April 10, 2023 2

Indicators, Predictive Analytics & Forecasting

Evangelos Otto Simos, Ph.D.

Economic Research

e-forecasting.com

April 10, 2023 3

Outline

• Going Beyond Raw Data • Forecasting with Hundreds of

Drivers• Case: Forecasting a Hotel

Market• US & Global Outlook

April 10, 2023 4

GoingBeyondRawData

April 10, 2023 5

Types of Indicators

• Quantitative• GDP, Industrial Production, Employment

• Qualitative • NAPM Survey, Consumer Surveys

• Analytics (Diffusion Indices) • Aggregates of Quantitative and/or Qualitative

Indicators

• Predictive Analytics• Fact-based forward-looking analytics (composite

leading indicators)

April 10, 2023 6

Case: PMI: NAPM Diffusion Index

NAPM History

20.0

30.0

40.0

50.0

60.0

70.0

80.0

Jan-59 Jan-64 Jan-69 Jan-74 Jan-79 Jan-84 Jan-89 Jan-94 Jan-99 Jan-04 Jan-09

April 10, 2023 7

A closer Look at PMI Analytic

Closer Look: NAPM 2012-13

48.0

49.0

50.0

51.0

52.0

53.0

54.0

55.0

56.0

Jan-12 Apr-12 Jul-12 Oct-12 Jan-13

• Last three months of 2012 average: 50.6

• Manufacturing was stagnant in the last quarter of 2012

New Orders % Better % Same % Worse Net Index

Jan 2013 28 51 21 +7 53.3

Dec 2012 24 45 31 -7 49.7

Nov 2012 26 43 31 -5 51.1

Oct 2012 24 47 29 -5 52.8

April 10, 2023 8

Housing Sales: Is that a Recovery?

April 10, 2023 9

Consumer Confidence

• Media pays attention to composite indices• Consumer spending is about 70% of GDP• Overall indices found to coincide even to lag

overall economic activity (GDP)• Q: Are they useless predictive analytics?• A: Not at all• Why?

April 10, 2023 10

Consumer Confidence (continued)

• Consumers are not forecasters • Overall indices are loaded with answers on questions

about the economy • Look at answers to survey’s questions related to

individual consumer activity, i.e. about them • Are you going to buy a car over the next six months?

New? Used? etc• Are you going to buy a house over the next six

months? New, Existing? etc• Are you going to travel over the next six months?

Domestic? Foreign? By Plane? By Car?

April 10, 2023 11

Economic Policy: Fed in a printing mode again

April 10, 2023 12

Nominal Vs. Real

• Retail Sales• Construction Spending• Orders (for Durables, Capital Goods)• Average Wage Rate ($ per hour)• Personal Income• Exports

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Dealing With Information

A modern data infrastructure

• Internal vs. external

• Timely and effective collection or updating tools

• Well-designed mining software

• Analytics processes to tap into meaningful & useful data for predictive intelligence

Analytics for predictive intelligence

• Eliminate transitory outliers, seasonally adjust and smooth out noise

• Identify hidden trends in economic, business and financial patterns

• Refine large information into a small set of market-centric composite predictors

April 10, 2023 14

Many Drivers: Blessing or Curse?

• Advances in information technology provide access to thousands of economic and business indicators

• In forecasting, "having many time series is a blessing not a curse”, James Stock1

• A new frontier in forecasting proposes to pool the information of all available predictors

1Department of Economics, Harvard University

April 10, 2023 15

Modeling Hundreds of Predictors

• Classical VS. Modern Modeling • Let y denote a business indicator to be

forecast (like Hotel Occupancy)• Let X be a small number of typical or

important variables that “classical” forecasters use as predictors, given statistical limitations (like GDP, Inflation, Unemployment)

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Forecasting with Hundreds of Predictors• Let Z be a set of a few “composite” or so-called

“diffusion” indicators that capture a large number (several hundreds) of individual predictors, which • are individually unimportant • collectively become important• provide useful “missed” information, when

properly grouped• their “combined” contribution to predicting y

may be as good as or better than the typical set of predictors X

April 10, 2023 17

The Modern Approach

• The General Modern Econometric Model is y = αX + βZ + ε • Origins of the methodology

• Burns and Mitchell (1947) in studying business cycle indicators with composites

• Sargent and Sims (1977) on factor analysis• Stock and Watson (1989, 2004) on" forecasting using

many predictors”• Bernanke and Boivin (2002) on monetary policy in a

data-rich environment

1In their research works Forecasting using diffusion indexes, and Forecasting using many predictors.

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Predictive Intelligence Modeling

Forecasting Model

IndustryMetrics

CompositeEngine

CompositeDrivers

Foreign MetricsNational

Metrics

RegionalMetrics

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Case: Forecasting Hotel Market

• History • Deloitte (London), Hotel Benchmark

Division• STR Global• TRI & e-forecasting

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Who Visits a Major City (London)?And, Stays in a Hotel

London

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Purpose of Trip: Why you visit a city? And, Stay in a Hotel

Tourists Business Government Affairs

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Where do Visitors come from? Domestic &

International Feeders to a Destination (Milan, Italy)

Origins of visitors – who stays in hotels - in a typical

destination city: Milan

• 41 percent of all visitors – 3 million - come from Italy (domestic feeder)

• 59 percent, or 4.3 million visitors come from the rest of the world• Majority of foreign visitors (76 percent) come from 31 countries.

Sample of some feeding countries:• 7.6% from the United Kingdom • 7.2% from the United States• 6.7% from Germany• 5.7% from France• 1.8% from Sweden• 0.6% from Mexico

April 10, 2023 23

What Drives Determine Visitors Volume for a Destination?

• Drivers for Tourism-Related Visitors• Personal Income, Employment Stability, Consumer

Confidence, Inflation, Capital Gains/Losses like Stock & Housing Prices, Interest Rates, Value of Domestic Currency (Exchange Rates) etc

• Drivers for Business-Related Visitors• Measures of Business Sentiment (Future production,

Sales, Incoming Orders), Investors Financial Optimism, Profitability, Interest Rates, Taxation, Energy costs, etc

• Drivers for Government-Related Visitors• Measures Affecting Revenues (Income, Employment

and Sales Taxes), Cyclical Spending (Unemployment), Public Investment, etc

April 10, 2023 24

Forecasting with Hundreds of Drivers?

The Challenge • Multifaceted Drivers

• Domestic

• Many countries (Top 30)

• Type of visitors (three)

• Indicators per dimension (about 10)

• The Upshot: Hundreds of Drivers

15GermanyDrivers

15India

Drivers

15 Brazil Drivers

15USA

Drivers

20DomesticDrivers

Hotel Market

April 10, 2023 25

Case: Modeling London Hotel Market• Modeling Approach

• Refining large information (indicators) into a small set of composite drivers (predictors) for the London hotel market

• Drivers are observable economic and business composites that influence business activity in London’s hotel market

• Occupancy (OCCU) and room rates (ADR) are interrelated

• Domestic and international drivers influence demand for and supply (including costs) of hotel services to determine future hotel performance

April 10, 2023 26

Adjustment of Hotel Market Indicators• To identify the “best fit” and generate most

accurate forecasts, hotel-market indicators are brought to conformity with single drivers and composites, which are seasonally adjusted by source or e−forecasting.com

• Hotel market indicators are adjusted for seasonality and special “transitory” events such as Royal weddings, sport events, political events, etc. Events are also treated in the future for prediction of performance metrics.

April 10, 2023 27

Composite Drivers

• Foreign Composites: Combine activity, current and future,

from all countries that feed London according to their relative importance (grouping of more than 1,000 indicators)

• Business expectations• Consumer confidence• Incomes• Changing value of assets• Exchange rates

• Domestic Composites: Combine activity, current and future, in the domestic economy

• Consumers• Business• Financial markets• Costs (wages, prices, energy)

April 10, 2023 28

London Forecasts: Occupancy & Room Rate

London Hotel Market: Occupancy Forecast

55

65

75

85

95

01 02 03 04 05 06

Forecast of Baseline Actual Baseline, SA&S

Forecast of Occupancy Actual Occupancy

Room Rate Forecast

£85

£95

£105

£115

£125

£135

£145

01 02 03 04 05 06

Forecast of Baseline Actual Baseline, SA&S

Forecast of Room Rate Actual Room Rate

April 10, 2023 29

US Economic Outlook

• Measuring long-term economic performance with stud metrics

• USA 1980-2007 model: 4x2• 4% growth & 2% Inflation

• European 1980-2007 model: 2x2• 2% growth & 2% Inflation

• Forecast: USA new reality: 2x2 or 2x4 ?

April 10, 2023 30

Growth in Monthly GDP

• Looking @ growth rate, can see depth of previous recession, upswing, and now slow recovery in economy

• When negative, recession; when positive, expansion

• Six-month growth rate, which signals confirmation of turning points, went up 1.2% in January, after going up 1.4% in December

30

April 10, 2023 31

Current Risks

• Ineffectiveness of low interest rates

• Asset inflation

• Deficits, Debts, Uncertainty and Fear – The European experience has moved west

• Free fall of dollar, panic, policy reversal, high interest rates, a “real” depression

• Geopolitical factors, Middle East conflict & oil prices

April 10, 2023 32

Dynamic Sector: US Manufacturing

Manufacturing recovery will slowly return to peak of 08 by 2015

April 10, 2023 33

Monetary Policy on the Horizon

• Look for the Fed Funds rate to remain near zero until 2015

• Bernanke’s ‘pledge’ to hold rates thru 2014

3-Month Treasury Bill: Secondary Market

-2

0

2

4

6

8

03:Jan 05:Jan 07:Jan 09:Jan 11:Jan 13:Jan 15:Jan

Percent

April 10, 2023 34

Global Outlook

• Looking @ major economic blocs and their leading indicators helps give an idea of turning points, which areas suffered more than others and which are recovering…

April 10, 2023 35

Long-Term Global Outlook

• Market Size Measured by GDP in $PPP Billion in 2011

April 10, 2023 36

Emerging Asia shows continued dominance in global economy

CONTRIBUTION OF REGIONS TO GLOBAL GROWTH

REGIONPercentage Points Contribution Relative Contribution, Percent

2011 2012 2013 2014 2011 2012 2013 2014

EUROPEAN UNION (EU27) 0.35 -0.04 0.02 0.26 9.4 -1.4 0.7 8.1

Euro Area (euro17) 0.23 -0.07 -0.05 0.13 6.1 -2.5 -1.8 4.1

Non-Euro Members (10) 0.12 0.03 0.06 0.13 3.3 1.1 2.5 4.0

OTHER EUROPE 0.30 0.17 0.20 0.24 8.0 6.1 7.9 7.4

NORTH AMERICA 0.52 0.52 0.32 0.54 13.8 18.4 12.5 17.1

United States 0.38 0.39 0.22 0.42 10.1 14.0 8.9 13.3

SOUTH AMERICA 0.28 0.17 0.20 0.24 7.5 6.0 7.9 7.4

ASIA & PACIFIC INDUSTRIAL 0.06 0.19 0.14 0.19 1.7 6.6 5.5 5.8

EMERGING ASIA 2.03 1.67 1.52 1.56 54.1 59.4 59.7 48.9

China & India 1.76 1.42 1.26 1.28 46.8 50.5 49.5 40.1

MIDDLE EAST & AFRICA 0.15 0.09 0.10 0.13 3.9 3.1 3.9 4.2

WORLD GROWTH1 3.8 2.8 2.5 3.2 100.0 100.0 100.0 100.0

1Sum of Regional Contributions Source: www.e-forecasting.com

April 10, 2023 37

Short term global forecast% change in real GDP growth

-1012345678

EUR

O A

REA

(17)

NO

N-E

UR

OAR

EA (1

0)

OTH

EREU

RO

PE

NO

RTH

AMER

ICA

SOU

THAM

ERIC

A

ASIA

&PA

CIF

ICIN

DU

STR

IAL

EMER

GIN

GAS

IA

MID

DLE

EAST

&AF

RIC

A

2011

2012

2013

2014

April 10, 2023 38

Long-term global forecast

-1

0

1

2

3

4

5

6

7

8

2011

2013

2015

2017

2019

2021

2023

2025

2027

2029

EURO AREA (16)

NON-EURO AREA (11)

OTHER EUROPE

NORTH AMERICA

SOUTH AMERICA

ASIA & PACIFIC INDUSTRIAL

EMERGING ASIA

MIDDLE EAST & AFRICA

April 10, 2023 39

Q&A

April 10, 2023 40

Thank You

For questions related to this presentation please contact

mesimos@e-forecasting.com