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Emerging Markets Finance and Trade

ISSN: 1540-496X (Print) 1558-0938 (Online) Journal homepage: http://www.tandfonline.com/loi/mree20

Individual Investor Sentiment and Stock Returns: Evidence from the Korean Stock Market Minhyuk Kim & Jinwoo Park To cite this article: Minhyuk Kim & Jinwoo Park (2015) Individual Investor Sentiment and Stock Returns: Evidence from the Korean Stock Market, Emerging Markets Finance and Trade, 51:sup5, S1-S20, DOI: 10.1080/1540496X.2015.1062305 To link to this article: http://dx.doi.org/10.1080/1540496X.2015.1062305

Published online: 25 Aug 2015.

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Emerging Markets Finance & Trade, 51:S1–S20, 2015 Copyright © Taylor & Francis Group, LLC ISSN: 1540-496X print/1558-0938 online DOI: 10.1080/1540496X.2015.1062305

SYMPOSIUM ARTICLES Individual Investor Sentiment and Stock Returns: Evidence from the Korean Stock Market Minhyuk Kim1 and Jinwoo Park2 Finance, Hankuk University of Foreign Studies, Seoul, Korea; 2College of Business Administration, Hankuk University of Foreign Studies, Seoul, Korea

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ABSTRACT: We investigate the dynamic relationship between individual investor sentiment and stock returns in the Korean stock market. The evidence indicates that individual investor sentiment has no significant explanatory power for cross-sectional stock returns. However, individual investors’ trades can move stock prices in certain stocks by their contrarian behavior, which leads them to implicitly provide liquidity to other market participants. In addition, individual investors earn a small market-adjusted excess return in the short-horizon future as compensation for liquidity provision. Our findings show that shorthorizon return predictability of individual investors does not come from their private information. KEY WORDS: individual investors, investor sentiment, return predictability, stock returns

Introduction Over the past several decades, traditional finance theory has been based on the notion that investors are rational and financial markets are efficient. In recent years, however, behavioral finance has come to the fore as an alternative finance theory in order to account for imperfections in the decision making of investors and anomalies of financial markets (Shefrin 2010; Thaler 1993). Based on the bounded rationality of investors in the market, behavioral finance models rely on a concept of noise traders who are prone to cognitive bias and decision-making errors. In particular, individual investors are found to be more prone to psychological biases and irrational behaviors. In fact, there are a number of studies indicating that the investment performance of individual investors is worse than that of institutional investors due to informational disadvantage as well as irrational investment decisions (see, among others, Bae, Min, and Jung 2011; Barber and Odean 2008; Barber et al. 2009; Griffin, Harris, and Topaloglu 2003; Grinblatt and Keloharju 2000; Hvidkjaer 2008; Kim and Nofsinger 2007; Odean 1998, 1999; Park and Kim 2014).1 Since the notion that systematic risk matters in asset pricing became tenuous, other characteristics have been suggested as relevant factors in the cross section of expected returns (Fama and French 1992, 1993; Haugen and Baker 1996; Jegadeesh and Titman 1993). More recently, the relationship between investor sentiment and stock returns is drawing attention from behavioral finance studies. Traditional finance hardly accepts the contention that investor sentiment affects the cross section of stock returns since an army of rational traders stand ready to offset any mispricing induced by sentiment, moving a stock price toward its fundamental value. Researchers of behavioral finance, however, argue that changes in investor sentiment cannot be fully arbitraged by rational informed traders, and the mispricing resulting from psychological biases of uninformed traders persists in the market (De Long et al. 1990; Shleifer and Summers 1990; Shleifer and Vishny 1997). In fact, we observe a quantity of empirical evidence regarding the relationship between investor sentiment and stock returns (Baker and Wurgler 2006, 2007; Brown and Cliff 2004; Kumar and Lee 2006). Address correspondence to Jinwoo Park, College of Business Administration, Hankuk University of Foreign Studies, 107 Imun-Ro Dongdaemun-Gu, Seoul 130-791, Korea. E-mail: [email protected]

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Thus, we investigate the relationship between individual investor sentiment and stock returns in the Korean stock market, specifically by focusing on how individual investor sentiment is related to past and contemporaneous returns as well as its ability to forecast future returns. In particular, as compared with developed markets, the Korean stock market provides a unique opportunity to investigate whether individual investor sentiment affects cross-sectional stock returns. First, unlike the stock markets in developed countries, where the financial system is relatively transparent and efficient and large institutional holdings and trading are dominating, emerging markets do not establish a sound financial system, although the participation of small individual investors in the market is significant.2 Despite this significant presence of individual investors in the emerging markets, relatively little evidence has been provided regarding the relationship between individual investor sentiment and stock returns. Second, examining the trading behavior of individual investors demands short-term high-frequency data for both buying and selling activities. Many attempts using the U.S. data have looked into individual investors’ accounts or transactions in order to analyze their trading records (Barber and Odean 2000, 2001; Kumar and Lee 2006; Odean 1998, 1999). While such an approach has proven effective in validating the effect of individual traits on one’s trading patterns and investment returns, at the same time the research was limited to individual investors from a handful of brokerage houses, thereby making it difficult for the limited sample group to represent all of the individual investor population. The Korea Exchange (KRX) provides daily buy and sell trading volumes of each type of investor classified as individuals, institutions, or foreigners for all firms listed on the KRX, which allows us to construct a daily measure of investor sentiment for all firms in the Korean stock market. Specifically, following Kumar and Lee (2006), we measure individual investor sentiment by calculating the buy-sell imbalance (BSI) from daily buy and sell trading activities of individual investors for a large cross section of stocks listed on the Korea Exchange during the period 2001– 09. Then, for the portfolios constructed by individuals’ share ownership and/or arbitrage costs we estimate multifactor time-series models in which the portfolio BSI is used as one of the explanatory variables; moreover, we investigate whether individual investor sentiment has an incremental power in explaining cross-sectional stock returns. In addition, following Kaniel, Saar, and Titman (2008), we examine the short-horizon dynamic relations between the BSI of individual investors and both previous and subsequent returns. The results are summarized as follows. First, the sentiment-return relationship observed in this article does not support the noise trader model, in which the systematic sentiment of individual investors affects the returns of stocks with high individual investor concentration.3 We do not find that individual investor sentiment has the incremental power in explaining the return comovement. However, we observe that in several portfolios with low individual investor concentration and with low arbitrage costs, the effect of individual investor sentiment on the comovement in stock returns is reliably negative. Hence, this result suggests that individual investors’ trades can move stock prices in certain stocks. Second, we find that the extent of the correlated sentiment of individual investors is negatively associated with individual investor concentration, individual investor trading intensity, and arbitrage costs. This evidence suggests that the systematic trading of individual investors might be driven by a passive reaction to the trading of institutional investors in the process of providing liquidity rather than by their own decisions. Third, by investigating the dynamic relations between individual investor sentiment and both previous and subsequent short-horizon returns, we find that individual investors tend to buy stocks following declines in the past and sell following price increases.4 This evidence suggests that the contrarian behavior of individual investors leads them to implicitly provide liquidity to other market participants who demand immediacy. In addition, we generally do not find the informed predictability of individual investor sentiment for future short-horizon stock returns. However, we find that in several portfolios with low individual investor concentration and low arbitrage costs, individual investors earn a small excess return in the short-horizon future as compensation for liquidity provision in the market.

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Review of the Literature According to behavioral finance, a mispricing is the result of both a sentiment-induced uninformed demand shock and a limit on arbitrage.5 One can therefore think of two distinct channels through which investor sentiment might affect the cross section of stock prices. The general finding of a sentiment-return relation is at odds with the standard finance theory, which predicts that stock prices reflect the discounted value of expected cash flows and the irrationalities among market participants are erased by arbitrageurs. Thus, sentiment does not play any role in this classic framework. Instead, the behavioral approach suggests that investors are subject to sentiment defined as a belief about future cash flows and investment risks that is not justified by the facts at hand. In addition, there are limits to arbitrage because betting against sentimental investors is costly and risky (Shleifer and Vishny 1997). Since a broad-based wave of sentiment has cross-sectional effects when sentiment-based demand or arbitrage constraints vary across stocks, waves of irrational sentiment can affect asset prices (De Long et al. 1990). Recent academic literature has seen a rise of studies investigating the impact of individual investor sentiment on stock returns.6 These studies can be classified into two types: first, studies on the effects of investor sentiment on contemporaneous cross-sectional stock returns (Baker and Wurgler 2006, 2007; Brown and Cliff 2004; Kumar and Lee 2006); second, studies of the stock price predictability of investor sentiment (Barber, Odean, and Zhu 2009; Canbaş and Kandır 2009; Dorn, Huberman, and Sengmueller 2008; Hvidkjaer 2008; Jackson 2003; Kaniel, Saar, and Titman 2008). The most prominent studies that have examined the effect of investor sentiment on contemporaneous cross-sectional stock returns are Baker and Wurgler (2006) and Kumar and Lee (2006). Baker and Wurgler (2006) form a composite index of sentiment based on a common variation in six underlying proxies of sentiment, including the closed-end fund discount (Lee, Shleifer, and Thaler 1991), New York Stock Exchange (NYSE) share turnover (Baker and Stein 2004), the number and average of first-day returns on IPOs (Ritter 1991), the equity share in new issues (Baker and Stein 2004), and the dividend premium (Baker and Wurgler 2004). They find that a wave of investor sentiment has larger effects on securities whose valuations are highly subjective and difficult to arbitrage. Kumar and Lee (2006) show that individual investor trades are systematically correlated. Moreover, consistent with noise trader models, they find that systematic individual investor trading explains return comovements for stocks with high individual investor concentration and with high arbitrage costs. Popular studies exploring the return predictability of investor sentiment include Dorn, Huberman, and Sengmueller (2008) and Kaniel, Saar, and Titman (2008). Using the order imbalance data of individual investors in the New York Stock Exchange (NYSE), Kaniel, Saar, and Titman (2008) develop a measure of net individual trading that can reflect the individuals’ sentiment and investigate whether net individual trading can be a predictor of future equity returns. They document positive excess returns in the four-week period following intense buying by individuals and negative excess returns after individuals sell. The results of the study by Kaniel, Saar, and Titman (2008) are in line with the “liquidity provision” hypothesis that retail investors earn a premium by taking the other side of institutional trades that exert temporary pressure on prices. Dorn, Huberman, and Sengmueller (2008) examine trading records at a German discount brokerage. They document that net limit order purchases correlate negatively with contemporaneous returns and positively with short-horizon future stock returns. Thus, limit-order traders seem to be compensated for providing liquidity to other market participants. To be in line with the literature developed so far, the aim of this research is to examine whether individual investor sentiment plays a role in the formation of returns and influences future returns. Data and Methodology Sample Selection Our data consist of common stocks listed on the Korea Exchange (KRX) for the period January 1, 2000–December 31, 2009. Firms whose fiscal year does not end in December are excluded from our

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sample. Companies that belong to the financial industry or have negative book values are also excluded. The number of firms analyzed ranges from 402 to 585 for each year. The data include daily stock prices that are adjusted for dividends, splits, and equity offerings; ninetyone-day CD rate as a risk-free rate of return; and other firm-specific financial information including firm size, (B/M), volatility, and trading volume by investor type. Firm size is calculated by the number of ordinary shares outstanding multiplied by the closing price at the end of each year. The B/M is the ratio of book value of equity to market value of equity. Volatility is measured by the standard deviation of monthly returns for the one-year period. We obtain the data from the FnGuide database.

Research Methodology

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Sentiment of Individual Investor We use the buy-sell imbalance (BSI) as a proxy for individual investor sentiment. Using weekly data, we construct a measure of investor sentiment by subtracting the value of the shares sold by individuals from the value of shares bought, and standardize the measure by the total value of shares sold and bought. The BSI of individual investors for an individual stock i during a period of t is defined as follows. PD t  BSIi;t ¼

j¼1 BVi;j;t PD t  j¼1 BVi;j;t

 SVi;j;t þ SVi;j;t

 ;

(1)

where Dt is the number of days in week t (from previous Thursday to this Wednesday); BVi;j;t ðSVi;j;t ) is the won-denominated buy (sell) volume for stock i on day j of week t: To remove the common dependence of the BSI on the market factor, we perform the following regression every week: BSIi;t ¼ b0 þ b1 ðMKTt  Rf ;t Þ þ εi;t ;

(2)

where BSIi;t is the week-t BSI index for stock i; ðMKTt  Rf ;t Þ is the week-t market return in excess of the risk-free rate; and εi;t is the week-t residual BSI for stock i. The purpose of this regression is to remove the common component in the investor net demand that is due to the overall market movements. In all our empirical analyses, we use this orthogonalized measure of investor trading activity. We generate a measure of portfolio BSI by calculating an equal-weighted average of the week-t residual BSI for stock i. Stock Return Weekly returns of individual stocks and the KOSPI are measured on their closing prices for the periods of one week extending from Wednesday to the following Wednesday. If Wednesday is not a normal trading day, the preceding trading day is used. Weekly returns falling on a week with less than three trading days are excluded. Weekly returns of individual stocks and the KOSPI over the periods are calculated as follows: riðmÞ;d ¼ lnðPiðmÞ;d Þ  lnðPiðmÞ;d1 Þ;

(3)

where riðmÞ;d is the weekly returns for stock i (market portfolio m) on week d. Arbitrage Cost Following Kumar and Lee (2006) and Wurgler and Zhuravskaya (2002), we measure the variance of the residual from the following capital asset pricing model (CAPM) regression (i.e., the idiosyncratic

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risk of each firm) as a proxy for the arbitrage cost.7 We use the monthly stock returns from the previous twelve months to estimate the CAPM regression.   ri;t  rf ;t ¼ β rm;t  rf ;t þ εi;t ;

(4)

where ri;t is the month-t stock returns for stock i; rf ;t is the ninety-one-day CD rate for month t for the risk-free rate; rm;t is the value-weighted KRX market index (known as the KOSPI index); and εi;t is the residual term of firm i at month t.

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Portfolio Construction We construct quartile portfolios according to individual investors’ shareholdings as of the year-end t-1 in order to examine the relationship between individual investor sentiment and stock returns.8 Portfolios are rebalanced every year. We also double sort stocks according to shareholdings and arbitrage cost in order to investigate the role of arbitrage cost in the relationship between investor sentiment and stock returns. In doing so, we look into how sensitively a stock with a higher arbitrage cost responds to changes in individual investor sentiment after controlling for the ownership concentration factor. We also investigate the dynamic relations between investor sentiment and stock returns of the past, present, and future by ranking stocks into decile portfolios according to the BSI of individual investors as well as into double-sorting portfolios according to individual ownership and the BSI. With respect to the double-sorting portfolio, we construct a total of twenty portfolios as we first sort stocks into quartile portfolios according to individual investor ownership, which are then respectively sorted into quintile portfolios according to the BSI. The aim is to examine whether individual investor sentiment is momentum or contrarian trading for the stock returns of the past and whether it has predictability on future returns.

Multivariate Time-Series Model To examine how the incremental ability of individual investor sentiment shifts to generate comovement in stock returns, our investigation follows procedures that have become standard in recent asset pricing studies. We employ a five-factor time-series model in which the first three factors are those of Fama and French (1992, 1993), the fourth factor is the momentum factor (e.g., Carhart 1997; Jegadeesh and Titman 1993), and the fifth factor is the appropriate portfolio BSI measure. That is, we estimate the following five-factor model: Rpt  Rft ¼ αp þ β1p ðMKTt  Rf ;t Þ þ β2p SMBt þ β3p HMLt þ β4p UMDt þ β5p BSIpt þ εpt ;

(5)

where Rpt is the portfolio rate of return; Rft is the risk-free rate of return; ðMKTt  Rf ;t Þ is the market return in excess of the risk-free 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 weeks t-12–t-2 and the value-weighted return of a portfolio of stocks with low returns during weeks t-12–t-2; BSIpt is the equal-weighted BSI of stocks in portfolio p ; and εpt is the residual return on the portfolio.

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Empirical Analysis

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Sentiment and Contemporaneous Stock Returns Following Kumar and Lee (2006), we examine whether changes in individual investor sentiment induce comovements in stock returns within a multifactor time-series modeling framework. We distinguish different retail investor habitats by individual ownership while adopting residual return dispersion that represents the idiosyncratic risk of individual stock in the CAPM for arbitrage cost. We construct quartile portfolios based on individual ownership and other quartile portfolios based on arbitrage cost. We also construct double-sorted portfolios based on ownership and arbitrage cost. Using these portfolios, we examine whether individual investor sentiment has a cross-sectional effect on contemporaneous stock returns. Table 1 presents the descriptive statistics for each portfolio based on the ownership, arbitrage cost, and ownership/arbitrage cost. Panel A illustrates the descriptive statistics of ownership concentration, trading intensity, and BSI time-series data for the ownership quartile portfolios.9 Individual investor ownership concentration and trading intensity are in line with our intention of the portfolio construction in that they are in a gradual decline from Q1 (the highest) to Q4 (the lowest). In Q1 the ownership stands at 80.29 percent and the trading intensity comes in at 94.06 percent whereas in Q4 the ownership and trading intensity post at 21.38 percent and 67.23 percent, respectively. The BSI means (medians) of Q1–Q4 are all negative, giving support to the theory that individual investors tend to sell irrespective of the ownership standing. The BSI mean of Q4 is −0.0962, which is larger in absolute terms compared to −0.0291 of Q1. The time-series standard deviation of BSI in Q4 marks 0.1257, which is higher than 0.0331 in Q1. Thus, we find that a strong tendency to sell stocks and a higher volatility of BSI is exhibited in Q4, with the lowest presence of individual investors. The results of Panel B, which distinguishes portfolios based on arbitrage cost, are similar in that a stronger tendency to sell stocks is displayed in the lowest quartile portfolio (Q4). Panel C, which represents the double-sorted portfolios of ownership/arbitrage cost, shows a strong tendency to sell stocks as the arbitrage cost declines within the same level of ownership portfolio. Table 2 documents the results of the multivariate time-series model on portfolios of individual investor ownership, arbitrage cost, and ownership/arbitrage cost.10 The results show a relatively weak relationship between individual investor sentiment and stock returns. In addition, the existing fourfactor model and the five-factor model in which the portfolio BSI is added as one of the explanatory variables do not show a significant difference in the ability to explain the comovement of stock returns. This is in opposition to Kumar and Lee (2006), who document that individual investor sentiment explains return comovements for stocks with high retail concentration or retail investor habitats.11 However, in portfolios with the lowest arbitrage cost we notice a statistically significant negative relationship between retail investor sentiment and stock returns for Panel B sorted on the arbitrage cost as well as Panel C sorted on the ownership/arbitrage cost. Retail investor sentiment has a significant negative relationship with stock returns when it comes to stocks in the portfolio with the lowest arbitrage cost and the smallest fraction of retail investors. In other words, this evidence demonstrates that retail investors pursue contrarian investment strategies by selling past winners and buying past losers to stand against those momentum traders in the market. Systematic Sentiment of Individual Investors For individual investor sentiment to affect stock returns requires the existence of a systematic (or common directional) component in the trades of retail investors. That is, the cognitive foibles that individuals commit do aggregate across the investing populous and are not canceled out by an army of rational arbitrageurs. As a result, individual irrationalities do result in systematic directional behavior across large groups of investors.12 If mistaken sentiment is not systematic, then there will be no room for sentiment to affect the equilibrium stock prices. Table 3 reports the results of a common directional component of retail investor sentiment for each portfolio on ownership, arbitrage cost, and the ownership/arbitrage cost. The values in the table are

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Table 1. Time-series descriptive statistics Panel A: Ownership quartile Statistic Ownership (%) Trading intensity (%) BSI Time series

Mean Median Standard deviation

Q1 (High)

Q2

Q3

Q4 (Low)

80.29 94.06 −0.0291 −0.0301 0.0331

57.82 87.29 −0.0917 −0.0968 0.1111

41.48 79.63 −0.1450 −0.1618 0.1391

21.38 67.23 −0.0962 −0.0909 0.1257

Panel B: Arbitrage cost quartile

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Statistic Arbitrage costs Ownership (%) Trading intensity (%) BSI Time series

Mean Median Standard deviation

Q1 (High)

Q2

Q3

Q4 (Low)

0.0608 55.76 90.02 0.0209 0.0141 0.0560

0.0235 50.52 82.10 −0.0109 −0.0120 0.0731

0.0140 48.71 79.70 −0.0906 −0.1268 0.1370

0.0067 45.77 76.26 −0.2619 −0.2870 0.1430

Panel C: Ownership/arbitrage cost double-sorted portfolios Arbitrage cost quartile Q1 (High) Trading intensity (%) Ownership quartile P1 (High) 95.41 P2 91.55 P3 86.43 P4 (Low) 81.82 Time-series average (median) for BSI Ownership quartile P1 (High) 0.0002 P2 0.0085 P3 0.0491 P4 (Low) 0.0538

Q2

93.59 86.73 78.75 69.36

Q3

93.77 85.40 78.43 62.89

(0.0057) 0.0228 (0.0037) −0.0210 (0.0046) −0.0195 (−0.0370) −0.1629 (0.0414) −0.0787 (−0.0267) −0.0919 (0.0637) 0.0313 (0.0487) −0.0846

Q4 (Low)

91.86 84.63 76.87 61.05 (−0.0160) (−0.1211) (−0.1030) (−0.0819)

−0.1607 −0.2146 −0.3686 −0.2830

(−0.1792) (−0.1861) (−0.4073) (−0.2466)

Notes: This table reports the cross-sectional time-series average for the stocks’ yearly variables and the weekly buy-sell imbalance (BSI) in each portfolio. We construct quartile portfolios based on individual investor ownership and other quartile portfolios based on arbitrage cost. We also construct double-sorted portfolios based on ownership and arbitrage cost. Individual investors’ ownership is the number of shares held by individual investors divided by the free float of each stock at the end of the year. Trading intensity is the number of shares traded by individuals divided by the number of shares traded in the market for each stock during the year. Arbitrage cost is the variance of the residuals from the CAPM regression (i.e., the idiosyncratic risk of each firm), which is estimated by using the monthly stock returns from PDt ðBVi;j;t SVi;j;t Þ the previous twelve months. The BSIi;t of individual investors for each stock is defined as Pj¼1 , where, Dt is Dt ðBVi;j;t þSVi;j;t Þ j¼1 the number of days in week t (from previous Thursday to this Wednesday), and BVi;j;t ðSVi;j;t ) is the won-denominated buy (sell) volume for stock i on day j of week t .

means of pair-wise correlations in the BSI time series between two stocks, which are generated by computing the correlations between two pairs of the weekly BSI time series for randomly chosen stocks in the same portfolios. We find that the average BSI correlation increases with a smaller fraction of retail investors (with a larger fraction of other market participants, such as institutional and foreign investors), and we find that correlations in the BSI time series increase in magnitude as the arbitrage

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Q4(Low)

Q3

Q2

Q1(High)

Q4(Low)

Q3

Q2

Q1(High)

MKT

1.0987***(72.43) 1.0997***(72.37) 1.0206***(71.34) 1.0205***(71.33) 0.9621***(68.31) 0.9623***(68.15) 0.9175***(62.08) 0.9180***(61.94)

MKT

1.2900***(60.78) 1.2901***(60.70) 1.1174***(70.47) 1.1166***(70.25) 0.9392***(68.24) 0.9392***(68.16) 0.6541***(51.37) 0.6560***(51.65)

Alpha

−0.0021***(−3.79) −0.0026***(−3.55) −0.0013**(−2.37) −0.0017**(−2.48) −0.0008(−1.58) −0.0009(−1.26) −0.0010*(−1.76) −0.0011*(−1.69)

Alpha

−0.0007(−0.83) −0.0006(−0.72) −0.0011*(−1.83) −0.0010*(−1.70) −0.0018***(−3.51) −0.0018***(−2.98) −0.0016***(−3.48) −0.0036***(−3.76)

Table 2. Multifactor time-series model results

0.2271***(8.07) 0.2262***(8.03) 0.2859***(10.77) 0.2853***(10.75) 0.2895***(11.08) 0.2896***(11.07) 0.2943***(10.73) 0.2938***(10.70)

HML

1.0803***(36.27) 1.0808***(36.14) 0.7579***(34.07) 0.7575***(34.02) 0.6266***(32.44) 0.6266***(32.39) 0.4681***(26.19) 0.4721***(26.42)

SMB 0.1993***(5.06) 0.1991***(5.05) 0.3205***(10.90) 0.3210***(10.90) 0.3194***(12.51) 0.3194***(12.49) 0.2575***(10.90) 0.2570***(10.93)

HML

Panel B: Arbitrage cost quartile

0.9924***(46.62) 0.9936***(46.60) 0.7703***(38.37) 0.7706***(38.38) 0.6690***(33.85) 0.6693***(33.75) 0.5010***(24.16) 0.5020***(24.04)

SMB

Panel A: Ownership quartile

−0.1026***(−3.53) −0.1024***(−3.52) 0.0145(0.67) 0.0147(0.68) −0.0072(−0.38) −0.0072(−0.38) 0.0116(0.67) 0.0115(0.66)

UMD

−0.0844***(−4.07) −0.0838***(−4.04) −0.0207(−1.06) −0.0217(−1.11) 0.0113(0.59) 0.0113(0.59) 0.0102(0.51) 0.0102(0.50)

UMD

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−0.0075**(−2.35)

−0.0002(−0.05)

0.0058(0.75)

−0.0025(−0.19)

BSI

−0.0020(−0.47)

−0.0008(−0.21)

−0.0046(−1.00)

−0.0169(−1.02)

BSI

0.9038 0.9036 0.9187 0.9186 0.9136 0.9134 0.8572 0.8586

Adjusted R2

0.9316 0.9316 0.9232 0.9232 0.9142 0.9141 0.8944 0.8943

Adjusted R2

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P1 (High) P2 P3 P4 (Low)

0.0079 −0.0080 −0.0079 0.0082

0.0014 −0.0031 0.0031 −0.0107

Q2

−0.0040 −0.0011 −0.0038 −0.0014

Q3

−0.0053 −0.0030 −0.0040 −0.0049**

Q4 (Low)

Notes: This table reports the BSI loadings of the multivariate time-series model. The portfolios are held constant throughout the following year. We estimate the following timeseries five-factor model: Rpt  Rft ¼ αp þ β1p ðMKTt  Rf ;t Þ þ β2p SMBt þ β3p HMLt þ β4p UMDt þ β5p BSIpt þ εpt where Rpt is the portfolio rate of return; Rft is the risk-free rate of return; ðMKTt  Rf ;t Þ is the market return in excess of the risk-free rate; SMBt is the difference between the value-weighted return of a portfolio of small stocks and the valueweighted 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 weeks t-12–t-2 and the value-weighted return of a portfolio of stocks with low returns during weeks t-12–t-2; BSIpt is the equal-weighted BSI of stocks in portfolio p ; and εpt is the residual return on the portfolio. t-statistics are in parentheses. *Significance at the 10 percent level; **significance at the 5 percent level; ***significance at the 1 percent level.

Portfolio BSI loadings Ownership quartile

Q1 (High)

Arbitrage cost quartile

Panel C: Ownership/arbitrage cost double-sorted portfolios

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Table 3. Common directional component and firm statistics Panel A: Ownership quartile

Pair-wise correlation Firm size Foreign ownership (%) Share price Volatility

Q1 (high)

Q2

Q3

Q4 (low)

F-statistic

Q1–Q4 (t-statistic)

0.0108 11.6935 3.07 10,165 0.1619

0.0097 12.4280 7.49 16,284 0.1468

0.0133 13.1896 10.98 24,250 0.1364

0.0219 14.3370 16.86 51,060 0.1343

5.83*** 27.07*** 41.27*** 15.18*** 2.52*

−0.0111***(2.98) −2.6435***(10.13) −13.79***(11.40) −40,895***(4.89) 0.0277**(2.54)

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Panel B: Arbitrage cost quartile

Pair-wise correlation Firm size Foreign ownership (%) Share price Volatility

Q1 (high)

Q2

Q3

Q4 (low)

F-statistic

Q1–Q4 (t-statistic)

0.0077 12.2584 5.57 13,743 0.2348

0.0109 13.0587 9.45 20,892 0.1505

0.0140 13.5086 10.82 29,550 0.1161

0.0273 13.6762 12.58 37,644 0.0786

9.56*** 7.33*** 6.86*** 5.91*** 68.38***

−0.0196***(3.92) −1.4178***(3.42) −7.01***(3.44) −23,901***(3.31) 0.1562***(13.53)

Panel C: Ownership/arbitrage cost double-sorted portfolios Arbitrage cost quartile Ownership quartile

Q1 (High)

Pair-wise correlation P1 (High) 0.0088 P2 0.0088 P3 0.0064 P4 (Low) 0.0106 F-statistic 0.22 P1–P4 −0.0018(0.35) (t-statistic)

Q2

Q3

Q4 (Low)

F-statistic

Q1–Q4 (t-statistic)

0.0141 0.0092 0.0158 0.0227 2.25* −0.0086(1.70)

0.0134 0.0128 0.0203 0.0275 2.74* −0.0141*(2.04)

0.0288 0.0248 0.0295 0.0386 0.60 −0.0098(0.84)

2.22 2.32* 4.99*** 5.55*** — —

−0.0200*(1.95) −0.0160*(1.83) −0.0231***(3.53) −0.0280***(3.81) — —

Notes: This table reports the time-series average for correlation statistics from the pair-wise correlation test for two stocks in the same portfolio, which examine the existence of a systematic component in the trading activities of individual investors. This table also shows the characteristics of stocks within the portfolios. Firm size is measured as the logarithm of the market capitalization (denominated by million Korean won) of the sample firm. Foreign ownership contains the year-end stock holdings of all foreign investors. Share price is the average daily price (denominated by Korean won) for the year. Volatility is calculated as the standard deviation of monthly returns measured over the year. To compute the tstatistics (in parentheses), we use a paired t-test for testing differences in the characteristics between high and low portfolios. The table also includes the F-statistics of the null hypothesis that the means across portfolios are equal. *Significance at the 10 percent level; **significance at the 5 percent level; ***significance at the 1 percent level.

cost declines within the same level of ownership portfolio. In turn, our correlation tests demonstrate that the presence of a systematic component among retail investor sentiment is more a function of arbitrage costs than a function of ownership. These results do not support the noise trader model, in which the systematic sentiment of individual investors affects the returns for stocks with high individual investor concentration, especially if those stocks are also more difficult to arbitrage. In several portfolios with low individual investor concentration and with low arbitrage cost, however, the effect of individual investor sentiment on comovement in stock returns is reliably negative, suggesting that individual

INDIVIDUAL INVESTOR SENTIMENT AND STOCK RETURNS

S11

investors generally serve as liquidity suppliers among these stocks. These results are consistent with Kaniel, Saar, and Titman (2008), who find that individual investors tend to provide liquidity to institutional investors. This systematic trading of individual investors, which is shown in several portfolios with low individual investor concentration and with low arbitrage cost, might be driven by a passive reaction to the trading of institutional investors in the process of providing liquidity rather than by their own decisions. This conjecture arises from prior empirical findings that the extent to which the sentiment of individual investors is correlated is more probable in the stock portfolios that are concentrated by individual investors due to shared psychological biases among individual investors.

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Dynamic Relation Between Investor Sentiment and Stock Returns As shown in the previous section, the effect of individual investor sentiment on comovement in stock returns is reliably negative in several portfolios with low individual investor concentrations and with low arbitrage costs. This phenomenon can be explained by the contrarian tendency of individual investors, who act as liquidity providers to institutional investors who require immediacy. In this case, institutional investors must offer price concessions in order to induce risk-averse individual investors to take the other side of their trades, thereby resulting in subsequent return reversals (Campbell, Grossman, and Wang 1993; Grossman and Miller 1988; Kaniel, Saar, and Titman 2008; Stoll 1978). Liquidity provision may be viewed as the interplay between different types of investors who populate the market, and in the interaction of different clientele, investor sentiment can affect returns. Thus, we examine the dynamic relations between investor sentiment and short-horizon stock returns. Table 4 shows the cumulative market-adjusted returns during the four weeks prior to or following the portfolio formation each week, by which one can place stocks into decile portfolios according to the weekly BSI of retail investors. Decile 1 is the intense-selling portfolio (i.e., 10 percent of stocks with the most negative BSI that week), and decile 10 is the intense-buying portfolio (i.e., 10 percent of stocks with the most positive BSI that week). Using these portfolios, we examine the trading behavior of retail investors on stock returns of the past and predictability of those individual investors on future returns. For the robustness test, we also present results for somewhat less intense trading by forming a selling portfolio from the stocks in deciles 1 and 2, and a buying portfolio from the stocks in deciles 9 and 10. First, we observe that Korean individual investors can be characterized as contrarians; that is, buying after prices go down and selling after prices go up. This result is consistent with the findings regarding individual investors in other countries. It is also interesting to note that excess returns during the intense trading week have opposite signs; that is, positive when individuals sell and negative when individuals buy. Table 4 also provides evidence on returns following intense individual buying and selling activity. We find that individual investor sentiment does not predict short-horizon future returns. This is in line with the traits of noise traders that, on average, the stock trades of individual investors are wealth reducing not wealth enhancing. Next, Table 5 shows the relationship between individual investor sentiment and stock returns after controlling for the stock preference (ownership concentrations) of individual investors. Similar to the results of Table 4 not controlled for the ownership, Table 5 displays a negative and significant relation between the retail investor sentiment and stock returns of the past and the contemporaneous in each portfolio, which shows a persistent contrarian investment tendency of retail investors, irrespective of ownership. Notwithstanding these results, the short-horizon return predictability of retail investors exists only when individual investors buy stocks in the portfolio with the lowest fraction of retail investors. The mean cumulative market-adjusted returns in the five days and twenty days following a week of intense buying by individuals are 0.17 percent and 0.47 percent, respectively, and both are significant at the 1 percent level.13 However, we find no significant relation between individual investor sentiment and short-horizon future returns in the portfolio (P4-Q1), which is a “selling” portfolio within the same

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−0.0013(−0.80)

−0.0021(−1.47)

−0.0024(−1.43)

Mean t-statistic

0.0073***(3.45)

−0.0015(−0.80)

0.0096***(3.87)

Mean t-statistic

0.0092***(4.13)

k - 15 days

Mean t-statistic

0.0123***(4.68)

Mean t-statistic

k - 20 days

−0.0022*(−1.82)

−0.0014(−1.03)

0.0051***(2.95)

0.0064***(3.46)

k - 10 days

k + 10 days

0.0008(0.61) 0.0015(0.84)

0.0009(0.65) 0.0019(1.00)

k + 5 days

−0.0042***(−4.11) 0.0011(1.07) 0.0016(1.22)

0.0048***(3.89)

0.0058***(4.37)

k=0

k + 20 days

0.0023(1.43) 0.0021(1.15)

0.0023(1.07) 0.0026(1.05)

0.0028(1.25) 0.0035(1.31)

k + 15 days

−0.0020**(−2.30) −0.0061***(−6.12) 0.0013(1.42) 0.0021*(1.72) 0.0026(1.79) 0.0027(1.62)

−0.0012(−1.24)

0.0031**(2.47)

0.0037***(2.75)

k - 5 days

Notes: This table presents the analysis of market-adjusted returns around the intense buying and selling activity of individuals as given by the individual trading imbalance (BSI). For each week in the sample period, we cross-sectionally sort the BSI measure and then form decile portfolios. Decile 1 is the intense-selling portfolio (i.e., 10 percent of stocks with the most negative BSI that week), and decile 10 is the intense-buying portfolio. We present the results for four portfolios: (1) decile 1, (2) deciles 1 and 2, (3) deciles 9 and 10, and (4) decile 10. The return on each portfolio is then adjusted by subtracting the value-weighted market returns (KOSPI returns). We present the time-series mean and t-statistic for each market-adjusted cumulative return measure during the intense trading week. *Significance at the 10 percent level; **significance at the 5 percent level; ***significance at the 1 percent level.

Intense selling (decile 1) Selling (deciles 1 and 2) Buying (deciles 9 and 10) Intense buying (decile 10)

Portfolio

Table 4. Individual investor sentiment and short-term stock returns

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(Q5)

(Q1) Buying

(Q5) Selling

(Q1) Buying

−0.0000(0.01)

0.0082***(3.11)

−0.0016(−0.76)

0.0135***(4.82)

0.0002(0.10)

0.0066***(2.93)

−0.0015(−0.81)

0.0102***(4.32)

−0.0008(−0.38)

0.0074***(3.27)

0.0002(0.06)

0.0045(1.61)

k - 15 days

−0.0005(−0.44)

0.0048***(2.50)

−0.0015(−0.94)

0.0071***(3.64)

−0.0008(−0.44)

0.0052***(2.79)

−0.0000(0.01)

0.0029(1.35)

k - 10 days

−0.0010(−1.01)

0.0032**(2.30)

−0.0012(−1.11)

0.0041***(3.06)

−0.0007(−0.61)

0.0032**(2.30)

−0.0001(−0.05)

0.0021(1.38)

k - 5 days

−0.0056***(−5.50)

0.0061***(4.42)

−0.0039***(−3.32)

0.0053***(3.84)

−0.0037***(−2.82)

0.0041***(3.02)

−0.0034**(−2.10)

0.0029*(1.89)

k=0

0.0017*(1.83)

0.0006(0.41)

0.0012(1.04)

0.0011(0.80)

0.0015(1.14)

0.0012(0.88)

0.0007(0.45)

0.0005(0.31)

k + 5 days

0.0031***(2.64)

0.0014(0.73)

0.0015(1.01)

0.0022(1.13)

0.0020(1.12)

0.0016(0.89)

0.0009(0.43)

0.0013(0.59)

k + 10 days

0.0041***(2.89)

0.0020(0.87)

0.0022(1.19)

0.0038(1.62)

0.0030(1.40)

0.0022(0.99)

0.0016(0.64)

0.0019(0.69)

k + 15 days

0.0047***(2.83)

0.0025(0.95)

0.0016(0.77)

0.0046*(1.72)

0.0026(1.12)

0.0015(0.60)

0.0019(0.67)

0.0018(0.56)

k + 20 days

Notes: This table presents the analysis of market-adjusted returns around the intense buying and selling activity of individuals after controlling for the stock preference (ownership concentrations) by individual investors. For each week in the sample period, we first sort stocks into quartile portfolios according to individual investor ownership. These portfolios are then respectively sorted into quintile portfolios according to the BSI. Q1 is the selling portfolio (i.e., 20 percent of stocks with the most negative BSI), and Q5 is the buying portfolio. We present the results for two portfolios: (1) Q1 (selling portfolio) and (2) Q5 (buying portfolio). *Significance at the 10 percent level; **significance at the 5 percent level; ***significance at the 1 percent level.

P4 (Low)

P3

(Q5) Selling

−0.0009(−0.37)

0.0098***(3.71)

(Q5) Selling

(Q1) Buying

0.0004(0.15)

(Q1) Buying

P2

0.0062*(1.93)

Selling

P1 (High)

k - 20 days

BSI

Ownership

Table 5. The relation between individual investor sentiment and short-term stock returns for ownership quartile

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level of ownership portfolio. This gives support to the interpretation that individual investors earn excess returns when they are buying as a compensation for providing liquidity to institutional sellers only in the portfolio, which is with a smaller fraction of retail investors and with a larger fraction of other market participants, such as institutional and foreign investors. However, we find that even at the similar ownership level, retail investors fail to profit when they take the other side of institutional buys. This evidence indicates that the liquidity supply is rewarded in an asymmetric manner in the market. Overall, this result is consistent with Saar’s (2001) contention that because buying activity of institutional investors is more likely to be motivated by information than their selling activity, individual investors fail to profit when they take the other side of institutional buys. Table 6 shows the cross-sectional time-series BSI average of retail, foreign, and institutional investors on the twenty portfolios constructed according to weekly ownership/investor sentiment (Panel A) and displays the average correlation statistics among the BSI of different groups of investors (Panel B). Specifically, we find the negative correlations between the BSI of individuals and that of institutions or foreign investors, whereas we find the positive correlations between the BSI of institutions and that of foreign investors. Because liquidity provision is viewed as the interplay between different types of investors who populate the market, these results support the view that the investment sentiments of retail investors and others are in opposition to each other, and thus individual investors participate in trading to provide other investors with liquidity.

Table 6. Interaction among various investor groups Panel A: Time-series average (median) for BSI Portfolio P1 (High) P2 P3 P4 (Low)

Selling Buying Selling Buying Selling Buying Selling Buying

(Q1) (Q5) (Q1) (Q5) (Q1) (Q5) (Q1) (Q5)

Individuals

Foreign investor

Institutions

−0.5311(−0.4562) 0.2790(0.2799) −1.0132(−0.9760) 0.5222(0.6485) −1.3950(−1.3647) 0.7565(0.7967) −1.5236(−1.4564) 0.9923(1.0665)

−0.1082(−0.1046) −0.3968(−0.3546) 0.0382(0.0234) −0.6205(−0.5803) 0.0374(0.0000) −1.0037(−0.9803) 0.2207(0.1447) −0.8837(−0.8421)

0.5862(0.2987) −0.5105(−0.4078) 0.8973(0.7091) −0.6554(−0.7153) 1.2511(1.0618) −0.6579(−0.7414) 1.1719(1.0195) −0.7606(−0.6315)

Panel B: Pair-wise correlation between various investor groups’ BSI Portfolio P1 (High) P2 P3 P4 (Low)

Selling Buying Selling Buying Selling Buying Selling Buying

(Q1) (Q5) (Q1) (Q5) (Q1) (Q5) (Q1) (Q5)

Individuals and foreign investors

Individuals and institutions

Institutions and foreign investors

−0.2835*** −0.3656*** −0.0300 −0.4758*** −0.0854* −0.8898*** −0.3549*** −0.3969***

−0.5866*** −0.5803*** −0.5518*** −0.8023*** −0.7680*** −0.7923*** −0.6762*** −0.8671***

0.6385*** 0.2659*** −0.2674*** 0.6987*** −0.0608 0.6001*** −0.0824* 0.0392

Notes: This table presents the cross-sectional time-series mean (median) for individuals’ trading imbalance (BSI), foreign investors’ BSI, and institutions’ BSI for the double-sorted portfolios. We first sort stocks into quartile portfolios according to individual investor ownership and then respectively sort them into quintile portfolios according to the BSI. This table also reports the average correlation statistics among different investor groups’ BSI for the portfolios. *Significance at the 10 percent level; **significance at the 5 percent level; ***significance at the 1 percent level.

INDIVIDUAL INVESTOR SENTIMENT AND STOCK RETURNS

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Robustness Tests Our empirical study on the Korean stock market may raise the question of whether stocks with a heavy retail investor concentration included in the sample have significantly affected the result. After all, stocks with over 90 percent of retail investor ownership may witness a net trading value of zero as most of their buy and sell trading is done by retail investors, which may affect the results by the distortion of BSI as a proxy for individual investor sentiment. In turn, we exclude stocks with excess concentration of retail investors and conduct an additional test on robustness. Tables 7 and 8 represent the results of the analysis on the new sample compiling stocks with less than 50 percent of free-float ownership held by retail investors. On the robustness test, we find that the relationship between individual investor sentiment and stock returns, whose results are shown in Table 7, is largely in line with the analysis on the entire sample. Table 8 shows that the contrarian trading behavior of retail investors is persistent throughout different portfolios irrespective of their ownership standing. The fact that future stock returns of buy portfolios exceed those of sell portfolios in the portfolio with the lowest fraction of retail investors is also qualitatively similar to the results in the entire sample. Notwithstanding this result, unlike the results of the lowest retail ownership portfolio, in the other ownership level of portfolio the future stock returns on the selling portfolio outweigh those of the buying portfolio in a statistically significant manner. Thus, this evidence confirms that individual investors are uninformed traders who do not rely on private information and supports the hypothesis that individual investors play the role of liquidity providers in the stock market.

Conclusion We investigate the relationship between individual investor sentiment and stock returns in the Korean stock market. For the KRX stocks in the period 2000–2009, we calculate the buy-sell imbalance (BSI) of individual investors and use it as a proxy for individual investor sentiment. We then construct quartile portfolios based on retail investor shareholdings in order to examine the effect of individual investor sentiment on contemporaneous stock returns by estimating the multifactor time-series models in which the portfolio BSI is added as an explanatory variable. We also examine whether the individual investor sentiment is momentum or contrarian trading for the stock returns of the past and, moreover, has predictability on future returns. Empirical results are summarized as follows. First, in the investigation regarding the relationship between individual investor sentiment and stock returns, we do not find that individual investor sentiment has an incremental power in explaining the return comovement. However, in several portfolios with low individual investor concentrations and with low arbitrage costs, the effect of individual investor sentiment on the comovement in stock returns is reliably negative. This evidence suggests that individual investors’ trades can move stock prices in certain stocks. Second, we find that the extent of the correlated sentiment of individual investors is negatively associated with individual investor concentration, individual investor trading intensity, and arbitrage cost. This evidence suggests that the systematic trading of individual investors might be driven by a passive reaction to the trading of institutional investors in the process of providing liquidity rather than by their own decisions. Third, by investigating the dynamic relations between individual investor sentiment and both previous and subsequent short-horizon returns, we find that individual investors behave as contrarians, who tend to buy stocks following declines in the past and sell following price increases, whereas institutional investors act as momentum traders. This evidence suggests that whatever the reason, the contrarian choices of individuals lead them to implicitly provide liquidity to other market participants (i.e., institutional investors) who demand immediacy. Further, we find that the predictability of individual investor sentiment for future short-horizon stock returns cannot be attributable to their private information. However, we find that in several portfolios with low individual investor

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t-statistic Mean

t-statistic Mean

t-statistic Mean

t-statistic

(decile 1) Selling (deciles

1 and 2) Buying (deciles

9 and 10) Intense buying

(decile 10)

−0.0012(−0.70)

−0.0007(−0.40)

0.0101***(3.97)

0.0136***(4.80)

k - 20 days

−0.0012(−0.85)

−0.0006(−0.40)

0.0079***(3.62)

0.0104***(4.37)

k - 15 days

−0.0014(−1.19)

−0.0010(−0.82)

0.0054***(2.97)

0.0075***(3.71)

k - 10 days

−0.0012(−1.44)

−0.0012(−1.38)

0.0032**(2.48)

0.0045***(3.22)

k - 5 days

−0.0071***(−7.98)

−0.0049***(−5.05)

0.0056***(4.24)

0.0063***(4.40)

k=0

0.0014(1.58)

0.0012(1.36)

0.0008(0.63)

0.0016(1.11)

k + 5 days

0.0021*(1.78)

0.0023*(1.90)

0.0017(0.91)

0.0026(1.26)

k + 10 days

0.0033**(2.30)

0.0029**(2.00)

0.0027(1.21)

0.0042*(1.73)

k + 15 days

0.0040**(2.42)

0.0030*(1.78)

0.0033(1.28)

0.0050*(1.74)

k + 20 days

Notes: This table reports the robust test results for the newly sampled firms in which individuals’ free-float holdings are below 50 percent. Our empirical study on the Korean stock market may raise the question of whether stocks with a heavy retail investor concentration included in the sample have significantly affected the result. After all, stocks with over 90 percent of retail investor ownership may witness a net trading value of zero as most of their buy and sell trading is done by retail investors, which may affect the results by the distortion of BSI as a proxy for individual investor sentiment. In turn, we exclude stocks with excess concentration of retail investors and conduct an additional test on robustness. It presents the analysis of market-adjusted returns around the intense buying and selling activity of individuals as given by the individual trading imbalance (BSI). For each week in the sample period, we cross-sectionally sort the BSI measure and then form decile portfolios. Decile 1 is the intense-selling portfolio (i.e., 10 percent of stocks with the most negative BSI that week), and decile 10 is the intense-buying portfolio. We present the results for four portfolios: (1) decile 1, (2) deciles 1 and 2, (3) deciles 9 and 10, and (4) decile 10. The return on each portfolio is then adjusted by subtracting the value-weighted market returns (KOSPI returns). We present the time-series mean and t-statistic for each market-adjusted cumulative return measure during the intense trading week. *Significance at the 10 percent level; **significance at the 5 percent level; ***significance at the 1 percent level.

Mean

Intense selling

Portfolio

Table 7. Robustness test: Individual investor sentiment and short-term stock returns

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0.0071**(2.47)

0.0021(1.14)

(Q1) Buying

(Q5)

−0.0019(−0.84)

(Q5) Selling

0.0137***(4.57)

−0.0025(−1.00)

0.0116***(4.49)

−0.0000(−0.00)

(Q1) Buying

(Q5) Selling

(Q1) Buying

(Q5) Selling

0.0086***(2.81)

k - 20 days

0.0016(1.01)

0.0055**(2.21)

−0.0008(−0.41)

0.0108***(4.28)

−0.0024(−1.09)

0.0086***(3.83)

0.0002(0.09)

0.0064**(2.47)

k - 15 days

0.0003(0.25)

0.0041**(2.00)

−0.0011(−0.66)

0.0073***(3.43)

−0.0018(−1.04)

0.0065***(3.41)

−0.0003(−0.20)

0.0048**(2.23)

k - 10 days

−0.0006(−0.54)

0.0028**(1.99)

−0.0013(−1.10)

0.0044***(2.81)

−0.0016(−1.27)

0.0039***(2.95)

−0.0011(−0.87)

0.0028*(1.89)

k - 5 days

−0.0059***(−5.33)

0.0070***(4.91)

−0.0054***(−4.10)

0.0061***(3.84)

−0.0044***(−3.36)

0.0053***(4.07)

−0.0037***(−2.97)

0.0036**(2.42)

k=0

0.0021*(1.93)

−0.0004(−0.25)

0.0014(1.11)

0.0019(1.21)

0.0009(0.72)

0.0015(1.07)

0.0012(0.98)

0.0005(0.30)

k + 5 days

0.0037***(2.65)

0.0002(0.08)

0.0025(1.57)

0.0033(1.57)

0.0009(0.54)

0.0033*(1.77)

0.0017(1.02)

0.0009(0.42)

k + 10 days

0.0054***(3.31)

−0.0000(−0.02)

0.0037*(1.94)

0.0058**(2.30)

0.0010(0.47)

0.0055**(2.46)

0.0020(1.00)

0.0009(0.36)

k + 15 days

0.0063***(3.26)

−0.0000(−0.00)

0.0046**(2.13)

0.0074**(2.50)

−0.0002(−0.08)

0.0071***(2.76)

0.0019(0.83)

0.0003(0.11)

k + 20 days

Notes: This table reports the robust test results for the newly sampled firms in which individuals’ free-float holdings are below 50 percent. Our empirical study on the Korean stock market may raise the question of whether stocks with a heavy retail investor concentration included in the sample have significantly affected the result. After all, stocks with over 90 percent of retail investor ownership may witness a net trading value of zero as most of their buy and sell trading is done by retail investors, which may affect the results by the distortion of BSI as a proxy for individual investor sentiment. In turn, we exclude stocks with excess concentration of retail investors and conduct an additional test on robustness. It presents the analysis of market-adjusted returns around intense buying and selling activity of individuals after controlling for the stock preference (ownership concentrations) by individual investors. For each week in the sample period, we first sort stocks into quartile portfolios according to individual investor ownership. We then respectively sort them into quintile portfolios according to the BSI. Q1 is the selling portfolio (i.e., 20 percent of stocks with the most negative BSI that week), and Q5 is the buying portfolio. We present the results for two portfolios: (1) Q1 (selling portfolio) and (2) Q5 (buying portfolio). *Significance at the 10 percent level; **significance at the 5 percent level; ***significance at the 1 percent level.

P4 (Low)

P3

P2

Selling

P1 (High)

(Q1) Buying

BSI

Ownership

Table 8. Robustness test: The relation between individual investor sentiment and short-term stock returns for ownership quartile

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concentration and with low arbitrage cost, individual investors earn a small excess return in the shorthorizon future as compensation for accommodating the liquidity needs of institutional investors. While most of the existing investor sentiment literature provides insights on the U.S. stock market, relatively little evidence has been provided regarding individual investor sentiment in emerging stock markets. This article extends the findings of the extant literature by providing out-of-sample evidence for emerging markets (KRX) that are characterized by a different composition of the investor population. Collectively, our results provide support for the hypothesis that the contrarian tendency of individuals leads them to act as liquidity providers to arbitrage traders that are institutional and foreign investors and that a statistically significant relationship between individual investor sentiment and stock returns exists to a limited degree through interplay among different investor clienteles in the Korean stock market. We suggest that the limitedness is due to the microstructure of the Korean stock market, which is characterized by an exceptionally high fraction of retail investors among its investor population. In turn, the dynamic interaction among different types of investors is possible only to certain stocks.

Notes 1. By contrast, a few studies suggest that some trades by individual investors are profitable (Choe, Kho, and Stulz 2005; Coval, Hirshleifer, and Shumway 2005; Ivković and Weisbenner 2005). 2. The Korea Exchange (KRX) reports that individual investors dominate trading volumes on the KRX, accounting for 88.19 percent of total market activity (institutions 5.37 percent, foreign investors 4.99 percent) over the period 2001–2009. Even on the basis of trading values, individuals’ share of total trading remains dominant at 61.32 percent of total market activity (institutions 17.27 percent, foreign investors 18.23 percent). 3. Our analysis is based on a clientele-based model documented by Kumar and Lee (2006), in which different investor groups restrict themselves to trading within different natural “habitats,” or groups of stocks. Kumar and Lee (2006) conjecture that the returns of individual stocks reflect not only fundamental risk, but also changes in sentiment of important investor groups. 4. Literature documents that individual investors tend to be contrarian traders while institutional investors rather tend to be momentum traders (see Choe, Kho, and Stulz 1999; Chordia, Goyal, and Jegadeesh 2011; Goetzmann and Massa 2002; Grinblatt and Keloharju 2000; Kaniel, Saar, and Titman 2008; Nofsinger and Sias 1999). 5. A mispricing is the result of an uninformed demand shock in the presence of a binding arbitrage constraint. In practice, these two distinct channels lead to quite similar predictions because stocks that are sensitive to speculative demand and subjective valuation also tend to be the riskiest and costliest to arbitrage (Baker and Wurgler 2006). 6. Existing literature can be classified by how they measure investor sentiment: first, measuring the sentiment by investor surveys (Brown and Cliff 2004), and second, examining order and trade imbalances that have become popular in recent studies (Barber, Odean, and Zhu 2009; Dorn, Huberman, and Sengmueller 2008; Hvidkjaer 2008; Jackson 2003; Kaniel, Saar, and Titman 2008; Kumar and Lee 2006). Others have looked into closed-end fund discounts (Lee, Shleifer, and Thaler 1991) or formed a composite sentiment index built on sentiment proxies: the closed-end fund discount, NYSE share turnover, the number and average first-day returns on IPOs, the equity share in new issues, and the dividend premium (Baker and Wurgler 2006). 7. Wurgler and Zhuravskaya (2002) document that the high idiosyncratic risk for a stock makes arbitrage especially risky. 8. We calculate the equity holdings ratio of individual investors as shares held by individual investors with less than 1 percent over the floating shares. Aitken and Comerton-Forde (2003) contend that this is particularly important for the emerging stock markets, where company founders typically hold large portions of stocks that are not freely traded. 9. We are interested not only in which stocks individual investors own, but also in how important their trades are in these stocks relative to the trades of other market participants. The trading intensity of individual investors in our sample is defined as the number of shares traded by individuals divided by the number of shares traded in the market. 10. The summary statistics for the factors as well as for the test portfolios are available upon request. 11. Our findings contrast with those of Kumar and Lee (2006), who examine correlations among trade imbalances of stocks traded by clients of a single U.S. discount broker. They find that their measure of trade imbalance is moderately correlated across stocks and conclude that there is evidence of a systematic component in retail investor trading. They also find that investor sentiment may have significant effects on the cross section of stock returns. One distinct difference between our article and Kumar and Lee (2006) lies in the data, in that Kumar

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and Lee (2006) look into trading data of clients of a specific discount brokerage house, whereas we analyze extensive retail trading data available for all firms listed on the KRX. Trading activities of investors from a particular brokerage firm are correlated because investors from the same brokerage are more likely to share a similar set of information or follow the same investment recommendations supplied by the brokerage firm (Barber, Odean, and Zhu 2009). 12. In addition to the common directional component, limits to arbitrage exist for sentiment to affect equilibrium stock prices (Shleifer and Vishny 1997). Barber, Odean, and Zhu (2009) document that trading by individuals is highly correlated, which is consistent with systematic noise trading that does not wash out in the aggregate. 13. It can be seen that statistically significant excess returns persist and grow in magnitude up to sixty days following a week when retail investors made buy transactions in the portfolio with the lowest fraction of retail investors (P4-Q5); we could not present it here due to space constraints.

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Acknowledgments The authors are grateful for helpful comments from Ali M. Kutan (the editor) and three anonymous referees. Funding This work was supported by the 2015 Hankuk University of Foreign Studies research fund. References Aitken, M., and C. Comerton-Forde. 2003. How should liquidity be measured? Pacific-Basin Finance Journal 11, no. 1: 45–59. doi:10.1016/S0927-538X(02)00093-8. Bae, S. C., J. H. Min, and S. Jung. 2011. Trading behavior, performance, and stock preference of foreigners, local institutions, and individual investors: Evidence from the Korean stock market. Asia-Pacific Journal of Financial Studies 40, no. 2: 199–239. doi:10.1111/ajfs.2011.40.issue-2. Baker, M., and J. C. Stein. 2004. Market liquidity as a sentiment indicator. Journal of Financial Markets 7, no. 3: 271–99. doi:10.1016/j.finmar.2003.11.005. Baker, M., and J. Wurgler. 2004. A catering theory of dividends. The Journal of Finance 59, no. 3: 1125–65. doi:10.1111/ jofi.2004.59.issue-3. Baker, M., and J. Wurgler. 2006. Investor sentiment and the cross-section of stock returns. The Journal of Finance 61, no. 4: 1645–80. doi:10.1111/jofi.2006.61.issue-4. Baker, M., and J. Wurgler. 2007. Investor sentiment in the stock market. Journal of Economic Perspectives 21, no. 2: 129–51. doi:10.1257/jep.21.2.129. Barber, B. M., Y.-T. Lee, Y.-J. Liu, and T. Odean. 2009. Just how much do individual investors lose by trading? Review of Financial Studies 22, no. 2: 609–32. doi:10.1093/rfs/hhn046. Barber, B. M., and T. Odean. 2000. Trading is hazardous to your wealth: The common stock investment performance of individual investors. The Journal of Finance 55, no. 2: 773–806. doi:10.1111/jofi.2000.55.issue-2. Barber, B. M., and T. Odean. 2001. Boys will be boys: Gender, overconfidence, and common stock investment. The Quarterly Journal of Economics 116, no. 1: 261–92. doi:10.1162/003355301556400. Barber, B. M., and T. Odean. 2008. All that glitters: The effect of attention and news on the buying behavior of individual and institutional investors. Review of Financial Studies 21, no. 2: 785–818. doi:10.1093/rfs/hhm079. Barber, B. M., T. Odean, and N. Zhu. 2009. Do retail trades move markets? Review of Financial Studies 22, no. 1: 151–86. doi:10.1093/rfs/hhn035. Brown, G. W., and M. T. Cliff. 2004. Investor sentiment and the near-term stock market. Journal of Empirical Finance 11, no. 1: 1–27. doi:10.1016/j.jempfin.2002.12.001. Campbell, J. Y., S. J. Grossman, and J. Wang. 1993. Trading volume and serial correlation in stock returns. The Quarterly Journal of Economics 108, no. 4: 905–39. doi:10.2307/2118454. Canbaş, S., and S. Y. Kandır. 2009. Investor sentiment and stock returns: Evidence from Turkey. Emerging Markets Finance & Trade 45, no. 4: 36–52. doi:10.2753/REE1540-496X450403. Carhart, M. 1997. On persistence in mutual fund performance. The Journal of Finance 52, no. 1: 57–82. doi:10.1111/j.15406261.1997.tb03808.x. Choe, H., B. C. Kho, and R. M. Stulz. 1999. Do foreign investors destabilize stock markets? The Korean experience in 1997. Journal of Financial Economics 54, no. 2: 227–64. doi:10.1016/S0304-405X(99)00037-9. Choe, H., B.-C. Kho, and R. M. Stulz. 2005. Do domestic investors have an edge? The trading experience of foreign investors in Korea. Review of Financial Studies 18, no. 3: 795–829. doi:10.1093/rfs/hhi028. Chordia, T., A. Goyal, and N. Jegadeesh. 2011. Buyers versus sellers: Who initiates trades and when? (2011, August 1). Swiss Finance Institute Research Paper, no. 11-43. http://ssrn.com/abstract=1914554.

Downloaded by [University of Malaya] at 20:35 22 February 2016

S20

M. KIM AND J. PARK

Coval, J. D., D. A. Hirshleifer, and T. Shumway. 2005. Can individual investors beat the market? Working paper, University of Michigan, Ann Arbor, MI. De Long, J. B., A. Shleifer, L. H. Summers, and R. J. Waldmann. 1990. Noise trader risk in financial markets. Journal of Political Economy 98, no. 4: 703−738. Dorn, D., G. Huberman, and P. Sengmueller. 2008. Correlated trading and returns. The Journal of Finance 63, no. 2: 885–920. doi:10.1111/j.1540-6261.2008.01334.x. Fama, E. F., and K. R. French. 1992. The cross-section of expected stock returns. The Journal of Finance 47, no. 2: 427–65. doi:10.1111/j.1540-6261.1992.tb04398.x. Fama, E. F., and K. R. French. 1993. Common risk factors in the returns on stocks and bonds. Journal of Financial Economics 33, no. 1: 3–56. doi:10.1016/0304-405X(93)90023-5. Goetzmann, W. N., and M. Massa. 2002. Daily momentum and contrarian behavior of index fund investors. Journal of Financial and Quantitative Analysis 37, no. 3: 375–89. Griffin, J., J. Harris, and S. Topaloglu. 2003. The dynamics of institutional and individual trading. The Journal of Finance 58, no. 6: 2285–320. doi:10.1046/j.1540-6261.2003.00606.x. Grinblatt, M., and M. Keloharju. 2000. The investment behavior and performance of various investor types: A study of finland’s unique data set. Journal of Financial Economics 55, no. 1: 43–67. doi:10.1016/S0304-405X(99)00044-6. Grossman, S., and M. H. Miller. 1988. Liquidity and market structure. The Journal of Finance 43, no. 3: 617–33. doi:10.1111/ j.1540-6261.1988.tb04594.x. Haugen, R., and N. Baker. 1996. Commonality in the determinants of expected stock returns. Journal of Financial Economics 41, no. 3: 401–39. doi:10.1016/0304-405X(95)00868-F. Hvidkjaer, S. 2008. Small trades and the cross-section of stock returns. Review of Financial Studies 21, no. 3: 1123–51. doi:10.1093/rfs/hhn049. Ivković, Z., and S. J. Weisbenner. 2005. Local does as local is: Information content of the geography of individual investors’ common stock investments. The Journal of Finance 60, no. 1: 267–306. doi:10.1111/jofi.2005.60.issue-1. Jackson, A. 2003. The aggregate behaviour of individual investors. Working paper, London Business School, London, UK. Jegadeesh, N., and S. Titman. 1993. Returns to buying winners and selling losers: Implications for stock market efficiency. The Journal of Finance 48, no. 1: 65–91. doi:10.1111/j.1540-6261.1993.tb04702.x. Kaniel, R., G. Saar, and S. Titman. 2008. Individual investor trading and stock returns. The Journal of Finance 63, no. 1: 273– 310. doi:10.1111/jofi.2008.63.issue-1. Kim, K. A., and J. R. Nofsinger. 2007. The behavior of Japanese individual investors during bull and bear markets. Journal of Behavioral Finance 8, no. 3: 138–53. doi:10.1080/15427560701545598. Kumar, A., and C. M. Lee. 2006. Retail investor sentiment and return comovements. The Journal of Finance 61, no. 5: 2451–86. doi:10.1111/jofi.2006.61.issue-5. Lee, C. M. C., A. Shleifer, and R. Thaler. 1991. Investor sentiment and the closed-end fund puzzle. The Journal of Finance 46, no. 1: 75–109. doi:10.1111/j.1540-6261.1991.tb03746.x. Nofsinger, J. R., and R. W. Sias. 1999. Herding and feedback trading by institutional and individual investors. The Journal of Finance 54, no. 6: 2263–95. doi:10.1111/jofi.1999.54.issue-6. Odean, T. 1998. Are investors reluctant to realize their losses? The Journal of Finance 53, no. 5: 1775–98. doi:10.1111/ jofi.1998.53.issue-5. Odean, T. 1999. Do investors trade too much? American Economic Review 89, no. 5: 1279–98. doi:10.1257/aer.89.5.1279. Park, J. W., and M. H. Kim. 2014. Investment performance of individual investors: Evidence from the Korean stock market. Emerging Markets Finance & Trade 50, no. s1: 194–211. doi:10.2753/REE1540-496X5001S113. Ritter, J. R. 1991. The long-run performance of initial public offerings. The Journal of Finance 46, no. 1: 3–27. doi:10.1111/ j.1540-6261.1991.tb03743.x. Saar, G. 2001. Price impact asymmetry of block trades: An institutional trading explanation. Review of Financial Studies 14, no. 4: 1153–81. doi:10.1093/rfs/14.4.1153. Shefrin, H. 2010. Behaviorizing finance. Hanover, MA: Now Publishers Inc. Shleifer, A., and L. H. Summers. 1990. The noise trader approach to finance. Journal of Economic Perspectives 4, no. 2: 19–33. doi:10.1257/jep.4.2.19. Shleifer, A., and R. Vishny. 1997. The limits of arbitrage. The Journal of Finance 52, no. 1: 35–55. doi:10.1111/j.15406261.1997.tb03807.x. Stoll, H. R. 1978. The supply of dealer services in securities markets. The Journal of Finance 33, no. 4: 1133–51. doi:10.1111/ j.1540-6261.1978.tb02053.x. Thaler, R. H. 1993. Advances in Behavioral Finance. New York, NY: Sage Press. Wurgler, J., and E. Zhuravskaya. 2002. Does arbitrage flatten demand curves for stocks? The Journal of Business 75, no. 4: 583–608. doi:10.1086/Jb.2002.75.issue-4.