If we can estimate the level of private information that has flowed into the capital market as a statistic, it can be very useful in many related fields. The first example is the probability of information-based trading (PIN) of Easley and O’Hara (1987). However, the structural model, PIN, has been criticized since the mid 2000s for not reflecting the data characteristics of the stock market, which has been turned into a surge of high frequency trading (HFT). Then, Duarte and Young (2009) proposed a modified PIN model, which extends the PIN model to overcome its shortcomings, and Odders-White and Ready (2008) suggested a model which is based on a fully different theoretical approach. This paper examines the validity of PIN, adjusted PIN (AdjPIN), and Odders-White and Ready (OWR), which have been extensively used as private information measures, in the Korean stock market. Applying them to the information asymmetry situation of the securities market before and after the merger announcement, we verify whether the estimates of these models are appropriate as private information measures. For this, we use the “Conditional Probability of an Information Event (CPIE)” of Duarte, Hu, and Young (2015, 2017). CPIE is estimated by model to enable to judge whether the private information reaches the market on a daily basis. The analyses in this paper include 29 cases of merger disclosures for the targeted companies listed on the KOSPI Market over an eight-and-a-half year period (2007. 5~2015. 10). We employ regression analysis, comparison between CPIE and its expected value, and the CPIE determinants analysis using the TAQ data for a total of 340 trading days before and after each event. As a result, only the OWR model among the three models appears to be useful as a measure of private information in the Korean securities market. Although there are differences in detail, this is essentially the same result as the US stock market. It suggests that in the Korea stock market, the OWR model based on orderflow imbalance, and day and over-night returns, is more appropriate as a measure of private information, rather than the PIN / AdjPIN model. The more detailed results are as follows. First, as a result of CPIE determinants analysis, the PIN-model-based CPIE is affected mostly by trade turnover and somewhat by order imbalance, while the AdjPIN-model-based CPIE is affected by trade turnover. Accordingly, the PIN/AdjPIN models are affected mainly by trade turnover, rather than order imbalance, on which they are theoretically based. On the other hand, most of the explanatory power of the OWR-model-based CPIE comes from over-night and intraday returns. The OWR-model-based CPIE reflects information consistent with its theoretical rationale, although it contrasts somewhat with the U.S. stock market, which is much more affected by either overnight return (in regression analysis) or intra-day return (in the comparison analysis between CPIE and its predicted value). Second, the PIN-/AdjPINmodel- based CPIEs are estimated at the boundary values 0 or 1 at least 81% of the time due to numerical overflow problems in the parameter estimation. This makes PIN or AdjPIN measurements more difficult to trust, even if the models are valid. Third, in all three models, the CPIE persists for up to 5~6 trading days after the announcement of the merger. However, after the announcement, the pattern shows a gradual decline to a lower level than before the announcement. Also, in all three models, the CPIE peaks one day after the announcement, rather than on the day of the announcement. This reflects a degree of inefficiency in the Korean stock market. Altogether, these results suggest that, among the PIN, AdjPIN, and OWR models, the OWR model best reflects the private information flow in the Korean stock market. In other words, they suggest that the OWR model based on order flow imbalance, over-night return, and intraday return, rather than the PIN/AjdPIN models based on order flow imbalance, is more appropriate as a measure of private information. The results on the Korean stock market are essentially the same as those of the U.S. stock market, though there are differences in detail, such as the relative importance of overnight and intraday returns.

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This study analyzes the mean reversion of trading volume, which allows us to predict future trading volumes from time-series data. The results have important implications for various related concerns, including the predictability of returns in relation to trading volumes, liquidity, and the use of practical indicators such as the VWAP (volume-weighted average price) and the CGO (capital gains overhang). We test the trading volumes of the indices, size-portfolios, and individual stocks on the Korean stock market from 1999 to 2017. All of the sample data are obtained from FnDataGuide. First, we test the autocorrelation of the trading volume. The results show that the trading volume has a positive autocorrelation, and that changes in trading volume have negative autocorrelations. Therefore, we confirm that the trading volume process (unlike the return process) does not follow an independent distribution. As mean reversion implies a correlated time-series, the autocorrelation of trading volumes serves as the premise for the mean reversion of trading volumes. Next, we use the Phillips-Perron test (Phillips and Perron, 1988) and the KPSS test (Kwiatkowski, Phillips, Schmidt, and Shin, 1992) to verify the mean reversion property of the trading volume. The results show that the trading volume of the indices, size-portfolios, and 96% of the individual stocks, all have a mean reversion property on the Korean stock market. In addition, we calculate the mean-reverting speed for each stock by applying the Ornstein–Uhlenbeck model (Uhlenbeck and Ornstein, 1930) to identify the variables that affect the mean reversion property of the trading volume. We regard the mean-reverting speed as a proxy variable that indicates the relative strength of the mean reversion property across sample stocks. This analysis of the mean-reverting speed enables us to confirm which variables affect the mean reversion of the trading volume. Before the regression analysis, we compare the actual mean-reverting duration of the trading volume with the duration calculated by using the Ornstein-Uhlenbeck model, which is our model for estimating the mean-reverting duration of the trading volume. As the implied error of the model has an acceptable scale, we confirm that our Ornstein-Uhlenbeck model can serve as a reasonable model for trading volume. The regression results on the mean-reverting speed of each stock shows that the smaller the size, the smaller the stock price volatility. In addition, we find that the smaller the ratio of the individual investors’ trading activity and the higher the number of analysts’ reports, the higher the mean-reverting speed. This set of findings suggests that the mean reversion of the trading volume can be explained by the presence of stealth trading (Kyle, 1985; Admati and Pfleiderer, 1988; Foster and Viswanathan, 1990; Wang, 1994) and by individual investors' attention-based trading (Barber and Odean, 2008). Heterogeneity between investors generates trading volume. This heterogeneity is resolved by opinion-sharing with trades. However, stealth trading by informed investors delays the incorporation of information, and attention-based trading by individual investors gives the trading volume a positive feedback. Thus, the mean-reverting speed of a stock is slower in trading environments where it is easier to hide information, and where individual investors trade more actively. Additionally, we show that the future trading volume can be estimated from its mean-reversion property. If we know the mean-reverting speed, the mean value, and the standard deviation of the trading volume, we can obtain the expected trading volume by applying the Ornstein-Uhlenbeck model. This study contributes to the literature in the following four ways. First, and most importantly, it expands research on trading volumes by demonstrating that the volume has a mean reversion property on the Korean stock market. Understanding this property takes us one step beyond making predictions based on the autocorrelation of the trading volume. Second, we find that the future trading volume can be predicted by its mean reversion property. This novel finding helps to expand the knowledge of market dynamics among academics, and it can help practitioners who want to build their positions without causing a serious market impact. Third, we show that the trading volume has a positive autocorrelation in the Korean stock market. Although such autocorrelation of trading volume has been previously studied in the U.S. stock market, it has not been investigated in the Korean stock market. As the scope for applying autocorrelation is wide, we believe that the verification of autocorrelation is also important. Last, we shed light on why the trading volume shows mean-reversion properties. We assess trade sizes, price volatility, the trading activity of individual investors, and the number of analysts’ earnings estimates, all of which influence the mean reversion of the trading volume. All of these factors can be partly explained by stealth trading and the attention-based trading of individual investors.

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The continuous increase in household debt in Korea over recent years has been widely noted as a potential risk to the country’s economy. In particular, the sustained increase in U.S. interest rates have raised the risk of default on household debt. To prevent the spread of household defaults in the economy as a whole, policies for consumer credit recovery should be re-evaluated, and institutional improvements are needed. This study considers the characteristics of delinquent borrowers and draws policy implications from a quantitative analysis of data from a representative consumer credit recovery program. In particular, we assemble a massive but precise micro-data set to identify the major factors that influence the process of consumer credit recovery. From the findings, we derive a set of substantial policy proposals. Our analysis classifies delinquent borrowers into three groups as follows: (i) group A, delinquent borrowers who are eligible for the credit recovery program; (ii) group B, delinquent borrowers who have applied to the program and are paying off their debt; and (iii) group C, delinquent borrowers who have completely paid off their debt and are no longer part of the program. Our results have several policy implications. First, the probability that delinquent borrowers will settle their debt by using the credit recovery program gradually decreases as the delinquency period increases. In addition, the probability of delinquent borrowers settling their debt through the credit recovery program is significantly affected by the amount of their debt, their age, and the period of delinquency. The empirical results also show that delinquent borrowers with smaller bonds, lower ages, and shorter delinquency periods have a higher tendency to join the program, and a greater willingness to recover their creditworthiness. A longer delinquency period and a higher age both reduce the probability of starting the program. Therefore, it is necessary to start the credit recovery program and manage it within the system before the delinquency period grows too long. Second, the probability of completing the program gradually increases as the delinquency period becomes longer. The successful completion of the program is mainly a function of the amount of debt and the debt reduction rate. A smaller bond and a higher debt reduction rate make it easier to repay the debt and complete the program. Notably, we find that borrowers with longer delinquency periods are more likely to pay off their debt completely, and this finding is robust after controlling for the debt reduction rate. This pattern emerges because a longer delinquency period increases the debt reduction rate, and improves the borrower’s ability to repay the debt. Over time, the causes of delinquency (e.g., unemployment and illness) tend to be resolved, and the borrower’s ability to repay increases. Therefore, it is necessary to consider expanding the grace period (i.e., the period in which a borrower does not need to follow the repayment plan), as this allows borrowers more time to recover their ability to repay the debt. Third, there is a time limit to the recovery in a borrower’s ability to repay. Borrowers with longer delinquency periods are more likely to pay off their debt completely, because the causes of delinquency are more likely to be resolved. However, borrowers who are overdue by more than 10 years are less likely to complete the credit recovery program. Currently, the government is promoting a debt relief program for delinquent borrowers whose total debt is less than KRW10 million, and whose delinquency period is longer than 10 years. Our results provide an empirical basis for this policy. In considering the empirical results regarding the relationship between the delinquency period and the probability of completing the program, we suggest that the program’s efficiency can be improved by restricting the number of extensions of the valid period for delinquent debt, restricting the extensions to small amounts of money, and offering such extensions to low-income borrowers only.

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In recent decades, share repurchases have greatly increased (Dittmar and Field, 2015; Floyd, Li, and Skinner, 2015; Sul, Kim, and Jang, 2004). Institutional ownership has also grown during this period, and institutional investors have had an increasingly significant effect on corporate decision making (Gugler, Mueller, and Yurtoglu, 2008; Yun and Kim, 2015). Even so, the existing literature has generally maintained that each institutional investor has unique characteristics. In particular, institutional investment horizons can be a notable dimension of heterogeneity among institutional investors (Bushee, 1998; Gaspar, Massa, and Matos, 2005; Chen, Harford, and Li, 2007; Aghion, Van Reenen, and Zingales, 2013). In this paper, we examine the relationship between institutional investment horizons and share repurchases. We hypothesize that firms with higher short-term institutional ownership engage in more share repurchases than those with lower short-term institutional ownership. Our conjecture is based on two considerations. First, short-term institutional investors are more likely to care more about the capital gains they expect from an increase in a firm’s stock price than they care about the firm’s long-term value. In financial markets with information asymmetry, share repurchases of overvalued firms benefit the shareholders who sell their shares, but such repurchases harm the investors who hold their shares. Accordingly, firms typically repurchase their stocks only when they are undervalued, and then stock repurchases typically stimulate an increase in stock prices (Lucas and McDonald, 1998). Although stock repurchases reduce the information asymmetry between firms and shareholders by providing the market with information on a firm’s intrinsic value, these repurchases may not enhance the firm’s growth or improve its operations. Given that short-term institutional investors may not remain shareholders of the firm for long, those investors are more likely to be concerned with the short-term capital gains that can result from share repurchases. Long-term institutional investors are more concerned with how share repurchases may affect a firm’s long-term value creation. Second, short-term institutional investors often have more information on a firm’s stock prices than long-term institutional investors (Yan and Zhang, 2009). More informed shareholders can make better decisions on a firm’s stock trades, and they can gain more profit from selling their shares. Therefore, more informed shareholders prefer share repurchases that can reap greater capital gains (Brenan and Thakor, 1990). Previous studies (Grinblatt and Titman, 1989; Wermers, 2000; Yan and Zhang, 2009) have indicated that short-term institutional investors are better informed about stock prices, and they tend to realize abnormal profits. Therefore, short-term institutional investors are more likely to prefer stock repurchases. To test our hypothesis, we measure institutional investors’ investment horizons with reference to turnover ratios. Following the literature (Gaspar et al., 2005; Attig, Cleary, El Ghoul, and Guedhami, 2013), we define a turnover ratio as the weighted average of the total portfolio’s churn rates among a firm’s institutional investors. We also use two other proxies for investment horizons, which are measured as the percentages of ownership of a firm held by long- and short-term investors. We define long-term (short-term) investors as those whose turnover ratios are in the bottom (top) tertile. In addition, a firm’s stock repurchases are measured as the amounts paid for stock repurchases, divided by the amounts of total payouts (that is, the sums of cash dividends and stock repurchases). Our final sample represents 3,404 Korean firm-year observations from 2004 to 2017. We find that the investment horizons of institutions are negatively related to share repurchases. In particular, firms that are mainly held by short-term institutional investors tend to make more repurchases. Our findings remain unchanged when we address endogeneity issues subject to measurement error and omitted variable bias. Specifically, our results are robust to using various measures of stock repurchases, and they hold after conducting a propensity score matching procedure and a change regression. Interestingly, we also find that when firms are classified as Chaebol or non-Chaebol firms, the negative association between institutional investment horizons and share repurchases is observed only in the Chaebol firms. Chaebol firms often have fewer financial constraints, due to the presence of internal capital markets. The controlling owners of Chaebol firms also tend to exercise greater control than owners of non-Chaebol firms. These characteristics of Chaebol firms are likely to provide a suitable environment for increased repurchases. Overall, our empirical findings suggest that shorter institutional investment horizons are among the most important factors explaining increased buybacks. Previous studies have analyzed the effects of institutional investors on corporate policies by regarding institutional investors as an homogeneous group. Relatively few researchers have investigated the heterogeneity of institutional investors. This study focuses on examining the differing investment horizons of institutions, and it makes a contribution to the corporate finance literature by examining the determinants of firms’ payout policies. Moreover, we add to the literature on Chaebol firms by showing that firm characteristics appear important for explaining the relationships between institutional investment horizons and stock repurchases.

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The informed investors’ order choice problem has been generally under-discussed, although this problem can exert a pervasive influence on trading and asset prices. In the literature, it has been long assumed that informed investors rely solely on market orders. Theoretical modeling of order choice under conditions of information asymmetry has been widely perceived to be complicated so far. Few empirical studies have been done on this subject, and even fewer have considered non-highfrequency data, primarily because detailed order and transaction data are rarely available. This article builds on the implications of several studies, and this article tests the hypothesis that investors with long-term private information prefer limit orders. When investors have information that is unlikely to spread among other market participants in the near future, the execution risk is low. In that case, the investors would wait discreetly for their limit orders to be executed at hopefully favorable prices and avoid influencing the market price, rather than rushing to take trading opportunities at a premium by placing market orders. As investors have information, the risk of adverse selection problem also remains low. To test this hypothesis, I investigate the trading practices of domestic institutional and individual investors. Compared with individual investors, institutional investors have been widely assumed to be better informed about future stock returns, at least for the intermediate-term of under a year. As institutional investors manage larger funds, they are more likely to spend resources to produce long-lived private information, and are concerned about the possibility of impacting prices. Individual investors are not usually considered to have superior information, but some evidences support their ability to predict future returns. In my simple preliminary analysis, I find that institutional investors are commonly able to predict the future returns for large stocks, and individual investors are often able to predict the future returns for small stocks. To provide empirical evidence regarding the aforementioned hypothesis, I use a unique dataset that includes every order and transaction of equity shares on the Korean Stock Exchange between January of 1999 and August 2009. I find that institutional investors make significantly more limit orders in purchasing (selling) stocks whose ex-post four-week returns are in the highest (lowest) quintile, and that they also return to use more limit orders after prices start to rise (fall) rapidly along with the general increase in market orders. In addition, according to our Fama-MacBeth (1973) cross-sectional regressions similar to those of Kelley and Tetlock (2013), institutional investors’ limit order imbalance positively predicts the following 4- to 12-week returns. In particular, the institutional investors’ return predictability on the biggest 30% stocks in particular is significant, both statistically and economically. These predictions do not reverse until 52 weeks later. These findings are robust when the order imbalances of individual and foreign investors are included as additional explanatory variables. The market order imbalances of institutional investors generally do not predict future returns. Concerning individual investors, no evidence is found that they use more limit orders when purchasing the stocks whose ex-post four-week returns are in the highest quintile. These investors use more limit orders in selling ex-post loser stocks, but the insignificant predictability of their negative limit order imbalances for predict future returns suggests that this increase in limit orders is unrelated to having private information. During the upward (downward) price movements, individual investors only tend to increase their market orders steadily for purchasing (selling) those winner (loser) stocks. Thus, on average, individual investors seem to be no better informed than the market as a whole. However, the limit order imbalances of individual investors strongly predict the 4- to 52-week returns on small stocks, especially when these limit order imbalances are positive. These overall results of individual investors can be compatible if a small number of individual investors do hold superior information on small-cap stocks, whereas it is more difficult for average investors to acquire information on these stocks, due to their deeper information asymmetry. In general, the market order imbalances of individual investors do not predict these stocks’ future returns. In summary, institutional investors prefer limit orders when they trade on the basis of private information on large stocks, and individual investors prefer limit orders when they trade small stocks on the basis of positive private information. Only a small fraction of the institutional investors and a smaller fraction of the individual investors seem to acquire high-quality long-lived information before prices fully converge to the reservation prices. My findings suggest that in some situations, investors can take profits due to having superior information. Such information can remain unrevealed to the public for longer periods than has been previously assumed according to efficient market theory. This article contributes to the discussion of order choice problems under information asymmetry. My findings can also help to reconcile the inconsistencies that domestic institutional and individual investors’ predictions on future returns show mixed results.

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Trade credits are offered by suppliers in the form of allowances for buyers to postpone their payments. Trade credits are non-market-based bilateral debt contracts between buyers and sellers, and these contracts incur higher costs of debt than market-based short-term financing sources such as bank financing and commercial paper. Trade credit financing is generally assumed to be more expensive than bank financing, as trade credit is commonly requested by credit-constrained borrowing firms. Another prevailing assumption is that reliance on trade credit financing tends to increase with the degree of credit rationing on financially constrained firms following adverse macroeconomic shocks. In the fields of corporate finance and financial intermediation, the question of how firms choose their financing sources has become increasingly important for researchers. Clearly, different sources involve different kinds of risk, and choices are made under conditions of information asymmetry in financial markets. In line with traditional research in this area, this paper examines how firms conduct trade credit financing in response to both macroeconomic and microeconomic factors, and it tests the substitutability and complementarity of bank financing and trade credit financing. The existing literature on trade credit financing suggests two main views on trade credit channels, namely the “substitution view” and the “complementarity view.” The substitution view posits that trade credit is a substitute for market-based, short-term financing sources such as bank revolving lines or commercial paper. Reliance on trade credit is more likely when the borrowing firms are constrained in their access to these market-based, short-term financing sources. Under such constraints, firms commonly turn to substitute sources of financing, such as trade credit. In this sense, trade credit and market-based financing are substitutes, and we can expect that trade credits will be more commonly used by firms under financial distress or in times of economic shocks, e.g., financial crises. The complementarity view holds in situations where the supply of trade credit and the access to bank financing or capital market funding are linked. When financial intermediaries are more effective in screening and monitoring the borrowing firms, financial intermediaries can provide financing directly to firms operating in capital markets. However, when the suppliers are more efficient in screening and monitoring, or in enforcing debt contracts, it may be optimal for these financial intermediaries to offer financing to the suppliers, who then relend to the purchasing firms. Such efficiency may arise if the suppliers have proprietary information about the purchasing firms, if they can threaten to suspend future deliveries, or if their opportunity costs for any repossessed inventory are higher than the costs of financial intermediaries. In some sense, non-financial suppliers act as “agents” for financial intermediaries. The main question raised in this paper is why financially vulnerable firms rely more on trade credit financing, even when seemingly cheaper sources of financing (such as bank revolving lines and commercial paper) are available in the short-term debt markets. In particular, our paper investigates how financial vulnerabilities and macroeconomic shocks operate in both separate and combined ways to affect firms’ choices for trade credit financing. This paper uses CP spread as a measure of credit crunches in short-term debt markets. In addition, the 2008~2009 global financial crisis is used as a natural event study factor, which represents an example of an extreme macroeconomic shock to both domestic and overseas financial markets. The effects of both CP spreads and macroeconomic shocks on trade credit financing are estimated. The empirical results show that adverse macroeconomic shocks increase trade credit financing, which is measured by accounts payable relative to sales. These results imply that trade credit financing may offset the contraction of bank financing, which can be triggered by adverse macroeconomic shocks and credit crunches in the Korean economy. In other words, trade credit financing can function as a financing instrument that can absorb macroeconomic shocks, thereby serving to maintain business operations and enable the management of working capital. Our paper also examines the effects of various firm-level financial vulnerabilities on trade credit financing. Following previous studies, we consider financial vulnerability factors, including the Whited-Wu index and the dividend payout ratio, both of which indicate the degrees of financial constraint. We also consider the modified Altman Z score and the default likelihood Indicator, both of which indicate the degree of financial distress. The empirical results show that financial vulnerability factors increase firms’ reliance on trade credit financing. Overall, our paper’s findings indicate that under conditions of macroeconomics shock, financially vulnerable borrowing firms are inclined to increase their reliance on trade credit financing. This evidence supports the hypothesis that trade credit financing serves as an alternative source of short-term corporate debt financing for financially vulnerable firms that are facing credit crunches. Our results suggest that trade credit financing plays a key role in helping firms to flexibly maintain their business operations and manage working capital under conditions of financial constraint or distress, such as those following macroeconomic shocks. Moreover, this paper provides evidence that the interactive effects of macroeconomic shocks and financial vulnerabilities tend to stimulate trade credit borrowing (i.e., accounts payable relative to sales). This pattern implies that firms are commonly forced into reliance on trade credit financing when adverse macroeconomic circumstances render them financially vulnerable.

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We examine the effects of a fund manager replacement on fund performance, risk, and money flows using data from January 2004 to March 2015. We define a fund as either a positive performance (PP) fund or a negative performance (NP) fund depending on whether it belongs to the upper 50% or lower 50% based on its performance in the previous year. We then compare the effects of manager replacement between the two groups. First, we find that the performance of an NP fund improves after the fund manager replacement, while that of a PP fund decreases. This phenomenon becomes more pronounced for the +2 year period compared with the +1 year period. This result implies that while it is feasible to find a qualified manager to replace a manager whose performance is below average, it is difficult to find a better manager to replace one whose performance is above average. Therefore, manager replacement can be a positive event for investors in an NP fund and a negative event for investors in a PP fund. However, investors should understand that it takes some time for fund performance to change, as the fund performance of a PP fund remains better than that of an NP fund for +1 year after the manager replacement. The improved performance of the NP fund and decreased performance of the PP fund become significant from the +2 year period after the fund manager replacement. Second, the risk level of the NP fund decreases after the manager replacement, whereas that of the PP fund remains virtually the same. We find that both the standard deviation of excess return and the one factor beta of the NP fund are higher than those of the PP fund for the -1 year period. We conjecture that this is because poorly performing fund managers take more risks to recover their inferior performance. This result is consistent with the U.S. market findings of Brown et al. (1996). The risk level of the NP fund decreases significantly after the fund manager replacement, whereas no significant change occurs for the PP fund. Third, we find a significant change in the style of the fund that survives more than two years after the manager replacement, with the fund manager replacement slowly contributing to the fund style change. Comparing the styles before and after the manager replacement, we find that the PP fund has a higher coefficient for the SMB and PR1YR factors and a lower coefficient for the HML factor. This implies that PP fund managers prefer small and medium-sized stocks, growth, and momentum. The meaningful style change is not found for the +1 year period for either the PP fund or NP fund, as it may require some time to change the fund portfolios and style. However, for the +2 year period, we find a significant style change for the NP fund; its negative SMB coefficient increases and its negative HML coefficient decreases, which means the proportion of large and value stocks increases in its portfolio. Fourth, after controlling for the various factors that may affect the fund money flows, we find no significant change in the new money flows before and after the fund manager replacement. We find that the money flows for both the NP and PP funds are negative for the +1 year period, although statistically significant so only for the NP fund. We use a number of control variables to check whether the inferior fund performance or the fund manager replacement causes the negative money flow for the NP fund. We use past performance, the excess return standard deviation, turnover, fund size, and management fee as control variables and run the regression on the new money flow for the fund manager replacement. Our regression results show that the fund manager replacement has no significant effect on the fund money flows. This indicates that important information such as the fund manager replacement is not delivered efficiently to fund investors in the Korean market. Therefore, we recommend that fund management companies and regulatory agencies establish practical methods to promptly deliver crucial information such as fund manager replacements to fund investors to protect the investors’ rights. This study is the first to examine the effects of fund manager replacement on fund performance and risk in the Korean market. However, fund manager research in Korea carries some limitations. We collect the fund manager replacement data from fund rating companies since 2004; therefore, the study period is rather short and can be period dependent. Furthermore, many fund management companies do not specify the name or responsible fund manager, but instead use the chief investment officer’s name or multiple manager names to identify the head manager of a fund. We hope future research on this topic will resolve the limits of our study and deliver further findings on the effect of fund manager replacement on fund performance.

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In this study, we investigate the determinants of credit ratings for companies affiliated with the Korean Chaebols. After controlling for endogeneity, we find that the average credit ratings for Chaebol-affiliated firms are significantly higher than those of non-Chaebol-affiliated firms. Next, we estimate the critical control threshold centrality and position established by Almedia et al. (2011) and apply those measures to the credit ratings of affiliated firms. The results show that affiliated firms with lower centrality measures tend to have higher credit ratings and that the relationship is not linear, suggesting that controlling shareholders use central firms to provide firms that have lower centrality with operational and financial support, that is, propping. Firms with lower centrality have higher credit ratings during a group-wide boom. This suggests that propping is more likely to emerge during an economic boom.

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Starting with the capital asset pricing model (CAPM), a variety of systematic risk factors have been suggested. The performance of equity funds investing primarily in equities is evaluated using these risk factors and serves as the basis for judging a fund manager’s ability. Jensen (1968) evaluates fund performance using the CAPM; Carhart (1997) relies on a four-factor model that includes Fama and French’s (1993) three factors and Jegadeesh and Titman’s (1993) momentum factor; Pastor and Stambaugh (2002a, 2002b) include industry factors; and Barber, Huang, and Odean (2016) include Fama and French’s (2015) profitability and investment factors. As more systematic risk factors are considered, fund performance can be evaluated more precisely, and the systematic risk factor explaining the fund’s abnormal returns can be found. However, Barber, Huang, and Odean (2016) demonstrate that fund investors are most sensitive to CAPM alpha and that more sophisticated investors use more systematic risk factors to assess fund performance. In other words, sophisticated investors may be aware that equity funds are not likely to have abnormal returns in excess of their benchmark returns. Based on Barber, Huang, and Odean (2016), this study reassesses equity fund performance using more information about the fund’s market excess returns, which are most easily accessible to fund investors. This approach enables us to indirectly evaluate fund performance from the perspective of sophisticated investors and identify risk factors that explain the market excess returns of equity funds. Fama and French (2010) argue that the cross-sectional average alpha of U.S. mutual funds is close to zero, while only very extreme performance groups have a significant alpha. Thus, extreme performance is due to luck, not the fund manager’s skill. This study attempts to identify the systematic risk factors that explain extreme performance using market excess return distribution. It examines whether the significance of fund performance changes when more systematic risk factors are used. When equity funds do not earn higher returns than the benchmark returns, performance changes insignificantly and idiosyncratic risk explains the extreme performance when more systematic risk factors are considered. For example, when Fama and French’s (1993) three factors completely explain the performance of a fund with significant market excess returns during the analysis period, a fund investor who evaluates performance using the three-factor model judges the fund managers as having no skill. In addition, fund managers use a widely known strategy in the market rather than adopting various investment strategies. In other words, style investing strategies that do not rely on the three factors do not exceed the market returns on average. This study uses the CAPM, Fama and French’s (1993) three-factor model, and Fama and French’s (2015) five-factor model. This study investigates the effects of idiosyncratic risk on fund performance. Highperforming funds with high idiosyncratic risk are overestimated when only return data are used. Therefore, this study reevaluates the performance using Bayesian inference, which considers the idiosyncratic risk. The Bayesian inference is measured by the weighted average of the actual return data and the prior distribution. The effect of the prior distribution is stronger when the idiosyncratic risk of the actual returns is high. I examine the Bayesian alpha rank of the market excess return groups to see whether a change in performance rank occurs. The absolute value of the Bayesian alpha decreases compared with the OLS alpha due to the effect of prior distribution, but the performance rank does not change if the fund alpha is due to the fund manager’s ability, as all of the funds have the same prior distribution. However, funds with a high idiosyncratic risk have a significant effect on prior distribution, leading to a significant change in performance rank. I use Jones and Shanken’s (2005) Bayesian inference method, which includes the information that the cross-sectional average alpha of the fund is zero. This study identifies several results. First, the average abnormal returns of both the net and gross returns are zero, and the distribution of the returns is close to normal. Second, although performance is ranked according to the market excess returns, only the top 1% (top 4%) of the funds in net returns (gross returns) indicates significant performance. This result indicates that during the analysis period, fund returns are mostly explained by market returns, and extreme returns are due to idiosyncratic risk. Third, the three-factor model completely explains the extremely high returns. Thus, fund managers increase systematic risks (three factors), which are well known in the market. In other words, style investing strategies other than the three factors do not exceed market returns on average during the analysis period. Fourth, the performance rank of the high-performing funds with high idiosyncratic risk changes drastically when Bayesian inference is used. In particular, the effect of Bayesian inference is stronger in the boom market periods, which have a high idiosyncratic risk. Thus, a fund manager’s skill does not result in extreme performance. These findings are empirical and based on fund managers, fund investors, and the development of the fund industry. Although investors’ interest in equity funds weakened after the global financial crisis, the exchange traded fund (ETF) market is growing rapidly. The ETF market is expected to pursue various investment styles while following the market returns with low fund fees.

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Given the wide variety of equity funds in the public offering market, fund investors find it difficult to select funds that match their investment preferences. Therefore, equity fund investors pay greater attention to funds that are recommended by sales professionals from banks or securities companies. They rely on brokers’ recommendations when selecting equity funds instead of conducting rigorous analysis. Therefore, brokers’ fund recommendations carry great importance from the perspective of investor protection. However, due to the limitations of data collection, research on this topic has not yet been conducted in Korea. Analysis of fund recommendations would have valuable implications for investor protection. Studies of equity funds have paid much attention to overall fund performance, fee structures, or the role of fund distributors (e.g., Won, 2009; Kim, 2011; Kho and Baek, 2013). However, although performance analysis of fund recommendations is important, research has paid little attention to the topic. To the best of our knowledge, no study has considered the real performance of recommended funds in Korea, as fund brokers do not voluntarily disclose data related to fund sales or the return performance of recommended funds. As each broker sells tens of funds, it is difficult to estimate the return performance of the funds of each broker. However, fund evaluation companies have recently begun to build data on funds recommended by each vendor, so analysis of the performance of recommended funds is possible. We empirically analyze the determinants of brokers’ fund recommendations and the performance of recommended funds in two ways. First, we analyze the factors that determine brokers’ fund recommendation decisions. Second, we analyze the future performance of the recommended funds. To confirm our findings, we use propensity score matching to compare the performances by matching recommended and non-recommended funds with similar characteristics. To investigate possible conflicts of interest in the fund recommendations, we focus on the levels of fees and commissions and the relationships between brokers and fund managers. We pay attention to the possibility that the seller, in recommending a fund to a customer, acts on behalf of the seller or affiliated fund manager rather than prioritizing the customer’s benefit. The results of this study are as follows. Funds with front-end loads and/or funds of asset management firms with no affiliated brokerage companies are more likely to receive recommendations from brokers. Furthermore, funds of large asset management companies receive more recommendations than those of medium or small management companies. In addition, new funds with short operating periods are selected as recommended funds, and new funds from affiliate management firms are more likely to be recommended. The performance of those recommended funds turns out to be inferior. Based on the results of analysis of the profitability of broker-recommended funds, we find that recommended funds do not perform better than funds that are not recommended by brokers. This result does not support the general perception that brokers have fund-selection ability. However, no evidence supports the view that the inferior performance of broker-recommended funds is caused by the self-interest of the broker who pursues the profit of a subsidiary asset management company. Recommended funds of affiliated asset management companies record better performance than those of non-affiliated companies. In particular, when the seller recommends a newly launched fund of the affiliated management company, its return performance is better. Thus, we conjecture that the inferior performance of a recommended fund is not due to the broker’s intention to subsidize an affiliated asset management company. This result suggests that a broker, through an information transfer from affiliated fund managers, plays a role in mitigating the information asymmetry of newly launched funds. It also supports the view that the affiliate broker analyzes the newly launched fund more accurately. Our study shows that broker-recommended funds generally exhibit inferior return performance. This result suggests that fund sellers are required to improve their fund selection ability to enhance the protection of fund investors. Sellers must improve their fund analysis capabilities through internal research and workforce reinforcement. Our study has some limitations in the sense that it covers a relatively short period due to the insufficient accumulation of fund recommendation data. Further research should be conducted to review the robustness of the analytical results over a wider analysis period after equity fund data are accumulated more broadly.

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