Investor sentiment and the cross-section of Japanese · PDF fileInvestor sentiment and the cross-section of ... to the Fama and French three-factor model. ... section of Japanese stock

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Investor sentiment and the cross-section of Japanese stock returns

by

Joyce Khuu*

Robert B. Durand

Lee A. Smales

This paper examines sentiment as an augmentation to the Fama and French three-

factor model. International tests of empirical asset pricing models show that only three

pricing factors are relevant, but perhaps not sufficient, in modelling the cross-section of

Japanese stock returns. We find that that sentiment can help explain the cross-section of

Japanese stock returns and is able to remove excess returns when added to the three-factor

model. We also observe asymmetric effects of sentiment on stocks in the cross-section of

size. Small stocks and large stocks are more affected by sentiment. This paper demonstrates

that the addition of a factor capturing sentiment should be considered when modelling

Japanese stock returns.

* Corresponding author. E-mail: [email protected]

The authors are from the School of Economics and Finance, Curtin University, Bentley,

Western Australia, Australia. We are grateful to our colleagues for their helpful input. All

errors are our own.

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Preface

Thesis title: Japan’s Lost Decade and The Role of Sentiment

Supervisors: Prof. Robert Durand, Dr. Lee Smales

Sentiment is often referred to as the overall market mood, and sentiment as the market mood

does not necessarily correspond to fundamental or economic information. A growing body

of literature suggests sentiment impacts stock market behaviour and can predict stock returns.

These findings are contradictory to the traditional efficient market hypothesis approach in

finance and are a behavioural explanation of asset pricing anomalies. Traditional models of

asset pricing have so far been unable to explain the puzzles that are the lost decades of the

Japanese equity market. The dissertation aims to explore whether market sentiment can be

used to help explain stock returns in the Japanese equity market during the latter part of this

period, January 2003 to October 2014. This time period encompasses the “second lost decade

of Japan” which describes part of 20 years of stagnation for the Japanese economy and equity

market. This research will encompass the first section of the dissertation. Further to this

research, there is currently no consensus within the literature as to how sentiment should be

measured or captured. To fill this gap in the literature, this dissertation also explores the

potential measurement issues, relationships and sensitivities of the sentiment proxies

currently used in the literature. The structure of this dissertation is as follows:

Chapter 1: Introduction

Chapter 2: Melancholia and Japanese stock returns

Chapter 3: Investor sentiment and the cross-section of Japanese stock returns

Chapter 4: Capturing sentiment the importance of measurement and proxies

Chapter 5 Conclusions

This paper has been taken from the third chapter of my dissertation and is adapted for a three

paper publication format. It examines the role that sentiment has in empirical asset pricing

within the Fama and French framework and focuses on the cross-section of Japanese stock

returns. It extends upon previous published (forthcoming) work in the dissertation which

suggested that negative market sentiment in Japan may be related to poor equity returns over

the time period examined.

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I. Introduction

Augmentations to the three-factor model such as momentum (Carhart, 1997),

profitability and investment (Fama and French 2015a), have achieved varied success in

explaining the cross-section of US and global stock market returns. However, these

additional factors have notably, and repeatedly, “failed” in the Japanese context (Cakici

2015; Fama and French 2015b). Unlike other developed markets international tests of

empirical asset pricing models reveal that only three pricing factors are relevant in modelling

the cross-section of Japanese stock returns (Fama and French 2012; 2015b). These three

factors are, the market premium (Rm - Rf), value premium (HML) and size premium (SMB)

which are based on seminal US studies (Fama and French 1993; 1996). However, the three

factors may not be sufficient to fully explain the cross-section of returns as unexplained

excess returns (α) seemingly persist. As standard factor augmentations have failed for Japan,

we examine whether sentiment is a priced factor and we find that sentiment is useful in

explaining stock returns. In this paper, we find that the addition of sentiment to the three

factors removes α in the majority of our sample portfolios. The effect of sentiment is greatest

for growth stocks (as expected given the extant literature) and also large stocks (which is

contrary to the literature).

A growing body of literature suggests that sentiment influences market behavior and

as a result stock prices and share markets (Baker and Wurgler 2006; Brown and Cliff 2005;

Tetlock 2007; Tetlock et al. 2008; Stambaugh et al. 2012). Sentiment may also provide a

useful addition to the Fama and French three-factor model. There are two potential reasons

for this: the first is that sentiment may have market wide effects and could influence the Fama

and French factors. Alternatively, sentiment may act as a separate additional factor.

Khuu et al. (2016) found that news sentiment can help explain the prolonged negative

average stock returns in Japan – a phenomenon which challenges the positive relationship

between risk and expected returns. They find a positive relationship between news sentiment

and stock returns, where on average the market has negative, (or near negative) sentiment

which they link to poor market returns in aggregate. They also document a relation between

sentiment and firm size that is common in the sentiment literature. Smaller stocks seem to

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be more susceptible to “sentiment” with “limits-to-arbitrage” presenting one explanation

(Baker and Wurgler 2006; 2007). 1 Size appears to be an important characteristic when

examining the effects of sentiment, and is explicitly priced in the Fama and French empirical

framework through SMB. This common variable in size suggests that sentiment might be

associated with SMB for Japan. Khuu et al. (2016) suggests that prolonged periods of

negative sentiment can help explain poor stock market returns in Japan. As Rm-Rf represents

the market premium, this may also be influenced by sentiment.

The Fama and French (1993) three-factor model provides an empirically-based

explanation for cross-sectional patterns in stock returns that were not captured by the single

factor CAPM model of Sharpe (1964) and Lintner (1965). In addition to the market risk

premium, the three-factor model includes two other factors, SMB and HML. SMB captures a

size premium where stocks with lower market capitalization earn higher returns, over stocks

with higher market capitalization. HML captures a value premium, where higher returns are

related to stocks with high book values of assets to market values than stocks which have

low book values to market values. The excess returns equation of this model is as follows:

, , , , ,( )p t f t p p m t f t p t p t p tR R R R SMB HML (1.1)

where Rp,t is the return of the portfolio; Rf,t is the return of a risk free asset; Rm,t is the

return of a market portfolio; HMLt is the difference between a portfolio of high book to

market and low book to market stocks; SMBt is the return of a portfolio of small minus big

stocks; εp,t is the error term. αp represents the intercept or abnormal return of the expected

return, which is equal to zero if the factors capture all the variation in expected returns. In

this model the factor loadings represent a risk premia associated with sensitivity to HML and

SMB. As Japanese stock returns are highly correlated to book to market (B/M) (Chan et al.,

1991), we would expect this to be captured by HML. Though this model is often augmented

by a momentum factor (Carhart 1997), we do not employ it here given that momentum effects

1 Berger and Turtle (2012) find that “sentiment prone” stocks are young, volatile, small firms with “opaque” characteristics. Brown and Cliff (2005),

Lemmon and Portniaguina (2006), and Schmeling (2009) also note that sentiment had a greater influence on small firms, although there is conflicting evidence

as to whether the effect is greatest for stocks categorized as value or growth.

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are commonly regarded as absent in Japan (Fama and French 2012). Recent evidence also

suggests that the new profitability and investment factors (Fama and French 2015) add little

to the three-factor model when applied to Japan (Cakici 2015; Fama and French 2015b)2.

Both rational and behavioral explanations have been offered for the pattern of Japanese stock

returns.

Sentiment is not directly observable, but is often associated with the market "mood" or

"feeling". While sentiment itself is not observable, its effects maybe which requires a proxy

(Chan et al. 2016). There is no commonly defined sentiment proxy and there are three

common approaches. One sentiment proxy includes Baker and Wurgler’s (2006; 2007)

macroeconomic based measure3 which captures market sentiment through the use of

macroeconomic and market variables. Papers which employ this metric include Baker and

Wurgler (2006; 2007), Tsuji (2006), Yu and Yuan (2011), Baker et al. (2012), Chung et al.

(2012) and Stambaugh et al. (2012). However, there is debate as to whether these proxies are

effective (Chen et al. 1993; Lemmon and Portniaguina 2006).

The second approach employs periodic survey based indices (Akhtar et al. 2011;

Antoniou et al. 2013; Brown and Cliff 2005; Lemmon and Portniaguina 2006). Examples

include the Conference Board Consumer Index (CBCI) and Michigan Consumer Sentiment

Index (MCSI), that poll market or household opinions on a regular basis.

The third approach, which we employ, is the use of text-based sentiment measures

which is becoming increasingly prevalent in the literature (Allen et al. 2015; Dzielinski 2011;

García 2013; Groß-Klußmann and Hautsch 2011; Smales 2014; Tetlock 2007; Tetlock et al.

2 Different explanations have been put forward for the pattern of Japanese stock returns. Daniel et al. (2001) argued that a characteristics based

model rather than a risk factor based model is more suitable for Japan. Chiao and Hueng (2005) finds evidence for overreaction in Japan (Chang et al, 1995;

Gunaratne and Yonesawa, 1997), which are independent of the characteristics and risk factor hypotheses. However, we do not focus on these explanations

here.

3 Baker and Wurgler (2006) report that the first measure of sentiment explains 49% of the sample variance of the set of candidate sentiment proxies

and that the second measure explains 51% of the cumulative variance of the orthogonalized proxies that they use. They utilize only the first principal component

and do not report the eigenvalues of their principal components analysis. Our attempts to replicate the PCA analysis finds that the second principal component

could also be considered.

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2008; Uhl 2014). We utilize Thomson Reuters News Analytics (TRNA) as a text-based proxy

of sentiment over our sample period. One advantage of using a sentiment proxy based on

text-based news is that it captures dynamic changes in sentiment; news is released and

updated frequently, eliciting changes in sentiment and influencing investor behavior. Tetlock

(2007) finds that media pessimism predicts lower stock returns on the Dow Jones Industrial

Average (DJIA), and this suggests the existence of a psychological link between news and

market prices. García (2013) analyzes the text of a Wall Street Journal (WSJ) news column

and finds that the predictive power of news sentiment is concentrated in recessions and notes

evidence of an irrational reaction to market news on days of pessimism and optimism. Uhl

(2014) reports that a text based sentiment measure performs better in forecasting returns than

in predicting macroeconomic factors. Dzielinski (2011) compared returns on positive and

negative news days, using the TRNA dataset, and found that US stock returns have above

(below) average returns on positive (negative) days. Aman (2013) identifies a potential

relationship between active media coverage (newspaper articles) and extreme market

volatility (crashes) in Japan. He finds that investors have extreme and large reactions to

increased intensity of news coverage.

The remainder of this paper is structured as follows: section II describes the data and

methodology utilized in this paper, section III presents our results and section IV concludes.

II. Data

Our study utilizes daily data for common stocks that are listed on the Tokyo Stock

Exchange (TSE) from January 2003 to July 2014.4 We choose daily data as it is more likely

to capture the dynamic relationship of sentiment on stock prices which would otherwise be

lost by using monthly data.

4 Our sample period is limited by the availability of the TRNA data provided by SIRCA used to construct our sentiment time

series.

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We compute our sentiment measure, Psent, using TRNA. TRNA is a contextual text-

based sentiment measure which captures the effect of news on stock markets. TRNA uses

neural network and machine learning to categorize sentiment associated with news stories as

“positive” (1), “negative” (-1) and “neutral” (0).5 Each news item is accompanied by a GMT

date and time stamp as well as a Reuters Instrument Code (RIC) that identifies the stock that

the news item is related to. We aggregate all daily news items for Japan during trading hours.

Any news articles that are released after close of trading are assigned to the following trading

day since that is when the news will be able to impact prices and returns. Prior to constructing

our sentiment measure, we filter the news items in the TRNA data set using several

information fields:

1. Sentiment probability: The measure of sentiment for a news article is

categorized as positive (1), neutral (0), or negative (-1). TRNA also assigns a probability that

the news item is correctly signed as positive, neutral or negative. For example, if there is an

80% probability that a news item is positive, the news item would be signed as positive (+1),

with 80% probability. From this we can construct a probability weighted sentiment score as

+0.8 (i.e. +1 x 80%).6 We utilize probability weighted scores in this study.

2. Relevance: A rating between 0 and 1 that indicates how relevant the news

item is to a specific firm. A score of 1 (0) means the news item is highly relevant (irrelevant).

We filter for news articles with a relevance score above 0.8 to ensure that the sentiment

measure we construct is relevant7 to stock prices and returns (Groß-Klußmann and Hautsch

2011; and Smales 2014) whilst filtering out noise. This filter does not necessarily mean that

5 Studies that have utilized this data set include Hendershott et al. (2015) and Smales (2014).

6 The TRNA sentiment scores provide measures of positivity (+1) and negativity (-1) of any news signal, as well as the magnitude (probability).

TRNA analysis provides an analysis of the sentiment likely opined from the perspective of the author of the News item for consistency, not how the market

perceives the news item. 7 Not all news items referring to a firm may be directly relevant to it. For example, a discussion about Firm A may also mention Firm B in passing.

TRNA provides information on relevancy to ensure that the sentiment being distilled from a news article is not mistakenly associated with firms which are not

necessarily the focus of the article. Relevant articles do not necessarily translate to fundamental information, as the relevancy field does not provide information

on the content of news articles.

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the news contains fundamental information, as this field does not distinguish between the

content or topic of the news articles.

3. Novelty: This measures how unique a particular news item is when compared

to previous similar news items within a defined period. We filter for content that is considered

“novel”, i.e. news items that are not similar to previous articles or “stale news”.

We construct our sentiment proxy using the sentiment classification (positive,

negative or neutral) attached to a news item and multiply this by the TRNA assigned

probability that the classification is correct. This provides a probability weighted sentiment

score Psent:

(1) ( ) ( 1) ( )

1;1positive negative

positive negative netural

P sentiment P sentimentPsent

nsentiment nsentiment nsentiment

(1.2)

where Psent is the sentiment of the market; P is the probability of classification; and

nsentiment is the number of sentiment news items with corresponding positive, negative or

neutral scores. As neutral news items have (0), or zero sentiment classification they do not

affect the numerator the equation. However, they weigh the denominator of this measure

towards neutral sentiment as the number of neutral news items increases.

Stock market and accounting data are taken from Thomson Reuters Datastream and

Bloomberg. The risk free rate Rf used in this study is the 30 day Gensaki repo rate which is

one of the most liquid proxies for the Japanese risk free rate and is commonly used in the

literature (Daniel et al., 2001) The market return Rm is the average return of the TOPIX. We

exclude stocks which do not have 24 months of returns before portfolio formation dates, as

well as stocks with negative book equity. Unlike firms in the United States, firms in Japan

tend to have fiscal years ending March 31. As a result we follow Daniel et al. (2001) and

Chiao and Hueng (2005), in the timing of all our portfolio formations, rather than the

traditional June to December formation periods. Return portfolios are formed on the first

trading day of October in year t and held until the last trading day of September t + 1. For

return portfolios, book equity (BE) of a firm is that which runs April t - 1 to March year t.

Book to market, is BE divided by market equity (ME) on the last trading day of March year

t and Size, is taken as the market equity of a firm on the last trading day of September year

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t. The 6-month lag between portfolio formation and fiscal year end is commonly used to

ensure that accounting information is publically available and has been disseminated.

To construct our Japanese specific Fama and French Factors, we follow Fama and

French (1993) to construct size (SMB) and book to market (HML) factors. We construct six

(2x3) size and book to market return portfolios from the intersection of two ME and three

B/M independent sorts. Stocks are first sorted into two portfolios by median market

capitalization at the end of March year t. We then independently sort stocks into three

portfolios by book to market using a split of 30:40:30 percentiles. We define the bottom 30th

percentile as low, the middle 40th percentile as medium and the top 30th percentile high.

These portfolios are rebalanced every year. The SMB factor is constructed as the average

return on the three small portfolios minus the average return of the three big portfolios. The

HML factor is constructed as the average return on the two high HML portfolios, minus the

average return of the two low HML portfolios. Table 1 presents the number of stocks in the

six (2x3) size and book to market return portfolios formed from the intersection of two ME

and three B/M independent sorts

Table 1 Average Number of Stocks in Each Portfolio 2x3 B/M and Size

B/M Low Med High

Small 280 434 549

Big 478 578 210

We also form twenty-five (5x5) size and book to market return portfolios from the

intersection of stocks sorted into quintiles by size and book to market. Stocks in our sample

are first sorted into market equity quintiles from small to large and then again independently

sorted into book to market quintiles from low to high. The value weighted daily returns are

calculated September year t to October t+1.8 Table 2 displays the number of stocks sorted in

to the (5x5) portfolios. Table 3 below presents the average excess returns and statistics for

8 As a check, we first download CRSP and Compustat data to replicate a subsample of Fama and French’s daily factors before making the required

adjustments to our programming code for Japanese data.

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the 5x5 size and book to market portfolios. These results demonstrate the puzzle of Japan’s

stock market as of recent times. The average excess return for the majority of 5x5 portfolios

are close to zero, however despite this there is significantly large variation in returns.

Table 2 Average Number of Stocks in Each Portfolio 5x5 B/M and Size

B/M Low 2 3 4 High

Small 76 67 73 98 190

2 77 71 88 117 154

3 85 86 107 127 100

4 109 114 130 106 47

Big 159 168 109 58 15

This persistent relationship of positive risk yet zero return contradicts Merton’s

proposition of positive risk and positive expected return. Khuu et al. (2016) report that

sentiment has a role in explaining this phemonema.

There are two potential channels through which sentiment could influence asset

prices: The first, is by acting through or influencing the factors themselves (which are

negatively correlated and statistically significant). Alternatively, sentiment could present as

a separate additional factor in itself. Therefore, to remove the influence of market wide

sentiment we consider orthogonalizing our Japanese factors to Psent. This allows us to

separate the effect of sentiment from the factors. To obtain the orthogonalized factors we

follow Durand et al. (2016) and regress each Fama and French factor against Psent, utilizing

the residuals as orthogonalized factors in the following analysis. These factors are denoted

by orthog in Table 4. Table 4 presents summary statistics of our constructed factors. Panel A

of Table 4 presents the summary statistics for the constructed factors, and sentiment measure.

Panel B presents correlations of the factors. Panel B of Table 4 indicates that there are

correlations between the factors and sentiment. Sentiment is positively and statistically

significantly correlated with the market premium (0.2688), and negatively related to SMB (-

0.1215) and HML (-0.0837). The reported correlations may indicate that positive (negative)

sentiment is related to positive (negative) premiums.

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Table 3 Average Daily Excess Returns for Twenty-five Portfolios Formed on Size and B/M

B/M Low 2 3 4 High

Excess Returns

Small -0.0001 0.0000 0.0001 0.0001 0.0002

2 0.0000 0.0004 0.0002 0.0003 0.0003

3 0.0000 0.0002 0.0002 0.0003 0.0004

4 0.0002 0.0003 0.0003 0.0003 0.0004

Big 0.0001 0.0002 0.0003 0.0004 0.0005

Std. Dev.

Small 0.0160 0.0125 0.0112 0.0105 0.0095

2 0.0144 0.0131 0.0106 0.0104 0.0105

3 0.0155 0.0124 0.0113 0.0114 0.0119

4 0.0139 0.0129 0.0125 0.0128 0.0142

Big 0.0134 0.0137 0.0136 0.0145 0.0161

(Continued on next page)

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B/M Low 2 3 4 High

Min

Small -0.2041 -0.1619 -0.1631 -0.1593 -0.1504

2 -0.1842 -0.1625 -0.1360 -0.1508 -0.1459

3 -0.1573 -0.1336 -0.1379 -0.1450 -0.1206

4 -0.1260 -0.1266 -0.1216 -0.1326 -0.1302

Big -0.0912 -0.0998 -0.1173 -0.1177 -0.1075

Max

Small 0.1007 0.0925 0.1109 0.1074 0.1014

2 0.1210 0.1306 0.1101 0.1131 0.1171

3 0.1263 0.1330 0.1109 0.1023 0.1006

4 0.1191 0.1261 0.1144 0.1142 0.1024

Big 0.1070 0.1320 0.1148 0.1341 0.1178

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Table 4 Summary Statistics for Constructed Factors

Panel A: Summary Statistics Factors Orthogonolized Factors

Psent SMB HML (Rm-Rf) SMBOrthog HMLOrthog (Rm-Rf) Orthog

N 3,000 3,000 3,000 3,000 3,000 3,000 3,000

Mean -0.0481 -0.0001 0.0003 0.0002 0.0000 0.0000 0.0000

Sd 0.1398 0.0072 0.0138 0.0042 0.0072 0.0042 0.0132

Min -0.5016 -0.0565 -0.0952 -0.0235 -0.0588 -0.0245 -0.0948

Max 0.3658 0.0435 0.1370 0.0339 0.0422 0.0327 0.1370

Panel B: Correlation Matrix

Psent

SMB -0.1215***

HML -0.0837*** -0.1403***

(Rm-Rf) 0.2688*** -0.5483*** -0.1817***

SMBOrthog 0.0000 0.9911*** -0.1527*** -0.5193***

HMLOrthog 0.0000 -0.1519*** 0.9962*** -0.1597*** -0.1532***

(Rm-Rf)Orthog 0.0000 -0.5345*** -0.1652*** 0.9630*** -0.5393*** -0.1659***

PsentOrthog 0.9623*** 0.0000 0.0000 0.0000 0.1178*** 0.0808*** -0.2686***

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Khuu et al. (2016) link positive sentiment to positive market premiums. Periods of high

(low) sentiment has also been associated with future reversals in the size premium (Baker and

Wurgler 2006) with exuberance and over confidence (Yu and Yuan 2011) providing one

explanation. The positive correlation with the market premium suggests that sentiment has market

wide effects.

To assess if sentiment is potentially useful we examine 6 different model specifications

centered around the three-factor Fama and French model. These models are run with and without

the orthogonalized factors. The first model is the standard three-factor model (excess returns):

, , , , ,( )p t f t p p m t f t p t p t p tR R R R SMB HML (1.3)

where Rp,t is the return of the portfolio on day t; Rf,t is the return of the risk free asset on day t; Rm,t

is the market return on day t; SMB is the size factor on day t; HML is the book to market factor on

day t; εp,t is the error term for the portfolio on day t; and αp is the intercept term of the portfolio p.

The second specification includes the addition of Psent, and we run this for both un-orthogonal

and orthogonalized factors:

, , , , ,( )p t f t p p m t f t p t p t p t p tR R R R SMB HML Psent (1.4)



day t; Psent is sentiment on day t; εp,t is the error term for the portfolio on day t; and αp is the

intercept term of the portfolio p.

The final specification includes dummy variables for January, March and July and day of the week

effects. 9 We run this regression for the 2x3 and 5x5 portfolio sorts.10,11 The final specification is

as follows:

9 July is the month when the majority of financial statements and accounts are finalized in Japan.

10 We run all regressions with robust standard errors.

11 We report only the 5x5 portfolio sorts for the sake of brevity.

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, , , ,

,

( )p t f t p p m t f t p t p t p t

p t p t p t p t p t

R R R R SMB HML Psent

j Jan March l July d Day

(1.5)



day t; Psent is sentiment on day t; εp,t is the error term for the portfolio on day t; and αp is the

intercept term of the portfolio p. Jan is a dummy variable for January, March is a dummy variable

for March, July is a dummy variable for July, and Day are day of the week dummies.

III. Results

Table 5 presents statistics commonly utilized to assess different model specifications

(Fama and French 2012). We examine the average absolute alpha for each model specification and

utilize a zero intercept rule as a selection criterion (Merton 1973). Panels A and B of Table 5

presents results for a Gibbons Ross Shanken test (GRS) test of finite sample.

Panel A focuses on tests for the 5x5 portfolio sorts whilst Panel B presents results for 2x3

portfolio sorts. In each case, we test the null hypothesis that the alphas for each specification of

our model are jointly equal to zero. As in Fama and French (2012), SR(α) is equal to 1/ 21

where α is the column vector of the 25 regression intercepts produced by a model when applied to

25 global or local Size and B/M portfolios, and Σ is the covariance matrix of regression residuals.

“Lower is better” for this statistic that is interpreted by Fama and French as a “Sharpe ratio for the

intercepts (unexplained average returns) of a model” (p.466).

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Table 5 Model Performance Statistics

Panel A 5x5 Portfolio Sorts Statistics Model GRS |α| R2 S(α) SR(α) (1) Japan three - factor model 1.90*** 0.0001 0.8855 7.56 x 10-5 0.3594 (2) Japan orthogonalized three - factor model 12.96*** 0.0002 0.8311 9.54 x 10-5 0.5803 (3) Japan three - factor model with psent 1.32 0.0001 0.8856 8.03 x 10-5 0.3382 (4) Japan orthogonalized three - factor model with psent 4083.12*** 0.0012 0.8850 8.01 x 10-5 2.5144 (5) Japan three-factor model with psent and control variables 1.10 0.0001 0.8856 1.78 x 10-4 0.4823 (6) Japan three-factor model with psent and control variables with orthogonalized factors

5.25x1014*** 0.0014 0.8856 1.78 x 10-4 2251.6660

(7) Japan three-factor model with Psent orthogonal to three factors 1.91*** 0.0001 0.8856 7.56 x 10-5 0.1296 Panel B 2x3 Portfolio Sorts Statistics Model GRS |α| R2 S(α) SR(α) (8) Japan three - factor model 4.25*** 0.0001 0.9676 4.01 x 10-5 0.0926 (9) Japan orthogonalized three - factor model 4090.63*** 0.0002 0.9070 6.99 x 10-5 2.8645 (10) Japan three - factor model with psent 2.66** 0.0001 0.9677 9.14 x 10-5 0.0779 (11) Japan orthogonalized three - factor model with psent 3635.64*** 0.0013 0.9671 4.27 x 10-5 2.8561 (12) Japan three-factor model with psent and control variables 0.67 0.0001 0.9677 9.45 x 10-5 0.0869 (13) Japan three-factor model with psent and control variables with orthogonalized factors

230.41*** 0.0014 0.9677 9.44 x 10-5 1.6087

(14) Japan three-factor model with Psent orthogonal to three factors 4.26*** 0.0001 0.9677 4.01 x 10-5 0.0927

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Based on Table 5, two models satisfy the factor criteria, that is they do not reject the null

hypothesis. These are (3), (5) and (12) which are models augmented with Psent.12 In addition, the

specification with the lowest values of SR(α) occurs in the three-factor model augmented with

Psent which suggests that the inclusion of Psent removes the excess returns left unexplained by

the other three factors. We present results for model (3), as model (5) and (12) adds little to the

discussion.13 Table 6 presents results for model (3). The reported results are based on 5x5 double

sorted portfolios. The results in Table 6 show that only two α’s remain in the 25 portfolios. The

market premium Rm - Rf is positively and statistically significant for all the portfolios, whilst SMB

is statistically significant except in the largest growth portfolio. HML is also significant in all but

two portfolios and depicts a monotonically increasing coefficient, from strongly negative to

strongly positive. These results indicate that the three factors are useful in explaining Japanese

stock returns.

Psent however, is only significant for five portfolios, with the majority of them occurring

in the smallest stocks and the largest growth stocks. The main contribution from sentiment is in

the removal of α when Psent is included in models (3) and (5) with the other three factors. The

identified pattern for Psent lends evidence to the most common patterns observed for sentiment

effects in the literature. There is an asymmetric effect which is explained in the sentiment literature

by Size. Small stocks and the largest stocks are those which tend to be most affected by sentiment,

as opposed to the stocks in the “middle”. Small stocks tend to be more “sentiment prone” as they

are more likely to be harder to value due to opaque characteristics. We also find that large growth

stocks are affected. Large growth stocks with low B/M may have characteristics, such as

intangibles, which make them more easily influenced by sentiment. Another potential reason is the

reaction to news. Luo et al. (2015) argues that institutional investors will react more to news in

larger stocks than in smaller stocks since their holdings are concentrated in larger stocks; we

observe evidence for this here. One further explanation is that the amount of news per firm, or

news coverage, is most concentrated in the largest stocks.

12 The same models also have the highest average adjusted R-squared values. 13 The results for models (5) and (12) are generally the same and various controls do not add to discussion materially.

18

Table 6 Time-Series Regressions of the 25 Size and Book to Market Sorted Portfolios Japan 3 Factor Model with Psent

B/M Low 2 3 4 High Low 2 3 4 High

α t(α)

Small -0.0002 -0.0002 -0.0001 -0.0001 -0.0000 -1.584 -1.367 -1.041 -0.965 -0.744

2 -0.0003*** 0.0002 -0.0001 -0.0000 -0.0001 -3.100 1.525 -0.999 -0.673 -1.185

3 -0.0002 -0.0001 -0.0001 -0.0001 0.0000 -1.559 -1.863 -1.707 -1.243 0.1290

4 -0.0001 -0.0000 -0.0001 -0.0002** -0.0001 -1.043 -0.621 -1.765 -2.259 -1.018

Big -0.0001 -0.0001 -0.0001 -0.0001 -0.0001 -1.100 -1.74 -1.752 -1.078 -0.539

β t(β)

Small 1.1416*** 0.9476*** 0.8959*** 0.8638*** 0.7979*** 41.372 55.241 50.886 53.324 59.652

2 1.1342*** 1.0025*** 0.9063*** 0.9179*** 0.9390*** 64.282 72.264 103.849 82.678 127.002

3 1.1672*** 1.0417*** 0.9713*** 0.9922*** 1.0344*** 61.349 104.848 88.036 70.384 53.377

4 1.0775*** 1.0388*** 1.0148*** 1.0357*** 1.1441*** 62.469 95.736 83.776 79.909 49.357

Big 0.9195*** 0.9497*** 0.9720*** 1.0614*** 1.1029*** 88.515 168.856 67.896 92.627 52.858

δ t(δ)

Small 1.6070*** 1.2831*** 1.1712*** 1.1168*** 1.0559*** 39.394 47.858 36.207 37.752 40.66

2 1.4667*** 1.2310*** 1.0303*** 0.9925*** 1.0203*** 50.995 43.247 61.63 48.386 72.43

3 1.2402*** 0.9367*** 0.8164*** 0.8447*** 0.8885*** 37.472 46.105 43.523 40.533 28.728

4 0.8197*** 0.5760*** 0.5219*** 0.5100*** 0.6512*** 27.059 26.912 24.588 23.310 19.579

Big -0.0127 -0.1084*** -0.0112 0.1027*** 0.1500*** -0.8430 -10.827 -0.472 4.4170 4.399

(Continued next page)

19

B/M Low 2 3 4 High Low 2 3 4 High

γ t(γ)

Small -0.2396*** 0.1894*** 0.3099*** 0.3599*** 0.5012*** -4.772 5.527 10.074 13.273 20.733

2 -0.1921*** -0.0682 0.3697*** 0.4579*** 0.7175*** -5.357 -1.326 18.614 26.041 49.265

3 -0.0807 0.1447*** 0.4120*** 0.5924*** 0.7768*** -1.863 6.281 19.682 28.26 30.358

4 -0.1152*** 0.2411*** 0.4134*** 0.6497*** 0.8615*** -2.939 8.579 16.147 24.962 25.279

Big -0.3798*** -0.0377*** 0.3134*** 0.5755*** 0.8626*** -21.547 -2.815 14.251 23.699 20.083

κ t(κ)

Small 0.0010 0.0019** 0.0013** 0.0013** 0.0019*** 0.824 2.154 1.990 2.177 3.935

2 -0.0014 -0.0010 0.0009 0.0005 -0.0000 -1.725 -1.027 1.866 1.310 -0.023

3 -0.0007 -0.0008 0.0002 0.0007 0.0006 -0.600 -1.476 0.517 1.398 1.005

4 -0.0012 -0.0002 -0.0007 -0.0006 0.0001 -1.473 -0.289 -1.173 -1.078 0.087

Big 0.0013*** 0.0003 -0.0000 -0.0009 -0.0006 3.055 0.958 -0.037 -1.594 -0.578

R-squared

Small 0.755 0.768 0.821 0.856 0.876

2 0.868 0.795 0.906 0.942 0.953

3 0.758 0.910 0.926 0.935 0.928

4 0.837 0.903 0.911 0.914 0.879

Big 0.961 0.977 0.944 0.921 0.785

This Table reports regression results over the period January 2003 – June 2014. This regression uses the daily three factors constructed for Japan and sentiment Psent. In March of year t, stocks are ranked by book to market, and independently sorted by median market capitalization at the end of September of year t. We form 25 stock portfolios based on quintiles for book to market and size. Value-weighted daily returns on these portfolios are from October to September in year t+1. The Model is specified as: , , ,( )p t f t p p m f t p t p t p t p tR R R R SMB HML psent

*** denotes significance at the 1% level, ** 5% level.

20

As discussed previously, we stated that market wide sentiment can potentially influence

stock returns via two channels. The first channel is indirectly, via through the three factors,

whilst the other is directly as an independent factor. The results presented in Table 5 supports

the first argument that sentiment works indirectly, that is primarily through the factors.

However, it is worthwhile discussing the orthogonalization of the three factors to Psent and the

subsequent results for Psent as a direct individual factor. Models (6) and (13) which

orthogonalize the three factors to Psent do not pass the GRS tests. However, orthogonalization

to Psent would theoretically remove the effects of sentiment on the three factors which

potentially indicates how Psent may work independently as a factor. Table 7 presents the results

of model (6). The estimated results indicate that the effect of sentiment is greatest for the largest

sized portfolios. There also appears to be a growth effect, with the coefficient for Psent

increasing for growth stocks and decreasing for value stocks. This pattern does not readily

emerge in Table 6 and model (3) where the 3 other factors are not orthogonalized to Psent.

Psent in model (6) seems to affect large stocks, more so than smaller stocks. This is a result

that is contradictory to results in Table 6, where smaller stocks and in particular smaller growth

stocks should be the most influenced by sentiment as they tend to have “opaque”

characteristics,14 yet large stocks have the largest loadings on Psent once the other factors are

orthogonalized. Again, this may be due to the fact that institutional investors react to news

(sentiment) in larger stocks than smaller stocks given their holdings in larger stocks (Luo et al.

2015).

As indicated by the failed GRS tests there are statistically significant alpha terms that

persist, indicating that this model does not explain all the cross-sectional variation in stock

returns. Psent is also significant for all portfolios which suggests that sentiment does play a

role in explaining excess portfolio returns. The day of the week dummies are insignificant

except for a few portfolios which show a significant coefficient on the Monday dummy and we

do not present those coefficients for sake of brevity.

14 Characteristics such as high information asymmetry, low liquidity and high transaction costs.

21

Table 7 Time-Series Regressions of the 25 Size and Book to Market Sorted Portfolios using Orthogonalized Factors

B/M Low 2 3 4 High Low 2 3 4 High α t(α) Small 0.0013*** 0.0011*** 0.0012*** 0.0010*** 0.0011*** 4.039 4.154 5.810 5.946 7.504 2 0.0007*** 0.0016*** 0.0011*** 0.0011*** 0.0012*** 3.521 6.075 8.977 10.030 11.901 3 0.0014*** 0.0012*** 0.0013*** 0.0012*** 0.0013*** 4.587 8.104 9.784 9.212 9.626 4 0.0018*** 0.0017*** 0.0015*** 0.0014*** 0.0017*** 7.723 9.315 9.096 8.147 8.225 Big 0.0015*** 0.0016*** 0.0017*** 0.0018*** 0.0023*** 13.538 16.755 12.608 10.820 7.197 β t(β) Small 1.1424*** 0.9475*** 0.8962*** 0.8640*** 0.7972*** 41.610 55.044 50.644 52.970 58.871 2 1.1346*** 1.0023*** 0.9066*** 0.9179*** 0.9384*** 63.586 72.548 103.324 81.542 126.315 3 1.1663*** 1.0417*** 0.9713*** 0.9923*** 1.0339*** 61.678 104.264 87.398 69.558 53.206 4 1.0769*** 1.0389*** 1.0144*** 1.0354*** 1.1441*** 62.396 95.329 83.683 79.130 48.983 Big 0.9190*** 0.9494*** 0.9722*** 1.0619*** 1.1036*** 88.141 168.689 67.564 93.172 52.306 δ t(δ) Small 1.6105*** 1.2822*** 1.1713*** 1.1171*** 1.0543*** 39.731 47.679 35.883 37.331 39.964 2 1.4688*** 1.2302*** 1.0317*** 0.9925*** 1.0190*** 50.155 43.228 61.056 47.444 71.698 3 1.2393*** 0.9376*** 0.8168*** 0.8452*** 0.8877*** 37.808 45.978 43.043 39.908 28.529 4 0.8205*** 0.5774*** 0.5218*** 0.5099*** 0.6523*** 26.937 26.906 24.449 22.977 19.395 Big -0.0132 -0.1093*** -0.0108 0.1045*** 0.1523*** -0.869 -10.887 -0.453 4.514 4.430


22

B/M Low 2 3 4 High Low 2 3 4 High γ t(γ) Small -0.2314*** 0.1894*** 0.3109*** 0.3616*** 0.4989*** -4.598 5.496 10.010 13.105 20.215 2 -0.1891*** -0.0669 0.3710*** 0.4577*** 0.7155*** -5.258 -1.294 18.834 25.615 49.218 3 -0.0852** 0.1446*** 0.4119*** 0.5921*** 0.7736*** -1.968 6.263 19.526 27.811 30.236 4 -0.1181*** 0.2409*** 0.4110*** 0.6474*** 0.8596*** -2.998 8.538 15.928 24.559 25.049 Big -0.3824*** -0.0387*** 0.3151*** 0.5793*** 0.8647*** -21.590 -2.875 14.006 23.832 20.003 κ t(κ) Small 0.0218*** 0.0184*** 0.0168*** 0.0162*** 0.0151*** 19.322 23.331 25.172 27.558 29.941 2 0.0197*** 0.0180*** 0.0174*** 0.0173*** 0.0166*** 27.301 20.602 37.680 45.016 51.168 3 0.0225*** 0.0205*** 0.0197*** 0.0201*** 0.0203*** 20.624 41.617 45.320 47.450 41.203 4 0.0225*** 0.0231*** 0.0218*** 0.0219*** 0.0240*** 30.218 41.766 42.476 42.567 34.040 Big 0.0267*** 0.0262*** 0.0250*** 0.0250*** 0.0255*** 70.677 89.780 52.929 45.778 24.460 j t(j) Small 0.0007 0.0016*** 0.0010*** 0.0009*** 0.0011*** 1.374 4.556 3.379 3.702 5.175 2 0.0009*** 0.0009*** 0.0004** 0.0007*** 0.0010*** 2.737 2.676 1.970 4.328 6.528 3 0.0012 0.0003 0.0004** 0.0003** 0.0005*** 1.388 1.338 1.891 1.718 2.998 4 -0.0002 -0.0004 -0.0002 -0.0001 -0.0002 -0.651 -1.606 -0.817 -0.463 -0.556 Big -0.0012*** -0.0007*** -0.0007*** -0.0006** -0.0008** -5.702 -5.289 -3.254 -2.270 -1.933


23

B/M Low 2 3 4 High Low 2 3 4 High t( )

Small -0.0009 -0.0002 -0.0002 -0.0001 0.0002 -1.638 -0.380 -0.555 -0.373 0.844 2 0.0001 -0.0004 -0.0001 0.0000 0.0002 0.243 -0.897 -0.634 -0.028 1.163 3 0.0008** 0.0001 0.0000 0.0001 0.0006** 1.871 0.401 0.166 0.341 2.140 4 0.0004 0.0002 0.0004 0.0003 0.0005 1.159 0.840 1.403 1.261 1.456 Big 0.0004** 0.0000 -0.0002 -0.0002 0.0000 2.050 0.236 -0.894 -0.767 -0.054 l t(l) Small -0.0003 -0.0008** -0.0006** -0.0006** -0.0006*** -0.453 -1.822 -1.856 -2.090 -2.813 2 -0.0009*** -0.0009** -0.0007** -0.0008*** -0.0009*** -2.667 -2.265 -2.497 -4.447 -5.893 3 -0.0013*** -0.0011*** -0.0008*** -0.0007*** -0.0007*** -2.878 -4.462 -3.585 -4.134 -3.570 4 -0.0010*** -0.0006* -0.0007*** -0.0009*** -0.0009*** -2.782 -2.573 -2.782 -3.929 -2.960 Big -0.0005*** -0.0004*** -0.0008*** -0.0010*** -0.0009** -2.783 -3.050 -4.091 -4.023 -1.759 R-squared Small 0.756 0.768 0.821 0.857 0.877 2 0.869 0.795 0.906 0.943 0.953 3 0.760 0.910 0.926 0.935 0.928 4 0.838 0.903 0.911 0.914 0.879 Big 0.961 0.977 0.944 0.922 0.785

This Table reports regression results over the period January 2003 – June 2014. This regression uses the daily three factors constructed for Japan and orthogonalized to our measure of daily news sentiment Psent. In March of year t, stocks are ranked by book to market, and independently sorted by median market capitalization at the end of September of year t. We form 25 stock portfolios based on quintiles for book to market and size. Value-weighted daily returns on these portfolios are from October to September in year t+1.

The Model is specified as: , , , , ,( )p t f t p p m t f t p t p t p t p t p t p t p t p tR R R R SMB HML psent j Jan March l July d Day

Where dpDay is a day of the week dummy. *** denotes significance at the 1% level, ** 5% level.

24

As a robustness check, we do a Wald test to jointly test whether the three coefficients

and Psent are different to zero. This aim is to test whether Psent adds to the three-factor model

by testing the null hypothesis that the factors (Psent in addition to the factors Rm-Rf, SMB and

HML) are jointly equal to zero. Table 8 presents these results.

Table 8 Wald tests for usefulness of factors.

Panel A: All Factors

χ 2 5120.16***

Panel B: All Factorsorthog

χ 2 5128.72***

This Table presents a Wald test (χ 2) of the null hypothesis that the coefficients reported in Table 6 and Table 7

for all the factors, are jointly equal to zero. Panel A presents results for the three factors and Psent. Panel B

presents results for the 3 factors orthogonalized to Psent.

*** denotes significance at the 1% level, ** denotes significance at the 5% level.

Panel A presents results for the test applied to the coefficients presented in Table 6

whilst panel B presents results for the coefficients presented in Table 7. Both of the tests reject

the null hypothesis which indicates that the coefficients are not jointly equal to zero. As both

tests reject the null hypothesis this presents further evidence that Psent should be considered

in addition to the three Fama and French factors for Japanese asset pricing.

It appears that sentiment has asymmetric effects, either through influencing the Fama

and French factors directly, and when used as an individual factor. However, the effect appears

to be relatively small relative to the three factors and tends to work in the extremes through

size. Future research could extend on this by examining multiple sentiment measures to test

the sensitivity of this to other metrics and by extending to other markets.

IV. Conclusion

The results presented in this paper suggests that there is a role for sentiment in the cross-

section of Japanese stock returns. Using the Fama and French three-factor model we find that

sentiment appears to primarily influence returns indirectly, via the Fama and French three

25

factors. The effects of sentiment are relatively small yet appear to add value to our

understanding of returns when included as a factor. We document asymmetric effects of

sentiment where small growth stocks and large stocks appear to be most affected. These results

suggest that the addition of a sentiment factor should be considered in asset pricing model and

in particular for Japan.

26

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