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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