This study suggests an investment sentiment index that exploits daily information on individual firm characteristics and individual investors’ trading behavior in the Korean stock market. We empirically examine the explanatory power of our sentiment indicator for the cross-sectional returns of individual stocks and portfolios after controlling for appropriate market risk factors. While previous studies use a single variable or multiple market-wide variables to measure investor sentiment, we efficiently measure sentiment by extracting common factors from various individual firm characteristic variables and trades by domestic individual investors, who are normally regarded as noisy and behaviorally biased. By extending methodologies suggested by Ryu, Kim, and Yang (2017), Yang, Ryu, and Ryu (2017), and Yang and Zhou (2015, 2016), we construct a composite sentiment indicator based on principal component analysis using five key variables: the relative strength index, psychological line index, adjusted turnover rate, logarithm of trading volume, and individual investor buy-sell imbalance. We analyze how the sentiment effect varies depending on firm and stock characteristics by constructing several portfolio groups classified according to firm size, stock price, book-to-market ratio, excess return, volatility of past returns, individual trading ratio, and foreign investors’ holding for individual companies. We categorize the portfolios into five quintile groups based on each criterion and generate an investor sentiment index for each portfolio group. Our sample data comprise a daily stock trading dataset for all available manufacturing companies listed on the KOSPI stock market from 2000 to 2015. This sample mitigates possible industry effects and biases and enables us to investigate the uncontaminated results for sentiment effects by maintaining homogeneity among the sample firms. To ensure consistency and clarity, we exclude companies facing trading suspension and/or administrative issues. We extend the literature on investor sentiment and contribute to research by constructing a composite sentiment indicator that includes information on various stock and firm characteristics. We use buy-sell order imbalance information on individual investors, as their investment strategies and trading patterns are likely to be affected by psychology, sentiment, and mood. Our sentiment indicator exhibits more robust explanatory power than existing sentiment measures do, as it successfully explains the cross-sectional asset returns after controlling for the four risk factors. Our empirical analyses using this sentiment indicator provide several important findings and implications. We find that our investor sentiment indicator may explain cross-sectional stock and portfolio returns, even after controlling for Fama and French factors (market factor, size factor, and book-to-market factor) and the additional momentum factor suggested by Carhart (1997). It is particularly interesting that the sentiment indicator maintains its explanatory powers after considering and controlling for the momentum effect, consisting of representative time-series stock price patterns driven by investor psychology and behavioral biases. This result indicates that the sentiment indicator may be an important factor in explaining asset price movements. The analyses considering firm and stock characteristics show that the sentiment effect varies significantly according to stock and firm characteristics, being more prominent for smaller firms as well as stocks that are lower priced, have higher book-to-market ratios, have greater excess returns, and are more volatile. The sentiment effect also increases for stocks exhibiting greater individual investor participations, while its effect is lower when stocks are mostly owned by foreign investors, who are mostly professional institutional investors. These results tend to suggest that domestic individual investors are uninformed and noisy traders, while foreign institutional investors are better informed and more sophisticated. Considering that our sentiment indicator captures firm and stock characteristics reflecting individual stock trading on a daily basis, our methodology can be easily applied to analyze sentiment issues in various industry sectors at a relatively high-frequency level. The sentiment indicator can also be used to examine the transmission and spillover effect of market sentiment and behavior across financial markets and countries.

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We analyze cause and effect of Korean institutional investors’ selling behavior of IPO shares right after initial trading. Most of IPO shares sold by institutions right after initial trading are assumed to be originally allocated to them in bookbuilding process. Thus, institutions’ selling behavior of IPO shares right after initial trading is interpreted as IPO flipping that is reported by Boehmer, Boehmer, and Fishe (2006). Following the methodology of Boehmer et al. (2006), we separately analyze the initial flipping on first three trading days after IPOs, and the second flipping thereafter to the twenty fifth trading days. Samples of our study cover firms that undertook IPOs in year 2003 through year 2014 in Korea. Firms’ financial data, stock prices, trading volumes, and market indexes are obtained from FnData Guide. IPO-specific data, such as bookbuilding results, allocation ratios, etc. are obtained from investment prospectus reported in DART (disclosure system of Financial Supervisory Services). Our samples covers 674 firms: 110 firms of Securities Market IPOs, and 564 firms of KOSDAQ IPOs. We have found that IPO initial returns are significantly negatively related to the first flipping, but significantly positively related to the second flipping. This result implies that Korean institutional investors that are allocated with IPO shares take profit in the second flipping. We have also found that the main players of short-term sales of IPOs are institutional investors. The voluntary lock-up of institutional investors does not play a meaningful role in constraining their selling behavior of IPO shares. Right after the voluntary lock-up period, institutional investors are selling IPO shares. The initialflippingon the first three days after IPOs are not related to the amount of institution allotments. We have also analyzed the effect of venture capital investment on the IPO stock return of the first trading day. Empirical results show that IPO firms with venture capital investments have significantly lower stock return on the first day of trading than those with no venture capital investment. This result would be interpreted as the certification effect of venture capitals. Institutional investors tend to sell more IPO shares both in the first and second flipping when firms have venture capital investments. In the analysis of long-term return behavior, we have found that the first flipping of institutional investors does not affect long-term market-adjusted returns of IPO shares. However, the second flipping activities of institutional investors significantly decrease long-term returns measured by market adjusted returns over 3, 6, and 9 month period after IPOs. This result implies that institutional investors allocated with IPO shares tend to flip shares right after IPOs regardless of long-term prospect of IPO firms. In contrast, the second flipping is mainly driven by venture capitals that have invested in IPO firms before these firms go public. Noting that the second flipping is negatively related to long-term return performance, venture capitals that have long relationship with firms have the ability to predict future operational prospect of IPO firms. The more shares are allocated to institutional investors, the worse are market-adjusted returns over the period of 6, 9, and 12 months after IPOs. This result is related to the dynamic information acquisition model of Benvenist and Spindt (1989). In a situation where IPO allotment ratio among investor groups are mostly fixed as in Korea, significant underpricing would be an effective way of compensation for institutional investors in the dynamic information acquisition model of Benvenist and Spindt (1989). Thus, when more IPO shares are allocated to institutional investors, those shares are more likely to suffer from underpricing.

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This paper examines how fund flows predict subsequent market returns, especially focusing on the validity of using fund flows as a measure of investor sentiment for the Korean financial market. For this purpose, we use a variety of fund flow indicators. In contrast to the previous literature in Korea, which has solely analyzed the fund new sales and redemptions, this article develops a fund transfer index, i.e., the shifts between equity funds and bond funds, and also uses it as a proxy variable for investigating the relationships between fund flows and market returns. Another contribution of the paper is that, based on the newly developed indicator, we investigate whether the fund transfer index has a significant impact on market returns. Therefore, this study can be expected to fill the gap in the literature which has mainly dealt with the relationships between equity fund flows and stock returns and between bond fund flows and bond returns. Against this backdrop, the paper attempts to address three research questions in order. First, we examine whether various fund flow indices such as fund-new-sales and redemptions-indices and fund-transfer-indices for equity and bond funds in Korean markets could be viewed as a relevant measure for investor sentiment. It is rather well documented in the literature that investor sentiment affects market returns. Here, a more important question is how to measure investor sentiment and quantify its effects. Generally speaking, investor sentiment is the overall attitude of investors toward future cash flows or fundamentals and the feeling or tone of investors as revealed through the activity and price movement of the securities traded in markets. Thus, the better sentiment measure should quantify the biases of market prices which are not explained by economic fundamentals. More specifically, positive sentiment pushes asset prices to extraordinarily higher levels in the short run but leads them to equilibrium in the long run. The underlying key assumption here is that individual investors are often noise traders potentially because they are less informed and less capable in information processing than institutional investors. Having said that, this paper verifies those fund flow indices could satisfy the prerequisites for a relevant measure of investor sentiment in the Korean financial markets. Second, by conducting the lead-lag analyses, we examine the relationships between fund flows and financial asset prices. That is, the paper checks whether contemporaneous flow indices and the accumulated flow indices affect market returns. In this process, we verify whether fund flows could measure investor sentiment and whether they could predict market returns originated from mispricing. Third, this article analyzes whether fund transfer flows have a significant effect on excess returns in equity returns and bond returns, respectively. This is in contrast to the previous studies which have focused only on the relationships between equity fund flows and excess returns in stock markets. Our main findings can be summarized as follows. First, we find that fund flows in Korea fail to satisfy the prerequisite conditions for investor sentiment, which is ‘short-run upsurge and long-run reversal’ pattern. This finding highlights the limitations of previous domestic studies that measure investor sentiment with fund flows. Based on this result, we argue that the previous studies, which use fund flows as a proxy for investor sentiment, have a consequential problem in common due to immature fund markets. This conjecture still seems to be consistent with the extant literature which points out the instability of fund flows and structural changes around the fund markets of Korea. Second, we find that fund flows have a substantial prediction power on market returns. For instance, while the equity fund net inflow index (denoted to NEAR) can predict stock market excess returns over three to six month horizons, the bond fund net inflow index (denoted to NBAR) and the fund transfer index (denoted to FAR) can predict bond market returns over one to six months ahead. Moreover, our finding confirms that the inclusion of the fund transfer index along with other economic variables could significantly increase the prediction power, measured in Adjusted R-squared, on market returns. Last but more importantly, we firstly document that fund transfer flows between equity funds and bond funds could be an useful indicator to explain excess returns in the Korean stock and bond markets. Together with the second finding, this result implies that the fund transfer between equity and bond funds has information regarding the asset allocation of investors, reflecting the prospect on future market conditions. On the contrary, the fund flows not including the fund transfer information contain only on the long-term savings and withdrawals.

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I demonstrate that the rank momentum strategy advanced by Chen, Chou, Ko, and Rhee (2016, Nonparametric momentum strategies, working paper, henceforth CCKR) is profitable in Korea. The strategy constructed on the basis of average past ranks of daily returns generates significant momentum profits for up to four years following portfolio formation. Further, I find evidence that the profitability is attributed to investor underreaction to non-salient information embedded in stock prices. Rank measures capture the non-salient component in stock prices largely neglected by investors, and therefore, the rank can predict the future changes in stock prices. The main findings of this study are as follows. First, rank momentum strategies generate significant returns for holding periods ranging from six months to four years subsequent to portfolio formation. In particular, the risk-adjusted return of the rank momentum strategy constructed by buying stocks with high average ranks and short selling those with low average ranks is 0.81% per month (t-statistics 2.90) when the long-short portfolio is held over six months. The profitability of rank momentum strategies is robust to controls of various cross-sectional effects such as size, book-to-market, illiquidity, and idiosyncratic volatility. Second, rank-based momentum strategies even work better than the price momentum strategies proposed by Jagadeesh and Titman (1993, Returns to buying winners and selling losers: implications for stock market efficiency, J. Finance 48, 65-91, henceforth JT). For example, over one year holding period following formation, the rank momentum earns an average monthly profit of 0.44%, while the profits of JT price momentum are even negative. The rank momentum strategies also outperform the 52-week high momentum strategies suggested by George and Hwang (2004, The 52-week high and momentum investing, J. Finance 59, 2145-2176). Third, rank momentum profits tend to fall for longer holding periods but are not reverted to become negative afterward in contrast with that of JT price momentum. The literature documents that investor overreaction leads to long-term reversals but investor underreaction does not. In this regard, the lack of reversals indicates that the rank momentum profits are not driven by the overreaction, but by the underreaction. Fourth, rank momentum profits primarily come from loser stocks’ underperformance. Fama-MacBeth (1973, Risk, return, and equilibrium: Empirical tests, J. Polit. Econ. 81, 607-636) regressions results reveal the short-leg of the long-short strategy yields persistent negative returns over holding periods ranging from six months to two years, while the profit of long-leg is positive only for the first six months. Consistent with the earlier findings in my study, this result also indicates that the rank momentum is associated with stock mispricing, which has not been eliminated by arbitragers. In particular, the rank momentum is closely related to stock overpricing and the resulting reduction. Arbitragers face greater impediments when they short-sell an overpriced stock than when they purchase an underpriced stock. The difficulty of short-selling deters arbitrage that reduces the overpricing. Accordingly, the underreaction of loser stocks to bad news is more persistent than underreaction of winner stocks to good news, which leads to persistent rank momentum profits for loser stocks. Lastly, the profitability of the rank momentum is strong among stocks with weak salient features, where the salience is proxied by the presence of extreme price changes. This evidence indicates that the rank momentum is the investors’ underreaction, rather than overreaction, is an underlying cause of the rank momentum. The literature including Bordalo, Gennaioli, and Shleifer (2013, Salience and asset prices, American Economic Review 103, 623-28) and CCKR asserts that investors tend to overreact to salient information and underreact to non-salient information. Weaker rank momentum in stocks with higher salient features lends support to the conjecture that the rank momentum profits are attributed to investor underreactions, not due to investor overreactions. Overall, our empirical findings for the Korean stock market is quite consistent with that for the U.S. stock market. As in CCKR, we provide ample evidence that rank captures the non-salient information embedded in stock prices, which is largely due to investors’ underreaction, and the profitability of rank momentum strategy primarily comes from the underreaction to bad news among loser stocks. This study contributes the literature on the momentum phenomenon in the Korean stock market. I explore the sources of the profitability of rank momentum strategies by comparing it with that of the traditional JT momentum strategies. Moreover, this study sheds light on the understanding of investors’ reactions to non-salient information in the Korean stock market. Investors tend to underreact to non-salient news, and the overpricing, which is due to the underreaction, and the resulting reduction produces momentum in stock prices.

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