It is generally observed that firms maintain relationships with multiple banks. Although most studies investigating the phenomenon of multiple banking relationships assume more or less equal borrowing weights among the banks, in practice creditor concentration or asymmetry in borrowing occurs, by which we mean that a firm tends to concentrate its borrowing on one main bank and to fill the remaining loan necessity from other arm’s-length lenders. A firm can benefit from establishing a close, long-term relationship with a single bank. For example, studies report evidence that a close relationship improves accessibility to funds and reduces the cost of capital and collateral requirements (Petersen and Rajan, 1994; Berger and Udell, 1995). However, the downside of a close relationship with a single bank is the tendency that a firm may find itself in a disadvantageous position for loan bargaining with the bank, which has acquired monopolistic information concerning the firm’s true credit worthiness. This is called the‘hold-up problem’, which can be a significant constraint, at least for informationally-opaque firms. According to von Thadden (1992), multiple banking relationships may be a solution to the hold-up problem because they create competition among banks, and thus prevent one bank from extracting the monopolistic rent when offering the loan. Multiple banking relationships are reported to reduce bankruptcy probability and the costs resulting from actual bankruptcy, and thus limit a firm’s potential financial distress. Finally, multiple relationships may reduce a firm’s liquidity problems by spreading the risks of liquidity problem of banks themselves. However, the negotiations for loan renewals are more complicated for multiple banking relationships, and when a firm fails and is liquidated, the liquidating value of the firm may be reduced due to the possible failure of coordination among banks (Bolton and Scharfstein, 1996). Considering the above discussion, we assert that creditor concentration (or asymmetric borrowing) with multiple banking relationships allows a firm to overcome the hold-up problem, as well as the possible failure in coordination among many banks. In other words, creditor concentration is a strategic solution for a firm to strike a balance between a single long-term relationship and multiple bank relationships. This paper investigates the influence of variables related to firm, bank, and market characteristics on the degree of creditor concentration in the Korean bank loan market. We use a data set from firms registered on the KISVALUE system of the NICE Information Service from 2006 to 2011. We use a Heckman selection model, and three proxies for measuring the degree of creditor concentration are included in the analysis: the Herfindahl-Hirschman Index (HHI), the largest financing share (CR1), and the Share of Inequality Index (SII). In the first stage of analysis, we estimate a probit for the probability of multiple banking relationships. In the second stage, we estimate the determinants of the degree of concentration, which is conditional on borrowing from multiple lenders. In this study, we find evidence of stronger creditor concentration for firms with more opaque information, more liquidation value of assets, and lower leverage. In addition, creditor concentration intensifies when a firm is more profitable and less risky. Regarding main banks’characteristics, we find that variables, such as bank size, capital adequacy, and bank ROA, do not significant explain creditor concentration. However, the number of employees to total loans, which can be viewed as a proxy for monitoring costs, is negatively related to creditor concentration. Market conditions are found to influence creditor concentration: there is evidence of stronger creditor concentration during boom times, which seems to be related to the tendency of firms to disperse loans to avoid the liquidity problem during recessions. In addition, a weaker creditor concentration is found in more competitive banking environments, during which banks offer better loan conditions, resulting in more dispersed loans among banks. We divide the sample into SMEs and large firms to examine asymmetric bank borrowing patterns. We find that SMEs display highly concentrated borrowing for the firms with more opaque information, higher liquidation value of assets, lower leverage, more profitable business, and better credit rating. These results are similar to those from the previous analysis of the full sample. However, the relations are weak for large firms, which have weak incentives to make strategic decisions on borrowing allocations because they have various alternative financing sources instead of bank loans, such as issuing stocks or bonds. Next, we analyze whether there is any difference in firms’ borrowing behavior during the financial crisis. The impact of firm and market characteristics on the degree of concentration in borrowing during the financial crisis is weaker than that during the normal period. We conjecture that the firms' strategic decisions are limited by the banks because of their strict lending attitude during the financial crisis. Taken together, these results indicate that creditor concentration is attributed to firms' strategic decisions on balancing the holdup problem of relationship lending with the coordination failure of multiple lending. In particular, SMEs behave more strategically relative to large firms. In this way, creditor concentration allows the borrower to retain some of the benefits of long-term relationship lending as well as to enjoy the insurance effect against liquidity risks originating from the relationship lender.

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We investigate the term structures of corporate credit default swap (CDS) spreads in the Korean market based on Pan and Singleton’s (2008) model. To this end, we first consider an algorithm of numerical methods to calculate the theoretical price of CDSs. Specifically, we consider the finite difference method (FDM) and Monte Carlo simulation, and then, we examine the time efficiency and accuracy of the FDM. Monte Carlo simulation might be an accurate way to calculate the model price but it is time-consuming and not appropriate for estimating the term structure in our empirical study. In contrast, our analysis shows that the FDM meets our empirical goal in terms of error and speed. In particular, we use the Crank-Nicholson FDM with a 120×120 grid in our empirical analysis, as it performs relatively well in terms of speed and accuracy. Next, by employing the suggested FDM, we estimate the default probability implicit in the term structure of the CDS spreads for 19 domestic firms over the sample period from 2006 to 2012. We use the ML estimation for our econometric framework, and we estimate the 1-year, 3-year, and 5-year CDS spreads by assuming that the spread of CDSs maturing in three years is observed without error. Our estimation reveals that investors require a significant amount of premiums for bearing the risk of future variation in intensity, defined as the distress risk premium (DRP), when they invest in CDSs. We verify the argument in several ways. First, the ML estimates based on the Q-measure are very different from those based on the P-measure. The ML estimates imply that investors view the intensity dynamics more negatively under the Q-measure than under the P-measure. This result in the Korean corporate CDS market supports the finding of Pan and Singleton (2008) in two emerging sovereign markets. To take a deeper look at the significant difference between the Q- and P-dynamics of intensity, we next compute and examine the default probabilities within five years with the estimated parameters and implied intensity under both the Q- and P-measures. The results show that the Q-probability of default is much higher than its P-counterpart, implying that investors require premiums by adding more probability on real default probability. Finally, we directly calculate the amount of DRP to see how much it accounts for CDS spreads. In doing so, we follow the methodology first suggested by Pan and Singleton (2008) and then employed by Longstaff, Pan, Pedersen, and Singleton (2011) and Díaz, Groba, Lafuente, and Serrano (2013). There are two steps involved in obtaining the DRP. We first calculate the “pseudo- CDS” spread, denoted CDSP using expectations under the P-measure instead of the Q-measure to evaluate the model’s spread. Then, the DRP is obtained from the difference between the market CDS spread and pseudo-CDS spread. The results show that, on the average, the DRP accounts for 42% of the CDS spreads observed in the market. We also find that the DRP varies over time as the economy state changes, and particularly, it soars during the recent financial crisis. To explain our empirical finding that the DRP accounts for a substantial portion of the CDS spread and varies over time, we further investigate what determines the DRP and what drives it to change. To this end, we analyze the principal components of the cross-section of the DRPs. Our investigation shows that the first principal component explains about 97% or more of DRP variation, which implies that Korean companies’ DRPs are associated with investors’ appetites for the market-wide risk. To find what drives the first principal component, we regress the first principal component on a couple of domestic and global financial variables, which serve as proxies for the market risk premium. Specifically, the Volatility Index (VIX) is used as a proxy for a global variable of risk premium, and equity market risk premium, term premium, credit premium, and sovereign distress risk premium are examined as proxies for domestic risk premiums. The result shows that the VIX is statistically significant and has a striking power to explain the time-variation of the first principal component of corporate DRP. Among the domestic variables, equity market risk premium, term premium, and credit premium are statistically significant. However, sovereign distress risk premium (DRP implied in the Korean sovereign CDS spread) subsumes the explanatory power of the other domestic variables when added to the multiple regression. This finding indicates that sovereign DRP is the most important determinant of the co-movement of corporate distress risk premiums among the domestic variables. To control the endogeneity problem, the sovereign distress risk premium is orthogonalized to all other premiums, including VIX, the equity market risk premium, the term premium, and the credit premium. We then regress the first principal component of corporate DRPs on the orthogonalized sovereign DRP. This experiment shows that the orthogonalized sovereign DRP is statistically significant and explains the first principal component of corporate DRPs. This finding implies that sovereign DRP has additional information that is not contained in other risk premium proxies.

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We estimate a two-factor structural mortgage valuation model using data on pool-level termination rates for fixed-rate mortgage loans issued by the Korea Housing Finance Corporation (KHFC) between 2004 and 2008. Our model is a modified version of that proposed by Downing, Stanton, and Wallace (DSW, 2005), in which borrowers rationally decide when to prepay and default in response to changes in house prices as well as interest rates. DSW (2005) find that incorporating house price movements into the model significantly improves its ability to match historical prepayment data over Stanton’s (1995) one-factor (interest rate only) model. In DSW (2005), house price changes are modeled to affect borrowers’ termination behavior only when prices decline by increasing the value of default option, but this particular specification does not bode well for our Korean sample period, during which house prices did not experience substantial or sharp declines. Consequently, we modify DSW’s (2005) two-factor model so that house price movements influence termination behavior when prices increase as well as when prices decline. In contrast to the structural model used in this paper, previous empirical research on mortgage prepayment in Korea has mostly used statistical models, in which termination behavior is modeled as a function of a set of exogenous variables such as interest rates and house prices that represent the factors affecting prepayment and default. While such statistical approach may produce estimated prepayment rates that are closer to the historical data, its out-of-sample forecasting power is likely to be low, and the model is not suitable for pricing mortgage-backed securities. The structural approach taken in this study, on the other hand, models prepayment and default as a rational borrower’s optimal responses to the changes in interest rates and house prices that represent the model’s state variables. Such structural models should perform better out of sample than statistical models, and they can be used in the valuation of mortgage-backed securities. The mortgage prepayment data used in this study are from 34 pools of fixed-rate mortgage loans (called Bogeumjari loans) issued by the KHFC. The sample period is from June 2004 to December 2010, and the prepayment rates at the pool-level, as published monthly by the KHFC, are used. The empirical results show that both interest rates and house price changes significantly affect prepayment rates. In particular, house prices and prepayment rates have a positive association, consistent with Park and Bang (2011) and Choi and Kim (2011), who use statistical models. In addition, incorporating house price movements into the model improves its performance in matching historical prepayment rates, compared to the model in which only interest rate movements are considered. The results of Davidson and MacKinnon’s (1981) J-test selecting between non-nested models show that the two-factor specification that includes house prices and interest rates is better for estimating prepayment rates than the one-factor (interest rate) specification. Moreover, we find that the two-factor model produces prepayment forecasts that are closer to the historical data than the one-factor model in an out-of-sample test. In a structural model, mortgage terminations (prepayment or default) are the result of borrowers’ optimizing behavior, and house price movements can affect the borrowers’ termination behavior as follows. When house prices fall, the value of the default option implicitly held by the borrower increases. And the movements in the value of the default option significantly affect the value of the borrower’s prepayment option and therefore, the likelihood of prepayment. However, we find that prepayment rates decrease with declining house prices. The house prices in Korea experienced some declines during our sample period, but not by any substantial degree. Our finding of lower prepayment rates in times of declining house prices is similar to Mattey and Wallace (2001), who find that weak house prices tend to depress refinancing and prepayments. When house prices increase, so does the collateral value of the houses and the value of the borrower’s equity, which influences the likelihood of prepayment if, for example, the borrower wants to cash out some of the increase in equity. This argument is consistent with the higher prepayment rates during times of increasing house prices found in this paper. More specifically, the period of increasing house prices in our sample is from late 2006 to early 2007, which coincides with rising interest rates. Despite the rising interest rates and penalties for prepayment, prepayment rates during that period increased in close association with rising house prices, suggesting that both interest rates and changes in house prices significantly affect mortgage prepayment rates in Korea.

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Given the numerous studies showing that firm size and book-to-market (BM) ratio are significant variables in explaining the cross-section of equity returns, size and BM effects have become the most notorious anomalies in asset pricing. Chan, Chen, and Hsieh (1985) and Chan and Chen (1991) argue that a default factor explains much of the size effect, and Fama and French (1992, 1993) show that the BM effect may be due to firms’ financial distress risks. As small firms with high BM ratios are likely to suffer from financial distress, the positive relationship between financial distress risk and equity returns may provide a rational risk-based explanation for size and BM effects. However, much of the research indicates that financially distressed stocks earn abnormally lower returns. In this study, we investigate the relationship between financial distress risk, proxied by corporate bond credit ratings, and equity returns in the Korean stock market. Dichev (1998) examines the relationship between bankruptcy risk and subsequent equity returns and finds that higher distress risk is not rewarded by higher returns. The result appears to be inconsistent with a distress factor explanation of size and BM effects. Griffin and Lemmon (2002) explore the relationship between BM ratio, distress risk, and stock returns, and document that stock returns are lower for firms with low BM ratios and high distress risk. Their study is consistent with the “overreaction hypothesis” associated with the BM effect, and the “underreaction hypothesis” for the distress effect, implying that the BM effect is not likely to be explained by the distress risk factor. Vassalou and Xing (2004) argue that firms with higher default likelihood indicators earn higher returns. They present their analysis as a risk-based explanation of the BM effect. However, Da and Gao (2010) show that their results are driven by penny stocks and first-month reversals. Campbell, Hilscher, and Szilagyi (2008), Garlappi, Shu, and Yan (2008), and Da and Gao (2010) also confirm the negative relationship between distress risk and equity returns. The finding that stocks with high distress risk are not compensated by high returns in the stock market suggests an anomaly called “the financial distress anomaly” or “the financial distress risk puzzle.” There are several ways to measure a firm’s default risk to examine the relationship between distress risk and stock returns. First, a logit model can be used to measure a firm’s distress risk (Ohlson, 1980; Shumway, 2001; Chava and Jarrow, 2004). Griffin and Lemmon (2002), Campbell et al. (2008), and Chava and Purnanandam (2010) adopt the hazard rate estimation methodology. The second approach is based on Merton’s (1974) call option pricing model. Vassalou and Xing (2004) and Kim and Park (2010) use the call option approach to measure a firm’s distress risk. The third measure is the credit ratings announced by credit rating agencies. Avramov, Chodia, Jostova, and Philipov (2009) investigate the relationship between the credit ratings provided by Standard & Poor's and stock returns. In this study, we use credit ratings to measure firms’ distress risk. The empirical results are summarized as follows. First, as credit ratings deteriorate, the average excess returns on the credit rating-sorted quintile portfolios decrease. The average monthly excess returns on the best (worst) rating quintile are 1.187% (-1.789%). The Capital Asset Pricing Model, Fama-French three-factor, and Carhart four-factor alphas for the credit rating quintiles also decline with the credit ratings. The return on the long-short portfolio holding stocks to the best rating quintile and shorting stocks to the worst rating quintile is significantly negative (-2.976%). These relationships hold after controlling for firm size, BM ratio, and momentum. Second, we run Fama-MacBeth regressions to examine the relationship between credit ratings and the cross-section of equity returns, and document that the coefficient on the credit rating variable is significantly negative. This finding implies that the credit rating is a significant factor in determining the cross-section of stock returns. For robustness checks, we confirm the negative relationship between distress risk and equity returns after removing downgraded firms and penny stocks from the sample. Finally, we report that market betas and loadings on the HML and SMB factors do not decrease as credit ratings decline, implying that a rational risk-based explanation is unlikely to account for abnormally low returns on stocks with bad credit ratings. Our empirical results do not support the conjecture that distress risk may be behind size and BM effects, due to evidence that distress risk is negatively related to equity returns. Kim and Park (2011) argue that default risk is positively related to stock returns in the Korean stock market. This study challenges them based on the “financial distress anomaly” in the Korean stock market.

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It is well known that venture capitals perform the certification role that reduces the underpricing of IPO firms by alleviating the information asymmetry between insiders and public investors. However, previous Korean studies suggest that the certification role played by Korean venture capitals is not supported—in contrast to the findings of foreign studies. It is also known that venture capitals perform the management support role that accommodates the growth of venture-backed firms and enhances the value of their portfolio companies—a direction explored by few in Korea. In this study, we analyze the short- and long-term performance of IPO firms and the certification and management support roles of venture capitals in a sample of 263 venture-backed IPOs and 213 non-venture-backed IPOs in the Kosdaq market from 2002 to 2012. Specifically, abnormal returns (AR) or cumulative abnormal returns (CAR) can be affected by the early sale of venture capitals’ equity stakes after IPO, so we test their roles by dividing venture-backed IPO firms into two groups: one in which equity shares are sold early and the other in which they are not sold by venture capitals after IPO. The results of previous domestic studies on these issues are not reliable due to a limited data sample problem, and the restrictions inherent in not considering important changes such as the determination method of first day opening price and the lock-up period in Korea. As early sales of venture capitals’ equity stakes can negatively affect the CAR of venture-backed IPOs, and the certification and management support roles played by venture capitals, we divide venture capitals into two groups based on whether they sell their equity stakes early or not. We then test for differences in the roles between the two groups. We find that Korean venture capitals perform the certification role after controlling the variables affecting the short-term performance of IPO firms and that the role strengthens as their equity stakes in IPO firms increase, based on the regression results for first day AR and one month CAR. However, the certification role played by venture capitals is not observed among venture-backed IPO firms whose equity stakes are sold early by venture capitals after the IPO. To analyze the effect that the equity sale of Korean venture capitals has on their certification role further, we divide venture-backed IPO firms into three groups: firms in which equity shares are sold by venture capitals immediately after IPO, those in which equity shares are sold by venture capitals within a month from the second day after IPO, and those in which equity shares are not sold at all by venture capitals within a month after IPO. The regression results show that for the second group, the certification role cannot be found at the 5% significance level and for the other groups, the certification role is not statistically significant. Our analysis of the regression results on three-year buy-and-hold average returns (BAHRs) show that Korean venture capitals do not perform the management support role after controlling the variables affecting the long-term performance of IPO firms. The long-term performance of venture-backed IPO firms decreases as the equity stakes of venture capitals increase. We also test whether the early equity sales of venture capitals prompt the underperformance of venture-backed IPO firms by dividing them into three groups again. The regression results show that as venture capitals sell more their equity shares of venture-backed IPO firms immediately or within a month from the second day after IPO—their BHARs decrease. In contrast, for venture-backed IPO firms in which equity shares are not sold by venture capitals, the BHARs increase as expected, although not with statistical significance. The results suggest that it is very difficult to anticipate the management support role of Korean venture capitals because they sell their equity stakes early (137 out of 263 portfolio companies). This implies that Korean venture capitals are trying to exit as early as possible through the IPOs of their portfolio companies instead of accommodating long-term, win-win growth. Korean President Park Geun-hye’s new government is concentrating its efforts on nurturing innovative small and medium-sized companies under the slogan, “Creative Economy.” Hence, the role of venture capitals as a main financing source and value creator for venture firms is more crucial than it has ever been. However, as this study shows, if the management support role is not adequately performed, venture capitals’ involvement will be limited to providing funding assistance. Therefore, more research on how to develop the management support role played by Korean venture capitals is needed for “Creative Economy” to be successful. The myth that the long-term performance of venture-backed IPO firms is worse than that of non-venture-backed IPO firms in Korea must be investigated further.

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In a backdoor listing, often called a “reverse merger,” an unlisted or “pearl” company obtains listed status by acquiring management control of a listed or “shell” company. Such backdoor listings provide unlisted companies with synergistic mergers and acquisitions (M&A) opportunities and the benefits of being listed. However, the reality is that the companies using this method are often involved in unfair trading scandals that harm investors, or become insolvent shortly after listing. For example, NeosemiTech was delisted in an accounting fraud scandal and issued an audit opinion of rejection on August 23, 2010, less than a year after its backdoor listing through its merger with Mono Solar, a KOSDAQ listed company. This backdoor listing practice raises many theoretical and regulatory issues because backdoor listings integrate aspects of both IPO and M&A. Adjei, Cyree, and Walker (2008) investigate the characteristics of companies using reverse mergers and Gleason, Rosenthal, and Wiggins III (2005) study the characteristics and performance of reverse mergers. In Korea, the research focuses on the factors affecting the choice between backdoor listing and IPO, the market reaction to backdoor listing, and the performance of the companies listed through reverse mergers (e.g., Kim and Lee, 2009; Choi and Lee, 2006; Park, Park, and Pae, 2009; Yun and Kang, 2009). While the literature documents the aforementioned typical features of backdoor listing, we focus on the deterioration of backdoor-listed companies—one of the most noted stylized facts in the literature that has barely been explored. In addition, our study is the first of its kind in the backdoor listing literature to investigate the corporate governance of shell companies as well as the financial characteristics of shell and pearl companies. In this study, we conducted an empirical analysis using 104 backdoor and 189 regular listing cases from July 2006 to June 2010 in the KOSDAQ market. Similar to the results of previous studies, backdoor listing was preferred over regular listing by companies that were smaller, had lower net profit margins, or were subject to information asymmetry. This held true regardless of whether the pearl companies met the quantitative listing requirements in the KOSDAQ market. Second, the shell companies were more likely to be businesses that could easily merged due to their smaller size, lower ownership of the largest shareholders, and poor performance. Third, an analysis of the public disclosures revealed that compared to firms that undertook regular listing, backdoor-listed firms reported the following cases more often: violation of regulations, changes in governance structure, and delisting. Backdoor-listed firms were also more likely to experience distress shortly after the listing, when the shell company’s largest shareholders had a low ownership share and the shell company saw frequent changes in its corporate governance, or when the pearl company experienced higher profits just before the listing. Fourth, high positive excess return was observed around the date when the backdoor listing was disclosed and negative excess return occurred after the designated date. The cumulative excess return before and after backdoor listing was high for the shell companies that violated regulations before a reverse merger, implying that the investors positively evaluated the reverse merger’s effect on the governance reorganization of the shell company. However, the cumulative abnormal return after the designated date was lower for the pearl companies that recorded higher profits right before backdoor listing, which shows that pearl companies sometimes window dress before backdoor listing. The findings in this study demonstrate that backdoor listing is likely to occur with a shell company that is small in size, has relatively lower ownership by the largest shareholders, exhibits poor performance, and whose business is irrelevant to that of the pearl company. Often, the motive for the backdoor listing is the acquisition of listed status rather than the creation of synergy, resulting in a high probability that pearl companies choose backdoor listing because they fail to meet the quantitative or qualitative requirements for a regular listing.. We found that backdoor listed companies that merged with shell companies under weak corporate governance experienced investor protection- related problems or delisting more frequently, emphasizing the importance of examining qualitative requirements during the backdoor listing process. Hence, our study ascertains that the qualitative inspection system for backdoor listing introduced in September 2010 is appropriate.

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In this study, we investigate the relationship between the ownership of controlling shareholders and the proportion of bank loans in total debt. According to the literature, when the ownership of controlling shareholders is large enough, it incentivizes the controlling shareholders to pursue their private interests. If it is low, however, outside investors and other stakeholders, including the governance system of the company, prompt the managers of the company to maximize the firm value (Demsetz, 1983; Fama and Jensen, 1983; Morck et al., 1988). Thus, there is anon-linear relationship between the ownership of controlling shareholders and the firm value that is, until a certain level of executive ownership is reached, it increases the firm value, but if it exceeds a critical level, the firm value decreases due to controlling shareholders’ pursuing their private interests (Stulz, 1988; Morck et al., 1988; McConnell and Servaes, 1990; Kim, 1992). According to the theories that controlling shareholders behave differently based on their ownership, in this study, we test whether there is a non-linear relationship between the ownership of controlling shareholders and different debt capital choices. We empirically confirm that the ownership of controlling shareholders has a non-linear relationshipwith the proportion of bank loans in total debt. Specifically, the controlling shareholders whose ownership is below (above) a certain level prefer (accept fewer) bank loans. We also consider the firm characteristics and the level of monitoring when investigating the relationship between the ownership of controlling shareholders and debt capital choices. Our analysis shows that a firm in financial distress seems to avoid the use of bank loans even when the ownership of controlling shareholders is low. We argue that the controlling shareholders of a firm in financial trouble are more likely to follow their private interests and evade the tight monitoring of the banks providing the loans (Johnson et al., 2000; Lin et al., 2013). Our investigation also addresses how a company’s age affects the relationship between the ownership of controlling shareholders and debt capital choices. If a firm is older and the ownership of controlling shareholders is high (low), a bank loan (public debt) is preferred. To analyze the effect that monitoring has on debt capital choices, we use several important proxies for monitoring, such as the ownership of foreign investors, institutional ownership, and the presence of multiple large shareholders. The test shows that foreign investors seem to effectively monitor firms, such that when foreign ownership is large enough, the improved monitoring encourages firms to take more bank loans (Baek et al., 2004; Shin et al., 2004). When there are multiple large shareholders, the ownership of controlling shareholders is positively related to the proportion of bank loans in total debt, even over the critical level of controlling shareholder ownership. We interpret this result as follows. Multiple large shareholders can monitor a firm and its controlling shareholders (Maury and Pajuste, 2005), which prompts the firm to use more bank loans despite the fairly large ownership of controlling shareholders. Finally, we implement additional analyses for robustness checks. We investigate the endogeneity between a firm’s ownership structure and its debt capital choices using a causal analysis. The investigation verifies our hypothesis that although the ownership of controlling shareholders affects debt capital choices, the latter does not affect the former. In addition, our results hold with different methods of inference, such as clustered error estimation or Fama and MacBeth’s (1973) method. Therefore, we confirm our hypothesis that a firm with less ownership of controlling shareholders takes more bank loans than one with more ownership. This study contributes to the literature in the following ways. First, we apply Panano and Roell’s (1998) theory on the decision between IPO and block sales of equity to debt capital choices. They argue that controlling shareholders consider the benefits and costs of IPO compared to the injection of new equity capital through other large shareholders. The cost of IPO is the listing cost and the benefit is less monitoring by diverse minority shareholders after the IPO. We apply this theory to debt capital choices and empirically verify it. Second, we expand on Lin et al. (2013) by including the ownership of controlling shareholders as a main variable in our investigation. Lin et al. use a wedge in their study and find a linear relationship between it and debt capital choices, whereas we find a non-linear relationship between the ownership of controlling shareholders and debt capital choices. Third, we consider the level of monitoring in our main investigation. Extant studies include a firm’s characteristics, but not the level of monitoring, in this vein of research. Fourth, the Korean financial market is one of the best testing platforms from which to observe controlling shareholders’ behavior, especially the pursuit of private interests, as most companies have concentrated ownership structures and are included in conglomerates known as “Chaebols.”

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In recent years, global major exchanges such as the NYSE, LSE, and TSE have faced an unprecedented amount of a specific type of trading known as high frequency trading (HFT). Although researchers and practitioners have not agreed on the precise definition of HFT, the Securities and Exchange Commission (SEC) loosely defines it as “a trading that uses high-speed computer systems to monitor market data and submit a large number of orders to the market.” HFT began in the late 1990s, and its proportion of total trading volume has rapidly increased during the past decade. In 2009, a rough estimation (by TABB group) indicated that HFT accounted for 60% and 30% of the total trading volumes in the US and European equity markets, respectively. To accommodate and process surging HFT-related orders, exchange-level projects to expand order processing capacity and reduce trade execution time are under way among a number of global exchanges, including the Korea Stock Exchange. On March 23, 2009, the Korea Exchange migrated to the EXTURE (‘EXchange + fuTURE’), an upgraded trade settlement system. The launch of this technically advanced trading system might enable investors to implement HFT in the Korean stock market. However, as far as the authors know, little is known about HFT in the Korean stock market. In this study, we find a special type of HFT has emerged as a conspicuous market phenomenon characterized by the extremely fast repetition of order submissions and cancellations. It closely resembles the ‘strategic runs’ observed on the NASDAQ (Hasbrouck and Saar (2013)). The strategic runs discovered in the Korean stock market consist of a chain of normal orders and its cancellation within a certain period of no more than one second between each consecutive order. We further examine the effect that strategic runs have on market quality in terms of short-term liquidity and volatility, measured in intraday ten-second intervals. To measure liquidity, we use proportional spread and the number of shares at the best bid, or ask prices divided by total number of shares outstanding. The highest price minus the lowest price divided by the bid-ask midpoint is used to calculate volatility. During the 10-second intervals when strategic runs are most concentrated, they attract liquidity by submitting higher proportions of marketable limit orders. This leads to increased spreads, decreased depth, and higher volatility. Furthermore, 10-second interval intraday vector auto regression (VAR) results indicate that strategic runs deteriorate market quality by decreasing market depth and raising volatility. We do not, however, find a statistically significant relationship between strategic runs and spreads. Overall, strategic runs appear to have negative effects on market quality. Our results must be interpreted with caution. Because the sample period of VAR analysis is as short as three months, one cannot make sure whether our main results still hold for the post sample period. Another problem is that strategic run is only a special type of high frequency trading. It implies that we cannot generalize our results based on strategic runs for other types of high frequency trading that we do not know yet. We hope these issues will be examined in the future research. While major exchanges are making considerable capital investments into IT infrastructure and network system recently to accommodate and attract high frequency trading, it is unclear what the socioeconomic benefit of the investment is and who makes profits (or suffers losses) in high frequency trading. As high frequency trading is increasingly dominant in the stock market, individual investors who cannot access stock market as fast as high frequency traders will leave the market, and this may lead to reduction in liquidity and eventually market failure. This paper contributes to the finance literature and the government’s policymaking as follows. First, this study is the first to not only verify that strategic runs exist in the Korean stock market, but also to examine their characteristics and effect on market quality. Second, this study finds strategic runs without incurring errors thanks to the use of account-level trades and quotes data. In contrast, the data used by Hasbrouck and Saar (2013) are subject to inherent limitation, as they do not include account level information. For example, they cannot identify whether two consecutive orders are submitted by the same trader, which makes it difficult to extract strategic runs from quote data. Our study is free from such problems because the data used enable us to identify which traders submit which orders. Finally, given that HFT is in the early stages in the Korean stock market, our results might provide useful information for financial authorities regarding whether they will need to regulate HFT in the Korean stock market.

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The term structure of interest rates is closely related to the macro economy, in that it contains information about the current state of the economy and reflects the bond market participants’ expectations regarding the economy’s future path. It can be effectively used to predict major economic variables, including inflation, and therefore has potential as an indicator variable in the establishment of monetary policy. Due to its economic importance, many researchers in the field of finance and macroeconomics have studied the modeling of the term structure of interest rates. In finance, the focus of such research has been the prediction of interest rates and the pricing of interest rate-related derivatives. In macroeconomics, researchers have mainly attempted to understand the changes in the term structure of interest rates in the context of monetary policy implementation. Since the pioneering work of Ang and Piazzesi (2003), however, macro-finance term structure models combining the advantages of the two traditional approaches have provided a useful means of interpreting the movements of the term structure in the economic context while maintaining the consistency between short- and long-term interest rates with the no-arbitrage restrictions. In this study, we apply the unspanned macro-finance term structure model (unspanned MTSM) to analyze the empirical relationship between the term structure of interest rates and the macroeconomic variables using Korean data. An ‘unspanned’ model is free from the unrealistic assumption that the macroeconomic variables can be entirely generated by the first Nth-principal components of the term structure (macro-spanning restriction). It is also more flexible in the sense that the model permits feedback between the term structure and macroeconomic variables. The unspanned MTSM incorporated in this paper was suggested in Joslin, Priebsch, and Singleton (2012) and extended to the multi-lag form in Joslin, Le, and Singleton (2013). In the empirical section, we first conduct the macro-spanning test to determine the validity of the empirical analysis using the unspanned MTSM. The test results show that the term structure factors only explain some of the variations in the real economy (R2 = 13.43%) and the price variables (R2 = 15.40%), confirming the validity of modeling ‘unspanned’ macro variables. Empirically, we identify the macroeconomic variables with the highest explanatory power over the term structure of the interest rate in Korea. We estimate the model with the three term structure factors (level, slope, and curvature) and two combinations of the 82 key macroeconomic variables (i.e. 82C2 = 3,321 models are estimated). The performance of each combination of macroeconomic variables is measured by the various information criteria (AIC, SIC, and HQIC). In our analysis, the combination of the ‘nonfarm payrolls’ in the real economy variable group and ‘consumer price index excluding agricultural products and oils’ in the inflation variable group provides the best performance in explaining the Korean term structure of interest rates. The robustness of these variables is maintained even when we add more macroeconomic variables (i.e. combining the three macroeconomic variables) to the macro-finance term structure model. In the three macroeconomic variable cases, the third additional variable with a high explanatory power is ‘credit spreads’, the spread between AAA-rated and BBB+-rated corporate bond spread. We believe that the ‘nonfarm payrolls’ has the highest explanatory power, as noted by Wu and Zhang (2008) in the US case, because this variable carries information about not only the real economy growth, but also inflation. Then, using the identified three macroeconomic variables, we estimate the unspanned MTSM and analyze the effect that macroeconomic factors have on the market price of risk. Due to the ambiguity of the economic meaning of ‘nonfarm payrolls’, we use the ‘gross domestic product’—the second-best performing real economy variable—in the estimation. The results of this analysis show that the inflation variables have a pro-cyclical effect on the market price of the slope factor while the real economy variables have a pro-cyclical (counter-cyclical) effect on the market price of the level (curvature) factor. However, the third macroeconomic variable—credit spreads—has a negligible effect on the bond risk premium. Finally, we conduct the impulse-response and variance decomposition analysis to show the empirical relationship between the three term structure factors and the three identified macroeconomic variables. We observe that a positive shock on the real economy variables increases the level factor, and as the credit spread widens, the level factor falls. A positive shock on the inflation variable also increases the slope factor, which widens the spread between short- and long-term yields. Finally, through variance decomposition, we confirm that the three macroeconomic variables explain 91% of the variation in the level factor, and 91.42% and 95.39% of the slope and curvature factors in the 36-month forecasting period.

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Based on the relationship between fund performance and the subsequent net flows, this study examines the net flow persistence. Earlier works have focused largely on the positive relationship between fund performance and the subsequent net flows. The most valuable information for fund investment is the past performance of each fund, yet one fund investor may consider the previous 6-month performance most important while another focuses on the previous 36-month performance. Investors’ interpretations of the concept of ‘past performance’ vary, and fund performance affects fund investors’ investment horizons differently. The economic motivation for this study is how fund investors use past fund performance to inform decision making, and how long their responses persist. In this study, we answer the following questions. First, what is the period for measuring past fund performance that has the most significant effect on net flows? Although earlier studies have fixed the past performance period, we use several past periods for measuring fund performance such as 1, 3, 6, 12, and 36 months. Some investors may chase the prior 1-year fund performance while others note the prior 2-year performance. Although different fund investors may make different investment decisions according to the prior fund performance, there could be a period that has the most influential effect on fund investment decisions. Second, how long does the fund performance affect the net flows? To the best of our knowledge, most studies have examined the relationship between the prior fund performance and the subsequent 1- or 3-month net flows. We take a different approach. The past performance of equity funds may affect future flows, but as time passes, the effect disappears. The net flows may follow fund performance for 1 month, 3 months, or longer. We define this duration effect of past performance as ‘flow persistence’ and it reflects the response duration of fund investors. The existence of flow persistence can be explained as follows. The fund performance information may be realized by fund investors after a time delay, or the fund investors may react to the information slowly. In this study, we test the flow persistence by examining the subsequent 1, 3, 6, 12, 24, and 36 months’ net flows following past performance. This concept of flow persistence is significantly different from the performance persistence of equity funds. Our goal is to provide an overall perspective on how fund investors react to past performance information. We measure the equity funds’ performance in multiple ways while analyzing the effects of past period performance and flow persistence; that is, past performance is measured by cumulative return, 1-factor alpha, 3-factor alpha, and 4-factor alpha. Each alpha is a risk-adjusted return. During the sample period, which ran from January 2004 to June 2012, the Korean fund markets were severely hit by the global financial crisis that originated in the U.S. Before the crisis, the fund markets were growing rapidly, but during the crisis, the global and Korean stock markets were sluggish, which led to the poor performance of equity funds. Numerous fund investors were surprised by plummeting fund returns. As a consequence, equity fund redemptions increased in the post-crisis period. In this study, we divide the entire period into two sub-periods, pre- and post-crisis, to determine the effect that the global financial crisis had on the relationship between fund performance and net flows. The empirical findings are as follows. First, the 1-year performance has the most significant influence on the net flows of equity funds, which implies that fund investors make their own investment decisions largely based on the prior 1-year performance rather than other performance periods. Our results support earlier studies that use prior 1-year performance as information to predict subsequent net flows. Second, the flow persistence lasts for 6 to 12 months. This reveals that prior fund performance affects the subsequent future net flows for 6 to 12 months. Third, we also test the effects of the past performance period and the flow persistence by measuring fund performance in many different ways. Additional tests prove the robustness of our results. Finally, we find that both the flow-performance relationship and the effect of flow persistence weaken during the post-crisis period. This study tests the effects of past fund performance as information for predicting subsequent net flow. We believe that our findings could provide new perspectives on how fund investors respond to past performance as information.

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