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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Until recently, the loan portfolios of savings banks in Korea have tended to concentrate on project financing with higher risks than the conventional loan portfolios of commercial banks. However, these concentrated loan portfolios and other disruptive behaviors led to massive bankruptcies among savings banks in 2011. In the past, many savings banks have experienced a similar bankruptcy process, largely due to the expansion of saving banks’ assets through mergers, high risk project financing loan projects, the illegal acts of large shareholders and executives, and the increasing loan concentration of high risk lenders. In particular, the concentration of real estate-related loans became a major management risk factor for savings banks in the 2000s, particularly in the deteriorating operating environment caused by the collapse of the real estate market after the global financial crisis. Furthermore, saving banks’ dependence on high-risk loans, such as project finance loans and unsecured personal loans, has intensified in recent years. This is a major factor in the failure of Korean savings banks in the event of external economic shocks. However, due to the limited data collection issue, only a few empirical studies have considered the relationship between the loan concentration ratio of savings banks and bank insolvency. The backdrop to the 2011 insolvency crisis of the savings banks is their large-scale growth and organizational change into a business group, which progressed rapidly up until 2010. Although mergers between savings banks were limited before December 2005, savings banks were already experiencing financial distress at that time due to the household credit crisis in 2003. The number of large savings banks with an asset size of over 1 trillion Korean won increased from 6 in June 2005 to 29 by the end of 2010. As of June 2010, 31 subsidiaries had been opened by 11 savings bank groups. The expansion and integration of these savings banks led to aggressive top-line expansion strategies, including high-risk lending operations and expansions. Over the 2011 to 2017 period, the average total assets of savings banks declined between 2011 and mid-2014 due to the failure of 31 savings banks after 2011, but the assets of the remaining savings banks rapidly increased after 2014. Research on savings bank credit concentration has been limited due to the lack of data, and most recent studies have analyzed only commercial banks or specific types of loans. Beck and Jonghe (2013) described the positive effects of loan concentration on commercial banks. The authors argued that commercial banks accumulate sufficient knowledge and experience in the field by focusing on loans to specific sectors over many years. This suggests that banks improve their ability to inspect and supervise loans and can reduce potential risks by identifying and addressing credit-related issues in a timely manner. Tabak, Fazio, and Cajueiro (2011) also argued that the concentration of lending to specific sectors in Brazilian banks shows that banks can enhance supervision, increase expertise in those areas, and lower bank risk while improving profitability. In this paper, we empirically examine the impact of concentrated loan portfolios on bank failures using the methodology of logistic regression. Our analysis shows that for saving banks the more concentrated the loan portfolio and the larger the number of mega loans (i.e., loans above 1 billion won), the higher the likelihood of failure. We also find that the more loans to relatively high risk borrowers and to borrowers with lower credit ratings in both the corporate and household sectors, including project financing and unsecured personal loans, the higher the probability of failure. However, mortgages with relatively good collateral turn out to have a significant negative relationship with savings bank insolvency.

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