Individual Investor Sentiment and Comovement in Small Stock Returns - Kumar, Lee

8/11/2019 Individual Investor Sentiment and Comovement in Small Stock Returns - Kumar, Lee

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Individual Investor Sentiment and

Comovement in Small Stock Returns

Alok KumarCornell University

Department of Economics

Charles M.C. LeeCornell University

Johnson Graduate School of Management

May 2003(First Version: September 2002)

Comments Welcomed

P l e a s e a d d r e s s a l l c o r r e s p o n d e n c e t o A l o k K u m a r , D e p a r t m e n t o f E c o n o m i c s , U r i s H a l l , C o r n e l l U n i -

v e r s i t y , I t h a c a , N Y 1 4 8 5 3 , e m a i l : a k 2 7 2 @ c o r n e l l . e d u O R C h a r l e s M . C . L e e , J o h n s o n G r a d u a t e S c h o o l o f

M a n a g e m e n t , C o r n e l l U n i v e r s i t y , I t h a c a , N Y 1 4 8 5 3 , e m a i l : C L 8 6 @ c o r n e l l . e d u . W e w o u l d l i k e t o t h a n k X i -

a n g C a i , R i c h a r d F r a n k e l , W i l l i a m G o e t z m a n n , D o n g H o n g , Z o r a n I v k o v i c , R o b e r t M a s s o n , V i c t o r M c G e e ,

T e d O D o n o g h u e , V i c e n t e P o n s , M a r k S e a s h o l e s , K e n t W o m a c k , a n d s e m i n a r p a r t i c i p a n t s a t M I T S l o a n

S c h o o l f o r s e v e r a l h e l p f u l d i s c u s s i o n s a n d v a l u a b l e c o m m e n t s . W e t h a n k I t a m a r S i m o n s o n f o r m a k i n g t h e

i n v e s t o r d a t a a v a i l a b l e t o u s a n d T e r r a n c e O d e a n f o r a n s w e r i n g n u m e r o u s q u e s t i o n s a b o u t t h e i n v e s t o r

d a t a b a s e . F i n a l l y , w e w o u l d l i k e t o t h a n k M a r k H u l b e r t a n d A n d r e w M e t r i c k f o r p r o v i d i n g t h e d a t a o n

i n v e s t m e n t n e w s l e t t e r r e c o m m e n d a t i o n s . A l l r e m a i n i n g e r r o r s a n d o m i s s i o n s a r e o u r o w n .


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I Introduction

The traditional view of returns comovement relies on the present value model of security

valuation where the current price of a stock reflects the present discounted value of a stream

of future earnings. According to this view, any comovement in stock prices (or returns)

reflects either the common movements in earnings (i.e., stock fundamentals) or changes in the

discount rates which in turn depend upon various macro-economic variables. Demand shocks

or shifts in investor sentiment have no role to play in this traditional view of comovement

where such shocks can be easily offset by the actions of arbitrageurs.

An alternative theory of returns comovement posits that stock prices are established

through a dynamic interplay between noise traders and rational arbitrageurs (e.g., Shiller

(1984), Shleifer and Summers (1999)). According to this view, in addition to innovations

in fundamentals and macro-economic variables, other factors such as the correlated trading

activities of noise traders, can induce comovement in stock returns. Arbitrage forces may

not be able to fully absorb these correlated demand shocks due to a variety of factors, 1 but

most notably due to a non-fundamental risk introduced by the correlated actions of these

investors themselves (Black 1986, DeLong, Shleifer, Summers, and Waldmann 1990).

In this study, we test a particular form of the noise trader model in which individual (or

retail) investor sentiment can affect stock returns. For example, DeLong, Shleifer, Summers,

and Waldmann (1990) (hereafter DSSW) conjecture that because the shares of closed-end

funds are held primarily by individual investors, the discounts on these funds capture the

differential sentiment of these investors. Consistent with this view, Lee, Shleifer, and Thaler

(1991) (hereafter LST) find that the returns of stocks with lower institutional ownership

and lower market capitalization are positively correlated with changes in closed-end fund

discounts. In the same spirit, Lakonishok and Maberly (1990) and Abraham and Ikenberry

(1994) find that the buy-sell imbalance in odd-lot trades exhibit weekly fluctuations consis-

tent with the Day-of-the-week effect.

1

S e e S h l e i f e r a n d V i s h n y ( 1 9 9 7 ) f o r a t h e o r e t i c a l e x p o s i t i o n o f t h i s a r g u m e n t a n d R a s h e s ( 2 0 0 1 ) f o r a n

e x a m p l e w h e r e a r b i t r a g e f a i l s t o d i s c i p l i n e t h e m a r k e t e v e n i n a v e r y s i m p l e s e t t i n g . I n t h e s a m e s p i r i t ,

L a m o n t a n d T h a l e r ( 2 0 0 2 ) p r e s e n t t h e c a s e o f e q u i t y c a r v e - o u t s w h e r e h i g h t r a n s a c t i o n c o s t s a s s o c i a t e d w i t h

s h o r t s a l e s s i g n i fi c a n t l y d i m i n i s h t h e p o w e r o f a r b i t r a g e .

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A key point of contention with these studies is whether closed-end fund discounts, or

odd-lot trades, are appropriate proxies for the sentiment of individual investors. The LST

findings, in particular, proved controversial and has spawned a number of follow-up studies

(e.g., DeLong and Shleifer (1991), Chen, Kan, and Miller (1993), Chopra, Lee, Shleifer, and

Thaler (1993), Elton, Gruber, and Busse (1998)). Although closed-end funds are mainly held

by individuals, questions remain as to what movements in the discounts represent. Similar

inference issues apply to the use of odd-lots trades. In short, so long as the proxy used

to measure small investor sentiment remains controversial, questions as to whether small

investor sentiment affects asset prices will remain.

In this study, we use a large set of investor trading data from a major discount brokerage

house to construct adirectmeasure of individual investor sentiment. Specifically, we computethe buy-sell imbalance (BSI) of trades initiated by individual investors across different stock

portfolios. We then compute a residual BSI by regressing each portfolio BSI on the overall

market returns. Using this residual measure of individual investor trading activity, we explore

the relation between individual investor sentiment and stock returns.

Our investigation is focused on three issues central to the noise trader framework. First,

we evaluate the extent to which the buy-sell activities of individual investors are correlated

across different stock portfolios. The noise trader model asserts that the aggregate behaviorof noise traders is affected by waves of common sentiment. In other words, the BSI of noise

traders should be correlated across non-overlapping stock portfolios. In the absence of this

type of systematic behavior, it is unlikely that noise trader sentiment can affect stock returns.

Our tests document a strong common component in trading activities of individual in-

vestors that is orthogonal to the overall market movement. Using the residual BSI measure,

we find that monthly pair-wise correlations for this variable between non-overlapping port-

folios average around 0.44. This evidence shows the buy-sell activities of individual investorsare correlated across stocks, even after controlling for overall market movements when they

buy (or sell) stocks in one portfolio, they also tend to do so in other portfolios.

Second, we examine the determinants of individual investor sentiment, i.e., what factors

seem to affect the proclivity of individual investors to buy or sell stocks? Noise trader models

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are generally silent with respect to the source of investor sentiment. Our tests are aimed at

providing additional insights on this issue. In addition to factors suggested by the empirical

literature, i.e., returns to mimicking portfolios based on firm size (SMB), book-to-market

(HML), price momentum (UMD), and macro-economic variables (unexpected inflation (UI),

monthly growth in industrial production (MP), value spread (VS), and term spread (TS)), we

also consider the effect of monthly changes in the expert advice obtained from investment

newsletters2 (NLBSI).

We find that changes in the aggregate sentiment of investment newsletters (residual

NLBSI) has a strong influence on the residual BSI measure these two measures have a

contemporaneous correlation of 0.50. Residual BSI is positively but weakly correlated with

the SMB and the HML factors (the correlations are 0.001 and 0

.054 respectively) and it

is negatively correlated with the UMD factor (the correlation is 0.389). Examining the

correlations between residual BSI and the macro-economic variables, we find that BSI is

negatively correlated with UI and MP (the correlations are 0.365 and 0.047 respectively)

but positively correlated with VS and TS (the correlations are 0.345 and 0.152 respectively).

In sum, these results suggest that aggregate individual investor sentiment is weakly related to

the standard risk factors, moderately related to the macro-economic variables, and strongly

related to the aggregate sentiment of investment newsletters.

Third, we examine the extent to which individual investor sentiment has incremental

explanatory power for cross-sectional as well as seasonal patterns in stock returns. Noise

trader models suggest that the effect of noise trading will be greatest in those stocks that they

dominate, and in stocks with relatively high arbitrage costs. To examine these hypotheses,

we form stock portfolios by firm size, stock prices, B/M ratios, and institutional ownership,

and examine the incremental effect of residual BSI on the returns of these portfolios, after

controlling for market excess returns (RMRF), SMB, HML, and UMD.We find that residual BSI has incremental explanatory power for small stocks, value

2

H u l b e r t F i n a n c i a l D i g e s t ( H F D ) t r a c k s t h e s t o c k r e c o m m e n d a t i o n s o f a l a r g e n u m b e r o f i n v e s t m e n t

n e w s l e t t e r s t h a t p r i m a r i l y t a r g e t i n d i v i d u a l i n v e s t o r s . T h e d a t a s e t c o n s i s t s o f s p e c i fi c s t o c k - p o r t f o l i o r e c -

o m m e n d a t i o n s w h e r e a n e w s l e t t e r e i t h e r e x p l i c i t l y r e c o m m e n d s a p o r t f o l i o o f s t o c k s ( a m o d e l p o r t f o l i o ) o r

p r o v i d e s a r a n k e d l i s t o f s t o c k s t h a t c a n b e u s e d t o c o n s t r u c t a p o r t f o l i o . A n e w s l e t t e r m a y r e c o m m e n d

m o r e t h a n o n e m o d e l p o r t f o l i o .

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stocks, stocks with low institutional ownership, and stocks with lower prices. The direction

of the relation indicates that when individual investors are relatively bullish (i.e., when BSI

is more positive), the stocks in these portfolios enjoy higher excess returns. The magnitude

of their influence is affected by factors associated with arbitrage costs, i.e., firm size, stock

price, and the percentage of institutional ownership. These findings are largely consistent

with the predictions of a noise trader model in which individual (retail) investor sentiment

affects returns, particularly among stocks in which these investors are concentrated.

To further explore the role of individual investor sentiment in the return generating

process, we consider the relation between residual BSI and two robust seasonal patterns in

stock returns the January effect and the Day-of-the-Week effect. We find strong evidence

that individual investor sentiment partially explains the January effect.

3

However, contraryto Lakonishok and Maberly (1990) and Abraham and Ikenberry (1994), we find no significant

relation between our measure of individual investor sentiment and weekly patterns in stock

returns.

In sum, our study provides a number of empirical findings consistent with the predictions

of noise trader models. First, we show that the buy-sell activities of individual investors are

correlated across non-overlapping stock portfolios. We find a strong degree of correlation even

after removing the effect of overall market movements. Second, we show that these buy-sellactivities are weakly related to macro-economic variables and excess returns to small firms

and value firms. Furthermore, investors trading activities are strongly influenced by changes

in the expert advice conveyed in investment newsletters. Finally, we show that individual

investor sentiment has incremental effect in explaining returns for small firms, low priced

firms, value firms, and firms with low institutional ownership. We also find some support for

the view that the January effect is related to individual investors propensity to buy small

stocks (sell large stocks) in January.

Although our paper is not the first to examine the role of individual investor sentiment

3

T h e J a n u a r y e ff e c t r e f e r s t o t h e u n u s u a l l y h i g h s t o c k r e t u r n s i n J a n u a r y , e s p e c i a l l y f o r s m a l l e r fi r m s , a n d

t h e D a y - o f - t h e - W e e k e ff e c t r e f e r s t o t h e g e n e r a l l y l o w e r r e t u r n s o b s e r v e d o n M o n d a y s . R i t t e r ( 1 9 8 8 ) s u g g e s t s

a l i n k b e t w e e n t h e J a n u a r y e ff e c t a n d i n d i v i d u a l i n v e s t o r s , L a k o n i s h o k a n d M a b e r l y ( 1 9 9 0 ) a n d A b r a h a m a n d

I k e n b e r r y ( 1 9 9 4 ) s u g g e s t t h e n e g a t i v e s e n t i m e n t o f s m a l l i n v e s t o r s m i g h t a c c o u n t f o r t h e D a y - o f - t h e - W e e k

e ff e c t .

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in explaining stock returns, we believe our results present the most convincing evidence so

far of small investor sentiment-induced comovement in stock returns. By using a direct,

trading-based measure of individual investor sentiment, we avoid the difficulties that arise

from establishing the appropriateness of sentiment proxies such as changes in closed-end

fund discounts or odd-lot trades.

The rest of the paper is organized as follows: in the following section, we review the

literature on sentiment-induced stock price movements. A brief description of the datasets

used in the study is provided in Section III. In Section IV, we define an investor sentiment

measure and establish that there exists a systematic component in the trading activities of

individual investors. In Section V, we provide insights into the determinants of individual

investor sentiment. Estimation of multi-factor time-series models that include the investor

sentiment measure are carried out in Section VI. In Section VII, we examine the role of

investor sentiment in explaining the seasonal variations in stock returns. Finally, in Section

VIII we summarize our findings and discuss future research.

II Related Research

Empirical evidence in support of a behavioral view of returns comovement is mounting.

Collectively this evidence suggests that investor sentiment is an integral part of the return

generating process. Lee, Shleifer, and Thaler (1991) find that the returns of stocks with lower

institutional ownership and lower market capitalization are positively correlated with changes

in closed-end fund discount rates. Because closed-end mutual funds are held primarily by

individual investors, they argue fluctuations in the discount of these funds reflect the changing

sentiment of these investors. Gemmill and Thomas (2002) use mutual fund flows as a more

direct measure of individual investor sentiment, and confirm that the fluctuations in closed-

end fund discounts are indeed influenced by the trading activities of individual investors.

Both results support the view that market segmentation can lead to comovement in returns.

This view is also supported by Swaminathan (1996) who suggests that market segmentation

may be responsible for the observed positive correlation between closed-end fund discounts

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and expected returns on small stocks.

Two other studies examine the effect of institutional trading on stock returns. Pindyck

and Rotemberg (1993) find that the degree of comovement in returns is high among stocks

that belong to different industries but have high institutional ownership. Neither currentmacro-economic conditions nor expectations of future macro variables fully explain this co-

movement. Similarly, Gompers and Metrick (2001) find that demand shocks generated by

shifts in institutional ownership influence future stock returns. The authors of both stud-

ies conjecture that these comovements in returns result from common institutional demand

across stocks.

Another stream of related research uses indirect measures of investor sentiment to exam-

ine the effect of individual investors on stock returns. For example, Lakonishok and Maberly

(1990) examine whether the trading activities of individual investors induce a weekly pattern

in returns. Using odd-lot trades as a proxy for the sentiment of individual investors, they

find that the lower returns on Monday (the weekend effect) correspond with disproportion-

ately large net selling by individual investors. Neal and Wheatley (1998) use three different

indirect measures of investor sentiment, namely, closed-end fund discounts, ratio of odd-lot

sales to purchases, and net mutual fund redemptions. They find that both closed-end fund

discounts and net mutual fund redemptions help predict the size premium.

Finally, our work is related to research that uses direct trading data to investigate the

effect of cognitive biases on the decisions of individual investors (e.g., Odean (1998, 1999),

Barber and Odean (2000, 2001), Grinblatt and Keloharju (2001), Shapira and Venezia (2001),

Zhu (2002), and Ivkovic and Weisbenner (2003)). For example, using the same data as in this

study, Odean (1998) investigates investors propensity to sell winners and retain losers in their

portfolios and Odean (1999) examines whether individual investors trade excessively due to

their over-confidence. In related studies, Goetzmann and Massa (2000) use trading data from

Fidelity Spartan Market Index Fund to show that mutual fund investors systematically act as

very short-term (daily) momentum traders and contrarians while Grinblatt and Keloharju

(2000) use Finnish trading data to show that both individual and institutional investors

systematically condition their trading decisions on past return patterns.

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In sum, various studies have attempted to derive indirect measures of individual investor

sentiment without direct data on investor trading activities. Those studies that use direct

trading data have focused on documenting individual psychological biases and do not address

the asset-pricing implications of individual investors systematic trading activities, which is

the main focus of our study.

III Data and Sample Selection

Several datasets are used in this study. A brief description of each one follows.

III.A Individual Investor Data

The primary data for this study consists of trades and monthly portfolio positions of investors

at a major discount brokerage house in the U.S. for the period of 1991-96. The database

consists of three types of data files: (i) position files that contain the end-of-month portfolios

of all investors, (ii) a trade file that contains all transactions carried out by the investors in

the database, and (iii) a demographics file that contains information such as age, gender,

marital status, income code, occupation code, geographical location (zip code), etc. for a

subset of investors. In this study, we primarily use the trades file. There are a total of 77 , 995

households in the database of which 62, 387 have traded in stocks. More than half of the

households in our database have 2 or more accounts. Approximately 27% of the households

have 2 accounts, 13% have 3 accounts, 6% have 4 accounts and 6% have 5 or more accounts.

Table I provides a summary of the key attributes of the investor database. The aggregate

value of investor portfolios in our sample is, on average, $2.18 billion in a given month. An

average investor holds a 4-stock portfolio (median is 3) with an average size of $35,629

(median is $13,869). Less than 10% of the investors hold portfolios over $100,000 and less

than 5% of them hold more than 10 stocks. The average portfolio turnover rate which

measures the frequency of trading is 7.59% (median is 2.53%). A typical investor executes

9 trades per year where the average trade size is $8,779 (median is $5,239) and the average

number of days an investor holds a stock is 187 trading days (median is 95).

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To gauge how representative our individual investor sample is of the overall population

of individual investors in the US, we compare the stock holdings of investors in our sample

with those reported by the Census Bureau4 (Survey of Income and Program Participation

(SIPP), 1995) and the Federal Reserve 5 (Survey of Consumer Finances (SCF), 1992, 1995).

According to the 1992 SCF, a typical household held $8,700 in stocks (median was $16,900).

The stock ownership declined marginally in the 1995 SCF where a typical US household

held $8,000 in stocks (median was $15,300). In another independent survey conducted by

the Census Bureau, the real median value of stocks and mutual funds held by households

increased from $7,331 in 1993 to $9,000 in 1995. The median portfolio size of an investor in

our sample ($13,869) matches quite well with the numbers reported in SCF 1992, SCF 1995,

and SIPP 1995. Overall, these comparisons suggest that our sample probably captures a

reasonable cross-section of U.S. households that invest in stocks.

III.B Institutional Ownership Data

We also obtained quarterly institutional ownership data for the stocks in our sample from

the CDA Spectrum database. Spectrum contains the end of quarter stock holdings of all

institutions that file form 13F with the Securities and Exchange Commission (SEC). Insti-

tutions with more than $100 million under management are required to file form 13F with

the SEC and common stock positions of more than 10 , 000 shares or more than $200,000 in

value must be reported on the form. Using the quarterly institutional holdings, we compute

the aggregate institutional ownership for each stock at the end of each quarter and this

aggregate ownership data is used to construct ownership portfolios at the end of the year. 6

Table II reports the summary statistics for investor trading activities and the level of

institutional ownership for the stocks in our sample. We find that the investors in our sample

execute 26, 000 trades in a typical month and 1, 244 trades on a typical day. Examining the

level of institutional ownership among stocks in our sample, we find that in a given year, on

4

S o u r c e : U S C e n s u s B u r e a u R e p o r t , A s s e t O w n e r s h i p o f H o u s e h o l d s , 1 9 9 5 . T h e d a t a i s a v a i l a b l e a t

h t t p : / / w w w . c e n s u s . g o v / h h e s / w w w / w e a l t h / 1 9 9 5 / w e a l t h 9 5 . h t m l .

5

T h e r e p o r t i s a v a i l a b l e a t h t t p : / / w w w . f e d e r a l r e s e r v e . g o v / p u b s / o s s / o s s 2 / 9 5 / s c f 9 5 h o m e . h t m l .

A l s o s e e K e n n i c k e l l , S t a r r - M c C l u e r , a n d S u n d e n ( 1 9 9 7 ) .

6

A d e t a i l e d d e s c r i p t i o n o f t h e i n s t i t u t i o n a l o w n e r s h i p d a t a c a n b e f o u n d i n G o m p e r s a n d M e t r i c k ( 2 0 0 1 ) .

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average, 19% of the stocks have zero institutional ownership and approximately 30% have low

(i.e., less than 5%) institutional holding. As expected, the level of institutional ownership

is even lower for small stocks. Panel B reports results for stocks in the smallest decile

using beginning-of-year NYSE size thresholds. These stocks are important because (i) they

represent a disproportionately large percentage of the stocks held by individual investors,

and (ii) individual investors are likely to play a larger role in the pricing of these stocks.

Panel B results show that approximately 25% of the decile 1 stocks have zero institutional

ownership, and approximately 42% have low (less than 5%) institutional holding. Overall,

these results show that individual investors are likely to be more highly concentrated among

the decile 1 stocks.

III.C Newsletter Analysts Stock Recommendations Data

We utilize a third data source to investigate the determinants of individual investors trad-

ing activities i.e., the stock recommendations of investment newsletter analysts. Hulbert

Financial Digest (HFD) tracks the stock recommendations of a large number of investment

newsletters that primarily target individual investors. A new recommendation is entered in

the database on the day a newsletter is received in the mail. This corresponds to the date

an investor is likely to have received the newsletter. In addition, some newsletters provide

telephone hotlines which are checked frequently to obtain updated recommendations. A

newsletter is not removed from the database after it ceases to exist, so this database is free

of any survivorship bias.

The dataset consists of specific stock-portfolio recommendations where a newsletter either

explicitly recommends a portfolio of stocks or provides a ranked list of stocks that can be

used to construct a portfolio7 . An addition (deletion) of a stock into a model portfolio or

an explicitly stated increase (decrease) in the weight of a stock in the model portfolio is

coded as a positive (negative) recommendation. Automatic changes in the portfolio weight

resulting from newsletters portfolio rebalancing decisions are ignored.

Table III reports the summary statistics of the newsletter database. There are 269

7

W e t h a n k A n d r e w M e t r i c k f o r p r o v i d i n g a c l e a n v e r s i o n o f t h e n e w s l e t t e r s t o c k r e c o m m e n d a t i o n s d a t a s e t .

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newsletter analysts in our sample and during the 1991-96 period, these analysts made 267, 278

recommendations on 4, 548 stocks. This includes recommendations on a considerable number

of stocks in all size and B/M based stock categories (see Panels B and C). The set contains

132, 872 negative and 134, 406 positive recommendations. In any given year, the group of

newsletters recommend approximately 2, 000 stocks and for each stock, as a group, they

make an average of 19 recommendations per year. A typical newsletter analyst made 994

recommendations during the 1991-96 sample period (median is 147). A quarter of them

made less than 38 recommendations, 5% of them made more than 3 , 600 recommendations,

and the most active newsletter analyst made 35, 539 recommendations.8

For each stock in our sample, we obtain the daily and the monthly security prices and re-

turns data from CRSP as well as the market capitalization and book value of common equity

data from COMPUSTAT. The monthly time-series of several macro-economic variables (un-

expected inflation, monthly growth in industrial production, value spread, and term spread)

are obtained from the economic data library of the Federal Reserve Bank of St. Louis.9

Finally, we obtain several datasets from Ken Frenchs data library. 1 0 Specifically, we use the

monthly time-series of the 3 Fama-French factors and the momentum factor. We also obtain

the NYSE size and B/M break-points for each month, which are used to construct size and

B/M portfolios.

IV Correlated Trading Activities of Individual Investors

IV.A Investor Sentiment Measure

Investors aggregate sentiment for a certain group of stocks (i.e., a stock portfolio) can be

measured in a variety of ways using their trading activities. One such sentiment measure is a

portfolios buy-sell imbalance (BSI) during a certain time-period t. To compute the portfolio

8

A d e t a i l e d d e s c r i p t i o n o f t h e n e w s l e t t e r d a t a s e t i s a v a i l a b l e i n M e t r i c k ( 1 9 9 9 ) . T h i s s t u d y i n v e s t i g a t e s

t h e s t o c k - s e l e c t i o n a b i l i t y o f n e w s l e t t e r a n a l y s t s a n d fi n d s t h a t , a t a n a g g r e g a t e l e v e l , n e w s l e t t e r s e x h i b i t

v e r y w e a k s t o c k - p i c k i n g a n d m a r k e t - t i m i n g a b i l i t i e s .

9

T h e e c o n o m i c d a t a l i b r a r y i s a v a i l a b l e a t h t t p : / / r e s e a r c h . s t l o u i s f e d . o r g / f r e d 2 /

1 0

K e n F r e n c h s d a t a l i b r a r y i s a v a i l a b l e a t h t t p : / / m b a . t u c k . d a r t m o u t h . e d u / p a g e s / f a c u l t y / k e n . f r e n c h / .

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BSI, we first define the BSI for stock iin month t as:

BSIi t

=

D

t

j = 1

VBi j t

D

t

j = 1

VSi j t

D

t

j = 1

VBi j t

+

D

t

j = 1

VSi j t

(1)

whereD

t

= Number of days in month t,

VBi j t

= Buy volume (measured in dollars) for stock i on day j in month t, and

VSi j t

= Sell volume (measured in dollars) for stock i on day j in month t.

An equal-weighted average of individual stock BSIs provides a measure of portfolio BSI:

BSI p t

=100

N p

N

p

i = 1

BSIi t

(2)

where N p

is the number of stocks in portfolio p. The portfolio BSI in any given month is

computed using only the stocks within the portfolio in which the investors in our sample

trade during that month. 1 1

Most of the analyses in this paper have been carried out using monthly aggregated mea-

sures of investor trading activities. Given the relatively sparse trading activities of individual

investors in our sample (see Table II), a daily buy-sell imbalance (BSI) measure for a par-

ticular stock is likely to be unreliable. On any given day, on average, less than 10% of the

stocks in a portfolio are likely to be traded by the investors in our sample. Therefore, to

ensure sufficient trading, we compute a BSI time-series for portfolios of at least 250 stocks.

This ensures, on average, approximately 20-25 stocks in each portfolio will be traded daily.

Using this approach, and monthly aggregation, we find that approximately 50-60% of the

stocks in a typical portfolio are traded each month by the investors in our sample.

IV.B Correlations among Random Non-Overlapping Portfolios

In this section, we test for the presence of a systematic component in the trading activities

of individual investors. To quantify the portfolio-level trading activities of investors, we1 1

O u r m e a s u r e o f p o r t f o l i o B S I g i v e s t h e s a m e w e i g h t t o e a c h s t o c k i n c o m p u t i n g t h e p o r t f o l i o s e n t i m e n t

m e a s u r e . A n a l t e r n a t i v e a p p r o a c h i s t o fi r s t c o m p u t e t h e a g g r e g a t e d o l l a r v o l u m e i n - fl o w ( A V B ) a n d a g g r e -

g a t e d o l l a r v o l u m e o u t - fl o w ( A V S ) f o r a l l t h e s t o c k s i n a p o r t f o l i o , a n d d e fi n e B S I a s

A V B A V S

A V B + A V S

. H o w e v e r ,

u n d e r t h i s a l t e r n a t i v e a p p r o a c h , a s i n g l e s t o c k c a n s t r o n g l y i n fl u e n c e t h e p o r t f o l i o B S I i n a p a r t i c u l a r m o n t h ,

e s p e c i a l l y a r o u n d i n f o r m a t i o n e v e n t s s u c h a s e a r n i n g s a n n o u n c e m e n t s a n d s t o c k r e c o m m e n d a t i o n c h a n g e s

w h e n t h e s t o c k t r a d i n g v o l u m e i s u n u s u a l l y h i g h . O u r m e a s u r e a v o i d s s u c h a b i a s .

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form non-overlapping portfolios and construct a monthly buy-sell imbalance time-series for

each portfolio. We then compute correlations between pairs of the buy-sell imbalance (BSI)

indices of these non-overlapping portfolios and generate an empirical distribution of BSI

correlation. Specifically, we form non-overlapping portfolio pairs where stocks are chosen

randomly from the set of stocks in our sample. 1000 pairs of non-overlapping 250-stock

portfolios are formed and a BSI time-series for each of the random portfolio is obtained.

To remove the common dependence of BSI on the market factor, we perform the following

regression:

BSI p t

=b0

+b1

RMRFt

+ p t

. (3)

Here, BSI p t

is the buy-sell imbalance index for portfoliop in month t, RMRFt

is the market

return in excess of the riskfree rate in month t, and p t is the residual BSI for portfoliop in

month t. We compute the correlations among the residual BSI time-series over 71 months

(January 1991 to November 1996) for each of the portfolio pairs.

Figure 1 shows an empirical distribution of these pairwise correlations. The average

residual BSI correlation is 0.44 (median is 0.45). The average residual BSI correlation de-

creases monotonically with portfolio size. For instance, for 100-stock portfolios, the average

residual BSI correlation is 0.34 while for 50-stock portfolios, this measure is 0.23. These re-

sults provide clear evidence of a systematic component in investor trading activities, which

is uncorrelated with the movements of the market index. In additional (untabulated) tests,

we find that these correlations are slightly higher when stocks are chosen from similar size

groups. For example, when stocks are chosen from size quintiles 1 and 5, the average residual

BSI correlations are 0.49 and 0.48 respectively.

We carry out Monte Carlo simulations to obtain an estimate of average residual BSI

correlation in the absence of a systematic component in the trading activities of our investors

(i.e., a benchmark average residual BSI correlation). Specifically, we generate a BSI matrix

where, for each stock, we keep the frequency and timing of trades fixed but we randomly

assign a BSI in each month where BSI (1, 1). Using this simulated BSI matrix and

following the procedure described earlier, we generate an empirical distribution of residual

BSI correlations by computing the correlations between 1000 pairs of non-overlapping 250-

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stock portfolios. This entire process is repeated 500 times and a distribution of the average

residual BSI correlation is obtained. 1 2

Figure 2 shows an example of the residual BSI correlation distribution obtained in one of

our randomization tests. Clearly, the average residual BSI correlation of 0.01 is significantlylower than the observed average residual BSI correlation of 0.44. In fact, we find that

the average BSI correlation is lower than 0.44 in each of the 500 repetitions. The evidence

indicates that the observed strong BSI correlations is unlikely to have occured by pure chance

(p-value < 0.002).

Overall, our results show that correlations between BSI indices are strongly positive over

non-overlapping stock portfolios. Our randomization tests suggest these strong correlations

could not have occured by chance alone. Taken together, the evidence points to the existence

of a systematic (or market-wide) component in the trading activities of individual investors.

Specifically, when individual investors buy (sell) one basket of stocks, they are likely to

simultaneously buy (sell) other stock baskets.

V Determinants of Investor Sentiment

What factors might induce systematic trading among individual investors? The noise traderliterature offers little guidance on this issue. For example, Fischer Black contrasts noise with

information, and defines noise trading as trading on noise as if it were information (Black

1986, pp. 529). Shiller (1984) suggests that common sentiments arise when investors trade

on pseudo-signals, such as price and volume patterns, popular models, or the forecasts of

Wall Street gurus.1 3 However, most past studies have attempted to document the existence

of investor sentiments rather than explore their origins.1 4 Our analysis begins with a credible

1 2

F o r r o b u s t n e s s , w e a l s o c a r r i e d o u t a s i m i l a r t e s t w h e r e i n s t e a d o f c h o o s i n g B S I r a n d o m l y f r o m ( 1 , 1 ) ,

w e r e s a m p l e B S I f r o m t h e o b s e r v e d B S I m a t r i x . I n a d d i t i o n , w e r e p e a t e d o u r t e s t s u s i n g 5 0 , 1 0 0 , a n d 5 0 0

s t o c k p o r t f o l i o s . T h e r e s u l t s a r e q u a l i t a t i v e l y s i m i l a r i n a l l t h e s e c a s e s .

1 3

P s e u d o - s i g n a l s a r e s i g n a l s t h a t a r e n o n - i n f o r m a t i v e i n e s t i m a t i n g a fi r m s f u n d a m e n t a l v a l u e , b u t t h a t

m a y b e n e v e r t h e l e s s p e r s u a s i v e i n t h e i r o w n r i g h t .

1 4

T h e r e a r e a f e w n o t a b l e e x c e p t i o n s . I n a r e c e n t s t u d y , B a r b e r , O d e a n , a n d Z h u ( 2 0 0 3 ) e x a m i n e w h e t h e r

p s y c h o l o g i c a l b i a s e s l e a d t o c o r r e l a t e d t r a d i n g a m o n g i n d i v i d u a l i n v e s t o r s . T h e y fi n d t h a t t h e c o r r e l a t e d

b u y i n g d e c i s i o n s a r e d r i v e n b y a t t e n t i o n ( B a r b e r a n d O d e a n 2 0 0 1 ) a n d e x t r a p o l a t i o n o f p a s t t r e n d s ( K a h -

n e m a n a n d T v e r s k y 1 9 7 3 ) w h i l e t h e i r c o r r e l a t e d s e l l i n g a c t i v i t i e s r e s u l t f r o m i n v e s t o r s r e l u c t a n c e t o r e a l i z e

l o s s e s ( S h e f r i n a n d S t a t m a n 1 9 8 5 ) . I n a n o t h e r r e c e n t s t u d y , u s i n g a C h i n e s e d a t a s e t , F e n g a n d S e a s h o l e s

14


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measure of sentiment, and provides some insights on how this sentiment is correlated with

several other commonly used variables in empirical finance.

As a starting point, we investigate the relations between our measure of investor sentiment

and several empirically-inspired risk factors that appear in the literature. Specifically, Fama

and French (1992) find that firms market capitalization (size) and book-to-market ratio

(B/M) explain a significant portion of the cross-sectional variation in stock returns. In a

related study, Fama and French (1993) form mimicking portfolios based on size (SMB) and

B/M (HML), and show that when the standard market model is augmented by these two

variables, a number of pricing anomalies disappear. In this study, we examine the relation

between BSI, SMB, and HML. We also include a price momentum factor (UMD), as suggested

by Jegadeesh and Titman (1993) and Carhart (1997).

In addition to these empirically inspired risk factors, we also examine whether individual

investor sentiment is influenced by innovations in macro-economic variables (Chen, Roll, and

Ross 1986). Under the present value model of security valuation, the current price of a stock

reflects the present discounted value of a stream of expected future cash flows. If investors

adopt a present value model (or some variant) to formulate their trading decisions, vari-

ables that influence expected future cash flows or the discount rate may influence individual

investor sentiment.

Following Chen, Roll, and Ross (1986) and Ferson and Schadt (1996), we consider the

following four macro-economic variables as potential determinants of investor sentiment: (i)

UI:unexpected inflation where the average of 12 most recent inflation realizations is used to

estimate the expected level of inflation, (ii) MP: monthly growth in industrial production,

(iii) TS: the term-spread (a measure of the term structure) which is the difference between the

yield of a constant-maturity 10-year Treasury bond and the yield of a 3-month Treasury bill,

and (iv)VS: the value-spread (a measure of risk premium) which represents the difference

between the yields of Moodys BAA-rated corporate bond and AAA-rated corporate bond

(or long term government bond).

Finally, we examine the relation between investor sentiment and the sentiment of a group

( 2 0 0 2 ) fi n d t h a t t h e t r a d i n g a c t i v i t i e s o f i n v e s t o r s t h a t l i v e w i t h i n a c e r t a i n g e o g r a p h i c r e g i o n a r e s t r o n g l y

c o r r e l a t e d .

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of investment newsletter writers (experts). It is possible that individual investors seek ex-

pert advice from professional information providers such as investment newsletter writers.

It is also possible that individual investors and newsletter writers are influenced by common

waves of investor sentiment. In either case, we would find a positive correlation between BSI

and the opinion of these newsletter writers.

To examine the relation between investor sentiment and the sentiment of investment

newsletters, we define a BSI-like measure for capturing the sentiment (or the degree of

bullishness) of newsletter analysts. Specifically, we code an increase (decrease) in portfolio

weight for a stock, or an addition (deletion) of a stock into a portfolio, as a positive (negative)

recommendation. We compute a newsletter sentiment measure for each stock i in month

t (NLBSIi t

) as:

NLBSIi t

=

D

t

j = 1

POSi j t

D

t

j = 1

NEGi j t

D

t

j = 1

POSi j t

+

D

t

j = 1

NEGi j t

(4)

where Dt

is the number of days in month t, POSi j t

is the number of new positive recom-

mendations for stock i on day j in month t, and NEGi j t

is the number of new negative

recommendations for stock ion day j in month t. We then define the newsletters degree of

bullishness for portfolio p in month t(NLBSI p t

) as:

NLBSI p t

=

100

N p

N

p

i = 1

NLBSIi t

(5)

whereN p

is the number of stocks in portfoliop for which new recommendations are available

in month t.

V.A Correlations: Sentiment Variables, Risk Factors, and Macro-Economic Variables

Table IV reports the correlations between aggregate investor sentiment (residual BSI) and

three sets of potential determinants of investor sentiment: the standard risk factors (SMB,

HML, and UMD), macro-economic variables (UI, MP, VS, and TS), and newsletter sentiment

(NLBSI). We find that residual BSI is positively but weakly correlated with the SMB and

the HML factors (the contemporaneous correlations are 0.001 and 0.054 respectively) and it

is negatively correlated with the UMD factor (the contemporaneous correlation is 0.389).

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19/51

V.B Estimation of the Lead-Lag Sentiment Relation using VAR

To better characterize the lead-lag relation between the aggregate investor and newsletter

sentiments (residual BSI and residual NLBSI respectively), we also estimated the following

bivariate vector auto-regression (VAR) model:

BSIt

NLBSIt

=

b1 0

b2 0

+

b1 1

b1 2

b2 1

b2 2

BSIt 1

NLBSIt 1

+

1 t

2 t

(7)

Table VI presents the VAR estimates and Granger causality probabilities. Both sentiments

exhibit strong persistence (b1 1

= 0.33 with a t-value of 2.18 and b2 2

= 0.53 with a t-value of

4.62). More interestingly, we find that the lagged aggregate newsletter sentiment (NLBSI)predicts the current aggregate investor sentiment (BSI), but lagged BSI has no power to

predict the current NLBSI. This evidence of individual investor sentiment predictability

suggests that newsletter recommendations influence individual investor trading behavior.

The p-values from Granger causality tests (see Table VI, Panel B) summarize and reinforce

these observations.1 5

V.C Cross-Sectional Variation in Investor-Newsletter Sentiment Relation

We also investigated the robustness of investor-newsletter sentiment relation across firm

size deciles. Smaller firms have more limited amounts of publicly available information. In

contrast, larger firms operate in an information-rich environment with significant analyst

coverage and higher levels of institutional ownership. The relative scarcity of publicly avail-

able information for smaller firms may induce individual investors to seek expert advice

from professional information providers such as investment newsletters. If so, the trading

1 5

B e c a u s e V A R e s t i m a t e s a n d t h e G r a n g e r c a u s a l i t y t e s t s a r e s e n s i t i v e t o t h e c h o i c e o f t h e l a g l e n g t h , w e

c a r r i e d o u t t h e V A R e s t i m a t i o n u s i n g l a g l e n g t h o f 1 , 2 , 4 , a n d 6 m o n t h s . I n a l l f o u r c a s e s , w e fi n d t h a t

l a g g e d a g g r e g a t e n e w s l e t t e r s e n t i m e n t ( N L B S I ) p r e d i c t s t h e c u r r e n t a g g r e g a t e i n v e s t o r s e n t i m e n t ( B S I ) b u t

l a g g e d B S I h a s n o p o w e r t o p r e d i c t c u r r e n t N L B S I . F o r r o b u s t n e s s , w e a l s o r e - e s t i m a t e d e q u a t i o n ( 7 ) i n t h e

p r e s e n c e o f f o u r c h o s e n m a c r o - e c o n o m i c v a r i a b l e s . W e fi n d t h a t t h e i n v e s t o r - n e w s l e t t e r s e n t i m e n t l e a d - l a g

r e l a t i o n i s r o b u s t t o t h e i n c l u s i o n o f m a c r o - e c o n o m i c v a r i a b l e s . T h e c o e ffi c i e n t e s t i m a t e s a n d t h e p - v a l u e s

f r o m t h e G r a n g e r c a u s a l i t y t e s t s f o r t h i s c o n d i t i o n a l V A R m o d e l a r e v i r t u a l l y u n c h a n g e d f r o m t h o s e r e p o r t e d

i n T a b l e I V . T h e s e r e s u l t s a r e a v a i l a b l e f r o m u s u p o n r e q u e s t .

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activities of investors among small stocks is likely to be more positively correlated with the

sentiment of investment newsletter analysts.

Figure 3 shows the residual BSI correlation between the newsletter and investor BSI

indices for each of the ten size-decile portfolios. Among the lower size-decile portfolios,the BSI correlation is strong and positive for the first three size deciles, the residual BSI

correlations are 0.464, 0.358, and 0.460 respectively. Moving from lower to higher size-decile

portfolios, we find that the BSI correlation decreases almost monotonically. In fact, for

both deciles 8 and 9, the BSI correlations are negative (-0.041 and -0.125 respectively). The

correlation increases again for the largest size decile where it is positive but weak (0.033).

Overall, our results are consistent with the view that for smaller stocks where publicly

available information is limited, investors are likely to seek expert advice from externalsources such as investment newsletters. 1 6

In sum, our results suggest that the sentiment of individual investors, in particular their

buy-sell activity among small-cap stocks, is strongly influenced by the sentiment of invest-

ment newsletters. Furthermore, individual investor sentiment is only weakly correlated with

the standard risk factors (SMB, HML, and UMD) and macro-economic variables (UI, MP,

TS, and VS). These results suggest that individual investor sentiment may give rise to an

orthogonal source of comovement in stock returns, particularly among small stocks.

VI Estimation of Multi-Factor Time-Series Models

In this section, we examine the incremental explanatory power of individual investor sen-

timent for cross-sectional stock returns. Our investigation follows procedures that have

become standard in recent asset pricing studies. For most of our tests, we employ variants of

a five-factor time-series model where the first three factors are from Fama and French (1993),

the fourth factor is the momentum factor (Jegadeesh and Titman 1993, Carhart 1997), and

the fifth factor is investors buy-sell imbalance measure for the portfolio. Specifically, the

1 6

I n t e r e s t i n g l y , o u r fi n d i n g c o i n c i d e w e l l w i t h F i s h e r a n d S t a t m a n ( 2 0 0 0 ) . T h e a u t h o r s u s e s u r v e y d a t a t o

s h o w t h a t i n d i v i d u a l i n v e s t o r s e n t i m e n t i s c o r r e l a t e d w i t h t h e r e c o m m e n d a t i o n s o f n e w s l e t t e r a n a l y s t s .

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following factor model is used to test the sentiment-return time-series relation:

R p t

Rf t

= p

+ 1 p

RMRFt

(8)

+ 2 p

SMBt

+3 p

HMLt

+4 p

UMDt

+ 5 p

BSI p t

+ p t

t= 1, 2, . . . , T .

Here, R p t

is the rate of return on the portfolio, Rf t

is the riskfree rate of return, RMRFt

is

the market return in excess of the riskfree rate, SMBt

is the difference between the value-

weighted return of a portfolio of small stocks and the value-weighted return of a portfolio

of large stocks, HMLt

is the difference between the value-weighted return of a portfolio of

high B/M stocks and the value-weighted return of a portfolio of low B/M stocks, UMDt

is

the difference between the value-weighted return of a portfolio of stocks with high returns

during months t 12 to t 2 and the value-weighted return of a portfolio of stocks with low

returns during months t 12 to t 2, BSI p t

is investors buy-sell imbalance for portfoliop

in month t, and p t

is the residual return on the portfolio.

VI.A Sorting on Size

To set the stage, we consider portfolios obtained from a uni-dimensional sort along the size

dimension. Noise trader models assert that the price impact of noise trading will be most

pronounced in stocks where these traders concentrate. We expect the effect of individual

investor sentiment to be most evident among small stocks.

Following the approach in Lee, Shleifer, and Thaler (1991), we construct five size-ranked

portfolios. At the end of each year, we sort the entire universe of stocks for which returns data

is available from CRSP according to their market capitalizations at the end of November.

Using the NYSE break-points, we group stocks into size quintiles. Portfolio membership is

not modified during the course of the year. Portfolio 1 consists of stocks with the lowest

market capitalization while portfolio 5 contains stocks with the largest market capitalization.

For each portfolio, we compute the monthly portfolio return as an equal-weighted average

of all stocks in the portfolio and construct a monthly portfolio return time-series.

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Table VII presents the results of a time-series factor model estimation for each of the five

size-quintile portfolios. For portfolio 1, the BSI loading is positive (0.069) and statistically

significant (t-value = 2.467). With a monthly standard deviation of 8.430% for quintile 1

BSI, one standard deviation shift in the sentiment measure corresponds to a 0 .582% monthly

shift in the portfolio return, which is economically significant. For the remaining 4 portfolios,

the BSI loadings are statistically insignificant. These results suggest stocks in the smallest

quintile earn positive (negative) excess returns when individual investor sentiment is more

bullish (bearish). Individual sentiment does not play a significant role in the comovement of

returns for other size quintiles.

The loadings on the standard risk factors are also revealing. As expected, the loading

on the SMB factor monotonically decreases as we move from portfolio 1 (smallest stocks) to

portfolio 5 (largest stocks). For portfolio 1, the loading on SMB is 1.409 and it decreases

to 0.160 in portfolio 5. However, we also find a decreasing trend in the loading on the

HML factor. For portfolio 1, the loading on HML is 0.634 and it decreases to 0.039 for

portfolio 5. This suggests that the smaller stocks held by our investors are likely to be value

(high B/M) stocks. However, given each investors relatively smaller portfolio size (see Table

I), this result may also reflect individual investors preference for lower priced stocks. The

loading on the UMD factor is negative for all 5 size-quintile portfolios, indicating an overall

preference for stocks that have performed poorly in the recent past.

VI.B Other Uni-dimensional Sorts: B/M, Institutional Ownership and Stock Price

To further examine the incremental power of investor sentiment in explaining returns, we

perform three other uni-dimensional sorts and carry out the five-factor model estimation for

each set of portfolios. Given that the loadings on the HML factor exhibit a decreasing trend

in the estimates of size-portfolios, it is possible that the B/M ratio rather than firm size is

driving our main results. Previous studies (Barber and Odean 2001, Hong and Kumar 2002)

have documented that investors exhibit value-seeking behavior around different types of

news events. Thus, individual investors may be more active among value stocks and their

sentiment may have a greater ability to explain value stock returns.

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To examine this possibility, we construct five B/M-ranked portfolios. As before, at the

end of each year, we sort all stocks in our sample according to their B/M ratio at the end of

December and assign each stock into a B/M decile. The estimation results are summarized in

Table VIII. The BSI loading is positive and statistically significant only for the value stocks

portfolio (B/M portfolio 5). This suggests that the concentration of individual investors is

likely to be higher among value stocks and their systematic trading activities is likely to be

a part of the return generating process of value stocks. Specifically, bottom quintile B/M

stocks earn positive (negative) excess returns when individual investor sentiment is bullish

(bearish).

We perform two more uni-dimensional sorts, one along the institutional ownership di-

mension and the second along the stock price dimension. These results are also summarized

in Table VIII. As expected, the sentiment loading varies almost monotonically across the

institutional ownership and stock price portfolios. This variable is strongly positive and

significant for lower institutional ownership and lower price portfolios. For the lowest in-

stitutional ownership and price quintile portfolios, the sentiment loadings are 0.052 (t-value

= 2.970) and 0.078 (t-value = 2.485) respectively. Specifically, low-institutional ownership

and low priced firms earn positive (negative) excess returns when individual investor senti-

ment is bullish (bearish).

Collectively, these results are consistent with the view that the impact of individual

investor sentiment is stronger among stocks where their concentration is higher.

VI.C Two-dimensional Sorts

To better understand the relative roles of firm size, level of institutional ownership, and stock

price in shaping the sentiment-return relationship, we perform double sorts using firm size,

institutional ownership, B/M, and stock price variables.

First, we perform a two-way nested sort on firm size and the level of institutional own-

ership. At the end of each year, we assign each stock into one of three size-categories based

on its market capitalization in November: small stocks (deciles 1-3), medium-size stocks

(deciles 4-7), and large stocks (deciles 8-10). Within each of these 3 categories, using the

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level of institutional ownership at the end of the year, we assign each stock into low (deciles

1-3), medium (deciles 4-7), and high (deciles 8-10) institutional holding categories. We use

this coarser 3 3 grid instead of the more standard 5 5 or 10 10 grid because of the

fewer number of stocks among the higher size deciles in our sample. However, the qualitative

nature of our results do not change with a finer grid.

The factor model estimation results for the 9 size-ownership portfolios are presented in

Panel A of Table IX. For brevity, we only report the BSI loadings. As expected, the BSI

loadings are stronger for stocks with lower institutional ownership. For small stocks with low

institutional ownership, the BSI loading is 0.072 with a t-value of 3.424. The BSI loading is

positive but statistically insignificant for small sizemedium ownership and small sizehigh

ownership portfolios. These results suggest that firm size and institutional ownership arejoint determinants of the strength of sentiment-return relation.

Panel B of Table IX reports results for a nested double-sort along firm size and B/M

dimensions. We find that among the small stocks category, the BSI loading is positive only

for high B/M portfolio (i.e, small-value stock portfolio). The BSI loading is insignificant

for all other size-B/M portfolios. Apparently small value stocks earn positive excess returns

when individual investors are more bullish.

Finally, we perform a two-way cut on firm size and stock price. Given the relativelysmall size of investor portfolios (see Table I), it is likely that our investors prefer lower priced

stocks. If this is the case, individual investor concentration should be higher, and the BSI

loadings would be stronger, among lower priced stocks. To examine this possibility, we

assign stocks into 9 categories using a nested double-sort on firm size and stock price. The

sentiment loading estimates for the 9 size-price portfolios are presented in Panel C of Table

IX. The BSI loading is strongest (0.115 with a t-value of = 2.941) for small, lower-priced

stocks. It is positive and statistically significant for small sizemedium price portfolio too.For all other size-price portfolios, the sentiment loading is statistically insignificant.

Overall, the factor model estimates for portfolios obtained from two-dimensional sorts

reveal quite clearly that the sentiment-return relation is influenced by all four stock char-

acteristics we considered, namely, firm size, institutional ownership, B/M ratio, and stock

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price. The sentiment-return relation is strongest for small-value stocks with lower level of

institutional ownership and lower prices. In general, these results support the behavioral

view of returns comovement where investor clienteles exist for certain sets of stocks and the

demand shocks generated by these clienteles induce comovement in returns.

VII Seasonality in Small Stock Returns: Role of Investor Sentiment

Various seasonal patterns in stock returns have been attributed to the trading behavior of

individual investors. For instance, Ritter (1988) suggests that the January effect, i.e., the

unusually high returns earned by firms in January, especially by small firms, may be a result

of systematic patterns in the trading activities of individual investors. Individual investors

are likely to engage in tax-loss selling in December. As this selling pressure subsides at

the end of December, there is a rebound in stock returns in January, especially during the

first 10-days of the month. In a recent study, Poterba and Weisbenner (2001) compare the

relationship between past stock returns and the turn-of-the-year returns across different tax

regimes from the perspective of individual investors. Their results support the view that tax-

loss selling by individual investors contributes to the unusual patterns in the turn-of-the-year

stock returns.

Other explanations for the January effect are also consistent with the observed patterns

of stock returns at the turn-of-the-year. For instance, Lakonishok, Shleifer, Thaler, and

Vishny (1991) suggest that window-dressing by institutional investors may be an important

determinant of the observed patterns in the turn-of-the-year returns. Managers may have a

strong incentive to sell the losers in their portfolios at end of the year before their portfolios

are evaluated. To avoid revealing that they have held losers during the year, they may

dispose of losing investments in December. However, the window-dressing hypothesis cannotexplain the stronger turn-of-the-year effect among smaller firms where the concentration of

institutional investors is low.

Another widely documented seasonal pattern in stock returns is the Monday effect or the

weekend effect. It has been observed that the expected stock returns vary with the day-of-

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the-week (French 1980) and in particular, the average Monday return is negative, especially

among smaller stocks. Prior studies have also attributed Monday effect to the behavior

of individual investors. Lakonishok and Maberly (1990) use odd-lot trades as proxy for

individual investor trading activities and find that the overall odd-lot trading is higher on

Mondays and more importantly, odd-lot sales are significantly higher on Mondays compared

with other days of the week. They use this evidence to suggest that the lower returns on

Monday may be due to a relatively higher concentration of individual trades and a higher

propensity of individual investors to be net sellers on Mondays. Abraham and Ikenberry

(1994) also use odd-lot trades as a proxy for individual investor trading activities and confirm

the findings of Lakonishok and Maberly (1990). They also analyze the conditional return

distributions on Mondays and go on to show that the weekend effect is exacerbated when

negative returns are observed on Fridays.

We use a direct measure of individual investor trading activities and test whether the

sentiment of individual investors is indeed responsible for these two seasonal patterns in

stock returns. Our dataset also allows us to identify the stocks associated with all the

individual trades. This feature of the data allows for more refined tests of our hypotheses.

If the systematic trading activities of individual investors contribute to the January and the

Monday effects, we expect the relation between investor sentiment and the seasonal return

patterns to be stronger among smaller stocks where the concentration of individual investors

is relatively higher.

VII.A The January Effect

Figure 4 depicts the monthly BSI time-series for the top and the bottom size-decile portfolios.

In addition, for each of the size decile portfolios, Table X reports the average monthly BSI and

average monthly returns during December, January, and February-November time-periods.

For the stocks in size decile 1, a distinct seasonal pattern in BSI is observed. During the year,

there is a downward trend in BSI, which reaches a bottom in December. 1 7 From December

1 7

T h e s e r e s u l t s a r e b r o a d l y c o n s i s t e n t w i t h t h e fi n d i n g s o f ( O d e a n 1 9 9 8 ) w h e r e i n v e s t o r s r e l u c t a n c e t o

r e a l i z e l o s s e s ( d i s p o s i t i o n e ff e c t ) d e c r e a s e s d u r i n g t h e y e a r , a t t a i n i n g a m i n i m u m i n D e c e m b e r .

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to January, we find an abrupt and a significant upward jump in BSI. This pattern is observed

for each of the 5 Januarys in our sample period. In December, the average BSI for portfolio

1 is 19.37% and in January it is 11.53% (see Table VIII), resulting in an average upward

move of 30.90%.

This seasonal pattern is not observed among the larger stock portfolios. Similar to smaller

stocks, we find a greater tendency by investors to sell throughout the year. 1 8 However, the

increase in year-end selling is not as pronounced as those for the smaller stock portfolios.

Moreover, we find little evidence of a January rebound in BSI for large stocks.

Figure 5 provides a closer look at the trading behavior of investors at the turn-of-the-

year. The average dailyvariation in the buy-sell imbalance (BSI) for small, medium, and

large stock portfolios during a 40-day period around the first trading date in January are

plotted. The abrupt change in the average BSI for small stocks from the last trading date in

December to the first trading date in January is rather striking. These results suggest that

the January bounce back in investors trading activities is strongest for smaller firms.

To investigate more formally the impact of investor sentiment on January returns, we

compare the average January residuals from different variants of the multi-factor time-series

model specified in equation (8), some of which include the BSI variable while others dont.

Our (untabulated) results show that, using a single-factor model containing only the market

risk factor, the average January residual is 5.54%. It decreases significantly to 2.29% when

the three-factor Fama-French model is used. With the introduction of the momentum factor,

we find a further reduction in the residual to 1.87%. Finally, when the BSI variable is added

to the model, the January residual is 1.49%. The difference between the residual returns on

the smallest and the largest size portfolio, i.e., the arbitrage return, is also the lowest for the

five-factor model that includes the investment sentiment factor. The arbitrage returns are

5.58%, 1.85%, 1.53%, and 1.19% for the 1, 3, 4, and 5-factor models respectively.

To summarize, our findings are strongly suggestive of a link between tax-loss selling

activities of individual investors, and the exceptional performance of stocks (in particular,

1 8

N o t e t h a t t h e n e g a t i v e a v e r a g e B S I f o r p o r t f o l i o s 4 - 1 0 t h r o u g h o u t t h e y e a r d o e s n o t m e a n t h a t i n e a c h

m o n t h i n v e s t o r s a r e n e t s e l l e r s o f s t o c k s i n e a c h o f t h e s e p o r t f o l i o s . S i n c e t h e p o r t f o l i o B S I i s c o m p u t e d b y

a v e r a g i n g i n d i v i d u a l s t o c k B S I s , t h i s s i m p l y m e a n s t h a t i n a n y g i v e n m o n t h , i n v e s t o r s a r e l i k e l y t o b e n e t

s e l l e r s f o r a g r e a t e r n u m b e r o f s t o c k s i n t h a t p o r t f o l i o .

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small value stocks) in January. However, with only 6 years of data on investor trading

activities, we are unable to establish this link conclusively.

VII.B TheWeekend Effect

Table XI reports the average daily return and the average daily BSI for each day of the week

and for each of the ten size decile portfolios. Consistent with previously reported results, for

stocks in deciles 1-4, the average return for Monday is negative (and statistically significant)

during our 1991-96 sample period. For higher size deciles, the average Monday returns

are either negative and statistically insignificant (deciles 5-8) or positive and statistically

insignificant (decile 9) or positive and statistically significant (decile 10). However, examining

the daily variation in the BSI pattern, we find that the average Monday BSI is not negative

but rather positive for most (1-7 and 10) size-decile portfolios. In addition, comparing the

BSIs across the days of the week for a particular size decile, we find that the average Monday

BSI is usually the highest. So, in our sample of individual investors, there is no evidence of

higher selling pressure (or even weaker buying pressure) on Mondays, even among smaller

stocks where the concentration of individual investors is considerably higher.

VIII Summary and Conclusion

The tendency in the financial press, and among capital market researchers, is to think of the

market as a single unit. This notion permeates our vocabulary and is well entrenched in our

thinking. The market is said to have gone up today, or the market responded negatively to

IBMs latest earnings report. In academic circles, inferences about the impact and usefulness

of a news release have been based on the aggregate market response. Market efficiency is

also defined in terms of the speed of the aggregate response to news events, or the extent to

which market prices (an aggregate measure) impound currently available information. In all

these cases, the market is regarded, perhaps too casually, as a monolithic whole.

An alternative view is to regard the market as being composed of various clienteles or

informational subgroups. These subgroups respond to different information stimuli and may

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respond differently to the same information stimulus. The reactions of these subgroups

are sometimes re-enforcing and sometimes off-setting. To the extent that reactions among

these groups are not perfectly correlated, any theory which attempts to track aggregate

market movements will encounter empirical anomalies caused by the differing reaction of

the subgroups. A richer theory of market behavior would explicitly incorporate the potential

of differing reactions from important market subgroups.

One potentially fruitful partition is between large institutional investors and small indi-

vidual traders. Extant evidence, primarily from surveys, shows individual investors trade

much less often, spend far less time on investment analysis and typically rely on a very dif-

ferent set of information sources from their professional counterparts.1 9 To the extent that

individual investors respond to different information signals, or have different time-varying

liquidity and consumption needs, the buy-sell imbalance in individual trades will differ sys-

tematically from overall market movements. If small investors buy-sell patterns do not move

in lock-step with those of larger investors, assets in market segments dominated by small

traders may be characterized by apparent pricing anomalies.

In this study, we have used a large set of data from a major discount brokerage house to

explore the effect of individual investor trading on stock returns. Our results show that the

buy-sell imbalance in individual investors trades contains a systematic component that is

uncorrelated with overall market movements. Using this common component as a measure

of individual investor sentiment, we examined its relation with a number of market pricing

anomalies.

We find that our measure of individual investor sentiment has incremental explanatory

power for small stocks, value stocks, stocks with low institutional ownership, and stocks

with lower prices. The direction of the relation indicates that when individual investors are

relatively bullish (i.e., when net BSI is more positive), the stocks in these portfolios enjoy

higher excess returns. The magnitude of their influence is affected by factors associated with

arbitrage costs, i.e., firm size, stock price, and the percentage of institutional ownership.

1 9

E v i d e n c e o n t r a d i n g f r e q u e n c y c a n b e d e r i v e d f r o m o w n e r s h i p a n d v o l u m e i n f o r m a t i o n p r o v i d e d b y N Y S E

s h a r e o w n e r s h i p s u r v e y s a s w e l l a s t h e S e c u r i t i e s I n d u s t r y A s s o c i a t i o n I n v e s t o r A c t i v i t y r e p o r t s . E v i d e n c e

o n d e c i s i o n s t y l e s a n d i n f o r m a t i o n s o u r c e s i s p r e s e n t e d i n L e a s e , L e w e l l e n , a n d S c h l a r b a u m ( 1 9 7 4 ) , L e w e l l e n ,

L e a s e , a n d S c h l a r b a u m ( 1 9 7 7 ) , Y u n k e r a n d K r e h b i e l ( 1 9 7 4 ) , a n d S h i l l e r a n d P o u n d ( 1 9 8 9 ) .

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Individual investor trading activities also exhibited strong seasonalities that partially explain

the January Effect.

Collectively, these findings are broadly consistent with the predictions of a noise trader

model in which the systematic activities of individual investors affect the returns of those

stocks in which they are concentrated. Our findings show that the magnitude of their

influence is affected by factors associated with arbitrage costs, i.e., firm size, stock price, and

the percentage of institutional ownership.

In sum, our findings establishes the existence of a systematic component in the trading

activities of individual investors, and show that this sentiment measure has implications for

stock returns. These findings raise a number of interesting issues. Although our sentiment

measure is correlated with changes in the recommendations of professional newsletters, many

questions remain as to the factors that affect the proclivity of individual investors to buy

or sell stocks. Indeed, these findings highlight the need to better understand the processes

by which individual investors formulate their trading decisions, including an identification of

the information sources they use in decision making and the nature of their belief updating

process. We hope to address some of these topics in future research.

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T a b l e I

Summary Statistics: The Investor Database

T h i s t a b l e s u m m a r i z e s t h e p o r t f o l i o a n d t r a d i n g c h a r a c t e r i s t i c s o f h o u s e h o l d s i n o u r s a m p l e . T h e p r i m a r y

d a t a s e t c o n s i s t s o f t r a d e s a n d m o n t h l y p o r t f o l i o p o s i t i o n s o f i n v e s t o r s a t a m a j o r d i s c o u n t b r o k e r a g e h o u s e

i n t h e U . S . f o r t h e p e r i o d o f 1 9 9 1 - 9 6 . T h e d a t a b a s e c o n s i s t s o f t h r e e t y p e s o f d a t a fi l e s : ( i ) p o s i t i o n fi l e s

t h a t c o n t a i n t h e e n d - o f - m o n t h p o r t f o l i o s o f a l l i n v e s t o r s , ( i i ) a t r a d e fi l e t h a t c o n t a i n s a l l t r a n s a c t i o n s c a r r i e d

o u t b y t h e i n v e s t o r s i n t h e d a t a b a s e , a n d ( i i i ) a d e m o g r a p h i c s fi l e t h a t c o n t a i n s i n f o r m a t i o n s u c h a s a g e ,

g e n d e r , m a r i t a l s t a t u s , i n c o m e c o d e , o c c u p a t i o n c o d e , g e o g r a p h i c a l l o c a t i o n ( z i p c o d e ) , e t c . f o r a s u b s e t o f

i n v e s t o r s .

Time Period: Jan. 1991 - Nov. 1996.

Panel A: Households

Number of households: 79, 995

Number of accounts: 158, 031

Number of households with position in equities: 62, 387

Panel B: Household Characteristics

Aggregate value of investor portfolios in a typical month: $2.18 billion

Average size of investor portfolios: $35,629 (Median = $13,869)

Average number of trades: 41 (Median = 19)

Average number of stocks in the portfolio: 4 (Median = 3)

Average age of the household: 50 (Median = 48)

Panel C: SecuritiesTotal number of traded common stocks: 10, 486

Number of common stocks for which data is available from CRSP: 9, 893

PanelD: Trades

Total number of trades: 2, 886, 912

Number of trades in common stocks: 1, 854, 776

Average Portfolio Turnover: 7.59% (Median: 2.53%)

Average Holding Period: 187 days (Median = 95)

35

Documents

Individual Investor Sentiment and Comovement in Small Stock Returns - Kumar, Lee