UNIVERSITEIT GENT

FACULTEIT ECONOMIE EN BEDRIJFSKUNDE

ACADEMIEJAAR 2012 – 2013

MACROECONOMIC CONSENSUS DATA AND FINANCIAL MARKETS

Masterproef voorgedragen tot het bekomen van de graad van

Master of Science in de Toegepaste Economische Wetenschappen: Handelsingenieur

Mathias Wambeke

onder leiding van

Prof. Dr. William De Vijlder

UNIVERSITEIT GENT

FACULTEIT ECONOMIE EN BEDRIJFSKUNDE

ACADEMIEJAAR 2012 – 2013

MACROECONOMIC CONSENSUS DATA AND FINANCIAL MARKETS

Masterproef voorgedragen tot het bekomen van de graad van

Master of Science in de Toegepaste Economische Wetenschappen: Handelsingenieur

Mathias Wambeke

onder leiding van

Prof. Dr. William De Vijlder

I

PERMISSION

The undersigned declares that the content of this thesis can be consulted and/or reproduced, subject

to acknowledgement of sources.

Mathias Wambeke

II

PREFACE

When writing this thesis, I had help from multiple sources. First of all, I would like to thank Prof. Dr.

William De Vijlder for his ongoing advice and support with this subject. Also, a word of thanks to

Kristjan Kasikov (foreign exchange quantitative analyst at Citigroup) for providing me with additional

data.

III

TABLE OF CONTENTS

PREFACE .................................................................................................................................................. II

TABLE OF CONTENTS .............................................................................................................................. III

ABBREVIATIONS ....................................................................................................................................... V

LIST OF TABLES ........................................................................................................................................ V

LIST OF FIGURES ..................................................................................................................................... VI

I. INTRODUCTION ............................................................................................................................... 1

II. MACROECONOMIC SURPRISES........................................................................................................ 3

II.1. Current literature ......................................................................................................................... 3

II.1.1. Research on the macroeconomic fundamentals of asset returns ........................................ 3

II.1.2. Research on macro surprises and asset returns ................................................................... 7

II.1.3. Surprise indices.................................................................................................................... 13

II.2. Data and descriptive analysis ..................................................................................................... 14

II.2.1. Data ..................................................................................................................................... 14

II.2.2. Descriptive analysis ............................................................................................................. 16

II.3. Method and results .................................................................................................................... 20

II.3.1. Surprise indices and long term government bond returns ................................................. 21

II.3.2. Timing government bond portfolios ................................................................................... 22

II.4. Conclusion .................................................................................................................................. 26

III. DISPERSION & CONSENSUS DATA ............................................................................................. 28

III.1. Current literature ...................................................................................................................... 28

III.1.1. Dispersion of micro consensus data ................................................................................... 28

III.1.2. Dispersion of macro consensus data .................................................................................. 31

III.1.3. Pricing models and the dispersion – asset return relationship .......................................... 35

III.1.4. Gaps in current research .................................................................................................... 36

III.2. Data ........................................................................................................................................... 38

III.3. Method and results ................................................................................................................... 42

III.3.1. Stock returns and macro dispersion ................................................................................... 42

III.3.2. Forecast errors and macro dispersion ................................................................................ 45

III.3.3. Default premia and macro dispersion ................................................................................ 47

III.3.4. Preliminary conclusion ....................................................................................................... 50

III.4. Conclusion ................................................................................................................................. 51

REFERENCES .......................................................................................................................................... VII

IV

APPENDIX ............................................................................................................................................. XIII

Appendix 1 - Out of sample bond timing returns for different derivatives ................................. XIII

Appendix 2 –Bond timing strategy based on US and domestic surprise index derivatives ......... XVI

Appendix 3 - Unit root tests ........................................................................................................ XVII

Appendix 4 –Bond timing strategy based on surprise index levels ............................................ XVIII

Appendix 5 – Chow breakpoint tests ........................................................................................... XIX

Appendix 6 – Market timing for the S&P500 based on macro dispersion .................................... XX

Appendix 7 – Plot of disagreement on TBOND and 6 month S&P500 returns ............................ XXI

Appendix 8 – Granger causality tests ........................................................................................... XXI

Appendix 9 – Market timing for the default premium based on macro dispersion ................... XXII

Appendix 10 – Plot of disagreement on CPI and the 1 month default spread change .............. XXIII

Appendix 11 – Nederlandse samenvatting ................................................................................ XXIV

V

ABBREVIATIONS

ADS Aruoba – Diebold - Scotti index

CESI Citi Economic Surprise Index

CESICAD Canada Citi Economic Surprise Index

CESIEUR EMU area Citi Economic Surprise Index

CESIGBP UK Citi Economic Surprise Index

CESIJPY Japan Citi Economic Surprise Index

CESIUSD USA Citi Economic Surprise Index

ECB European Central Bank

HML High Minus Low – book to market factor

SMB Small Minus Big – size factor

SPF Survey of Professional Forecasters

UMD Up Minus Down – momentum factor

LIST OF TABLES

Table 1; Early studies on asset returns and macroeconomic variables .................................................. 3

Table 2; macroeconomic surprises and stock returns............................................................................. 8

Table 3; macroeconomic surprises and foreign exchange returns ......................................................... 9

Table 4; macroeconomic surprises and fixed income returns .............................................................. 11

Table 5; correlations between Citi Economic Surprise Indices ............................................................. 15

Table 6; long term government bond portfolios ................................................................................... 15

Table 7; money market rates ................................................................................................................ 16

Table 8; correlations between 3 month % forex returns and surprise indices ..................................... 18

Table 9; correlations between surprise indices and 3 month % returns of long term government

bonds ..................................................................................................................................................... 20

Table 10; Regressions of bond returns on surprise indices ................................................................... 21

Table 11; Market timing statistics ......................................................................................................... 25

Table 12; Philadelphia SPF macroeconomic estimates ......................................................................... 38

Table 13; correlations between dispersion measures .......................................................................... 41

Table 14; regressions of excess stock returns on macro disagreement ............................................... 42

Table 15; market timing for the S&P500 based on macro dispersion .................................................. 44

Table 16; regressions of absolute forecast errors on macro disagreement ......................................... 45

Table 17; regressions of absolute forecast errors on NBER recessions and macro disagreement ....... 46

Table 18; regressions of default premia on GDP growth, VIX and macro disagreement...................... 47

Table 19; default premium regressed on its macro determinants ....................................................... 49

Table 20; market timing for the default spread based on macro dispersion ........................................ 50

VI

LIST OF FIGURES

Figure 1; CESIUSD and S&P500 returns ................................................................................................. 17

Figure 2; CESIUSD and Euro Stoxx 50 returns ....................................................................................... 17

Figure 3; CESIEUR-CESIUSD and EURUSD returns ................................................................................. 18

Figure 4; CESIUSD-CESIJPY and USDJPY returns .................................................................................... 18

Figure 5; CESIUSD and 3 month log returns of a 30 year T bond portfolio ........................................... 19

Figure 6; CESIUSD and 3 month log returns of a 30 year EMU bond portfolio ..................................... 19

Figure 7; different measures of dispersion. .......................................................................................... 32

1

I. INTRODUCTION

Macroeconomic consensus data are pooled estimates or predictions on variables that have the

potential to determine the current state of the economy. These predictions are provided by banks or

forecasting departments of large industrial companies, and aggregated trough services such as

Bloomberg.

Although decent macro consensus data were almost non-existent about twenty years ago, they have

now gained considerable importance in financial markets and academic research. The reason for this

is quite straightforward; raw macro releases do not provide enough information to trade, as financial

markets only react to unexpected components of news. Therefore, it will be the difference between

macro releases and the corresponding consensus that will be determinant on the reaction of

investors. Indeed, simple macroeconomic releases do provide insight into the overall state of the

economy, but they aren’t useful for financial markets unless they are considered simultaneously with

their respective consensus estimates.

The increasing interest of financial market actors in macro consensus estimates is clearly linked to

the wide availability of this type of data. For example, Bloomberg now provides a large set of pooled

USA macro estimates, ranging from GDP growth rates and nonfarm payrolls to unit labor costs.

Reviews dedicated solely to macro consensus data have emerged, e.g. ConsensusEconomics®.

Central banks are also clearly interested in macro estimates; both the ECB and the Federal Reserve

Bank of Philadelphia now manage their own Survey of Professional Forecasters (SPF).

Literature dealing with the effect of macroeconomic surprises1 on asset markets has become quite

mature over time. Numerous studies have shown that financial markets (including stock markets,

fixed income and forex) react to the surprise of macro releases. These researches often apply

sophisticated econometric models (see for example Andersen, Bollerslev, Diebold and Vega, 2003)

and managed to get, over time, some agreement on which macro surprises affect asset markets

under which conditions (cf. supra).

Another interesting application of macro consensus data resides in its potential to proxy for

uncertainty. Arnold and Vrugt (2008) and Glansbeek and Ivo (2011) use the dispersion of macro

estimates to establish a link between volatility in financial markets and macroeconomic uncertainty.

Furthermore, Dopke and Fritsche (2006) find that macro uncertainty is particularly high before and

during recessions.

In this thesis, multiple gaps in current literature on macro consensus data are identified. First of all,

the market timing potential of macro surprise indices will be assessed. These indices aggregate the

surprises of multiple macroeconomic series into a comprehensive surprise measure. Although this

1 defined as the standardized difference between a macro release and the corresponding consensus

estimate

2

type of data provides an interesting way of dealing with the surprise of macro consensus estimates,

it has hardly been discussed in literature so far.

A second gap in extant research is the application of macroeconomic uncertainty in relationship to

stock returns. The past use of dispersion in macro consensus data was limited to assessing its impact

on asset volatility or other more descriptive approaches. In thesis, it will be verified whether this

macro uncertainty is a measure of non-diversifiable risk, and whether it is therefore linked to

innovations in stock markets. Furthermore, dispersion in macro estimates will be used in two other

domains; we will check how dispersion and macro surprises are related, and whether macro

uncertainty can explain default premia.

The structure of this thesis is as follows; the first part will deal with macro surprises and starts with

an overview of current literature about the effect of macro variables, macro surprises and macro

surprise indices on asset prices. Subsequently, data and descriptive analysis are provided, followed

by market timing models for government bonds based on macro surprise indices. Next, the results

and preliminary conclusion are given.

The second part of this thesis will deal with the dispersion of consensus estimates. It starts with an

overview of related literature on micro and macro consensus data, after which multiple gaps in

current research are identified. Subsequently information is provided on consensus forecasts

obtained from the Philadelphia SPF. The following section describes our econometric models and

provides the results; the last part concludes.

In the next pages, this thesis will present that macro surprise indices have the potential of

determining a profitable market timing strategy for long term government bonds, though to a limited

extent. Also documented is a clear effect of macro dispersion on subsequent surprises and default

premia. However, the dispersion of macroeconomic consensus data does not appear to have a clear

relationship to stock returns, nor can it be used for a stock market timing or default premium timing

strategy.

3

II. MACROECONOMIC SURPRISES

II.1. Current literature

This literature overview starts with some early studies on the link between asset returns and macro

fundamentals. Next, a review of papers studying the effect of macro surprises on stock, forex and

bond returns is provided. The section ends with some notes on macro surprise indices. Unless

mentioned otherwise, results are reported for US markets and US macro variables.

II.1.1. Research on the macroeconomic fundamentals of asset returns

II.1.1.1. Stock markets

The first articles on the link between stock returns and macro variables were published around the

year 1980. These early studies simply used regressions of asset returns on current, lagged or future

innovations of macro variables. There is a large discrepancy in the conclusions of the researches

applying this method, with some authors revealing significant coefficients for macro variables (e.g.

Fama, 1990), while others admit having discovered no relationship at all (e.g. Cutler, Poterba, and

Summers, 1989). Many authors discern a significant negative relationship between inflation, interest

rates and stock returns, while evidence for real activity measures (industrial production, GNP) is

mixed at best. The table below provides a short overview of these early publications.

Table 1; Early studies on asset returns and macroeconomic variables

Note; (+) indicates a positive relationship with stock returns, (-) indicates a negative relationship,

(0) indicates no relationship

Author (year of publication) Coefficients evaluated

C.R. Nelson (1976) Inflation (-)

Fama, G.W. Schwert (1977) Inflation (-)

Fama (1981) Inflation (-), future capital expenditures (+), future

industrial production (+), future real GNP (+)

Solnick (1984) Interest rates (-)

Kaul (1987) Inflation (-), M1 (-),industrial production (+), real GDP (+)

Asprem (1989) Employment (-), imports (-), inflation (-) interest rates (-

), future industrial production (+), capital expenditures

(0)), measures for money supply (+) and the U.S. yield

curve (+), consumption (0)

Fama and French (1989) Default premium (+), term premium (+)

Cutler, Poterba, Summers, 1989 Industrial production (0), CPI (0), M1 (0), long-term

interest rates (0), 3 month t bill rate (0)

Wasserfallen (1989) Real GNP (0), Industrial production (0), real consumption

(0), real investment (0), consumer prices (0), money

4

supply (0), monetary base (0), real exports (0), import

prices (0), nominal interest rate (0), real interest rate (0).

Fama (1990) Default premium (+), term premium (+), future industrial

production(+)

Schwert (1990) Future industrial production (+)

Balvers, Cosimano, McDonald

(1990)

Industrial production (-)

Chen (1991) Past GNP (-), future GNP (+), term structure (+), default

spread (+), industrial production (-), t bill rate (-),

dividend yield (+)

Marathe and Shawky (1994) The permanent component of industrial production (-)

Conover, Jensen, Johnson (1999) Central bank discount rates (-)

Durham (2001) discount rate (0) (measured in nominal and real terms, as

well as spread with a 3 month t bill), M1 growth (0)

Fifield, D.M. Power, C.D. Sinclair

(2000)

GDP, inflation, money supply, interest rates, world

industrial production and world inflation

Rapach, Wohar, Rangvid (2005) Money market rate (-), 3-month Treasury bill rate (-),

long-term government bond yield (-), term spread (0),

inflation rate (-), industrial production (0), narrow money

(0), broad money (0), unemployment rate (0)

Ang and Bekaert (2007) Short term interest rate (-)

Over time, more advanced methods have been developed to assess the stock market – macro

variables relationship. Vector autoregression (VAR) is one such novel technique that was introduced

about two decades ago in this research area. For example, Lee (1992) uses a VAR to find that

industrial production granger causes stock returns, while no relationship was discovered between

stock returns and inflation. Kaneko and Lee (1995) also employ a VAR analysis and establish that

risk premia, term premia and industrial production are significant for predicting US stock returns,

whereas inflation is only slightly important.

Cointegration analysis is another often used approach to examine the link between stock markets

and economic variables. If two variables are cointegrated, their long term equilibrium relationship,

as well as the corresponding error correction model (ECM) can be established through a regression.

For example, Siklos and Kwok (1999) use a cointegrating VAR and find a negative relationship

between stock returns and inflation. They argue that this result is driven by central bank debt

monetization. Nasseh and Strauss (2000) demonstrate trough cointegration analysis that stock

returns are significantly related to industrial production (+), business surveys of manufacturing

orders (+) , short-term interest rates (+), long-term interest rates (-) and CPI (+). Likewise, Humpe

and Macmillan (2009) show that stock returns are related to industrial production (+), the long term

interest rate (-) and CPI (-). Binswanger (2004) however, argues that for the longer 1950-2000

5

period, no clear cointegrating relationship can be found in G-7 countries for real GDP, industrial

production, and stock returns.

A recent strand in literature searches for regime dependent macro effects on stock returns. An often

used approach is to construct a two state Markov model for stock returns, which in practice almost

always results in a high return – low variance and a low return – high variance regime. Next, the

influence of macro variables on stock returns is assessed for each of the two regimes, (potentially)

together with the effect of macro variables on transition probabilities. For example, Perez-Quiros and

Timmermann (2000) find a significant influence of the 1 month t bill rate (-) and default premium

(+) during the low return – high variance regime, while these variables don’t affect stock returns

during high return – low variance periods. Similarly, Chang (2009) finds that the 3 month t bill rate

(-) and default premium (+) significantly affects stock markets during the low return – high volatility

regime, but not so for high return – low volatility periods. Chen (2007) shows that M2 growth (+),

the federal funds rate (-) and discount rate (-) significantly affect stock markets, but this relationship

appears to be stronger during bear market regimes. Furthermore, it is shown that decreasing

discount rates lead to a higher probability of switching to a bear-market period.

In summary, there is a vast amount of literature available on the link between macroeconomic factors

and stock market returns. Although multiple authors find a significant relationship between inflation

(-), interest rates (-) and stock returns, results for measures of real economic activity (industrial

production, GDP, employment etc.) are mixed at best. It could be argued that this lack of clarity is

partially due to the fact that only a limited number of papers take into account time varying

coefficients for macro variables. Moreover, all of the above papers ignore the existence of consensus

data. This seems problematic, as classic investment theory states that asset prices only respond to

the unexpected component of (macroeconomic) news. Therefore, only taking into account macro

series without their respective consensus estimates seems like an invalid measure of macroeconomic

influences, therefore making spurious data mining results likely. The next section (II.1.2) will thus

pay considerable attention to researches that do take into account macro surprises, not just raw

macroeconomic announcements.

II.1.1.2. Foreign exchange

Most of the research about macroeconomic effects on forex rates has focused on the so-called

fundamental models. These include (i) the flexible price monetary model, (ii) sticky price monetary

model, (iii) the productivity differential model and (iv) models based on the Taylor rule.

The flexible price (Frenkel-Bilson) model defines an exchange rate as the relative price resulting from

the demand and supply for two moneys. Other key assumptions made by this model include that (1)

domestic and foreign assets are perfect substitutes; (2) purchasing power parity (PPP) holds at all

times; and (3) the uncovered interest parity (UIP) holds at all times. The resulting model is given

by;

s = a0 + a1*(m-mf) + a2(y-yf) + a3*(rs-rsf) + u

6

where s is the logarithm of the domestic price of foreign currency, m is the logarithm of money

supply, y is the logarithm of real income, rs is the short-term interest rate, and u is an error term.

The subscript f indicates foreign variables.

The sticky-price (Dornbusch-Frankel) model does not assume the PPP to hold continuously; the goods

market prices are presumed to be sticky, at least in the short run. Exchange rates and interest rates

therefore have to compensate for this price stickiness, and thus exchange rates can “overshoot” their

long-run equilibrium rates. The resulting equation is given by;

s = a0 + a1*(m-mf) + a2(y-yf) + a3*(rs-rsf) + a4*(e-ef) + u

wheree is the expected long-run inflation.

The alternative sticky price (Hooper-Merton) model also allows the long-run real exchange rate to

fluctuate. These real exchange rate changes are presumed to be caused by unanticipated trade

balance shocks. The resulting equation is;

s = a0 + a1*(m-mf) + a2(y-yf) + a3*(rs-rsf) + a4*(e-ef) + a5*TB + a6*TBf + u

where TB is the cumulated trade balance.

A third type of fundamental models accords a central role to productivity differentials in explaining

real exchange rate fluctuations. These Balassa–Samuelson models hypothesize that PPP only holds for

tradable goods, whereas non-tradables are a function of productivity differentials (z). In other words,

the Balassa-Samuelson model holds if (1) the productivity differential between traded and non-traded

sectors are positively correlated to relative prices; (2) the ratio of traded versus non-traded good prices

increases with per capita GDP; (3) real exchange rate are positively correlated to relative prices of non-

tradables. A generic version of this model is thus given by;

s = a0 + a1*(m-mf) + a2(y-yf) + a3*(rs-rsf) + a4*(z) + u

A final type of fundamental model is based on the Taylor rule. Assuming that the UIP holds, this

model gives;

s = a0 + a1* - a2*f + a3*y – a4*yf + a5*q + a6*rs – a7*rsf + u

where q is the real exchange rate.

An elaborate strand in literature investigates whether these fundamental models have out-of-sample

explanatory or predictive power. In a well-known paper, Meese and Rogoff (1983) find that a random

walk performs as well as the Frenkel-Bilson, Dornbusch-Frankel and Hooper-Morton models in terms

of out-of-sample forecasting accuracy, even when including future realized values of explanatory

variables.

In the more recent literature, multiple researchers have found significant out-of-sample predictability

for fundamental models, though mainly in the long run. These researchers include Chinn and Meese

(1995), who find some predictive power at a 3 year horizon for the Frenkel-Bilson, Dornbush-Frankel

and Balassa–Samuelson model. Kim and Mo (1995) corroborate these findings, while MacDonald and

Taylor (1994), Mark (1995) and Mark and Sul (2001) use the Frenkel-Bilson model to conclude that

it has predictive ability for the short run (1 month) as well as the long run (up to 4 years).

7

Recent literature has also found some significant short term out-of-sample predictive ability for

fundamental models based on the Taylor rule. Molodtsova, Nikolsko and Papell (2008) come to this

conclusion by using the USD/DM exchange rate, while Molodtsova and Papell (2009) confirm these

findings for 11 different currencies (out of 12 tested) vis-à-vis the U.S. dollar. Molodtsova, Nikolsko

and Papell (2008) also find evidence of short-term predictability using the EUR/USD rate.

However, these findings on significant predictability have not remained without criticism. Berben and

van Dijik (1998), for example, casts doubt on the “stylized fact” that predictability increases for

longer horizons. While the cointegration is generally assumed in papers about forex fundamentals,

it can be shown that alternative critical values under the null of no cointegration can counter classic

findings of significant long-run predictability. These results are corroborated by Berkowitz and

Giorgianni (2001). Kilian (1999) emphasizes biases due to small-samples and spurious regression

fits; he develops a novel bootstrap method to account for small-sample inference, and consequently

finds no long-run forex predictability using this new technique. Nikolsko-Rzhevskyy and Prodan

(2012) argue that the alleged predictive ability of fundamental models is partly due to the inclusion

of constant drift terms. He models this drift term as a Markov switching model and finds robust

evidence for short term as well as long-run (1 year) forex predictive ability. Lastly, Cheung, Chinn,

and Pascual (2005) don’t find any predictive power for the PPP, Dornbusch-Frankel, Balassa–

Samuelson or composite model at horizons ranging from 1 up to 20 quarters. Just like Meese and

Rogoff (1983), they conclude that no fundamental model is consistently superior to a normal random

walk.

Research on the Balassa-Samuelson (B-S) hypothesis also produced mixed results. For example,

Solanes, Portero and Flores (2008) do find that productivity differentials between traded and non-

traded sectors are positively correlated to relative prices, but they cannot prove that inter-country

productivities are linked to real exchange rate innovations, and therefore find no evidence in favor

of the B-S model. These findings hold for new as well as old member states of the EU. Drine and

Rault (2005) on the other hand, confirm the B-S hypothesis for 8 out of 12 OECD countries, similar

to Chong, Jorda and Taylor (2012), who confirm the B-S model for 21 OECD member states, although

they find a substantial variation in results across countries. Lastly, Dumrongrittikul (2012) found

that, for 17 developing countries, in favor of the B-S model, higher productivity for traded goods

leads to real forex rate appreciation, while opposite results hold for 16 developed countries.

It can be concluded that the results for these fundamental models, relating forex rates to

macroeconomic fundamentals, are mixed at best. As Meese and Rogoff (1983) already pointed out,

this might be due to not properly taking into account expectations of explanatory variables. Again,

the argument is that looking for a macro surprise effect on short term forex rates might be a

fundamentally better way of searching for link between forex and macro variables.

II.1.2. Research on macro surprises and asset returns

The disappointing results from conventional regressions denoted in the previous part have made

academics look for more advanced methods for research on the link between macro variables and

asset returns. Literature using macroeconomic surprises (defined as the standardized difference

8

between a macro release and the corresponding consensus estimate) is becoming more and more

common practice. This type of research often uses high frequency returns to reduce the potential

effect of other, non-macroeconomic variables. The econometric models of this approach have become

quite mature over time, with the regressions including GARCH terms to account for conditional

heteroscedasticity of daily returns, calendar effects to account for intraday patterns of volatilities etc.

Results are also often controlled for different states of the economy, as it could be that, e.g.

unemployment news has a time-varying effect on asset returns, dependent on whether the current

state is defined as a recession or expansion. These different economic states can be determined

based on several indices or variables, such as the trend of industrial production, Aruoba-Diebold-

Scotti (ADS) index or the NBER classification of economic states.

The following section will briefly examine the main results of the prevailing literature concerning the

effect of macro surprises on stock markets, foreign exchange and bond markets.

II.1.2.1. Stock markets

An overview of literature (see table 2) reveals some consistent results on the macro variable – stock

return relationship; CPI, PPI and money supply have an overall negative effect on stock returns,

nonfarm payrolls have a negative effect on stock markets during economic expansion and a positive

effect on stock returns during recession, while the opposite holds for unemployment.

A lot of variables that are commonly used to proxy for economic activity, such as industrial

production, GDP, retail sales etc. do not show any relationship with stock returns.

Table 2; macroeconomic surprises and stock returns

Note; (+) indicates a positive relationship with stock returns, (-) indicates a negative relationship,

(0) indicates no relationship

Author Surprise coefficients evaluated

Pearce and Roley

(1983)

Money supply (-).

Pearce and Roley

(1985)

Money supply (-), discount rate (-), PPI (-). CPI (0), industrial production

(0), unemployment (0).

McQueen and Roley

(1993)

Money supply (-) and PPI (-), not conditional on the state of the economy.

Industrial Production (- during expansions) Merchandise trade deficit (-

during expansions), CPI (- in medium state), unemployment (+during

expansions). Nonfarm payrolls (0), discount rate (0).

Flannery and

Protopapadakis (2002)

CPI (-), PPI (-), M1 (-). Several of the lagged conditioning variables also

have significant coefficients in the expected returns equation; the value-

weighted market index (+), 3 month treasury bill rate (-), treasury term

premium (-), and the dividend-price ratio (+).

Balance of trade (0), consumer credit (0), construction spending (0),

nonfarm payrolls (0), unemployment, new home sales (0), housing starts

(0), industrial production (0), leading indicators (0), M2 (0), personal

consumption (0), personal income (0), Real GDP (0), retail sales (0).

9

Kim, McKenzie & Faff

(2004)

CPI (-) and PPI (-).

Balance of trade (0), GDP (0), unemployment (0), retail sales (0).

Adams, McQueen and

Wood (2004)

PPI (-) CPI (-).

Poitras (2004) Nonfarm payrolls (- during expansions, + during recessions), CPI (-), PPI

(-), and the discount rate(-).

Industrial production (0), unemployment rate (0), M1 (0).

Boyd et al. (2005) Unemployment (+ during expansions, - during contractions).

Andersen et al. (2007) Nonfarm payrolls (- during expansions, + during contractions), durable

goods orders (- during expansions, + during contractions), initial

unemployment claims (0 during expansions, - during recessions), PPI (-

during expansions, 0 during contractions).

Cenesizoglu (2011) Nonfarm Payrolls (- during expansions, 0 during recessions), hourly

earnings (-), trade Balance (+), export price index (-) and core CPI (-),

fed funds target rate (-).

Real GDP (0), unemployment rate (0), retail sales (0), industrial

production (0), personal income (0), consumer credit (0), new home

sales (0), PCE (0), construction spending (0), import price index (0), PPI

(0), core PPI (0), CPI (0), housing starts (0), leading indicators (0).

II.1.2.2. Foreign exchange

Compared to research on forex fundamental models, an approach using surprises allows assessment

of a much wider array of macro variables. Whereas fundamental models are generally limited to

money supply, inflation, interest rates, real income and trade balances, the surprise approach does

not a priori exclude any macroeconomic release. Theoretically, it is expected that positive domestic

economic surprises will increase the domestic interest rates, leading to an appreciation of the

exchange rate. The main results show that foreign exchange markets react to macro surprises as

expected a priori (see table 3). Although this literature overview shows some variability with respect

to which macro variables have a significant effect on forex rates, it is clear that these inconsistencies

are mainly due to the length of the data series employed or other irregularities in the measurement

of returns or surprises. For example, Kim, McKenzie and Faff (2004) use daily returns instead of high

frequency returns, leading to a large amount of insignificant surprise coefficients. In general, foreign

exchange markets react significantly to surprises of economic activity (GDP, industrial production,

retail sales, durable goods orders), nonfarm payrolls, unemployment, balance of trade and the

federal funds rate. These consistent results for a large number of macroeconomic releases clearly

show the value of using consensus data in this type of research.

Table 3; macroeconomic surprises and foreign exchange returns

Author (year) Surprise variable with theoretically

expected coefficient

Surprise variable with

insignificant coefficient

10

Andersen, Bollerslev,

Diebold, and Vega

(2003)

GDP (advance and preliminary),

nonfarm payrolls, retail sales,

industrial production, durable goods

orders, construction spending, factory

orders, trade balance, PPI, CPI,

consumer confidence index, NAPM

index, housing starts, target federal

funds rate, initial unemployment

claims, M1, M2 and M3.

Capacity utilization, personal

income, consumer credit,

personal consumption

expenditures, new home sales

business inventories,

government budget deficit,

index of leading indicators.

Kim, McKenzie, Faff

(2004)

Balance of trade. GDP, unemployment, retail

sales, PPI, CPI.

Andersen, Bollerslev,

Diebold and Vega

(2007)

Nonfarm payrolls, durable goods

orders, initial unemployment claims.

PPI.

Faust, Rogers, Wang,

and Wright, (2007)

Federal funds rate, GDP, initial

unemployment claims, nonfarm

payrolls, retail sales, trade balance

and unemployment

CPI, PPI, housing starts.

II.1.2.3. Fixed income

From a theoretical perspective, it is expected that “good” economic surprises (i.e. macro

announcements indicating a stronger than expected economy) will increase interest rates, and

therefore reduce bond and bill prices. An overview of the macro surprise effect on fixed income

returns (see table 4) largely confirms this theoretical viewpoint; in general surprises denoting “good”

news indeed increase fixed income rates. The variables that generally support this empirical finding

include GDP, industrial production, nonfarm payrolls, initial jobless claims, unemployment, retail

sales, factory orders, durable goods orders, consumer confidence, the NAPM index, housing starts,

money supply, CPI, and PPI. The only macro variables which are consistently categorized as

insignificant are business inventories, the balance of trade and balance of trade proxies.

A priori we don’t expect to see any time-varying effects of macro surprises on interest rates. Only a

few papers report research on whether the state of the economy could influence the macro surprise

– interest rate relationship; McQueen and Roley (1993) and Andersen, Bollerslev, Diebold and Vega

(2007) find constant effects of surprises on interest rates, regardless of the state of the business

cycle. On the other hand, Boyd et al. (2005) find that unemployment surprises have a negative effect

on interest rates in expansions, but no effect during economic contractions.

The papers listed in table 4 report returns of long term bonds as well as short term bills. In general,

all maturities (from 3 month bills up to 30 year bonds) react significantly to surprise in macro

announcements. However, the term structure of US (and foreign) interest rates doesn’t simply move

vertically in response to macroeconomic news; McQueen and Roley (1993) and Faust, Rogers, Wang,

and Wright (2007) show that short and medium term interest rates are more sensitive to macro

surprises than long term rates, or as Faust et al. (2007, p. 1057) put it; “the effect is hump shaped

11

with a maximum effect at about 2 years”. Even though long term rates are less sensitive to macro

surprises, it is clear that, due to their higher duration, prices of long term bond portfolios will react

more strongly than short term bills. This is clearly shown by Balduzzi, Elton, and Green (2001), who

compare prices of 3 month bills, 2 and 10 year notes and 30 year bonds.

It is interesting to note that the effect of US macro surprises reported in table 4 holds for US as well

as EMU fixed income markets; indeed, different authors such as Ehrmann and Fratzscher (2005),

Andersson, Overby and Sebestyen (2009) and Andersen, Bollerslev, Diebold and Vega (2007) find

that Euro area bonds react significantly to US macro surprises, and that this effect is stronger than

for the equivalent euro area surprises. As Ehrmann and Fratzscher (2005, p. 928) put it; “In recent

years certain US macroeconomic news affect euro area money markets and have become good

leading indicators for the euro area”. Similarly, Andersson et al. (2009) find that German government

bond futures react more strongly to US macro surprises compared to German and euro area

announcements. They find that the effect of US releases has become more important during the

period considered (’99-’05).

Finally, it should be noted that two papers in table 4 found a rather high number of insignificant

surprise coefficients; this is notably the case for Kim, McKenzie and Faff (2004) and Ehrmann and

Fratzscher (2005). This is mainly because these two papers use daily return data, whereas other

papers generally use high-frequency returns.

Table 4; macroeconomic surprises and fixed income returns

Author (year) Surprise variables with a theoretically

expected coefficient

Surprise variable with an

insignificant coefficient

McQueen and

Roley (1993)

Industrial production, unemployment,

nonfarm payrolls, PPI, CPI, M1, fed funds

discount rate.

Merchandise trade deficit.

Balduzzi, Elton,

and Green

(2001)

Durable goods orders, housing starts, initial

jobless claims, nonfarm payrolls, PPI, CPI,

consumer confidence, NAPM index, new home

sales, unemployment, retail sales, capacity

utilization, industrial production, factory

orders, M2.

Leading indicators,

merchandise trade balance,

US imports, US exports,

business inventories,

construction spending,

personal consumption,

personal income, treasury

budget, M1, M3.

Hautsch and

Hess (2002)

Nonfarm payrolls, unemployment, hourly

earnings, NAPM index, overall CPI, core CPI,

housing starts, M2, Retail Sales.

Kim, McKenzie,

Faff (2004)

Retail sales, PPI and CPI. Balance of trade, GDP,

unemployment

Boyd et al.

(2005)

Unemployment news in expansions Unemployment news in

contraction

12

Ehrmann and

Fratzscher

(2005)

Feral funds target rate, NAPM index, nonfarm

payrolls, consumer confidence, retail sales.

Industrial production, GDP,

CPI, unemployment rate, PPI,

housing starts, trade balance.

Andersen,

Bollerslev,

Diebold and Vega

(2007)

Durable goods orders, nonfarm payrolls, initial

jobless claims, PPI.

Faust, Rogers,

Wang, and

Wright (2007)

GDP, retail sales, housing starts, initial jobless

claims, unemployment, nonfarm payrolls, PPI,

CPI, trade balance, Federal funds rate.

Andersson,

Overby,

Sebestyen

(2009)

GDP, industrial production, nonfarm payrolls,

initial jobless claims, retail sales, factory

orders, durable goods orders, University of

Michigan consumer sentiment index, Chicago

PMI, consumer confidence, Philadelphia Fed

Business Outlook Survey, ISM non-

manufacturing confidence, CPI, PPI.

Business inventories.

From the discussion above, it is clear that the use of consensus data and the corresponding macro

surprises is a relevant way of searching for the macro variable – asset return relationship. Whereas

previous literature was generally not able to link asset prices to their macro fundamentals, the novel

papers combining consensus data and high frequency returns have provided more consistent findings.

This holds for stock markets, forex as well as bond returns. Andersen, Bollerslev, Diebold and Vega

(2007, p. 251), who search for the effect of macro surprises on different asset classes, conclude; “We

find that news produces conditional mean jumps; hence high-frequency stock, bond and exchange rate

dynamics are linked to fundamentals”.

However, this approach using macro surprises to explain high frequency returns entails some

downsides;

- The interaction or aggregation of macro surprises has hardly been discussed in literature so far.

For example, Andersen et al. (2003, 2007) only consider the joint effect of macro surprises if the

corresponding announcements are released at the same time.

- Macro expectations often show long periods of too optimistic forecasts, followed by long periods

of too pessimistic forecasts (cf. supra). This effect is ignored by the papers listed above.

- High frequency returns provide, per definition, only very limited and short term insight into the

evolution of asset prices. The results from these papers can hardly be used for an asset allocation

or market timing strategy.

Surprise indices, discussed in the next paragraphs, are a novel way of searching for a macro surprise –

asset return relationship, and potentially can surpass some of the shortcomings listed above.

13

II.1.3. Surprise indices

Surprise indices allow to track the performance of economic forecasts by aggregating past

macroeconomic surprises into a comprehensive index. The first surprise indices emerged some 15

years ago, and often have been quoted in popular press2 ever since. The popularity of surprise indices

is also clearly denoted by the large number of banks who have now created their own surprise index.

The best known examples include the Citigroup Economic Surprise index (CESI), Bloomberg

Economic Surprise index, HSBC US activity index, Schroders’ index, and the JP Morgan Economic

Activity Surprise Index (EASI). In a recent press release3, JP Morgan claimed that; “Almost all large

dollar drops in recent years have coincided with phases of pessimism as defined by the EASI, (…)

and trading EASI signals would have delivered annual returns of 8.2%”.

Despite the apparent popularity and usefulness of surprise indices, literature on this type of data is

almost nonexistent. The only noteworthy paper is by Scotti (2012), who compares the US Citigroup

Economic Surprise index with a self-created macro surprise index. In this paper, surprise indices are

used in an ordinary regression to explain foreign exchange returns. Although the R² of these

regressions are rather low (<0,05), the surprise indices are often significant and generally have the

right sign (a positive change in the US surprise index appreciating the US dollar and vice versa).

Because of the apparent lack of literature on surprise indices (except for the work of Scotti, 2012),

these indices will be subsequently discussed further in this thesis. More concretely, investigation will

be carried out as to whether these indices are linked to past asset returns, and whether they can

predict future returns. It will be researched whether it is possible to deduct an asset allocation

strategy from the evolution of a surprise index; specifically, checking whether these indices can be

used for the market timing of bond portfolios with a high duration.

By doing so, this thesis will be different from conventional macro surprise research (cf. infra II.1.2)

because;

- Individual surprises are aggregated into an all-inclusive surprise index. In such a setting,

individual surprises are not of interest anymore; on the contrary, this type of research investigates

whether it is the aggregation of surprises that contains additional information.

- High frequency returns are not used; rather it is assessed whether longer term asset returns

respond to aggregated surprises.

- This thesis is not limited to a descriptive approach; the aim is to look for an effective market

timing strategy.

2 See, for example, Friedman (2012) or Levkovich (2012). 3 JPMorgan, February 7, 2002, JPMorgan introduces the economic activity surprise index, URL; <http://investor.shareholder.com/jpmorganchase/releasedetail.cfm?releaseid=145456>

14

II.2. Data and descriptive analysis

This section starts with a brief description of the surprise indices and asset returns used in the

empirical models of this thesis. This is followed by a visual comparison of surprise indices and the

returns on different asset classes, and calculations of the corresponding correlations.

II.2.1. Data

The specific surprise index used in this thesis will be the Citigroup Economic Surprise Index (CESI).

Bloomberg provides the following definition; “The Citigroup Economic Surprise Indices are objective

and quantitative measures of economic news. They are defined as weighted historical standard

deviations of data surprises (actual releases vs Bloomberg survey median). A positive reading of the

Economic Surprise Index suggests that economic releases have on balance been beating consensus.

The indices are calculated daily in a rolling three-month window. The weights of economic indicators

are derived from relative high-frequency spot FX impacts of 1 standard deviation data surprises. The

indices also employ a time decay function to replicate the limited memory of markets.”

CESI series have been obtained for a total of 11 different regions (Australia, Canada, Switzerland,

Euro area, Japan, Norway, New Zealand, Sweden, United Kingdom, United States and the G10), with

daily data ranging from April 1998 until January 2013. Table 5 provides correlations between the

different indices. Except for the United States, most CESI indices have a rather poor coverage of

expectation surveys in the years 1998 until 2003. Therefore, interpretation of CESI indices in this

early period might be misleading.

The method of calculating CESI indices is also subject to criticism. As Citigroup calculates weights

based on reaction of forex markets to news surprises, it is depicted as subjective in the sense that it

might leave out otherwise important macro announcements. Scotti (2012, p. 19), on the other hand

calculates weights based on the contribution of the macro announcement to an unobserved common

factor, and therefore argues that this method “represents a more objective measure of deviation

from consensus expectations”. Alternatively, HSBC activity indices are simply calculated by the sum

of all economic surprises since the creation of the indices, which therefore allows visualization of runs

of surprises in little-watched data.

Despite this criticism, it was decided to continue with the CESI as it is the best known surprise index

and one of the only indices with publicly available data.

The government bond portfolio returns used in section II.3 are obtained from Datastream.

Specifically considered are bond portfolios with a high duration because, as demonstrated by Balduzzi

et al. (2001), long term bond portfolios have the strongest reaction to macro surprises. Table 6

provides an overview of the specific portfolios and their data range.

15

Table 5; correlations between Citi Economic Surprise Indices

CESI

CAD

CESI

EUR

CESI

G10

CESI

JPY

CESI

NZD

CESI

NOK

CESI

SEK

CESI

CHF

CESI

GBP

CESI

USD

CESI

AUD

CESICAD 1.0000

CESIEUR 0.1180 1.0000

CESIG10 0.2274 0.7241 1.0000

CESIJPY 0.0162 0.0784 0.2287 1.0000

CESINZD 0.1427 0.1001 -0.0078 -0.0615 1.0000

CESINOK -0.0144 -0.1033 -0.0521 -0.1542 -0.0816 1.0000

CESISEK -0.0756 0.1871 0.1472 -0.0787 -0.0827 0.0954 1.0000

CESICHF 0.3122 0.3352 0.3395 0.0703 0.0322 -0.1349 0.0633 1.0000

CESIGBP -0.0803 0.1641 0.3521 0.0093 -0.0448 -0.0259 0.1417 -0.1410 1.0000

CESIUSD 0.1271 0.1873 0.7874 0.0498 -0.1048 0.0197 0.0493 0.17026 0.2862 1.0000

CESIAUD 0.1797 -0.0141 0.1104 -0.0034 0.0054 0.1243 -0.0101 0.0352 0.0795 0.0950 1.0000

Table 6; long term government bond portfolios

Portfolio Datastream

mnemonic Data range

Canada 30 year government bond return

index BMCN30Y(RI) 1/4/’98 – 31/1/’13

EMU 30 year government bond return index BMEM30Y(RI) 1/1/’99 – 31/1/’13

Japan 30 year government bond return index BMJP30Y(RI) 31/12/’99 – 31/1/’13

UK 30 year government bond return index BMUK30Y(RI) 1/4/’98 – 31/1/’13

UK 50 year government bond return index BMUK50Y(RI) 31/5/’05 – 31/1/’13

USA 30 year government bond return index BMUS30Y(RI) 1/4/’98 – 31/1/’13

The return indices are calculated by the formula;

𝑅𝐼𝑡 = 𝑅𝐼𝑡−1 ∗ ∑ (𝑃𝑖,𝑡 + 𝐴𝑖,𝑡 + 𝐶𝑃𝑖,𝑡 + 𝐺𝑖,𝑡) ∗ 𝑁𝑖,𝑡−1𝑖

∑ (𝑃𝑖,𝑡−1 + 𝐴𝑖,𝑡−1 + 𝐶𝑃𝑖,𝑡−1) ∗ 𝑁𝑖,𝑡−1𝑖

Where P is the middle price of the bond, A is the accrued interest, Gi,t is the value of any coupon

payment received from the ith bond at time t since time t-1, N is the nominal value of the amount

outstanding, CP is the value equal to the next coupon payment in the ex-dividend period or 0 outside

the ex-dividend period.

The money market rates used in section II.3 are presented in table 7. The overnight currency

deposits are obtained from Bloomberg, while the Japanese overnight uncollateralized call money rate

is obtained from Datastream.

Other series used in section II.2.2 include the S&P500, Euro Stoxx 50, and USD exchange rates,

which are obtained from Bloomberg. Data ranges from April 1998 up to January 2013. Returns are

generally calculated using past 3 month percentages or natural log differences.

16

Table 7; money market rates

Portfolio Mnemonic Data range

CAD overnight deposit CDDR1T 1/4/’98 – 31/1/’13

EUR overnight deposit EUDR1T 1/1/’99 – 31/1/’13

Japan overnight uncollateralized call money rate JPCALLO(IR) 31/12/’99 – 31/1/’13

GBP overnight deposit BPDR1T 1/4/’98 – 31/1/’13

GBP overnight deposit BPDR1T 31/5/’05 – 31/1/’13

USD overnight deposit USDR1T 1/4/’98 – 31/1/’13

II.2.2. Descriptive analysis

This section provides graphs and correlation tables to show how surprise indices and asset returns

of the USA, as well as foreign markets are related. The discussion starts with stock markets, followed

by foreign exchange , and finally fixed income markets.

II.2.2.1. Stock markets

The figure below presents the USA Citigroup Economic Surprise Index (CESIUSD) together with the

S&P500 3 month % return. This long horizon for S&P returns has been modeled specifically because

the CESI indices are calculated using an aggregate of the past 3 month surprises, too. However,

mind that this definition of the CESI (aggregate of 3 month past surprises) and S&P returns (3 month

past returns) induces a form of autocorrelation; the value of a particular day will be very close to the

value on the previous day.

Drawing S&P500 returns with surprises of other regions (such as the CESI of the G10 or Euro area,

or a difference of these indices - not shown here), does not display a clear fit such as in figure 1. The

relationship between the CESIUSD and S&P500 returns is visibly dependent on the state of the

economy. When the economy is in clear expansion (mid-2004 up to the end of 2007), there is a

negative correlation between surprises and S&P500 returns. Otherwise (mid-1998 up to mid-2004

and 2008 up to now), the correlation is positive.

Figure 2 shows the relationship between the Euro Stoxx 50 and the CESIUSD. Drawing the Euro

Stoxx 50 together with other surprise indices (such as the CESI of the Euro area or other regions –

not shown here) does not display a clear fit such as in figure 2. Again, the correlation between these

two series is clearly dependent on the state of the economy; in the pre-2004 and post-2008 period,

the correlation is clearly positive, whereas in in the period between mid-2004 and the end of 2007,

the correlation turns negative.

17

Figure 1; CESIUSD and S&P500 returns

Figure 2; CESIUSD and Euro Stoxx 50 returns

II.2.2.2. Foreign exchange

A priori, it is expected that the EURUSD exchange rate should move in line with CESIEUR-CESIUSD

innovations; when CESIEUR is higher than its American counterpart, the EURUSD should appreciate.

Figure 3 shows that this relationship holds, although there appears to be a lot of noise in the data.

In general, the relationship between forex returns and their respective surprise indices is not as clear

as for the EURUSD case. Table 8 provides correlations for some important exchange rates. It appears

from this table that for the EURUSD exchange rate, the relationship with its surprise indices is quite

strong, while this is rarely the case for other foreign exchange returns.

-0,5

-0,4

-0,3

-0,2

-0,1

0

0,1

0,2

0,3

0,4

0,5 CESIUSD/500 S&P500 3 month % return

-0,4

-0,3

-0,2

-0,1

0

0,1

0,2

0,3

0,4

0,5

0,6 CESIUSD/500 Euro Stoxx 50 3 month % returns

18

Figure 3; CESIEUR-CESIUSD and EURUSD returns

Table 8; correlations between 3 month % forex returns and surprise indices

Exchange rate EURUSD USDJPY USDCAD GBPUSD AUDUSD

Surprise indices CESIEUR-CESIUSD

CESIUSD-CESIJPY

CESIUSD-CESICAD

CESIGBP-CESIUSD

CESIAUD-CESIUSD

Correlation 0,2917 0,2203 -0,1099 -0,0276 -0,0474

Exchange rate USDCHF USDSEK NZDUSD USDNOK

Surprise indices CESIUSD-CESICHF

CESIUSD-CESISEK

CESINZD-CESIUSD

CESIUSD-CESINOK

Correlation 0,2111 -0,1112 0,0749 -0,1056

As an illustration, provided is a figure of 3 month USDJPY returns together with the surprise index

CESIUSD-CESIJPY. It appears from this figure that the relationship between the two series is rather

unclear.

Figure 4; CESIUSD-CESIJPY and USDJPY returns

-0,3

-0,25

-0,2

-0,15

-0,1

-0,05

0

0,05

0,1

0,15

0,2 (CESIEUR-CESIUSD)/1000 EURUSD 3 month log return

-200

-150

-100

-50

0

50

100

150

200

-0,25

-0,2

-0,15

-0,1

-0,05

0

0,05

0,1

0,15 USDJPY3 month % return CESIUSD-CESIJPY

19

II.2.2.3. Fixed income

A priori, a strong relation is expected between surprise indices and fixed income returns; positive

surprises should result in higher interest rates and therefore lower prices of bond portfolios. As

previous research (cf. II.1.2.3) already demonstrated a strong effect of macro surprises on interest

rates, it is expected that also surprise indices should have a strong relationship with bond returns.

Figure 5 compares the CESIUSD with the return on a portfolio of 30 year US government bonds;

visibly, the link between these two series is very strong.

Figure 5; CESIUSD and 3 month log returns of a 30 year T bond portfolio

As explained in section II.1.2.3, Euro area bonds have a stronger link with USA surprises compared

to domestic news. Figure 6 therefore compares the returns of 30 year EMU bonds with an index of

USA surprises. Obviously, the relationship is not as good as in figure 5, but it is still clear that the

CESIUSD index can explain a considerable part of the innovations in the 30 year EMU government

bond portfolio. Visibly, the link between the two series has become stronger over time; this has

already been documented by Andersson et al. (2009), who also find that the reaction of German

bond markets to US releases has become more significant during the period 1999-2005.

Figure 6; CESIUSD and 3 month log returns of a 30 year EMU bond portfolio

-0,3

-0,2

-0,1

0

0,1

0,2

0,3

0,4 CESIUSD/500 (inverted) 3 month log return

-0,3

-0,2

-0,1

0

0,1

0,2

0,3

0,4 CESIUSD/500 (inverted) 3 month log return

20

The table below provides a complete overview of the correlations between surprise indices and

returns on the long term government bond portfolios which will be used in section 3.

Table 9; correlations between surprise indices and 3 month % returns of long term government

bonds

Bond portfolio Canada 30Y

EMU 30Y Japan 30Y UK 30Y UK 50Y USA 30Y

Surprise index CESICAD CESIEUR CESIJPY CESIGBP CESIGBP CESIUSD

Correlation -0,1805 -0,2647 -0,3065 -0,0707 -0,0364 -0,4688

Bond portfolio Canada 30Y

EMU 30Y Japan 30Y UK 30Y UK 50Y USA 30Y

Surprise index CESIUSD CESIUSD CESIUSD CESIUSD CESIUSD CESIUSD

Correlation -0,3362 -0,3848 -0,3843 -0,2793 -0,2405 -0,4688

It appears from this table that, as expected, bond portfolio returns have high (absolute) correlations

with surprise indices. It is apparent that these portfolios have consistently higher correlations with

US surprises compared to their respective domestic surprise indices. This finding is in line with

Ehrmann and Fratzscher (2005) and Andersson et al. (2009), who both comment on high spillover

effects from US news on foreign bond markets.

II.2.2.4. Concluding notes

Looking at the three asset classes discussed above, it can be stated that there is overall a clearly

visible relationship between the Citi Economic Surprise Indices and asset returns. Again, this is

evidence that asset returns are linked to macro fundamentals. The CESI has an apparent relationship

with stock indices for different regions. Consistent with other research, it was found that this

relationship is dependent on the state of the economy. The link between foreign exchange rates and

surprise indices is often not that clear, although the correlation between the EURUSD and CESIEUR-

CESIUSD is quite high. This high correlation specifically for the EURUSD is not a coincidence; after

all, this is the most liquid currency pair with the highest volume in the forex market4. Consistent with

the findings of Andersen et al. (2007), it was found that fixed income markets react most strongly

to macroeconomic surprises. Just like previous research, it is shown that interest rates are more

related to the USA surprises than domestic surprise indices.

II.3. Method and results

The previous section has shown that, of all asset classes considered, the fixed income market has

the clearest relationship with surprise indices. Specifically, it was decided to make use of long term

bonds in our models. Although Faust et al. (2007) have shown that long term interest rates have a

4 The Foreign Exchange Committee, October 2012, FX volume survey, URL; <http://www.newyorkfed.org/FXC/volumesurvey/>

21

comparatively small reaction to macro surprises, Balduzzi et al. (2011) reported that the price

reaction of long term bonds, due to their high duration, is relatively bigger compared to shorter

maturities. Therefore, long term bonds should also have the clearest reaction to changes in surprise

indices. In section 3.1, the relationship between high duration government bond portfolios and

surprise indices will be formalized. Section 3.2. will explain how surprise indices can potentially be

used to actually time the fixed income market. To end is the simulation of a market timing strategy

for six different bond portfolios.

II.3.1. Surprise indices and long term government bond returns

Previous literature has already shown a clear effect of macro surprises on high frequency bond

returns (cf. 1.2.3). In line with the descriptive statistics in section 2, the intention is to formally test

whether the effect on bond returns is also significant for macro surprise indices. To this end, we

regress the following equation;

𝑟(𝑡) = 𝛼 + 𝛽𝑟(𝑡 − 1) + 𝛾1∆𝐶𝐸𝑆𝐼𝑈𝑆𝐷(𝑡) + 𝛾2∆𝐶𝐸𝑆𝐼𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐(𝑡) + 𝜀(𝑡)

With r(t) the daily return, measured as the first difference of natural logarithms, of a long term

government bond portfolio. This return is regressed on its lagged value, together with the first

difference of the CESIUSD index and, when applicable, the first difference of the domestic CESI

index. We use White heteroscedastic robust errors to account for heterogeneity. In general, no

autocorrelation is found, nor was an important degree of multicollinearity detected. Normality for the

error terms is generally rejected, but does not seem problematic due to the large sample size (>2000

observations). Table 10 shows the results;

Table 10; Regressions of bond returns on surprise indices

Note; numbers in parentheses are t-statistics. *, ** and *** denote significance at a 10%, 5% or

1%, respectively.

R²

Canada 30 years 0.0002*** 0.0477** -0.0001*** -0,00004*** 0.0229

(3.1171)*** (2.3344)** (-6.5786)*** (-3.8958)***

EMU 30 years 0.0002** 0.1288*** -0.0001*** -0,00004*** 0.0281

(2.0879)** (5.9755)*** (-5.6319)*** (-2.9038)***

Japan 30 years 0.0001 0.1167*** -0,00003** -0,00002 0.0155

(1.0730) (4.3276)*** (-1.9622)** (-1.1914)

UK 30 years 0.0002** 0.0715*** -0.0001*** -0,00004*** 0.0163

(2.0585)** (2.9957)*** (-5.3528)*** (-2.9823)***

UK 50 years 0.0002 0.0800** -0.0001*** -0,00006*** 0.0175

(1.0319) (2.3779)** (-3.2647)*** (-2.7211)***

USA 30 years 0.0002* 0.0147 -0.0002*** 0.0251

(1.7391)* (0.6959) (-9.1413)***

22

Whereas previous literature has already shown that bond markets react significantly to macro

surprises, the results in table 10 now also confirm that bond returns are significantly connected to

macro surprise indices. The coefficients 1 and 2 are negative, as a priori expected, and often

significant at the 1% level. Similar to previous literature on macro surprises, R² statistics are quite

low (<0,029).

For all bond portfolios, it appears that the coefficients of the CESIUSD index are bigger (in absolute

value) and more significant than their respective domestic CESI counterparts. Again, this is in line

with the research of Ehrmann and Fratzscher (2005) and Andersson et al. (2009), who both find high

spillover effects from US surprises on foreign bond markets.

II.3.2. Timing government bond portfolios

II.3.2.1. Market timing strategy

Now that the relationship between bond returns and the CESI has been formally established, it could

be asked whether these surprise indices can be used for market timing purposes. The logic behind

the timing strategy is as follows; when the derivative of a CESI index is high, this means that recently

some positive macro news has been published. This is a sign of an economy in expansion, which

should result in higher interest rates, and therefore lower the return on a bond portfolio. As

graphically illustrated in section II.2.2, surprise indices are generally persistent, in the sense that

long periods of rising surprise indices (overly optimistic forecasts) are visible, followed by long periods

of declining surprise indices (overly pessimistic forecasts). This finding is useful for market timing,

as this could mean that a rising surprise index (implying rising interest rates) is a signal to sell long

term bond portfolios in the favor of cash or other money market instruments. This reasoning will be

empirically validated in the next paragraphs.

For this market timing strategy, an investor is modeled who has the choice between investing either

in long term government bonds of one specific country, or overnight money market deposits of the

same currency. The portfolio can be adapted every day. Although government bond returns of 5

different regions are available, the investor will only look at the CESIUSD index (and not the domestic

CESI index). Indeed, section II.2.2. has shown the correlations between USA surprises and

government bond returns to be higher compared to domestic surprises. When the first derivative of

the CESIUSD index is high (low), the investor will invest 100% in overnight currency deposits (a long

term government bond portfolio).

For most government bond portfolios, data is available from 1998 until 2013. This range will be

divided into an in-sample and out-of-sample period. Looking at other research on market timing,

e.g. Resnick & Shoesmith (2002) use an in sample period of 1/4 of the total data range , Rapach et

al. (2005) use an in sample period of 3/5 of the total data range, and Chen (2009) uses an in sample

period of 1/5 of the total data range. In this thesis, an in-sample period of 3 years is chosen compared

to an out-of-sample period of 12 years.

The in-sample period serves for two purposes. First of all, a precise definition of “derivative” will be

determined; the derivative of a surprise index can be measured as difference in levels between two

subsequent days, 3 subsequent days, 10 subsequent days etc. It is clear that, when a derivative is

23

defined as a short term (e.g. 2 day) difference of the level of a surprise index, the investor will

rebalance his portfolio more frequently compared to when the derivative is defined as a long term

(e.g. 20 day) difference in the level of the CESI. The in-sample period will determine the definition

of “derivative” which is the most profitable for market timing. Second, a threshold has to be

determined for when a derivative is categorized is “high”. In the in-sample period, the percentile of

derivatives will be calculated for which the corresponding threshold leads to the highest profitability

for our market timing strategy. These definitions of the derivative and threshold level are continued

throughout the remaining years of the out-of-sample period.

Afterwards, the profitability of this market timing strategy is compared with a buy and hold of the

corresponding long term government bond portfolio and overnight currency deposits.

In order to assess whether the market timing ability of our model is statistically significant, the

Pesaran and Timmermann nonparametric test is conducted. As Pesaran and Timmermann (1992, p.

461) state, this test is particularly useful when “the focus of the analysis is on the correct prediction

of the direction of change in the variable under consideration”, as is the case in this model. This test

does not a priori restrict the distribution of variables, and holds as long as the government bond

portfolio returns and the first derivative of the CESIUSD are symmetrically distributed around 0.

Another advantage of this test is that, contrary to risk based measures such as the Sharpe ratio,

Jensen’s alpha or the two beta model of Hendriksson and Merton, the nonparametric test of Pesaran

and Timmermann does not require the definition of a market and risk free portfolio. This test statistic

is defined as follows;

𝑆 =𝑝 − 𝑝 ∗

√𝜎𝑝2 − 𝜎𝑝∗

2 ~𝑁(0,1)

Where p is the proportion of times that the sign of yt is predicted correctly; p* = pypx + (1-py)(1-px);

py = Pr(yt>0); px = Pr(xt>0); s2p = n-1p*(1-p*); s2

p* = n-1(2py-1)2px(1-px) + n-1(2px-1)2py(1-py) +

4n-2pypx(1-py)(1-px); xt is the CESIUSD, yt is the daily return of a long term government bond

portfolio minus the overnight rate on a currency deposit.

Lastly, transaction costs must be accounted for. As it is not clear what the average cost would be of

buying or selling portfolios of government bonds, the number of transactions are simply counted

over the period under consideration and a theoretical transaction cost (in % of total portfolio value)

that would make the return of our market timing strategy equal to the bond benchmark is calculated.

II.3.2.2. Market timing results

The results of the market timing strategy are presented in table 11. For 5 out of 6 bond portfolios,

the out-of-sample return of the market timing strategy clearly outperforms the respective

benchmarks. Likewise, the Peseran and Timmermann test is significant at a 5% level for all portfolios.

Keep in mind that all six of the investment strategies are based on the CESIUSD Index. From that

perspective, it is remarkable that this strategy works equally well for US government bonds as well

as most foreign bond portfolios. Market timing does not seem to work for 30 year Canadian

government bonds, although the Pesaran Timmerman statistic is still significant (p-value of 0,0342).

The disappointing out of sample return of Canadian government bonds might be due to their

relatively low volatility. After all, it is more difficult to apply a market timing model to a bond portfolio

with nearly continuously rising prices, compared to a region with much more volatile bond returns.

Table 9 therefore also presents the volatility of the different bond portfolios, measured as the

24

standard deviation of weekly natural log returns. It clearly appears from this table that the portfolio

of 30 year Canadian government bonds is the least volatile.

The yearly returns presented in table 11 don’t take into account transaction costs. The last row of

table 11 therefore shows the theoretical transaction cost (in % of the portfolio value) that would

make the return of our market timing strategy equal to the bond benchmark. These transaction costs

are often quite low (0,086% or smaller), which casts doubt on the ability of this market timing

strategy to yield above benchmark returns in the real world.

A consistent finding emerges from the table below; for most bond portfolios, 5 and 6 day CESIUSD

derivatives appear to provide the best trading strategies. The market timing strategy for US T-bonds

seems to diverge from other bond portfolios with an optimal strategy for 25 day CESIUSD derivatives;

however, keep in mind that this derivative is chosen based on a rather short in-sample period of

about three years. Over the full sample (15 years), a five day strategy seems optimal, anyway.

An important cautionary note is necessary when measuring derivatives over such a short 5 day or 6

day interval; not only will a short interval entail considerable transaction costs, but also such a short

time period will only contain one or two macro releases. This entails that this market timing method

is not very different from conventional research which looks at the effect of a single macro surprise

on asset returns. As an illustration, appendix 1 graphically presents out of sample returns for different

derivative definitions. This is shown for the 70th and 75th percentile strategies for all of the six bond

portfolios. It appears from these figures that, in general, 5 day derivatives are associated with a peak

in out of sample return, while strategies based on longer derivatives are unprofitable.

II.3.2.3. An alternative strategy based on domestic surprise index derivatives

Previous academic literature has shown that fixed income markets react more to US macro surprises

compared to domestic macro events (cf. infra II.1.2.3). This was corroborated by presenting the

correlations between surprise indices and government bond returns (cf. infra II.2.2.3). Similarly, the

market timing strategy shown in the previous paragraph, although based solely on the CESIUSD,

worked equally well for US government bonds as well as foreign bond portfolios. Nevertheless, it

might still be interesting to reproduce a market timing strategy for foreign government bonds by

simultaneously taking into account domestic surprise indices.

The method for this timing strategy is thus similar to the one presented in II.3.2.1. The same

government bond portfolios are used and the same distinction between in sample and out-of-sample

periods remains. However, now the CESIUSD and the domestic surprise index are considered

simultaneously; the investor will buy government bonds once the CESIUSD and the domestic surprise

index derivatives fall below a certain level. This level is determined as the percentile of US and

domestic surprise index derivatives which maximizes profits in the in sample period. All other details

of this market timing strategy are similar to II.3.2.1.

The results of this timing strategy are presented in appendix 2. It appears that in sample as well as

out-of-sample returns are systematically lower compared to the a market timing strategy based

solely on the CESIUSD. Similarly, the Pesaran and Timmermann test statistics are less significant

compared to the p-values presented in table 11. Again, this confirms the finding that bond markets

react more to US macro news compared to domestic macro surprises.

25

Table 11; Market timing statistics

Bond portfolio Canada 30 years EMU 30 years Japan 30 years UK 30 years UK 50 years USA 30 years

In sample return (yearly %) 6,45% 10,21% 18,37% 13,55% 9,41% 9,67%

In sample benchmark government

bonds (yearly %)

4,32% 0,47% 11,25% 8,87% 3,77% 4,96%

In sample benchmark o/n deposit

(yearly %)

3,68% 2,62% 0,04% 4,32% 3,73% 4,09%

Out of sample return (yearly %) 8,23% 10,50% 3,85% 7,93% 9,40% 8,22%

Out of sample benchmark government

bonds (yearly %)

9,03% 8,79% 0,36% 6,20% 7,84% 7,00%

Out of sample benchmark o/n deposit

(yearly %)

1,71% 1,55% 0,11% 2,31% 0,81% 1,40%

Derivative 6 days 5 days 6 days 6 days 5 days 25 days

Percentile of derivative values 75 70 75 75 70 75

Pesaran and Timmermann test

statistic

1,8218 2,8928 2,2709 1,8866 1,8815 2,5340

P-value 0,0342 0,0019 0,0116 0,0296 0,0300 0,0056

Volatility 0,0910 0,1203 0,0989 0,1126 0,1425 0,1358

Transaction cost which makes surplus

return = 0

0,0384% 0,0863% 0,0431% 0,0493% 0,0657%

26

II.3.2.4. An alternative strategy based on surprise index levels

An interesting finding emerges when looking at the CESI plots presented in II.2.2; CESI series appear

to be mean reverting. Over the past 15 years, CESI indices appear to fluctuate around 0, with long

periods of positive surprises followed by long periods of negative surprises. Therefore, if a CESI index

is at an extreme level, two things may happen; either forecasters will adapt their estimates in the

right direction, either subsequent macro releases will be more in line with their respective forecasts.

In the two cases, the CESI will go back to 0. More formally, unit root tests (see appendix 3) confirm

that surprise indices are mean reverting.

It could therefore be asked whether extreme levels of surprise indices provide information to trade.

E.g. when the CESIUSD is at a high level, this could mean that future surprises will be lower (as the

CESI always mean reverts), that interest rates will decline, and that therefore the value of long term

government bond portfolios will increase.

Thus, it could be interesting to check whether the level of the CESI can be used in a bond timing

strategy. As a preliminary analysis, the levels of the CESIUSD are divided into different “hurdles” or

percentiles. Subsequently, the corresponding mean future government bond returns are calculated.

These future bond returns are defined as the percentage change over a horizon of 2 weeks and 1, 2,

3 and 6 months. Appendix 4 provides the results. Contrary to intuition, these government bond

portfolio sorts don’t show any meaningful pattern. The same sorts were also conducted for domestic

surprise indices, which again showed no clear results. Therefore, we conclude that the level of the

CESI is not useful for a government bond timing strategy.

II.4. Conclusion

Macro consensus data have gained considerable attention in recent research on the link between

macro surprises and asset returns. The literature overview in section II.1 shows that previous

research using conventional fundamental models and levels of macro variables has not been able to

present stylized facts on the macro variable – asset return relationship. Research using macro

surprises, on the other hand, has obtained some consistent and significant results; foreign exchange

markets, for example, appear to have a strong link with a wide range of macro surprises, while

previous fundamental models weren’t able to produce any consistent results.

Literature on macro surprises has largely ignored the interaction between different surprises. Hence,

this thesis applied macro surprise indices to have a new way of dealing with the macro surprise -

asset return relationship. The descriptive analysis in section II.2 shows that stock returns, forex rates

and bond returns have a clear relationship with these surprise indices.

In section II.3, a market timing strategy is proposed, based on the first derivative of the CESIUSD

index. Results show that, before taking into account transaction costs, this strategy achieves high

excess returns for multiple long term government bond portfolios. These market timing returns are,

however, quite tentative because of the short time frame applied, the short term definition of

derivative (i.e. a 5 or 6 day difference) and because it is unlikely that this strategy yield high returns

27

after taking into account transaction fees. Related government bond timing models based on

domestic surprise index derivatives or levels of surprise indices appear to be unprofitable. In any

case, the market timing returns presented in II.3.2.2 demonstrate that surprise indices have the

potential of being used for asset allocation purposes.

Because surprise indices have been overlooked in current literature up until now, there are many

possibilities for further research. A more formal (descriptive) analysis of the relationship between

surprise indices and asset returns could be useful. Market timing models for other asset classes such

as stock markets and foreign exchange could be interesting, in which it would be challenging to

model state dependent returns. It is clear that many avenues for research on surprise indices are

possible, and that this research could have interesting applications for asset allocation and the macro

variable - asset return relationship.

28

III. DISPERSION & CONSENSUS DATA

III.1. Current literature

This section provides an overview of literature on the dispersion of consensus data and asset returns.

We start by looking at the dispersion of earnings estimates (i.e. micro consensus data), of which

previous empirical research found a clear link with the cross section of stock market returns. Past

literature on the dispersion of macro consensus data is, however, less developed. Empirical studies

on macro dispersion are generally limited to explaining forecast dispersion using other macro factors,

or searching for a link between this forecast dispersion and the variance in asset returns. There also

exists a large set of theoretical models on dispersion, which have however largely remained without

empirical validation. The next paragraphs go deeper into these strands of literature, and give a basis

for building an asset allocation strategy.

III.1.1. Dispersion of micro consensus data

III.1.1.1. Basic empirical research on the micro dispersion – stock return relationship

Ackert and Athanassakos (1997) are among the first to explore the effect of earnings estimate

dispersion on stock returns. They divide a sample of US stocks into different quartiles according to

the standard deviation of earnings estimates and calculate the “overoptimism” (i.e. the difference

between earnings releases and the preceding estimates) for each quartile. A positive relation between

over optimism and uncertainty is found, with little or no optimism for low uncertainty stocks.

Subsequently, average 20 month (excess) returns are calculated for each quartile, where significantly

higher returns are found for low dispersion stocks. This paper finds an annual compounded return

difference between high and low dispersion stocks of 11,35%.

The results of this paper have been confirmed numerous times in subsequent research. Diether,

Malloy and Scherbina (2002), for example, show that going long on low dispersion stocks and short

on high dispersion stocks, generates a 9,5% annual return. They sort this dispersion effect on size,

momentum and book-to-market (BTM) factors, and show that the effect is strongest for small stocks

and shares that have performed poorly over the previous year. They run a series of Fama and

MacBeth (1973) yearly cross sectional regressions and show that the dispersion effect cannot be

explained by a traditional risk framework. They show that the returns are more or less robust to

different specifications in portfolio formations, holding periods, earnings forecasts and sub-periods,

although the return differential is somewhat lower in the 90’s compared to the 80’s.

Hintikka (2008) confirms the findings of Diether et al. (2002) by using data of 7 European countries.

Gharghoria, Seeb and Veeraraghavanc (2011) find similar effects for the Australian stock market.

Ang and Ciccone (2001) again confirm for US stocks that low dispersion portfolios outperform high

dispersion shares. They find that this result cannot be explained by momentum, liquidity, industry,

or traditional risk measures.

Berkman et al. (2009) also find a significant effect of differences of opinion on stock returns. They

use 5 different measures for difference of opinion, including dispersion of earnings forecasts. Other

29

measures such as historical income volatility, stock return volatility, firm age and average daily

turnover often provide even higher return differentials. They use Fama and MacBeth (1973)

regressions to show that this return differential is higher for stocks that are difficult to sell short.

These results are robust after controlling for size, BTM, momentum, leverage, and volume around

earnings announcements.

Dische (2002) shows that the results of dispersion strategy can be improved by simultaneously

sorting the portfolios for an earnings momentum effect. Buying low dispersion shares with positive

earnings revisions and simultaneously shorting high dispersion shares with negative earnings

revisions yields a monthly return of 1,48%. This effect is robust for different sub-periods (‘87–‘91,

‘91–‘95 and ‘95–’00), although the dispersion effect declines over time.

Chahine (2004) specifically looks for an effect of dispersion at earnings announcement days, and

finds that the excess return on the announcement day is negatively related to forecast dispersion

after the preannouncement of earnings.

III.1.1.2. Theories explaining the micro dispersion – stock return relationship

The papers above thus present common findings that a high dispersion in earnings estimates leads

to lower future returns. This is rather surprising, as intuitively, one would expect high dispersion in

micro consensus data to be a proxy for risk, which should lead, on average, to higher returns. One

common explanation for this counterintuitive finding, advocated by a.o. Ackert and Athanassakos

(1997) and Diether et al. (2002), is based on analysts’ behavior. It is a well-known phenomenon

that sell-side analysts have incentives to issue optimistic recommendations. These incentives are

connected to maintaining good relations with client firms and supporting brokerage commissions.

When the dispersion in earnings forecasts is low, a sell-side analyst may opt not to stand out of the

crowd and therefore issue a forecast that is not overly optimistic. However, if a large variation in

forecasts exists, analysts face less problems to act on their incentives to issue an optimistic estimate.

This in turn leads to overvaluation of high dispersion stocks, which inevitably entails poor future

returns when the actual earnings announcements are below expectations.

Another explanation, advocated by a.o. Hintikka (2008) and Gharghoria et al. (2011) is based on the

theory of Miller (1977). In this setting, dispersion in micro consensus estimates are seen as a proxy

for differences of opinion among stock market participants. The theory of Miller then states that,

whenever investors have differing opinions of asset valuations, pricing of these assets will reflect the

view of more optimistic participants if this stock market is characterized by restricted short selling.

This optimistic view thus leads to overvaluations, and therefore low future returns.

The framework of Miller (1977) is often seen as valid explanation, as the research previously

mentioned in this chapter consistently finds that the high dispersion stock portfolio is responsible for

the bulk of the return differential. As it is the high dispersion portfolio that needs to be shorted,

restricted short selling indeed could entail that pricing of this specific portfolio is too high. Also, it is

often documented that the return differential is more significant for small stocks, or more in general,

stocks that are difficult to sell short (see e.g. Diether et al., 2002, or Berkman et al., 2009). Again,

this is consistent with the optimism framework of Miller.

Johnson (2004), on the other hand, provides a theoretical framework which can explain the returns

differentials mentioned above, without presuming any irrationality, frictions, or anomalous analyst

behavior. Johnson constructs a theoretical model for the risk premium of levered firms, based on

30

elementary options pricing. Under the hypothesis that dispersion in a proxy for idiosyncratic risk, it

is shown that dispersion will lower expected returns. This pricing model also states that the strength

of the dispersion – expected return relationship should increase with leverage. This is empirically

validated using Fama-MacBetch cross-sectional regressions.

III.1.1.3. Related empirical research

An interesting variation to the research explained above is the work of Zhang (2006). He also finds

a negative, though insignificant, relation between dispersion of earnings forecasts and future stock

returns. Additionally, Zhang researches whether uncertainty (defined as standard deviation of

earnings forecasts) has an influence on momentum profits (defined as earnings momentum or price

momentum). It is shown that greater uncertainty leads to more significant momentum returns. These

results are also found when abnormal returns are defined as the intercept of a four factor model, and

remain valid for different sub-periods, although the returns decline for larger lags in portfolio

formations. A trading strategy which shorts low momentum, high dispersion stocks and buys high

momentum, high dispersion stocks creates monthly profits of 2,30%. These results are consistent

for other measures of micro uncertainty, such as firm size, firm age, analyst coverage, return

volatility, and cash flow volatility. Zhang therefore suggests that uncertainty postpones the flow of

information into stock prices. These findings are corroborated by Verardo (2009), who also looks for

the effect of micro dispersion on price momentum. Again, it is found that higher dispersion of earnings

estimates leads to larger momentum profits. These findings are robust after controlling for size,

book-to-market, analyst coverage (as a proxy for information diffusion) and idiosyncratic volatility.

However, not all papers on micro dispersion lead to the finding that high dispersion stocks have

consistently lower future returns. Leippold and Lohre (2012), for example, show that this micro

dispersion effect is not consistent for different time frames. As a matter of fact, they do find that

high dispersion portfolios lead to lower future returns, but they show the bulk of this return

differential originates in a narrow time frame of three years around the technology bubble. Therefore,

they conclude that the dispersion effect cannot be used as a long term asset allocation strategy.

Doukas, Kim and Pantzalis (2004) obtain results in contradiction to the conventional micro dispersion

– stock return relationship. They test whether dispersion of analysts’ earnings estimates can explain

the return differential between value and growth stocks. By dividing stocks into quintiles according

to their BTM ratio and calculating the corresponding earnings forecast dispersions, they show that

value stocks have a higher earnings forecasts dispersion compared to growth portfolios. Hence, in

this setting, higher dispersion is actually a proxy for higher risk. This is corroborated by adding a

micro dispersion variable to a Fama and French (1993) three factor model.

Dispersion in earnings forecasts has also been a topic for asset classes other than shares. Güntay

and Hackbarth (2010), for example, look for an effect of earnings dispersion on corporate bond

returns. They show that high dispersion firms have on average higher credit spreads, and that

changes in the level of dispersion can significantly predict changes in these credit spreads. Thus, for

the corporate bond market, dispersion is a proxy for cash flow uncertainty (i.e. risk), in contradiction

with the theory of Miller (1977). This, in turn, is explained by the limited short-sale constraints of

bond markets.

31

III.1.2. Dispersion of macro consensus data

The previous section has shown that a wide range in literature finds a consistent, negative

relationship between micro dispersion and future stock returns. Literature on macro dispersion,

however, is less developed. In the next paragraphs, it is discussed how to measure macro dispersion.

Subsequently, an overview of related empirical research is given.

III.1.2.1. Measures of macro uncertainty

Macro uncertainty has previously been identified using macroeconomic consensus data, stock return

volatility (e.g. the VIX index), news measures, and economic policy uncertainty (e.g. Baker, Bloom

and Davis, 2012). In this thesis, the focus is on the dispersion in macroeconomic consensus data as

a proxy for uncertainty.

Several authors such as Zarnowitz and Lambros (1987), Giordani and Soderlind (2003), and Bowles

et al. (2007) have compared different measures of macro uncertainty and commented on their

usefulness for different purposes. The main measures of macro uncertainty include (1) the dispersion

across forecasters, (2) average standard deviation of individual distributions, (3) the standard

deviation of the aggregate distribution, (4) uncertainty measures based on surprise indices, and (5)

measures based on forecasting errors or historical volatility.

1. The standard deviation (variance) of point estimates is also commonly referred to as the

disagreement among forecasters. The measure is easy to compute and even directly available on

services such as Bloomberg and the SPF of the Philadelphia FED. However, disagreement might

be related to the number of forecasters, and is also problematic when forecasters are highly

uncertain about the economy, but nevertheless agree with each other on the most likely outcome

(see figure 7a). In that case, the standard deviation of these point forecasters would be very

small, although the overall level of uncertainty has peaked. Giordani and Soderlind (2003)

calculate disagreement as half the distance between the 16th and 84th percentile of point forecasts;

this is a robust measure of dispersion, whereas the conventional standard deviation is more

sensitive to outliers. They find that this robust measure, for Philadelphia SPF data, has a high

correlation with other, more complicated measures of uncertainty, and is therefore a valid

measure of uncertainty. These findings are corroborated by Zarnowitz and Lambros (1987) for a

shorter timeframe of the Philadelphia SPF; their reported correlations between disagreement and

individual uncertainty measures are generally positive and significant. They do warn that

disagreement might overstate the variations in uncertainty, as its fluctuations are far higher than

what should be expected from economic variables (GDP growth, inflation), characterized by

resilience and gradual adjustments.

2. The average standard deviation (variance) of individual distributions is an interesting measure

because it captures the uncertainty of representative agents. However, because this dispersion

measure doesn’t take into account the disagreement across individuals, it is possible that that

forecasters report the same individual uncertainty from one period to another, but disagree among

each other about the mean estimate (see figure 7b). In such a case, simply taking the average

standard deviation of individual distributions might not capture the overall uncertainty of

32

forecasters. Nevertheless, Giordani and Soderlind (2003) see this individual uncertainty as a valid

benchmark for comparison and show that also this measure has a high correlation with the

standard deviation of the aggregate distribution of forecasters and other measures of dispersion.

Likewise, Zarnowitz and Lambros (1987) note that this dispersion measure is overall a good

approximation for the overall level of uncertainty and the variation in uncertainty.

3. The standard deviation (variance) of the aggregate distribution, or aggregate uncertainty, takes

into account both the disagreement and uncertainty of individual forecasters. It can be shown

that the variance of the aggregate distribution is equal to the sum of the average variance of the

individual distributions (=individual uncertainty) and the variance of point estimates

(=disagreement). Because this measure takes into account the two previous dispersion measures,

it could be seen as an encompassing uncertainty measure. It is of particular interest as it is often

shown in empirical literature (e.g. Giordani and Soderlind, 2003, 2006) that individual forecasters

are overconfident about their own estimates, and therefore underestimate uncertainty.

4. Scotti (2012, p.3) recently proposed a new measure of uncertainty, based on consensus data,

calculated as the root of a weighted average of squared surprises. The author argues that;

“Forecast disagreement measures divergence of opinions among forecasters rather than just the

underlying uncertainty about the economy […] My uncertainty measure is a cleaner measure of

the uncertainty regarding the current state of the economy and is available daily.”

Figure 7; different measures of dispersion.

When comparing the first three measures described above, Giordani and Soderlind (2003) find that,

for inflation expectations extracted from the Philadelphia SPF, all three dispersion measures have

high correlations and are generally equally volatile. They therefore conclude that, disagreement, by

far the easiest dispersion metric to compute, is, in their setting a valid measure for uncertainty.

Bowles et al. (2007), on the other hand, compare dispersion measures for inflation, unemployment

and GDP growth from the ECB Survey of Professional Forecasters. They claim that standard deviation

of the aggregate distribution, contrary to disagreement or individual volatility, is the most valid

measure as it corresponds broadly to the historical volatility of the actual data series. However, most

measures of dispersion, including the standard deviation of the aggregate distribution, were

remarkably stable during the recession period centered around 2001. Bowles et al. therefore argue

that these ECB dispersion measures are inconsistent because they haven’t been able to accurately

assess the risks associated with economic downturn.

(a) low disagreement, high individual uncertainty (b) high disagreement, low individual uncertainty

33

Giordani and Soderlind (2003) also compare the three dispersion measures described above with

time series measures of uncertainty. They use the standard deviation of forecast errors from different

models as proxies for uncertainty. Their employed models include a VAR with homoscedastic errors,

a standard GARCH model and an asymmetric GARCH. The authors refute the use of these time series

measures as a proxy for uncertainty because of the low correlations with other dispersion measures

and their inability to account for structural breaks (e.g. a change in inflation rate targets). These

findings are corroborated by Lahiri and Liu (2010), who specifically finds that ARCH estimates tend

to diverge significantly from survey estimates of uncertainty during periods of regime change or

structural break. Arnold and Vrugt (2008) also discuss time series measures of uncertainty from a

theoretical viewpoint; they also refute the use of these time series measures because “uncertainty”

should be an ex ante measure, whereas time series models are backward looking. Neither is there

agreement on a universal time-series model to be used in this context. Finally, as stressed by the

Arnold and Vrugt, time-series volatility just measures one particular ex post realization of a macro

variation out of many ex ante possibilities. It might thus be that ex ante considerable uncertainty

exists about the innovations of macroeconomic factors, whilst this is not necessarily captured by time

series models.

Lahiri and Sheng (2010) also provide interesting insights on the relationship between different

measures of macro uncertainty. They deconstruct the average variance of individual distributions

into disagreement among forecasters, added to the perceived variance of future cumulated shocks.

Therefore, in this setting, the validity of disagreement as a measure for uncertainty is dependent on

the stability of the macroeconomic setting and the length of the forecast horizon. These assumptions

are empirically verified using the Philadelphia FED SPF; disagreement appears to be a reliable proxy

for periods with low volatility of aggregate shocks and short forecast horizons.

Lahiri and Sheng also show that the squared error of consensus estimates is not a good proxy for

variance of future aggregate shocks; augmenting disagreement with the squared error of forecasts

actually performs worse as a measure of uncertainty than disagreement alone. This casts doubt on

the validity of the uncertainty measure proposed by Scotti (2012), who also merely uses a weighted

average of squared surprise as an uncertainty measure.

III.1.2.2. Macro dispersion and asset volatility

The previous section has shown what measures of dispersion in macroeconomic consensus data can

be used as valid proxies for uncertainty. In the next paragraphs, these measures are used in empirical

research on asset volatility.

Whereas previous empirical research on asset volatility has mainly used time series based measures

for uncertainty, Arnold and Vrugt (2008) were among the first to use disagreement as a macro

uncertainty measure in this setting. Their paper includes several interesting findings; firstly, they

calculate a disagreement measure for ten macro variables from the Philadelphia SPF and find that all

of these dispersion measures are significantly higher during NBER defined recessions. Also, it appears

that the disagreement is highly correlated among these different variables, which leads the authors

to conclude that this uncertainty measure is able to capture moments of general economic unease

34

relatively well. Next, by simply regressing S&P500 volatility on the disagreement for different

variables, they show that a large part of these disagreement coefficients are significant, thus

uncertainty about future macroeconomic variables provides information on stock market volatility.

The authors do warn that, starting from 1997, this relationship becomes insignificant, consistent with

other papers who found the structural break in asset volatility around this period, possible caused by

the dotcom bubble. Subsequently, a VAR model is estimated to look for granger causality between

S&P500 volatility and disagreement for different macro variables; results show that five out of ten

disagreement measures have significant predictive power. All the above tests were also conducted

for times series measures of macroeconomic uncertainty, which, compared to disagreement,

generally show a very limited or no relationship with asset volatility.

Glasbeek and Ivo (2011) mainly confirm the aforementioned findings using SPF data of the ECB. The

authors also use disagreement as a measure for macroeconomic uncertainty. Glansbeek and Ivo

conduct an ordinary regression to search for a link between asset volatility and macro uncertainty,

and just like Arnold and Vrugt, they find a significant positive relationship between stock market

volatility and disagreement on different macro variables (real GDP, unemployment and inflation). In

addition, they find that bond market volatility is mainly affected by disagreement on inflation. Finally,

the authors also report on long-run cointegration relationships between stock and bond market

volatilities and disagreement on all three macroeconomic uncertainties (real GDP, inflation, and

unemployment).

III.1.2.3. Macro dispersion and the levels of other macro variables

The two previous sections compared different measures of macro uncertainty and searched for a link

with asset volatility. Interesting questions remain whether macro uncertainty is linked with the state

of the economy, and whether this uncertainty is linked to the level of other macro variables.

Dopke and Fritsche (2006), for example, use ordinary regressions to search for a relationship

between recession periods and disagreement on GDP growth and inflation in Germany. Their results

show that disagreement on GDP growth is significantly larger before and during recessions, while

disagreement is actually lower during early upturns of the economy. Therefore, this finding is broadly

consistent with Arnold and Vrugt (2008). With respect to disagreement on inflation, however, Dopke

and Fritsche find no, or only a very weak positive relationship with recession periods. These differing

results are explained by stating that forecasters might frequently disagree on the current length of

a business cycle, whereas the state of this cycle might not be a good indicator for inflationary

pressures.

D’Amico and Orphanides (2008) use the Philadelphia SPF to search for a link between inflation

uncertainty measures and term premia. They calculate correlations between mean forecasts,

disagreement, average individual uncertainty and 2, 5 and 10 year term premia. All of these premia

have positive correlation coefficients with mean forecasts of inflation (>0,31) as well as with average

individual uncertainty (>0,62), and disagreement (>0,23). These findings are corroborated by

regressing the term premia on each of the previous inflation measures separately. Again, the

relationship is strongest for the average individual uncertainty (R² of 0,38), compared to mean

forecasts of inflation (R² of 0,16) or disagreement (R² of 0,13).

35

D’amico and Orphanides also visually compare series of disagreement and average individual

uncertainty with the level of inflation. They find that level of inflation clearly moves together with

these two dispersion measures. Correlation coefficients confirm these findings; a coefficient of 0,68

is found between the mean inflation forecast and the disagreement on inflation, while this coefficient

is slightly lower for the dispersion of individual distributions.

Mankiw, Reis, Wolfers (2004) corroborate the findings of D’amico and Orphanides and show through

a visual analysis and several regressions that disagreement on inflation forecasts is linked to the

level and change in inflation. On the other hand, the authors find no relationship between

disagreement and the output gap.

Capistran and Timmermann (2009) provide a theoretical framework for the fact that disagreement

in inflation forecasts varies with the level and variance of inflation. Their theoretical model is based

on asymmetric loss (i.e. the cost of over- or underpricing inflation might be uneven), differences in

forecasters’ loss functions, and a constant loss term (i.e. a constant tendency to over- or underpredict

inflation). They empirically validate their model with Philadelphia SPF data, and find evidence for

asymmetric loss, different loss functions and a constant loss term. They find that inflation forecasts

are indeed dependent on the level and conditional variance of current inflation.

III.1.3. Pricing models and the dispersion – asset return relationship

Section 1.1 listed numerous papers which have found a negative relationship between stock returns

and micro dispersion. These empirical findings were shown to be consistent with the framework of

Miller (1977) and the option based pricing theory of Johnson (2004). Besides these two theories on

micro dispersion, there are a large number of other theoretical models available that deal with the

effect of (macro) dispersion on asset returns. These theoretical asset pricing models often have a

basis of aggregate consumption and rely heavily on mathematical deductions. However, a large part

of these models has not been empirically validated yet.

Varian (1985), for example, finds that in an Arrow-Debreu equilibrium, asset prices depend solely on

aggregate consumption and the agent’s subjective probability distributions. For relative risk averse

utility functions, equilibrium asset prices are found to decrease when dispersion of opinion increases.

Soderlind (2006) again considers an Arrow-Debreu equilibrium but deducts a different model

compared to the one of Varian (1985). Soderlind’s modes suggests that the equity premium is

approximately γσ2+δ2, where γ is the risk aversion coefficient, σ2 denotes the variance of an

investor’s individual beliefs on future output, and δ is the disagreement among investors on output

estimates. The implied variance of a stock option is approximately equal to the equity premium

divided by γ. Therefore, the uncertainty of a representative investor equals σ²+δ²/γ. Soderlind thus

states that, if the coefficient of risk aversion is not too small, disagreement will not be very significant

for asset pricing. This effect is amplified by empirical findings from Philadelphia SPF data, which show

that individual uncertainty (σ²) is more than twice as large as disagreement (δ²).

Anderson, Ghysels and Juergens (2009) suggest a very specific definition of uncertainty; they extract

expectations on aggregate profits from the Philadelphia SPF and use these to calculate expected

returns for each forecaster using the Gordon-Shapiro model. The variance across forecasters in these

expected returns is used to proxy for uncertainty. Anderson, Ghysels and Juergens subsequently

deduct a theoretical model, based on Merton (1973), which states that both risk (i.e. the volatility of

36

an asset) and uncertainty (i.e. the variance of expected returns across forecasters) carry a positive

premium. Formally, their model states for the aggregate stock market that;

Etret+1=γVt+θMt

With ret+1 the quarterly excess returns of the stock market over a risk-free bond between t and t+1; Vt

the variance of the market and Mt the uncertainty of the market.

They deduct for an individual stock k;

Etrkt+1=βvkγVt+βukθMt

With βvk and βuk could be seen as regression coefficients of respectively the risk in asset k on market

risk and of the uncertainty in asset k on market uncertainty.

They empirically validate their model and show that in the equation for aggregate excess stock

market returns, θ is highly significant. In addition, the correlation between uncertainty (Mt) and

aggregate excess returns is 0.28 whereas the correlation of risk (Vt) with these excess returns is

only half the size. For the cross section of individual stock returns, Anderson et al. empirically test

whether risk and uncertainty can explain stock returns in a Fama and French factor model. Results

show that uncertainty is significantly priced and helps to explain the cross section of excess stock

returns.

Another paper by Anderson, Ghysels and Juergens (2005), focusing on micro dispersion, suggests a

consumption based model which confirms that dispersion is priced in asset markets. They augment

the three factor model with micro dispersion measures, based on analysts’ disagreement on short-

and long-term expected earnings, and find that these additional factors are significantly priced. They

also examine whether disagreement among analysts can be used to forecast return volatility; results

show that models incorporating dispersion measures often produce a better fit with actual series

compared to other multi-factor models.

There are also theoretical models of asset pricing that hypothesize a link with uncertainty, but without

a priori presuming a directional effect of uncertainty on asset returns. Detemple and Murthy (1994),

for example, present a model where agents have different beliefs about macro news innovations and

the interpretation of the corresponding news. This dispersion in macro estimates leads to different

investment strategies across agents. Detemple and Murthy conclude from their model that asset

prices are a wealth weighted average of the agents’ differing estimated prices, and equilibrium prices

oscillate in response to fluctuations with the wealth shares.

III.1.4. Gaps in current research

The previous sections provided an overview of a wide range in literature showing a clear link between

stock returns and dispersion in micro consensus data. This finding is backed by several independent

theoretical models on micro dispersion, such as the frameworks of Miller (1997) and Johnson (2004).

Where previous research came up with clear asset allocation strategies for micro dispersion, this is

far from the case for macro dispersion. Empirical research on macro dispersion is mostly limited to

looking for a relationship with asset volatility or levels in some other macro variables. Several papers

also provide abstract theories on the link between macro dispersion and stock returns, which however

often remained without empirical validation. Hence, different paths exist to expand knowledge in this

area. The next paragraphs will consider three topics that haven’t been discussed in literature so far.

37

III.1.4.1. Stock returns and macro dispersion

Several authors, such as Arnold and Vrugt (2008), claim that macro dispersion is a good proxy for

overall uncertainty. It could therefore be asked whether macro dispersion could be seen as a measure

for non-diversifiable risk, and whether it is remunerated in stock markets.

In the same vein, it would be interesting to empirically validate the theoretical models of Varian

(1985) and Soderlind (2006). Varian’s model posits that asset prices should decrease when

dispersion of opinion (on the probability of an economic state) increases. Soderlind’s model, on the

other hand, posits that the equity premium increases linearly with individual uncertainty and

disagreement on output estimates. Both of these models will be verified with actual stock market

returns in paragraph 3.1.

III.1.4.2. Forecast errors and macro dispersion

Several researchers have studied whether macro consensus data are unbiased i.e., whether macro

forecasts have a zero mean forecast error. These researchers include Andrew, Bekaert and Wei

(2007), Baghestani (2011) and Dominitz and Grether (2009), just to name a few. However, it has

not been discussed yet under which conditions mean forecasts will deviate from the actual release.

In this perspective, it could be asked whether periods of high macro dispersion (i.e. high overall

uncertainty), are linked to higher forecast errors. This will be further discussed under paragraph

III.3.2.

III.1.4.3. Default premium and macro dispersion

D’Amico and Orphanides (2008) have already shown that dispersion in inflation estimates is

significantly correlated with term premia. In a similar way, it could be interesting to check whether

dispersion in inflation forecasts, or generally, dispersion in macro consensus data are linked to the

default premium.

A theoretical underpinning of firm-level default spreads can be found in Merton (1974, 1977), whose

fundamental models have been subsequently refined by Black and Cox (1976), Longstaff and

Schwartz (1995) and Cathcart and El-Jahel (1998). In its simplest form, default is defined as the

state at which a company’s value is below the nominal value of its outstanding loans. This is

formalized using option theory. It can be derived from this model that credit spreads are positively

related to the value of a company and the volatility in the company’s value.

In empirical research, the link between the default premium and other macroeconomic factors has

already been discussed by Pedrosa and Roll (1998), Duffee (1998), Das and Tufano (1996),

Papageorgiou and Skinner (2006), and Ewing (2003), among others. These papers found links

between default spread, the term premium, long and short term interest rates, stock index returns,

inflation, monetary policy and GDP growth. These variables were expected to be significantly related

to the default premium because they proxy for the state of the economy, and therefore convey

information on the aggregate company value in the structural model of Merton.

The effect of volatility measures on the default spread has also been discussed in recent literature.

Collin-Dufresne, Goldstein and Martin (2001) and Bhar and Handzic (2011), for example, report a

positive relationship between default premia and the VIX.

38

To the extent that volatility in a firm’s value is a proxy for uncertainty, it can be questioned whether,

at a macro level, the default premium is positively related to dispersion in macroeconomic consensus

data. Previous researchers already found a relationship between the VIX and default premia; it might

therefore be interesting to check how well disagreement performs in comparison to the VIX as a

measure for macro uncertainty. This will be further discussed in paragraph III.3.3.

III.2. Data

In this chapter, macroeconomic consensus data will be used from the Philadelphia FED Survey of

Professional Forecasters (SPF). This is a quarterly survey, started in 1968Q4 and originally managed

by the American Statistical Association (ASA) and the National Bureau of Economic Research (NBER).

In 1990Q2, the Philadelphia FED took over the administration of the SPF. This survey provides

projections on 32 different economic variables, of which some have been included since 1968Q4,

some others were added in 1981Q3 or even later. Forecasters are not publicized with their names in

SPF data sheets, but are assigned a confidential identification number. As Croushore (1993, p. 8)

puts it; “This anonymity is designed to encourage people to provide their best forecasts, without

fearing the consequences of making forecast errors. In this way, an economist can feel comfortable

in forecasting what she really believes will happen to interest rates, even if it contradicts her firm's

official position”. Laster, Bennett and Geoum (1999) also note that macro forecasters might

deliberately disclose extreme macroeconomic forecasters for publicity reasons; producing an extreme

forecast that outperforms all other competing forecasts might generate comparatively more publicity

than an average forecast which also proves to be correct. Again, this publicity bias should not be a

problem as forecasters report their estimates anonymously.

Generally, forecasters are asked to provide quarterly projections for the current and four upcoming

quarters and annual projections for the current year and the following year. In this thesis, only data

of current quarter estimates will be used. Forecasters can also estimate revisions of past quarter

announcements, but this is rarely done in practice; as shown by Arnold and Vrugt (2008), median

forecasts of the preceding quarter have a perfect fit with initial unrevised data.

For this thesis, macroeconomic variables have been selected that have previously proven to have a

significant effect on stock market returns (cf. II.1.1.1. and II.1.2.1) or that should, generally

speaking, convey information about the state of the economy. Furthermore, the selected

macroeconomic series are those with a history of at least 20 years of forecasts. This results in a

database of 12 variables from the Philadelphia SPF (see table 12). The extractions contain data until

2012Q4.

Table 12; Philadelphia SPF macroeconomic estimates

Code Description Start date

NGDP Nominal GDP. Prior to 1992; nominal GNP. 1968Q4

CPROF Corporate profits after tax. Prior to 2006Q1; excluding inventory value adjustment and capital consumption adjustment.

1968Q4

UNEMP Unemployment rate 1968Q4

INDPROD Index of industrial production 1968Q4

39

HOUSING Housing starts 1968Q4

PGDP5 GDP price index. Prior to 1996 GDP implicit price deflator. Prior to 1992; GNP deflator.

1970Q4

CPI Consumer price index, inflation rate 1981Q3

TBILL Three-month Treasury bill rate 1981Q3

RGDP Real GDP. Prior to 1992; real GNP. Prior to 1981Q3; computed using the formula NGDP/PGDP*100.

1968Q4

RCONSUM Real consumption expenditures 1981Q3

BOND Moody’s AAA corporate bond yield. Prior to 1990Q4; new, high-grade corporate bond yield

1981Q3

TBOND 10-year Treasury bond rate 1992Q1

Some comments or cautionary notes are necessary when using SPF data. First of all, several

variables, as denoted in table 10, have undergone one or more changes in definition over history of

the Philadelphia SPF. Secondly, the variables PGDP, INDPROD, RGDP and RCONSUM are based on

indices with a varying base year, which complicates the calculation of growth rates and other

measures. A third note regards the timing of this survey. The deadline for the SPF is around the third

week of the middle month of each quarter. When the Philadelphia FED took over the SPF, they were

too late to send out the questionnaire for 1990Q2. The survey for this period was therefore sent out

together with the questionnaire of 1990Q3, asking forecasters only to fill in the 1990Q2 survey if

they had a written record of this forecast. This resulted in a small number of correspondents for the

1990Q2 survey. However, the statistics presented in this chapter are generally robust to in the

inclusion of 1990Q2 data.

The number of forecasters included in the survey can vary significantly over different periods, from

more than 60 correspondents in the 1970’s to about 30 forecasters in more recent years. In addition,

the turnover of forecasters can be quite high over some periods. Therefore, authors such as

Zarnowitz and Lambros (1987) exclude from their statistics forecasters who contributed in less than

12 surveys. D’Amico and Orphanides (2008, p. 14) similarly control SPF statistics for the inclusion of

irregular forecasts and conclude that “The estimates are essentially similar regardless of whether the

inflation forecast attributes employed are based on only regular survey respondents or all

respondents, including irregular ones.” In this thesis therefore, all SPF panelists will be considered,

regardless of the number of surveys they participated in.

The Philadelphia FED does not only provide aggregate mean and median forecasts, but also makes

the forecaster’s individual point estimates publicly available. This facilitates the calculation of cross-

sectional forecast dispersion, also denoted as “disagreement”. As mentioned under 1.2.1, several

authors such as Zarnowitz and Lambros (1987), Giordani and Söderlind (2003) and Arnold and Vrugt

(2008), claim that disagreement has a high correlation with other measures of dispersion and is

generally a good measure of uncertainty. Following Giordani and Soderlind (2003), we start by

5 Data for PGDP are available since 1968Q4. However, we leave out first quarters because forecasters

rounded index estimates to the nearest integer, which resulted in identical forecasts and therefore

zero values for dispersion.

40

calculating disagreement as half the distance between the 16th and 84th percentile of point forecasts.

This “quasi-standard deviation” or qStd, is a robust measure of dispersion, whereas the conventional

standard deviation is more sensitive to outliers. This is the definition of disagreement that will be

used for the variables CPI and TBILL. A possible disadvantage of this quasi-standard deviation is that

it might increase simply because the underlying variable increases in value over time. For example,

nominal GDP increases about 18 times in value over the period 1968Q4-2012Q4. It is therefore very

likely that the disagreement surrounding NGDP will also increase linearly over time. Hence, for all

variables except CPI and TBILL6, disagreement will be calculated as the difference in natural logs of

the 16th and 84th percentile of point forecasts.

Bowles et al. (2007) indicate that it might be interesting to calculate a cross-sectional average of

disagreement series. To this end, all disagreement series of table 12 are transformed into an index.

Subsequently, the arithmetic mean is calculated for these 12 indices, denoted as MEAN.

Disagreement series starting in 1968Q4 are transformed into an index with value 1 in 1968Q4.

Disagreement series with a start date later than 1968Q4 are transformed to an index with value of

the MEAN index at their start date.

Table 13 provides correlations between the disagreement measures of different variables and the

MEAN disagreement index. The large majority of these correlations, except for the disagreement

about unemployment and the T-bill rate, are significant at the 5% level. Also note the high and

significant correlations between the disagreement variables and the MEAN index. This is a first

indication that our dispersion measures are able to capture periods of general macro uncertainty.

Other measures of dispersion described in 1.2.1, such as the average standard deviation of individual

distributions, are theoretically quite appealing, but are in practice very difficult to compute. No

database, including the Philadelphia SPF, provides detailed individual forecasts of high quality that

could be used for measuring these more advanced types of dispersion. The Philadelphia FED does

ask forecasters to give probabilities that GDP and GDP price index will fall in a particular range or

category. These data are difficult to work with, because the number and width of categories changes

over time, the sometimes large probabilities in border categories, and the changing definition of

variables (GDP or GNP). Furthermore, the forecasting horizon changes over time; as the SPF asks

panelists to provide forecasts for the next calendar year, the survey of the first quarter has a longer

forecasting horizon than e.g. the survey of the fourth quarter. In sum, these individual probability

data are generally not of sufficient quality to use in academic research. Therefore, disagreement will

be considered as the only macro dispersion measure.

6 Natural logarithms of CPI and TBILL are not calculated, because both of these variables have

become negative or close to zero in recent years, which would result in unrealistic disagreement

values.

41

Table 13; correlations between dispersion measures

Note; bold numbers denote significance at a 5% level.

NGDP CPROF UNEMP INDPROD HOUSING PGDP CPI TBILL RGDP RCONSUM BOND TBOND MEAN VIX

NGDP 1.0000

CPROF 0.4769 1.0000

UNEMP 0.1279 0.1320 1.0000

INDPROD 0.6512 0.4425 0.0525 1.0000

HOUSING 0.6225 0.5265 0.0945 0.5913 1.0000

PGDP 0.7075 0.4184 0.0550 0.5570 0.6315 1.0000

CPI 0.5155 0.3940 0.1918 0.3114 0.5242 0.5617 1.0000

TBILL 0.0782 -0.0190 0.0352 -0.0594 -0.0433 -0.0506 0.0599 1.0000

RGDP 0.7349 0.3935 0.1444 0.6025 0.4658 0.4283 0.2823 0.1305 1.0000

RCONSUM 0.5479 0.3551 0.2197 0.5106 0.2757 0.2409 0.2506 0.1323 0.6655 1.0000

BOND 0.4417 0.3869 -0.1245 0.2612 0.3898 0.3114 0.3978 0.0270 0.2486 0.1538 1.0000

TBOND 0.3458 0.2602 -0.1983 0.2793 0.4574 0.3095 0.2494 -0.2339 0.0786 -0.0308 0.7276 1.0000

MEAN 0.8030 0.7542 0.1526 0.7351 0.8240 0.7109 0.6711 0.0604 0.6163 0.5085 0.6178 0.5299 1.0000

VIX 0.5306 0.1128 0.1168 0.3986 0.4116 0.3606 0.4777 0.0843 0.4375 0.3888 0.4105 0.2961 0.5044 1.0000

42

Data for excess stock returns, default premia and the VIX index are extracted from Bloomberg and

Datastream. The correlation between macro dispersion measures and the VIX can be found in table

13. Excess stock returns are measured as the quarterly S&P500 return (in %) minus the three-month

Treasury bill rate. The default premium is measured as the difference between corporate bond rates

with a Moody’s rating Baa and Aaa. This premium is measured at the end of the middle month of

each quarter. Thus, the default premium is determined about a week after the SPD deadline. Data

ranges from 1968Q4 to 2012Q4.

III.3. Method and results

III.3.1. Stock returns and macro dispersion

III.3.1.1. Is macro uncertainty remunerated in stock markets?

As mentioned under III.1.4.1, it could be interesting to check whether uncertainty (measured by

macro dispersion) is linked to higher stock market returns. At the same time, theoretical models of

Varian (1985) and Soderlind (2006) could be empirically validated. To this end, the following

equations are regressed;

𝑟(𝑡) = 𝛼 + 𝛽 ∗ 𝜎𝑥(𝑡) + 𝜀(𝑡) (1)

𝑟(𝑡) = 𝛼 + 𝛽 ∗ ∆𝜎𝑥(𝑡) + 𝜀(𝑡) (2)

Where r(t) is the excess S&P500 return over the calendar quarter t, σx is the cross-sectional

dispersion in point forecasts of variable x released in the middle of quarter t. σx is either measured

as the quasi-standard deviation or the difference in natural logs of the 16th and 84th percentile of

point forecasts. Unit root tests of these variables are presented in appendix 3. Substituting

disagreement about GDP in equation 1 is more in line with Soderlind (2006), while equation 2 is

more in line with the framework of Varian (1985). We use these specifications with a limited number

of explanatory variables to avoid multicollinearity problems, as the previously reported descriptive

statistics have shown large correlations among disagreement measures. The coefficients are

estimated with least squares, and we subsequently check for heteroscedasticity and autocorrelation.

When necessary, White heteroscedastic robust errors or Newey-West HAC errors are used. Quite

often, the hypothesis of normality is rejected for residuals. However, this doesn’t seem to be a big

problem, as the regressions generally have a quite large sample size (>100 observations). The

results of these regressions are presented in table 14.

Table 14; regressions of excess stock returns on macro disagreement

Note; *, ** and *** denote significance at a 10%, 5% or 1%, respectively.

Level of disagreement First difference of disagreement

t-stat R² t-stat R²

MEAN -0.010932 -0.202890 0.000235 -0.183650* -1.919217 0.020730

NGDP -5.363190 -0.411673 0.001674 -15.50301 -1.286658 0.009425

CPROF 0.406174 0.485592 0.002061 -0.597783 -0.897071 0.004604

UNEMP -1.225042 -0.490474 0.003576 0.627839 0.334691 0.001293

43

INDPROD 2.802401 0.698890 0.002783 -0.984196 -0.208155 0.000249

HOUSING -0.613666 -0.586915 0.003632 -1.771704* -1.96532* 0.021716

PGDP -26.95365* -1.75733* 0.018156 -35.09823** -2.0107** 0.025210

CPI -0.147099** -2.1244** 0.035119 -0.020389 -0.267420 0.000581

TBILL -0.086696 -0.470233 0.001780 0.033085 0.177233 0.000255

RGDP -7.415244 -0.891822 0.004524 -17.67908 -1.362543 0.010557

RCONSUM 0.047216 0.003638 0.000000 -18.08633 -1.317280 0.013911

BOND 0.799069 0.553533 0.003921 -0.586937 -0.438396 0.002597

TBOND 1.455678 0.989583 0.022572 -0.845234 -0.808583 0.008007

The results show only very few significant coefficients and R² statistics which are generally very low.

The MEAN index of disagreement series is significant in equation 2, but the level of significance and

R² is definitely not convincing enough to provide any generalization of this finding. The statement

therefore cannot be validated that disagreement on macro fundamentals is a form of non-diversifiable

risk. The theory of Soderlind (2006), which stated that disagreement on output should be positively

related to the equity premium, is not in line with the discussed data. Neither can the framework of

Varian (1985) be empirically validated, which stated that asset prices should decrease when

dispersion of opinion increases. Whereas micro dispersion has a clear link with stock returns, this is

far from the case for dispersion of macro estimates. The finding that dispersion in macro estimates

and stock returns are essentially unrelated, remains when controlling for different definitions of

returns (calculating returns over longer horizons, using other stock market indices), other definitions

of dispersion (standard deviation, quasi-standard deviation, inter quartile range, difference of natural

logs between the 16th and 84th percentile, using macro estimates for future quarters instead of the

current quarter, using dispersion of monthly Bloomberg macro estimates instead of SPF data) or only

taking into account quarters with extremely high disagreement levels.

Besides MEAN, two other variables in table 14 revealed significant coefficients; CPI and first

differences of PGDP. The stability of these results should be verified. After all, inflation rate has

declined significantly over time, with an average inflation of about 6% in 1981-1992 compared to

3% during the period 1992-2012. Therefore, a Chow breakpoint test is conducted (see appendix 5).

The null hypothesis of no structural break is not rejected for any of these two variables. However, it

is clear that the significance of CPI and PGDP in table 14 is not convincing enough to providing any

generalization of these findings.

III.3.1.2. Market timing strategies

Although the previous section was not able to show a link between macro dispersion and equity

returns (measured as the return over the calendar quarter in which the SPF was released), it is not

impossible that a profitable market timing strategy could be deduced from dispersion in macro

consensus data. It could still be that an investor who buys or sells equity based on macro

disagreement signals obtains positive excess returns. To this end, the following equation is

regressed;

𝑟𝑛(𝑡) = 𝛼 + 𝛽 ∗ 𝜎𝑥(𝑡) + 𝜀(𝑡) (3)

44

Where rn(t) is the S&P500 annualized % return measured over n months, beginning in the third week

of the middle month of quarter t (i.e. beginning after the release of the SPF). In these regressions,

n={1,2,3,6} has been chosen. These regressions are therefore useful for an investor who wishes to

trade based on macro dispersion just after a new SPF is released. The same regression method is

used as for equation 1. Table 15 provides the results for 1 month returns. 2, 3 and 6 month regression

results are provided in appendix 6.

Table 15; market timing for the S&P500 based on macro dispersion

Dispersion

measure t-stat R²

MEAN 0.136991 1.180011 0.007894

NGDP 1.033999 0.048249 0.000013

CPROF -0.658692 -0.450562 0.001159

UNEMP -1.150902 -0.343746 0.000675

INDPROD 12.67423 1.468366 0.012171

HOUSING -0.599902 -0.360491 0.000742

PGDP -15.30675 -0.452447 0.001224

CPI 0.132350 0.799984 0.005135

TBILL 1.922758 1.335856 0.158129

RGDP -0.236601 -0.013127 0.000001

RCONSUM 52.63056 1.676604 0.022167

BOND 3.270675 1.220105 0.011863

TBOND 1.300764 0.905284 0.009895

The results presented in the table above are rather disappointing. None of the coefficients are

significantly different from zero, and R² statistics are very low. The 1 month marketing timing

strategy for TBILL seems to be an exception with a R² of 0,158. Furthermore, regressions with BOND

and TBOND show significant coefficients for longer return horizons (see appendix 6). However, one

should caution for generalizing these significant coefficients to future periods. After all, most of the

dispersion series only a have a few observations for truly high dispersions, and in general, the

regressions presented for these market timing strategies are quite sensitive to outliers. Appendix 7,

for example, provides a scatterplot for 6 month S&P500 returns and disagreement on TBOND. This

plot clearly shows that the market timing regression for TBOND is quite biased due to the low number

of observations with a dispersion higher than 0,12. Other regressions often show the same problems.

Therefore, the results of TBILL, BOND and TBOND cannot be generalized to other variables, neither

can they be used in a reliable market timing strategy.

Likewise, regressions based on first differences of macro dispersion instead of its levels, result in

insignificant coefficients which can’t be turned into a realistic market timing strategy.

45

III.3.2. Forecast errors and macro dispersion

As stated under III.1.4.2, it has not been discussed yet in empirical literature under which conditions

macro consensus forecasts deviate from actual releases. From this perspective, it might be

interesting to check whether disagreement on macro forecasts (as a proxy for uncertainty) is

positively related to forecast error. Therefore the following regression is conducted;

|𝑒𝑟𝑟𝑜𝑟𝑥(𝑡)| = 𝛼 + 𝛽 ∗ 𝜎𝑥(𝑡) + 𝜀(𝑡) (4)

Where errorx(t), the forecast error of variable x in quarter t, is calculated by the Philadelphia SPF as

the median growth rate forecast minus the revised release. σx is the cross-sectional dispersion in

point forecasts of variable x in quarter t. Unit root tests of these variables are presented in appendix

3. The specification of equation 4, with a limited number of explanatory variables, is chosen to avoid

multicollinearity problems. White heteroscedastic robust errors or Newey-West HAC errors are used

to account for autocorrelation and/or heteroscedasticity. The results of these regressions are

presented in table 16.

Table 16; regressions of absolute forecast errors on macro disagreement

Note; *, ** and *** denote significance at a 10%, 5% or 1%, respectively.

t-stat R²

NGDP*** 253.4185 3.283708 0.139795

UNEMP 0.636403 1.632342 0.013757

INDPROD*** 112.3494 3.439826 0.091246

HOUSING 0.120485 0.898124 0.005520

PGDP*** 110.7668 3.020518 0.079860

CPI*** 1.102448 3.467397 0.301343

TBILL** 0.331731 2.289321 0.188317

RGDP*** 147.5136 3.182939 0.092194

RCONSUM* 91.75796 1.881438 0.028197

BOND*** 4.067703 3.264261 0.254957

TBOND 0.536527 1.175615 0.026864

As can be seen from the table above, all of the -coefficients have positive values and the majority

of them is significant at the 1% level. R² statistics range from 0,005 to 0,301. Thus, in general, the

hypothesis can be confirmed that larger macro uncertainty (proxied by disagreement) results in

larger forecast errors. The results remain essentially unchanged when forecast error is measured as

the difference between the median forecast and initial instead of revised release.

Besides an ordinary regression of forecast errors on macro disagreement, it was also chosen to model

a vector autoregression for each pair of error and dispersion series. The big advantage of a VAR is

that it treats the dispersion measures as being endogenous. The appropriate amount of lags were

chosen with the likelihood ratio test, Akaike information criterion, and Schwarz information criterion,

among others. In general, the models, reported in appendix 8, contain between 1 and 6 lags.

Subsequently checks for autocorrelation were undertaken using the Box-Pierce Q-statistic and the

46

autocorrelation LM test. Granger causality tests were performed to check the hypothesis that

disagreement causes larger forecast errors, and not the other way around. Results are reported in

appendix 8. For eight out of eleven variables, the hypothesis is rejected that disagreement does not

cause subsequent forecast errors. On the other hand, only for three out of eleven equations, the

hypothesis is rejected that forecast error does not cause subsequent disagreement. Therefore, the

statement that macro disagreement is causally prior therefore seems to be largely affirmed by the

data.

The results of the linear regressions and VARs have an interesting implication for asset returns; as

the absolute forecast error, or “surprise” will generally be larger when disagreement is higher, asset

price reactions to macro releases will be larger, too. This results in higher asset price volatility in

periods of high disagreement. Therefore, this could be a partial explanation for the findings of Arnold

and Vrugt (2008) and Glansbeek and Ivo (2011), who find that disagreement has predictive ability

for asset volatility.

A cautionary note is however in place with respect to the regressions and Granger causality tests

presented in this section. Dopke and Fritsche (2006) and Arnold and Vrugt (2008) have already shown

that macro dispersion measures are significantly higher during recession periods. It could therefore be

questioned whether these the previously reported relationship between forecast error and dispersion

is actually caused by a third variable, i.e. some kind of variable measuring the state of the economy.

We experimented with different recession measures, a.o. the NBER definition of economic states and

the ADS index. Overall, the NBER definition of economic states performed best in our models. We

therefore conduct the following regression;

|𝑒𝑟𝑟𝑜𝑟𝑥(𝑡)| = 𝛼 + 𝛽1 ∗ 𝑁𝐵𝐸𝑅(𝑡) + 𝛽2 ∗ 𝜎𝑥(𝑡) + 𝜀(𝑡) (5)

Where NBER(t) is a dummy variable equal to 0 during recession periods and 1 otherwise. We apply the

same methodology as in equation 4. The results are presented in table 17.

Table 17; regressions of absolute forecast errors on NBER recessions and macro disagreement

Note; *, ** and *** denote significance at a 10%, 5% or 1%, respectively.

t-stat t-stat R²

NGDP 0.002785 0.005943 253.5863*** 2.684193 0.139795

UNEMP -0.055931** -2.557117 0.220386 0.515951 0.060020

INDPROD -1.160951 -1.305805 88.61401*** 2.090376 0.119367

HOUSING 0.006040 0.454637 0.148039 0.793385 0.006912

PGDP 0.099854 0.678651 118.4057*** 3.770283 0.082436

CPI -0.400367* -1.672084 0.933832*** 2.947106 0.319518

TBILL 0.005765 0.144076 0.337182** 2.197887 0.188517

RGDP 0.088923 0.262228 150.7345*** 3.071431 0.092629

RCONSUM -0.411069 -1.232139 72.09900 1.407756 0.040239

BOND -0.087239 -0.759886 3.627375*** 4.229997 0.272559

TBOND -0.052411 -1.339628 0.449066 1.229863 0.048480

47

The NBER recession measure does generally not subsume the explanatory power of dispersion

measures. Quite on the contrary, the recession measures are often insignificant, while dispersion

measures have overall highly significant t-statistics. It can therefore be stated that the previous

specification of equation 4, not including a recession measure, was correct.

III.3.3. Default premia and macro dispersion

III.3.3.1. Is the default premium linked to macro uncertainty?

As described under III.1.4.3, the fundamental models of Merton (1974) state that default premia are

positively related to volatility in a company’s value. To the extent that volatility in a firm’s value is a

proxy for uncertainty, it can be questioned whether, at a macro level, the default premium is

positively related to dispersion in macroeconomic consensus data. Previous literature on default

premia has already used the VIX as a measure of volatility or uncertainty in firm values. It might

therefore be interesting to check how well the VIX performs in comparison to disagreement as a

measure for macro uncertainty. In addition, GDP growth is introduced into the equation for the

default premium. GDP growth is expected to be significantly related to the default premium because

it proxies for the state of the economy, and therefore conveys information on the aggregate company

value in the structural model of Merton. Altogether, we regress the following equation;

𝑑𝑒𝑓𝑎𝑢𝑙𝑡(𝑡) = 𝛼 + 𝛽1 ∗ 𝑅𝑔𝑑𝑝𝑦𝑜𝑦(𝑡) + 𝛽2 ∗ 𝑉𝐼𝑋(𝑡) + 𝛽3 ∗ 𝜎𝑥(𝑡) + 𝜀(𝑡) (6)

Where the default premium, default(t), is measured as the difference between corporate bond rates

with a Moody’s rating Baa and Aaa. Rgdpyoy(t) is the year on year growth rate of Real GDP in quarter

t. σx(t) is the cross-sectional dispersion in point forecasts of variable x in quarter t. Unit root tests

for these variables are presented in appendix 3. The specification of equation 6, which only takes

into account one macro dispersion measure in every regression, is chosen to avoid multicollinearity

problems. White heteroscedastic robust errors or Newey-West HAC errors are used to account for

autocorrelation and/or heteroscedasticity.

The results of these regressions will be compared with a baseline models using RGDP growth and the

VIX;

𝑑𝑒𝑓𝑎𝑢𝑙𝑡(𝑡) = 𝛼 + 𝛽1 ∗ 𝑅𝑔𝑑𝑝𝑦𝑜𝑦(𝑡) + 𝛽2 ∗ 𝑉𝐼𝑋(𝑡) + 𝜀(𝑡) (7)

This model will be estimated using the same method as for equation 5. The results of these

regressions are presented in table 18.

Table 18; regressions of default premia on GDP growth, VIX and macro disagreement

Note; *, ** and *** denote significance at a 10%, 5% or 1%, respectively. Values in parentheses

are HAC corrected t statistics.

Dispersion

measure 1 t-stat 2 t-stat 3 t-stat R² Adj. R²

Eq. (5) -14.306*** -6.0562 0.0208*** 4.2845 0.7229 0.7166

MEAN -6.9857*** -3.1921 0.0162*** 3.9236 0.6447*** 4.0080 0.7748 0.7671

NGDP -12.429*** -5.0546 0.0192*** 3.6472 37.895 1.3084 0.7315 0.7223

CPROF -11.630*** -4.8486 0.0222*** 4.8485 2.1254*** 2.7880 0.7459 0.7373

UNEMP -14.438*** -6.1128 0.0212*** 4.5692 -3.2804* -1.9474 0.7299 0.7207

48

INDPROD -12.916*** -5.2487 0.0201*** 4.1059 9.3777 1.0614 0.7271 0.7178

HOUSING -11.749*** -5.9089 0.0190*** 4.2022 2.7373** 2.0528 0.7421 0.7333

PGDP -12.956*** -4.7422 0.0205*** 4.2026 34.771 0.7847 0.7259 0.7165

CPI -12.621*** -4.8985 0.0184*** 4.5534 0.1620** 2.0483 0.7368 0.7278

TBILL -14.330*** -6.1754 0.0210*** 4.1848 -0.4306 -0.8308 0.7262 0.7168

RGDP -14.629*** -5.7356 0.0211*** 4.1501 -11.017 -0.4053 0.7233 0.7139

RCONSUM -13.516*** 6.3178 0.0197*** 3.9228 29.753 1.6033 0.7306 0.7214

BOND -13.707*** -6.1289 0.0178*** 3.7615 2.7387* 1.8248 0.7436 0.7348

TBOND -14.562*** -6.5228 0.0177*** 3.5300 2.0487* 1.7289 0.7640 0.7551

In the baseline model (equation 7), the reported RGDP growth and VIX coefficients are highly

significant and show high (adjusted) R² statistics. This again confirms that the default premium is

significantly related to the VIX and the overall state of the economy.

Looking at the estimation results for equation 6, it appears that the coefficient for the MEAN

dispersion index is highly significant and shows a remarkably higher (adjusted) R² than the baseline

model. In addition, other macro dispersion series such as CPROF and HOUSING show significant

coefficients. Therefore, it seems that the VIX generally does not subsume the explanatory power of

macro disagreement variables. This might indicate that the VIX index and the measures of macro

disagreement capture a different form of uncertainty, while being both significantly related to the

default premium. In general, the reported coefficients for RGDP growth, VIX, and macro

disagreement are able to confirm the model of Merton (1974) at a macro level; i.e. the default

premium is significantly related to macro uncertainty and the overall state of the economy.

Again, an interesting finding of table 18 is that multiple disagreement variables have highly significant

coefficients and are thus able to proxy for uncertainty. While previous research has mainly focused

on disagreement on inflation forecasts (see, for example, D’Amico and Orphanides, 2008, Mankiw et

al. 2004, Lahiri and Liu, 2010), these results show significant coefficients for a wide range of macro

variables, including but not limited to the CPI inflation rate.

The results in table 18 are also interesting because RGDP growth does not appear to subsume

explanatory power of macro disagreement. After all, previous research has already shown that

dispersion measures vary according to the state of the economy (see for example Arnold and Vrugt,

2008 and Dopke and Fritsche, 2006). Now, the regressions presented in table 18 have shown that

macro disagreement significantly affects default premia, even when taking into account other RGDP

growth. Therefore, when the economy picks up, there will be a “double whammy” effect on default

spreads; on the one hand, default premia will decline because GDP increases. On the other hand,

default spreads will further decrease because dispersion will decline, too. This also has an interesting

asset allocation implication; as an investor, if you believe that macro disagreement will rise/decline,

you have a signal to trade, as default spreads move together with dispersion in macro consensus

data.

49

Following the extant literature, more macro variables could be included into a specification for the

default premium. For example, S&P500 returns could be included as a measure for the business

climate (see e.g. Bhar et al., 2011). The rate on 10 year Treasury bond returns is also often used to

explain default premia (see e.g. Longstaff and Schwartz, 1995 and Collin-Dufresne et al. 2001).

Lastly, as the term premium is associated to economic activity (i.e. a lower term premium indicates

a higher probability of a recession), it can also be related to default premia (see e.g., Duffee, 1998

and Papageorgiou and Skinner (2006). Therefore, the following specification is regressed;

𝑑𝑒𝑓𝑎𝑢𝑙𝑡(𝑡) = 𝛼 + 𝛽1 ∗ 𝑟𝑔𝑑𝑝𝑦𝑜𝑦(𝑡) + 𝛽2 ∗ 𝑆𝑃𝑋(𝑡) + 𝛽3 ∗ ∆𝑇𝑏𝑜𝑛𝑑(𝑡)+

𝛽4 ∗ 𝑇𝑒𝑟𝑚(𝑡) + 𝛽5 ∗ 𝑉𝐼𝑋(𝑡) + 𝛽6 ∗ 𝜎𝑀𝐸𝐴𝑁(𝑡) + 𝜀(𝑡) (8)

Where SPX(t) is the quarterly S&P500 return; Tbond(t) is the yield on a 10 year Treasury bond;

Term(t) is the difference between the rate of a 10 year Treasury bond and a 3 month T Bill. All of

these variables are measured at the end of the second month of quarter t. In this extended

specification, it is chosen to only model the MEAN disagreement index, without any other dispersion

measures. Newey-West HAC errors are used to account for heteroscedasticity and autocorrelation.

Table 19 presents the results.

Table 19; default premium regressed on its macro determinants

Note; *** denotes significance at 1% level. HAC corrected t-statistics are given in parentheses.

1 2 3 4 5 6 0.1431 -6.8684*** -0.2845 0.0544 0.0153 0.0159*** 0.6279***

(0.6199) (-3.2804) (-0.7191) (1.4330) (0.7922) (4.1945) (3.6732)

R²= 0.781981 ; Adj. R² = 0.766592

As it appears from the table above, the three additional macro variables don’t add explanatory power

to our initial specification presented in equation 6. Their t-statistics are insignificant, and the

(adjusted) R² remains essentially unchanged compared to the results of table 18. Therefore, we

reject this specification and keep our original regression of equation 6.

III.3.3.2. Market timing strategies

As the previous section has shown a significant relationship between default premia and macro

uncertainty, it can be questioned whether disagreement in macro estimates can also be used for a

market timing strategy. Similar to III.3.1.2, we model the following regression;

∆𝑛𝑠𝑝𝑟𝑒𝑎𝑑(𝑡) = 𝛼 + 𝛽 ∗ 𝜎𝑥(𝑡) + 𝜀(𝑡) (9)

Where nspread(t) is the difference in the default spread (in percentage point) measured over n

months, beginning in the third week of the middle month of quarter t (i.e. beginning after the release

of the SPF). In these regressions, n={1,2,3,6} has been chosen. These regressions are therefore

useful for an investor who wishes to trade based on macro dispersion just after a new SPF is released.

The same regression method is used as for equation 6. Table 20 provides the results for 1 month

default spread changes. 2, 3 and 6 month regression results are provided in appendix 9.

50

Table 20; market timing for the default spread based on macro dispersion

Dispersion

measure t-stat R²

MEAN 0.034460 1.498407 0.017334

NGDP 3.636087 0.836066 0.005708

CPROF 0.147884 0.478204 0.002027

UNEMP -0.015749 -0.027699 0.000004

INDPROD 1.632408 0.747469 0.007006

HOUSING 0.326150 0.925304 0.007611

PGDP 6.031265 0.915080 0.006434

CPI 0.059939** 2.329118 0.041915

TBILL 0.079387 1.159661 0.010729

RGDP 2.339806 0.777875 0.003342

RCONSUM -0.572156 -0.113713 0.000104

BOND 0.325613 0.538783 0.004680

TBOND -0.096967 -0.232487 0.000659

The results presented in the table above are rather disappointing. Almost every coefficient is

insignificant, and R² statistics are extremely low. The majority of the ’s in table 20 appear to be

positive, but for longer horizons (see appendix 9), this is no longer the case. There is no clear pattern

in R² statistics compared over different timing horizons.

The coefficient for CPI disagreement seems to be significantly positive for a 1 month default spread

timing strategy. However, one should note that there are only very few observations for high

dispersions. Furthermore, these regressions are generally quite sensitive to outliers. Appendix 10

provides the scatterplot of CPI for a 1 month timing horizon; it appears that the reported positive

coefficient in table 19 is mainly due to one observation (2008Q4). The regression for CPI is therefore

difficult to interpret.

Although the default premium has a significant connection to dispersion measured in the same

quarter (see equation 6), these results cannot be transposed to a reliable market timing strategy.

III.3.4. Preliminary conclusion

This section has been able to provide some interesting links between macro disagreement and other

macro series. Among others, it has been shown that absolute forecast errors are related to macro

uncertainty. In particular, causality seems to go mainly from disagreement to absolute forecast error.

This relationship could be one of the explanations why previous literature (e.g. Glansbeek and Ivo,

2011), has found significant links between asset volatility and macro disagreement.

Furthermore, a significant relationship is shown between macro uncertainty and the default premium.

This relationship remains when controlling for the effect of RGDP growth and the VIX index . On the

other hand, macro dispersion doesn’t seem to work in any market timing strategy.

51

It is somewhat surprising that we found a significant relationship between macro uncertainty and

default spreads, while such a significant relationship has not been found for stock returns. From a

theoretical perspective, however, the risk priced in stock markets should be fundamentally connected

to default premia. This goes back to Merton (1974), who relates the default premium to the volatility

in firm value. Jarrow and Turnbull (2000, p. 272) also state that “If the market value of the firm’s

assets unexpectedly changes – generating market risk – this affects the probability of default –

generating credit risk. Conversely, if the probability of default unexpectedly changes – generating

credit risk – this affects the market value of the firm – generating market risk.” In empirical research,

a significant link between aggregate stock returns and the default premium has already been found

by Shane (1994), Huang and Kong (2003), Collin-Dufresne et al. (2001) and Bhar and Handzic

(2011). From this perspective, it remains puzzling that it was not possible to relate macro uncertainty

to stock returns.

III.4. Conclusion

This chapter started with an overview of a wide range in literature showing a clear link between stock

returns and dispersion in micro consensus data. Several authors such as Ackert and Athanassakos

(1997), Diether et al. (2002) and Hintikka (2008) have shown that shares with a lower disagreement

about earnings forecasts will outperform the stock market. This finding is backed by several

independent theoretical models on micro dispersion, such as the frameworks of Miller (1997) and

Johnson (2004).

Where previous research came up with clear asset allocation strategies for micro dispersion, this is

far from the case for macro dispersion. There are some theoretical models on the link between macro

dispersion and stock returns (see e.g. Varian, 1985 and Soderlind, 2006), but these models have

generally remained without empirical validation. The extant empirical literature has previously

performed research on the link between dispersion and recession periods (e.g. Dopke and Fritsche,

2006), dispersion and asset volatility (e.g. Glansbeek and Ivo, 2011), dispersion and term premia

(D’Amico and Orphanides, 2008) and dispersion and levels of inflation (e.g. Mankiw et al. 2004).

In this thesis, literature on macro dispersion is augmented by showing that disagreement has a clear

link with future macro surprises and default spreads. Previous empirical literature (e.g. D’Amico and

Orphanides, 2008, Mankiw et al. 2004, Lahiri and Liu, 2010) has mainly focused on disagreement on

inflation forecasts. An interesting finding of this thesis is that disagreement in other macro variables

is equally useful in empirical research. Also, an index of macro dispersion, as suggested by Bowles

et al. (2007) provides significant coefficients in most of the models tested in this chapter.

Altogether, in line with papers by Zarnowitz and Lambros (1987), Giordani and Soderlind (2003),

Arnold and Vrugt (2008), Glansbeek and Ivo (2011), and Dopke and Fritsche (2006), the results

presented in this thesis can be seen as additional evidence that disagreement on macro estimates is

a good proxy for uncertainty.

52

Another part of this chapter focused on the link between excess stock returns and dispersion in macro

consensus data. These regressions, however, were not able to provide significant results. Therefore

it was not possible to empirically validate research by Varian (1985) or Soderlind (2006). Hence,

macro uncertainty (proxied by disagreement) does not appear to be a source of non-diversifiable

risk. Furthermore, dispersion in macro estimates does not appear to be useful for stock market

timing, nor default spread timing.

Any further research that could explain why the tests for excess stock market returns were not able

to provide significant coefficients, or any research that could nevertheless empirically demonstrate a

link between macro uncertainty and equity returns, would be very helpful.

VII

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XIII

APPENDIX

Appendix 1 - Out of sample bond timing returns for different derivatives

Note; “70” and “75” denote the percentiles of CESI derivatives. In the in sample period, when a

derivative is above the 70th or 75th percentile, government bonds will be sold and the investor will

place its cash as an overnight currency deposit.

0,00%

2,00%

4,00%

6,00%

8,00%

10,00%

12,00%

14,00%

1 2 3 4 5 6 7 8 9 10 11 12 14 16 18 19 20 21 25 30 35 40 45 50 55 60 65 70

out

of sam

ple

retu

rn (

yearly %

)

Derivative

USA 30 years

70

75

0,00%

2,00%

4,00%

6,00%

8,00%

10,00%

12,00%

1 2 3 4 5 6 7 8 9 10 11 12 14 16 18 19 20 21 25 30 35 40 45 50 55 60 65 70

out

of sam

ple

retu

rn (

yearly %

)

Derivative

Canada 30 years

70

75

XIV

0,00%

2,00%

4,00%

6,00%

8,00%

10,00%

12,00%

1 2 3 4 5 6 7 8 9 10 11 12 14 16 18 19 20 21 25 30 35 40 45 50 55 60 65 70out

of sam

ple

retu

rn (

yearly %

)

Derivative

EMU 30 years

70

75

0,00%

1,00%

2,00%

3,00%

4,00%

5,00%

6,00%

7,00%

8,00%

1 2 3 4 5 6 7 8 9 10 11 12 14 16 18 19 20 21 25 30 35 40 45 50 55 60 65 70

out

of sam

ple

retu

rn (

yearly %

)

Derivative

Japan 30 years

70

75

0,00%

2,00%

4,00%

6,00%

8,00%

10,00%

12,00%

1 2 3 4 5 6 7 8 9 10 11 12 14 16 18 19 20 21 25 30 35 40 45 50 55 60 65 70out

of sam

ple

retu

rn (

yearly %

)

Derivative

UK 30 years

70

75

XV

0,00%

2,00%

4,00%

6,00%

8,00%

10,00%

12,00%

14,00%

16,00%

18,00%

1 2 3 4 5 6 7 8 9 10 11 12 14 16 18 19 20 21 25 30 35 40 45 50 55 60 65 70out

of sam

ple

retu

rn (

yearly %

)

Derivative

UK 50 years

70

75

XVI

Appendix 2 –Bond timing strategy based on US and domestic surprise index derivatives

Bond portfolio Canada 30 years EMU 30 years Japan 30 years UK 30 years UK 50 years

In sample return (yearly %) 6,45% 11,69% 17,20% 13,42% 9,39%

In sample benchmark government

bonds (yearly %)

4,32% 0,47% 11,25% 8,87% 3,77%

In sample benchmark o/n deposit

(yearly %)

3,68% 2,62% 0,04% 4,32% 3,73%

Out of sample return (yearly %) 8,23% 8,55 6,19% 5,58% 4,66%

Out of sample benchmark government

bonds (yearly %)

9,03% 8,79% 0,36% 6,20% 7,84%

Out of sample benchmark o/n deposit

(yearly %)

1,71% 1,55% 0,11% 2,31% 0,81%

Derivative 6 days 9 days 5 days 5 days 5 days

Percentile of derivative values 75 80 75 75 70

Pesaran and Timmermann test

statistic

-105,55 2,6512 3,8348 1,3086 0,81472

P-value 1,0000 0,00401 0,00006 0,09533 0,20762

Transaction cost which makes surplus

return = 0

0,1284%

XVII

Appendix 3 - Unit root tests

Unit root tests statistics reported below are ADF tests for a model with intercept and trend. Lag

lengths are selected with the Schwarz Info Criterion.

Surprise

index

First differences Levels

t-Statistic P-value t-Statistic P-value

CESIUSD -59.64498 0.0000 -4.295934 0.0032

CESICAD -58.90872 0.0000 -5.440529 0.0000

CESIEUR -56.88596 0.0000 -4.201009 0.0044

CESIGBP -60.05852 0.0000 -5.943305 0.0000

CESIJPY -62.11908 0.0000 -5.298766 0.0000

Dispersion

measure

First differences Levels

t-Statistic P-value t-Statistic P-value

MEAN -11.67676 0.0000 -4.689528 0.0010

NGDP -14.04614 0.0000 -4.896064 0.0005

CPROF -14.53474 0.0000 -8.005310 0.0000

UNEMP -10.26932 0.0000 -10.92476 0.0000

INDPROD -19.21301 0.0000 -7.332860 0.0000

HOUSING -13.76182 0.0000 -4.325636 0.0036

PGDP -16.87118 0.0000 -6.955815 0.0000

CPI -11.64922 0.0000 -5.819661 0.0000

TBILL -18.21903 0.0000 -7.610267 0.0000

RGDP -13.54701 0.0000 -6.686918 0.0000

RCONSUM -10.84753 0.0000 -7.585414 0.0000

BOND -11.78617 0.0000 -3.546256 0.0389

TBOND -9.620649 0.0000 -6.661180 0.0000

Absolute

error

measure

First differences Levels

t-Statistic P-value t-Statistic P-value

NGDP -11.02267 0.0000 -11.46131 0.0000

UNEMP -11.01369 0.0000 -10.75453 0.0000

INDPROD -12.61764 0.0000 -9.163862 0.0000

HOUSING -13.08469 0.0000 -12.06409 0.0000

PGDP -11.89318 0.0000 -11.32074 0.0000

CPI -8.417468 0.0000 -10.02741 0.0000

TBILL -9.263930 0.0000 -8.501837 0.0000

RGDP -8.055123 0.0000 -13.14970 0.0000

RCONSUM -13.67281 0.0000 -11.39460 0.0000

BOND -7.692831 0.0000 -7.105738 0.0000

XVIII

TBOND -6.546613 0.0000 -10.89335 0.0000

First differences Levels

t-Statistic P-value t-Statistic P-value

Default -14.39212 0.0000 -4.216240 0.0052

Rgdpyoy -7.140652 0.0000 -3.588521 0.0339

VIX -8.229661 0.0000 -5.147136 0.0003

Tbond -12.43731 0.0000 -12.63894 0.0000

Term -13.21141 0.0000 -3.763947 0.0208

SPX -15.39542 0.0000 -11.58876 0.0000

Appendix 4 –Bond timing strategy based on surprise index levels

In the tables below, levels of the CESIUSD are divided into different “hurdles” or percentiles. The

corresponding mean future government bond returns are calculated. These future bond returns are

defined as the percentage change over a horizon of 2 weeks and 1, 2, 3 and 6 months. Arbitrarily,

the hurdles are chosen as the 10th, 20th, 80th and 90th percentiles of CESIUSD levels. The average

government bond return is presented in the middle row of each table.

USA government bonds and CESIUSD

CESI percentile 2 weeks 1 month 2 months 3 months 6 months

0,9 0,4246% 0,6122% 0,5866% 2,2617% 6,5553%

0,8 0,4415% 0,7583% 0,9946% 2,5075% 6,3829%

average 0,3092% 0,6267% 1,2434% 1,8851% 3,7896%

0,2 0,5936% 1,0974% 1,2019% 0,9304% 1,8272%

0,1 1,3467% 2,5693% 3,1951% 2,7545% 3,3502%

Canadian government bonds and CESIUSD

CESI percentile 2 weeks 1 month 2 months 3 months 6 months

0,9 0,4490% 0,5360% 0,6355% 1,7984% 4,6134%

0,8 0,3576% 0,5060% 0,7147% 1,9243% 4,8013%

average 0,3391% 0,6794% 1,3384% 2,0367% 4,1687%

0,2 0,4945% 0,9836% 1,4594% 1,8503% 3,5063%

0,1 1,0043% 1,9154% 2,9146% 3,3355% 4,8761%

EMU government bonds and CESIUSD

CESI percentile 2 weeks 1 month 2 months 3 months 6 months

0,9 0,1135% 0,3264% 0,5564% 1,8660% 4,7383%

0,8 0,3069% 0,5809% 1,1389% 2,4517% 5,4048%

average 0,3275% 0,6452% 1,2796% 1,9911% 4,1630%

XIX

0,2 0,3896% 0,7902% 1,0716% 1,3421% 2,7951%

0,1 0,9281% 2,0676% 2,8370% 3,1706% 5,2275%

Japanese government bonds and CESIUSD

CESI percentile 2 weeks 1 month 2 months 3 months 6 months

0,9 0,4814% 0,9791% 1,0699% 2,0612% 2,9803%

0,8 0,2080% 0,4800% 0,8973% 1,6510% 2,3050%

average 0,1466% 0,2932% 0,5714% 0,8621% 1,6581%

0,2 0,4268% 0,7210% 0,7405% 0,3189% 0,9851%

0,1 0,9176% 1,6285% 1,8378% 0,9164% 1,0067%

UK 30 year government bonds and CESIUSD

CESI percentile 2 weeks 1 month 2 months 3 months 6 months

0,9 0,1463% 0,1734% -0,1241% 0,8137% 4,1969%

0,8 0,2569% 0,3787% 0,4632% 1,3123% 3,8797%

average 0,2960% 0,5879% 1,1597% 1,7595% 3,5223%

0,2 0,2779% 0,6034% 0,8782% 1,0058% 1,6927%

0,1 0,5149% 1,1552% 1,5541% 1,6650% 3,1653%

UK 50 year government bonds and CESIUSD

CESI percentile 2 weeks 1 month 2 months 3 months 6 months

0,9 0,5884% 0,7534% 0,2625% 1,6856% 4,5998%

0,8 0,4158% 0,6711% 0,7220% 1,6238% 4,0860%

average 0,2820% 0,5485% 1,0977% 1,7186% 3,5566%

0,2 0,4234% 0,8397% 1,2557% 1,2822% 2,6863%

0,1 0,7757% 1,4532% 1,8632% 2,7935% 5,2826%

Appendix 5 – Chow breakpoint tests

Chow Breakpoint test for CPI in equation 1

We conduct the regression r(t) = + *cpi(t) for two subperiods;

t-stat t-stat R² RSS

1981Q4-1991Q4 0.200740 1.648333 -0.118117 -0.967168 0.022851 5.292267

1992Q1-2012Q4 0.209066 2.965330 -0.180662 -2.055250 0.048989 9.065624

F-statistic = 0.294329. Prob F(2,122) = 0.7456.

Therefore, the null hypothesis of no breaks at 1992Q1 is not rejected.

Chow Breakpoint test for PGDP in equation 2

XX

We conduct the regression r(t) = + *pgdp(t) for two subperiods;

t-stat t-stat R² RSS

1971Q1-1991Q4 0.058828 1.537109 -36.10420 -1.864081 0.042802 10.01731

1992Q1-2012Q4 0.084559 2.282009 -29.45176 -0.701901 0.006023 9.475210

F-statistic = 0.127018. Prob F(2,164) = 0.8808.

Therefore, the null hypothesis of no breaks at 1992Q1 is not rejected

Appendix 6 – Market timing for the S&P500 based on macro dispersion

Dispersion

measure

2 month future returns

t-stat R²

MEAN 0.0933 1.3949 0.0110

NGDP -0.1613 -0.0130 0.0000

CPROF 0.4727 0.5607 0.0018

UNEMP -1.3707 -0.7105 0.0029

INDPROD 9.0218* 1.8178 0.0185

HOUSING 0.4251 0.4430 0.0011

PGDP -10.591 -0.5465 0.0018

CPI -0.0221 -0.2368 0.0005

TBILL 0.7690 1.0464 0.0802

RGDP -1.6346 -0.1572 0.0001

RCONSUM 15.698 0.8832 0.0063

BOND 2.1378 1.4229 0.0161

TBOND 2.1013** 2.4312 0.0414

Dispersion

measure

3 month future returns

t-stat R²

MEAN 0.0683 1.3203 0.0099

NGDP 0.6516 0.0682 0.0000

CPROF 0.7595 1.1684 0.0077

UNEMP -1.6215 -1.0888 0.0067

INDPROD 6.4621 1.6331 0.0159

HOUSING 0.2936 0.3954 0.0009

PGDP -11.728 -0.7822 0.0037

CPI -0.0565 -0.7617 0.0056

TBILL 0.3882 0.8954 0.0386

RGDP 1.0429 0.1297 0.0001

RCONSUM 18.852 1.4650 0.0170

BOND 1.9378* 1.7799 0.0249

XXI

TBOND 1.6284** 2.2710 0.0435

Dispersion

measure

6 month future returns

t-stat R²

MEAN 0.0338 0.9324 0.0050

NGDP 2.8816 0.3966 0.0011

CPROF 0.3210 0.6648 0.0028

UNEMP -1.6211 -1.0543 0.0138

INDPROD 2.4685 0.8231 0.0048

HOUSING 0.2872 0.4249 0.0018

PGDP -3.1547 -0.2720 0.0006

CPI -0.0427 -0.7487 0.0068

TBILL 0.1863 0.7862 0.0188

RGDP -1.0420 -0.1754 0.0002

RCONSUM 9.3667 1.0237 0.0089

BOND 1.8517** 2.2629 0.0484

TBOND 1.5351*** 2.6638 0.0614

Appendix 7 – Plot of disagreement on TBOND and 6 month S&P500 returns

Appendix 8 – Granger causality tests

Null hypothesis Lags in VAR F-statistic P-value

BOND does not Granger cause errorx(t) 3 5.71513 0.0011

errorx(t) does not Granger cause BOND 1.21180 0.3088

HOUSING does not Granger cause errorx(t) 3 0.68451 0.5627

XXII

errorx(t) does not Granger cause HOUSING 2.09101 0.1034

INDPROD does not Granger cause errorx(t) 2 3.55200 0.0309

errorx(t) does not Granger cause INDPROD 1.72733 0.1809

NGDP does not Granger cause errorx(t) 2 7.64966 0.0007

errorx(t) does not Granger cause NGDP 5.01780 0.0077

PGDP does not Granger cause errorx(t) 1 9.00695 0.0031

errorx(t) does not Granger cause PGDP 1.78076 0.1839

RCONSUM does not Granger cause errorx(t) 4 0.87698 0.4803

errorx(t) does not Granger cause RCONSUM 2.48310 0.0478

RGDP does not Granger cause errorx(t) 6 2.98348 0.0087

errorx(t) does not Granger cause RGDP 2.93438 0.0097

TBOND does not Granger cause errorx(t) 1 5.49165 0.0217

errorx(t) does not Granger cause TBOND 1.15675 0.2855

UNEMP does not Granger cause errorx(t) 1 1.79363 0.1823

errorx(t) does not Granger cause UNEMP 0.62632 0.4298

CPI does not Granger cause errorx(t) 1 54.6077 0.0000

errorx(t) does not Granger cause CPI 2.27444 0.1342

TBILL does not Granger cause errorx(t) 4 14.9320 0.0000

errorx(t) does not Granger cause TBILL 0.33977 0.8506

Appendix 9 – Market timing for the default premium based on macro dispersion

Dispersion

measure

2 month spread change

t-stat R²

NGDP 4.780610 0.588831 0.004030

CPROF 0.066213 0.147145 0.000166

UNEMP -0.532144 -0.598768 0.002045

INDPROD -0.611181 -0.208746 0.000401

HOUSING 0.495034 0.850953 0.007162

PGDP 8.189044 0.860509 0.004802

CPI 0.063028 1.445266 0.020638

TBILL -0.056229 -0.545814 0.002397

RGDP 1.623100 0.281069 0.000657

RCONSUM -9.336435 -1.245886 0.012363

BOND -0.020457 -0.031936 0.000008

TBOND 0.146454 0.233489 0.000664

Dispersion

measure

3 month spread change

t-stat R²

NGDP 3.015316 0.327320 0.000926

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CPROF -0.414777 -0.812837 0.003761

UNEMP 0.165620 0.141495 0.000114

INDPROD -4.822249 -1.173626 0.014423

HOUSING 0.026485 0.031032 0.000012

PGDP 7.624517 0.643611 0.002449

CPI 0.046481 0.863771 0.006031

TBILL 0.081926 0.583057 0.002734

RGDP 1.556016 0.255273 0.000349

RCONSUM -12.88866 -1.260956 0.012660

BOND -0.820736 -0.942590 0.007114

TBOND -1.272934 -1.626338 0.031248

Dispersion

measure

6 month spread change

t-stat R²

NGDP -2.448566 -0.202898 0.000322

CPROF -0.641358 -0.500431 0.004770

UNEMP 1.439088 0.976194 0.004580

INDPROD -9.407502 -1.323007 0.029119

HOUSING -1.053880 -0.716032 0.009950

PGDP -5.732203 -0.288561 0.000735

CPI -0.032374 -0.316415 0.001510

TBILL 0.008401 0.032846 0.000015

RGDP 2.485414 0.311226 0.000470

RCONSUM -21.15173 -1.488051 0.017426

BOND -2.960416* -1.706522 0.047769

TBOND -3.586003 -1.463515 0.113983

Appendix 10 – Plot of disagreement on CPI and the 1 month default spread change

-.8

-.6

-.4

-.2

.0

.2

.4

.6

.8

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

CPI

1 m

on

th d

efa

ult

sp

rea

d c

ha

ng

e

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Appendix 11 – Nederlandse samenvatting

Macro verrassingen

Macro-economische voorspellingen hebben aanzienlijk meer aandacht gekregen in recent empirisch

onderzoek. Dit type van data is namelijk zeer nuttig om het verband tussen macro verrassingen en

rendementen van verschillende activa klassen te onderzoeken.

Het literatuuronderzoek in deel II.1 toont dat vele academische publicaties gebruik maken van

conventionele fundamentele modellen of gewone levels in macro variabelen om een verband te

onderzoeken tussen asset returns en macro-economische variabelen. Dit type onderzoek resulteerde

echter in veel insignificante variabelen. Anderzijds, publicaties die gebruik maakten van macro

verrassingen waren wel in staat om consistente resultaten aan te tonen. Forex rendementen,

bijvoorbeeld, hebben een sterk verband met een brede waaier aan macro verrassingen.

Fundamentele modellen voor forex waren daarentegen niet in staat om systematisch goede

resultaten aan te tonen.

Literatuur over macro verrassingen heeft vaak de interactie tussen verrassingen van verschillende

variabelen genegeerd. Daarom heeft deze thesis gebruikt gemaakt van indices van macro

verrassingen om zo een nieuwe methode voor te stellen om het verband tussen activa rendementen

en macro verrassingen te onderzoeken. De descriptieve analyse van deel II.2 toont aan dat aandelen,

forex en obligaties een duidelijk verband hebben met indices van macro verrassingen.

In deel II.3 wordt een obligatie timing strategie voorgesteld, gebaseerd op de afgeleide van de

CESIUSD index. De resultaten tonen aan dat, voordat transactiekosten in rekening worden gebracht,

deze strategie hoge abnormale rendementen behaalt voor verschillende portefeuilles van lange

termijn overheidsobligaties. Deze timing strategie heeft echter ook beperkingen. De afgeleide wordt

in deze modellen gedefinieerd als het verschil in index niveau over 5 of 6 dagen. Deze korte termijn

definitie zorgt daarom bijvoorbeeld voor hoge transactiekosten.

Gerelateerde obligatie timing modellen die gebaseerd zijn op indices van macro verrassingen van

andere landen dan de USA, of modellen gebaseerd op het niveau (“hurdle”) van indices van macro

verrassingen, zijn vaak niet winstgevend.

In elk geval tonen de positieve resultaten van het obligatie timing model, gebaseerd op de eerste

afgeleide van de CESIUSD, dat macro surprise indices potentieel hebben om gebruikt te worden in

asset allocatie beslissingen.

Verder onderzoek over indices van macro verrassingen zou betrekking kunnen hebben op een meer

formele (descriptieve) analyse over het verband tussen deze indices en activa rendementen. Market

timing modellen voor andere activa klassen, zoals aandelen kunnen ook interessant zijn. Hierbij is

het vooral een uitdaging om het tijdsafhankelijk verband te modelleren dat vaak wordt gevonden in

onderzoek over macro verrassingen en aandelenreturns. Klaarblijkelijk zijn er verschillende

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mogelijke invalshoeken om indices van macro verrassingen verder te bestuderen. Verder onderzoek

kan interessante applicaties hebben voor asset allocatie beslissingen.

Dispersie & consensus data

Dit deel begint met een literatuuronderzoek over de invloed van micro consensusdata op

aandelenrendementen. Verschillende auteurs zoals Ackert en Athanassakos (1997), Diether et al.

(2002) en Hintikka (2008) hebben aangetoond dat aandelen met een lage dispersie in

winstvoorspellingen gemiddeld hogere rendementen zullen genereren. Deze empirische bevinding

worden ondersteund door theoretische modellen van micro dispersie, waaronder de theorie van Miller

(1977) en Johnson (2004).

Alhoewel academisch onderzoek in staat is geweest om duidelijke asset allocatie strategieën te

formuleren voor micro dispersie, is dit lang niet het geval voor dispersie in macro voorspellingen. Er

zijn wel enkele theoretische modellen die het verband tussen macro dispersie en

aandelenrendementen behandelen (bv. Varian, 1985 en Soderlind, 2006). Echter, deze modellen

werden niet empirisch gevalideerd. De emprische literatuur over macro dispersie heeft reeds een

verbanden aangetoond tussen dispersie en economische recessies (bv. Dopke en Fritsche, 2006),

dispersie en volatiliteit in verschillende activa klassen (bv. Glansbeek en Ivo, 2011), dispersie en de

term premium (D’Amico en Orphanides, 2008) en dispersie en inflatie (bv. Mankiw et al. 2004).

Deze thesis draagt bij tot de literatuur over macro dispersie door aan te tonen dat dispersie in macro

voorspellingen een duidelijk verband heeft met macro verrassingen en default spreads. Vorig

onderzoek heeft vooral een focus op dispersie in inflatie voorspellingen (bv. D’Amico en Orphanides,

2008, Mankiw et al. 2004, Lahiri en Liu, 2010). Een interessante bevinding van deze thesis is dat

dispersie in andere macro variabelen even nuttig kan zijn voor academisch onderzoek. Verder wordt

er ook aangetoond dat een index van gemiddelde macro dispersie eveneens significante resultaten

toont in de meeste modellen van dit hoofdstuk.

In overeenstemming met ander onderzoek door Zarnowitz en Lambros (1987), Giordani en Soderlind

(2003), Arnold en Vrugt (2008), Glansbeek en Ivo (2011), en Dopke en Fritsche (2006), tonen de

resultaten van deze masterproef aan dat dispersie in macro consensus data een goede proxy is voor

onzekerheid.

Een volgend deel van deze thesis behandelde het verband tussen aandelenrendementen en dispersie

in macro consensus data. Deze regressies toonden echter geen significant resultaat. Het was dus

niet mogelijk om de publicaties van Varian (1985) of Soderlind (2009) empirisch te valideren. Macro

onzekerheid blijkt met andere woorden geen vorm van niet-diversifieerbaar risico te zijn.

Daarenboven kan dispersie in macro voorspellingen niet gebruikt worden voor een stock market

timing of default spread timing strategie.

Verder onderzoek dat zou kunnen uitleggen waarom aandelenrendementen geen significant verband

hebben met macro dispersie, of elk onderzoek dat toch een link tussen macro onzekerheid en

aandelenrendementen zou kunnen aantonen, zou in dit opzicht zeer nuttig zijn.

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