Almost all prior studies on the Korean accrual anomaly focus on the stock market. In contrast to prior studies, our study examines whether or not the accrual anomaly exists in the Korean corporate bond market. Using the corporate bond data traded in the Korean market from 2000 to 2011, we find supporting evidence of the accrual anomaly in the Korean corporate bond market using three approaches: (1) hedge portfolio return test, (2) the Fama-French five factor (three equity market factors plus two bond market factors) time series regression test, and (3) the Fama-MacBeth cross sectional test. Next, we examine whether the unique Korean corporate governance system, chaebol, affects the magnitude of the accrual mispricing in the corporate bond market and find evidence that the accrual anomaly exists only for bonds issued by non-chaebol firms. This finding indicates that bond investors’ expectation about explicit or implicit subsidy from chaebol-affiliated subsidiaries dampens accrual mispricing of a chaebol firm. Taken together, our results suggest that investors in the Korean corporate bond market fixate on earnings, in particular, for non-chaebol firms.

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The essence of strategic asset allocation (SAA) is to derive the optimal asset allocation taking into account the diversification effect of each asset class under the same risk and return profile structure. The mean variance optimization method is useful when the structure of the risk and return of assets is homogeneous. However, due to the emergence of various alternative investments, methods for determining optimal asset allocation are the subject of controversy. We review the problems of the traditional SAA method that is used by pension funds and propose a new SAA method that considers multiple factors. We examine how to apply the new model to domestic pension funds. We provide a new asset allocation paradigm that provides a reasonable weighting for alternative investments and a basis for determining foreign currency hedge ratios for foreign investments. A review of domestic pension funds’ portfolios, assuming a similar target rate of returns and risk tolerance limits, identifies big differences in the weights given to alternative investments. The variation occurs because most pension funds predetermine the portion of the portfolio that will be alternative investments and then perform an SAA only for the remaining asset classes. As decisions on the weights of alternative investment consider the entire portfolio, it is necessary to improve the current asset-based SAA method that performs the allocation after excluding the alternative investment. Factor-based SAA can be a useful method for incorporating alternative investments into the asset allocation framework and can identify methodologies for integrating risk with existing assets such as stocks and bonds. There are four steps in factor-based SAA: selecting an asset mapping the asset class to each corresponding factor determining the target factor weight and re-mapping it to the asset class. In the first step, an appropriate factor must be selected to explain the payoff of each asset class. We use a principal component analysis to extract the common elements in all of the asset classes and then conduct a correlation analysis with the factor candidates to determine growth, inflation, real interest rates, credit, and FX. It is interesting to note that the inflation factor has a higher negative correlation with the other factors during an economic crisis, confirming that investing in the inflation factor during a crisis can effectively hedge a portfolio’s returns. Second, to estimate the factor coefficients for each asset using a multifactor model it is necessary to map the asset class to each corresponding factor. The estimated coefficients are then used as a link in determining the target factor exposure and to determine the optimal asset allocation. The current six asset classes are re-defined and divided into 17 asset classes based on the cash flow and risk characteristics of each asset class. In particular, we classify the alternative investments into equity and debt types. In this way, we can make the risk and profit structure of each asset class as homogeneous as possible. Third, the target factor weight is determined by selecting an appropriate factor exposure for the risk profile of the pension fund. The target factor weight should be consistent with the pension fund’s investment objectives and risk tolerance limits and is also a key element in determining the optimal allocation of investment assets. To determine the target factor exposure, we first derive a factor-based efficient frontier. Once the factor-based efficient frontier has been acquired, the optimal factor exposure with the highest Sharpe ratio is selected from the alternatives that meet the target returns and risk tolerances of the pension fund. In this case, the target factor exposure is based on the implied factor exposure obtained from the current portfolio’s starting point. Fourth, to remap to the asset class, we determine the asset class that meets the target factor weight. This process usually uses an optimization method. We set up a hypothetical portfolio of pension funds and analyze these portfolios. The implications of the case analysis are summarized as follows. First, to increase the Sharpe ratio, it is desirable to reduce the domestic equity investments and increase the portion of overseas investments. Second, the foreign exchange hedge policy of the pension fund is too conservative and should be expanded. Third, the impact of alternative investments on the portfolio’s risk-return profile varies by its composition, such that the weights should be distributed based on the correlation of factors and factor exposures. In addition, factor-based SAA allows monitoring of the exposure of investment assets to each of the corresponding risk factors at all of the times when effective risk monitoring for alternative investments is expected.

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A substantial body of research has examined the customer-supplier relationship in Korea from both the economic and social perspectives. The majority of the Korean studies on the customer-supplier relationship have focused on the performance of the suppliers of large companies (Chung, 2007; Shim, 2011; Kim and Shin, 2015). In this study, we extend this line of research by investigating the effect of the customer-supplier relationship on the cash holdings of the supplier firms. The customer-supplier relationship can directly affect a firm’s cash flow, which in turn can significantly affect the supplier firm’s working capital management, sales expenditures (Kalwani and Narayandas, 1995), leverage (Titman and Wessels, 1988; Kale and Shahrur, 2007; Banerjee, Dasgupta, and Kim, 2008), cost of debt (Campello and Gao, 2014; Cen, Dasgupta, Elkamhi, and Pungaliya, 2015), and cost of equity (Dhaliwal, Judd, Serfling, and Shaikh, 2016). Firms hold cash as a buffer to protect themselves from adverse cash flow shocks. If a customer accounts for a large proportion of a supplier’s sales, then the loss of that customer will result in a large adverse cash flow shock. As a precaution against the additional operating risk associated with having an important relationship with a customer, suppliers tend to hold cash in proportion to the importance of the customer-supplier relationship (Itzkowitz, 2013). Bae and Wang (2015) find that the relationship-specific investments of firms in customersupplier relationships are associated with the high cash holdings of the supplier firms. Firms that rely on relationship-specific investments tend to maintain high cash reserves as a cushion to sustain their relationship-specific investments when negative shocks occur (Titman, 1984). Using R&D based measures and durable-goods industries as proxies for the extent of relationship-specific investment, Bae and Wang show that the positive relationship is more pronounced for firms with positive R&D expenses and firms in the durable-goods sector. Kim, Jinn, and Han (2012) find that the leverage of Korean firms is negatively related to the R&D intensity of their suppliers and customers on the industry level but not the firm level. The primary goal of this study is to determine how the customer-supplier relationship affects the suppliers’ cash holdings. We use the percentage of sales to major customers and the major customer concentration to measure the customer-supplier relationship. Firms’ sales data from 2006 to 2012, including the proportion of sales and the customer names obtained at the firm level from Korea Enterprise Data (KED), are used to calculate the customer-supplier relationship variables. As a supplier’s dependence on major customers increases, the suppliers can potentially lose a significant proportion of sales, which could cripple their financial health. Therefore, the suppliers are likely to hold additional cash to protect themselves against the operating risk induced by their buyers. Our results indicate that suppliers are motivated to increase their cash holdings as a precautionary measure. This positive relationship between a supplier’s cash holdings and the supplier’s reliance on customers is also found in Korean supplier firms. The suppliers of customers that have considerable bargaining power may face higher uncertainty of keeping their contracts. In the case of big customers, such as leading companies, the suppliers face a higher risk of losing sales. Therefore, the suppliers of leading companies have an incentive to hold more cash as a precaution. Accordingly, the results of this study indicate that the positive relation between customer–supplier relationships and cash holdings is more pronounced for firms with big customers. Following Bae and Wang (2015), the sample is divided into subsamples based on a measure of relationship-specific investments that uses firm level R&D intensity as a proxy for relationship-specific investment. The results indicate that firms with more relationship-specific investments hold more cash than other firms. Research suggests that the precautionary motive is stronger during recession periods when firms have less access to credit and their customers are more likely to cancel purchases (Ikowitz, 2013). In line with this, the findings of this study show that during the 2008 and 2009 financial crisis, suppliers held more cash relative to the strength of their relationships than during other periods.Although the results of this study show that a supplier’s reliance on major customers induces the supplier to hold more cash, the customers may prefer to deal with financially stable suppliers and thus purchase more from such suppliers. To handle this endogeneity problem, a two-stage least squares (2SLS) regression is conducted in which the instrumental variable is a dummy variable that is set to one if a supplier is located in the same area with one or more customers, and zero otherwise. The results show that although the instrumental variable is associated with the proportion of a supplier’s sales, the relationship does not affect the firm’s cash holdings. Thus, the 2SLS analysis supports the main results of this study.

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Equity funds that invest primarily in equities are exposed to both market risk and cross-sectional systematic risks. However, Barber, Huang, and Odean (2016) and Berk and van Binsbergen (2016) show that investors in the U.S. mutual fund industry mostly consider the market risk (beta) when evaluating funds and treat the returns attributable to other systematic risks as alpha. Thus, the capital asset pricing model (CAPM) alphas are the best predictors of fund flows among the competing performance evaluation models. Indeed, Barber, Huang, and Odean (2016) and Berk and van Binsbergen (2016) show that investors take into account the realizations of aggregate risk factors when assessing the performance of mutual funds. Similarly, Berk and Green (2004) use a rational model to demonstrate that investors learn about a manager’s ability from past returns. Based on the work of Berk and Green (2004), Barber, Huang, and Odean (2016), and Berk and van Binsbergen (2016), Franzoni and Schmalz (2017) investigate the flow-performance sensitivity of mutual funds across market states and show that the sensitivity changes across market states. They assume that the fund investors are Bayesian investors who are uncertain about the degree to which the fund returns are exposed to systematic risk. In extreme periods, defined as periods of very high or low market excess returns, they find that the flow-performance sensitivity is weaker because fund investors have difficulty estimating the skill of fund managers. In line with Franzoni and Schmalz (2017), Starks and Sun (2016) find that the fund flow-performance sensitivity decreases during periods of economic uncertainty. In this study, we investigate the equity fund flow-performance sensitivity across market states in the Korean fund market. Sirri and Tufano (1998) and Huang, Wei, and Yan (2007) show that search costs or participation costs are an important determinant of fund flows, emphasizing the convex relationship between flow and performance. Because a fund investor cannot evaluate all of the funds in the fund market, the fund flows are concentrated in high-performing funds. However, Ferreira, Keswani, Miguel, and Ramos (2012) show that the participation costs have declined over time as the U.S. mutual fund industry has matured, thereby weakening the convex flow-performance relationship. However, in the emerging fund market of Korea, the flow- performance relationship is strongly convex because the investors are less sophisticated. In fact, during the 2008 global financial crisis, there was a massive redemption of equity funds because the less sophisticated investors were not aware that the fund returns could move in line with the market returns. In contrast to the U.S. mutual fund market, in which investors acknowledge the fund market risk, there may be a lack of awareness of the fund market risk in the Korean fund market. Thus, we also examine the effect of market risk on equity funds in the flow-performance relationship. The aims of this study are as follows. First, drawing on Franzoni and Schmalz (2017), we analyze the flow-performance sensitivity across different stock market conditions. Using Fama and Macbeth’s (1973) monthly cross-sectional regression, we divide the entire period into three periods of down-, moderate-, and up-market depending on the market risk premium and divide the periods into negative and positive periods. Second, to examine the effects of an equity fund’s market risk, we investigate the fund flow response to the fund’s raw returns (i.e., the absolute returns without adjusting the benchmark returns) and market-adjusted returns (i.e., the raw returns minus the market returns). We then use the pooled regression model to estimate how the fund flows respond to the raw returns and the market-adjusted returns divided by the negative and positive returns, respectively. Our results are as follows. First, in contrast to Franzoni and Schmalz (2017), we find that the flow-performance sensitivity in the Korean fund market increases significantly during the up-market periods and the positive periods in which the market risk premium is high. This suggests that Korean equity fund investors expect high absolute positive returns and respond more sensitively to the performance of the funds in such market conditions. Second, we find that the fund risk does not affect the flow-performance relationship, thus indicating that the fund investors do not respond to the fund risk. Third, the flow-performance relationship is significant only when the raw returns are positive for the entire period. Finally, the flow-performance relationship is only observed when the raw returns are positive regardless of the fund size, age, and performance evaluation period. The results of this study suggest that unlike in the developed U.S. mutual fund market, the fund investors in the Korean equity fund market do not regard market risk as a risk and expect high absolute raw fund returns. This suggests that fund investors in Korea need to recognize the inevitably of market risk and invest in funds as a means of long-term investment.

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This study investigates the impact of the trading of the national pension fund (NPF, hereafter) on the stock markets in Korea. In 2017, the Korean national pension system, which was introduced in 1988, was ranked as the third largest public pension fund after Japan and Norway, with assets of 6.8 trillion Korean won. Moreover, the NPF accounts for 6.75 percent of the Korean equity market, which is higher than the 5.06 percent of equity held by the Japanese pension fund. As the NPF has increased its investment in the Korean stock market, its trading strategies and possible influence on the stock market have attracted increasing attention from market participants and regulators. However, despite the growing interest in the subject, few studies have examined the strategies and influence of the NPF and there is little research data on the trading in the past decade. In addition, the NPF recently announced that it plans to increase the proportion of investment in stocks in its portfolio over the next five years. Thus, the NPF is expected to have an increasing influence on the Korean stock market. Given the significant size of the NPF, the increased proportion of stocks held by the fund could lead to price pressures unrelated to the intrinsic value of firms and result in price movements induced by follow-up investors. For these reasons, there has been considerable debate over the effect of the NPF’s trading on the stock market. Some have argued that the NPF’s investment decisions are based on political judgement rather than information whereas other institutional and foreign investors conduct information-driven transactions. In line with this, the NPF recently provided an official explanation of why its stock portfolio only achieved average returns of 5%, which is significantly lower than the 22.15% average returns on the Korea Stock Exchange (KSE) and 24.75% returns on the Korea Securities Dealers Automated Quotation (KODAQ) market. Taken together, these concerns suggest that there is a pressing need to comprehensively examine whether the NPF’s transactions on the stock markets distort the markets and worsen the market quality in terms of liquidity and volatility. In this study, we attempt to address these questions by examining the influence of the NPF’s transactions on the Korean stock markets. Our dataset comprises the combined data from the ownership change and trading records of the KSE and KODAQ over the 12-year period from August 2005 to July 2017. Specifically, we combine the insider equity ownership disclosures, disclosures of large equity ownership, and information on the trading record files and infer detailed information on the dates, quantity, and prices of the stocks traded by the NPF. Based on the inferred accounts of the NPF, we analyze the trades of the NPF from 2005 to 2017 at both the aggregate market and individual stock levels. Some of our major findings are as follows. First, consistent with the literature, we find that the NPF uses a contrarian trading strategy such that the fund tends to buy stocks when the market return goes down and sell them when the return goes up. This phenomenon is also observed at the individual stock level even after controlling for various stock characteristics, and the results are consistent across the yearly analyses. Second, the market volatility decreases when the NPF’s net buying increases. Therefore, the NPF’s stock transactions do not appear to destabilize the stock market. Furthermore, in contrast to the general concerns, our empirical results suggest that the NPF’s stock transactions contribute to market stabilization. Third, we do not find evidence showing that the trading of the NPF influences the trading of other investors, including the other sub-groups of institutional investors and individual investors. However, we find that among the various sub-institutional investor groups, the increased net buying of the NPF is associated with increased net buying by investment companies and miscellaneous financial investors. These sub-groups are influenced by the net buying of the NPF after two to three days, indicating follow-up transactions. Together, these findings suggest that some financial investors may consider the NPF to possess a strong capacity to analyze information.

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This study analyzes the relationship between tail risk as a risk indicator and price limits as a price control system. We investigate the impact of price limits on tail risk and examine the predictive power of tail risk on future stock returns. We use the measure of tail risk proposed by Kelly and Jiang (2014), which is based on the commonality inherent in the tail risk of individual stocks and is useful for capturing the common factors associated with tail risk at each time point. We analyze the sensitivity of Oh, Park, and Kee’s (2017) expected value model of tail risk under price limits and simulate the random returns under various price limit conditions. The sensitivity analysis for the expected value model of tail risk shows that the tail risk is underestimated when the price limits are tight. The sensitivity results can also be supplemented to find the appropriate price limits needed to achieve the policy objectives of mitigating the extreme fluctuation of stock prices and minimizing the price distortions caused by the upper and lower limits. We then examine these findings using a simulation of 50,000 random returns with leptokurtic distribution. The tail risk measured suggests that the tail risk is underestimated as the price limit becomes increasingly tight. However, with a well-expanded price limit, the tail risk based on leptokurtic distribution is estimated to be about 20% to 30% higher than that based on the standard normal distribution and the negative impact of the price limit on tail risk is generally attenuated after a price limit greater than 12% is applied. The price limit system, which limits the range of prices within which stocks are allowed to be traded, restricts the intrinsic fluctuation demand of the stock market and is believed to have a significant impact on tail risk. Price limit systems have been implemented in many Asian countries such as Japan and China, which are the third and fourth in the world in terms of stock market capitalization, and Korea, Thailand, Malaysia, and Taiwan. Our empirical analysis focuses on the Korean stock market because the price limit has been gradually eased from an average of 4.6% to the current 30%. Our sample covers trading on the Korean stock market from January 1990 to October 2015. We find that the tail risk during the period with the price limit of 12% or more increases more than twice compared to that of previous period. We interpret this as reflecting the inherent nature of the tail risk associated with stock movements without being subject to the artificial control of the price limit. We separately analyze the predictive power of tail risk in the KOSPI and KOSDAQ markets and examine the effect of firm size on the predictive power of tail risk for future Korean stock returns. Our main empirical results are as follows. First, among groups of the same firm size, the coefficient of tail risk increases as the forecasting period becomes longer, which means that the sensitivity to tail risk persists over a long period. Second, when examining the size effect in relation to the forecasting period, we find that in the short term, the sensitivity of tail risk increases as the firm size increases whereas for longer forecasting periods, the sensitivity diminishes as the firm size increases. Third, in the largest firm size group, we find statistically significant predictive power in the short-term forecasting period for the whole sample period. We find that the stock returns of large firms are less affected by price limits than those of small firms. Fourth, in all of the forecasting periods for the KOSDAQ market and in the short-term period of the KOSPI market, the tail risk has statistically significant predictive power on future stock returns. In the KOSDAQ market, the longer the forecasting period, the more significant the predictive power and the more sensitive the tail risk. In contrast, in the KOSPI market, the tail risk is only found to have predictive power in a less than one year forecasting period and the sensitivity of the tail risk is lower than that of the KOSDAQ market. Fifth, by estimating the predictive power of tail risk on the future skewness, we find that the coefficients of tail risk have negative values for all forecasting periods and Newey West t-statistics are sufficiently high. This confirms that the two variables share the same dynamics and the tail risk is closely related to the left-skewed distribution.

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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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