Asset allocation is one of the most important decisions made in the asset management process to achieve a portfolio’s target return within the constraints of its risk tolerance level. Brinson et al. (1986, 1991) conclude that asset allocation policy explains 93.6% or 91.5% of performance. This greatly helps practitioners understand the importance of asset allocation in asset management. However, at the same time, it triggers the publication of other studies with different views on its empirical testing and different interpretations of its results. For example, Ibbotson et al. (2000) and Vardharaj et al. (2007) conduct empirical tests using regression models to measure the explanatory powers of asset allocation in fund performance and find that the dispersion of R2 in their models is wider than those of Brinson et al. Alternatively, Xiong et al. (2010) decompose the total return into three components: market movement, asset allocation policy return in excess of the market return, and active return. They then run time-series and cross-sectional regressions on total returns and excess market returns for both equity and blend funds, respectively. Based on their regression analyses, Xiong et al. (2010) conclude that market movement dominates the other two components. Additionally, they find that the explanatory powers of passive management (asset allocation policy) and active management are at similar levels. In sum, although multiple studies propose different conclusions from Brinson et al. (1986), they all agree that asset allocation is significant in the asset management process. Applying the methodologies of Brinson et al. (1986) and Xiong et al. (2010), this study performs time-series and cross-sectional analyses of equity and blend fund returns in Korea, the U.S., the U.K. and Australia, based on each country’s style benchmark and leading market index returns. A performance attribution analysis is conducted to determine the contribution of passive and active management to fund performance by comparing the coefficients of determination (R2) for each country’s regression models. The results indicate that in accordance with the time-series regression analysis of Brinson et al. (1986), the explanatory power of asset allocation reaches its highest level when the total fund return is explained by benchmark returns reflecting market movement. However, in all four countries, when market movement is separated, it’s the greatest contribution to the performance of both equity and blend funds. When a market-movement-excluded excess return is used, in three countries, excluding the U.K., active management has higher explanatory power for equity funds and passive management has higher explanatory power for blend funds. When a cross-sectional regression analysis is conducted using the methodology of Xiong et al. (2010) to measure the explanatory power of asset allocation performance without market movement, the explanatory power of active management is higher for equity funds in all four countries. In the case of blend funds, however, the explanatory power of active management is higher in the U.S. and U.K., where equities hold a larger portion of funds than bonds. Finally, passive management shows higher explanatory power in Korea, where its funds are composed of similar levels of equities and bonds. Comprehensively reviewing the results of this study from a mid- and long-term investment policy perspective, we conclude that the selected variables in strategic asset allocation consist of investors’ risk preferences and asset class risk premiums, not short-term market movements. Therefore, it is difficult to agree with Xiong et al. (2010) that it is reasonable to exclude market movements from returns because the contribution to investment performance for mid- and long-term market movements are already reflected in risk premiums. We add, however, that if market movements are under control, which can be explained as constant asset class risk premiums, then we support the finding of preceding studies that active management is as significant as passive management in terms of its contribution to performance.

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This study examines the relationship between employee pay and the capital structure of publicly listed firms in Korea. Consistent with Hanka (1998) and Hovakimian and Li (2011), we find that higher leverage is associated with lower average employee pay. However, our findings contrast with the theory of Berk, Stanton, and Zechner (2010) and the theory of the compensating wage differential. When firms add more debt to their balance sheet, the probability of bankruptcy increases and their employees face a greater risk of unemployment. Studies have suggested that workers bear significant economic and psychological costs from involuntary unemployment. In this context, the economic theory of Berk et al. (2010) predicts that firms compensate for this risk by increasing employee pay to increase the leverage related to higher average wages. However, in contrast to Berk et al. (2010), several studies have documented a negative correlation between leverage and employee wages. Hanka (1998) and Hovakimian and Li (2011) find that increased leverage is associated with lower employee pay. They interpret this as being a result of the risk shifting from shareholders and managers to employees. In addition to the risk-shifting motive, Perotti and Spier (1993) argue that firms use leverage as a bargaining tool against labor, especially when in financial distress. Moreover, Benmelech, Bergman, and Enriquez (2012) find that firms tend to renegotiate employee wages downward when they are under financial distress. Thus, from this perspective, higher leverage is associated with lower average employee pay among firms that are in financial difficulty. In summary, studies that predict a negative relationship between leverage and wages have suggested that increased leverage imposes disciplinary effects on labor by reducing employee wages. In our empirical analysis, we estimate panel fixed effect models that control for the time-invariant heterogeneity among firms and general time trends. In firm fixed effect regressions controlling solely for leverage, we find that leverage is associated with lower average employee pay. Moreover, in regressions that control for other firm characteristics, we find that higher leverage is associated with lower average employee pay. The Fama and MacBeth (1973) regressions deliver the same results. These findings suggest that the disciplinary effect of leverage is more pronounced than the compensating effect of higher bankruptcy risk. As our sample period covers the periods before and after the 2008 global financial crisis, we test whether the global financial crisis had any significant effects on the relationship between leverage and wages. We conduct the same regression analyses for the pre- and post-financial crisis periods (2003~2007, 2010~2015), and our empirical results suggest that the negative relationship between leverage and wages significantly intensified after the financial crisis. Moreover, the negative relationship between leverage and wages was only significant for the post-crisis period. This result confirms that the disciplinary effect of leverage was more pronounced after the financial crisis. We also consider industry heterogeneity and the labor market conditions in our analysis of the variation in the leverage-wage relationship. We expect employees’ wage levels to vary significantly among different industries, especially technology firms and non-technology firms (Chemmanur, Cheng, and Zhang, 2013). We examine the variations across industries and markets by conducting comparative analyses of KOSPI and KOSDAQ firms, and firms in the technology industry and those in other industries classified according to the KSIC code. We find that the leverage-wage relationship is significantly more negative among non-technology firms. We also predict that the disciplinary effect of leverage is stronger in industries with lower unionization and shortage rates, as employees in these industries have less bargaining power against the firms. Consistent with our prediction, we find that the negative relationship between leverage and wages is strengthened in industries with lower unionization and shortage rates. In summary, among Korean public firms, we find that higher leverage is associated with lower employee pay. This result substantiates the view that debt disciplines labor. However, our findings do not support the theory of Berk et al. (2010) that firms compensate for increased unemployment risk by increasing wages to gain greater leverage. In addition, we find that after the financial crisis (2008~2009), the negative relationship between leverage and wages increased significantly. Our findings suggest that the disciplinary effect of debt was significantly pronounced after the financial crisis. We also examine the effects of the labor market conditions and industry heterogeneity on the leverage-wage relationship. Consistent with our prediction, the negative relationship between leverage and wages is significantly strengthened among firms in non-technology industries and those belonging to industries with lower unionization and shortage rates. One limitation of our study is that we cannot fully control for the endogeneity of leverage. We expect that future research will mitigate this problem. Our evidence contributes to the growing body of literature connecting labor economics and corporate finance and to the research on the relationship between leverage and wages by providing additional evidence to support the disciplinary effect of debt.

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This paper examines the mechanism of systemic risk propagation through system- wide latent leverage channel. We focus on the hidden leverage-induced asset value dynamics in the financial markets, intertwined with balance-sheet components of the banking system. We propose a latent leverage index by estimating smooth transition regression models based on the intrinsic element of the financial system, off-balance-sheet transaction, and cross-border activities of the Korean commercial banking system. We find that a shock to the latent leverage index impacts the macroeconomy with the lag of three quarters. This finding provides an important policy-oriented implication for macroprudential supervision of banking system.

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This paper investigates the effect of permanence of firm’s earning on the dividend policy of Korean firms. We decompose earnings into a transitory component and a permanent one, employing the approach of Beveridge and Nelson (1981). We focus on the relation between dividends and permanent earnings. Lee (1996) presented the empirical results that dynamic dividend behavior was more accounted for primarily by changes in permanent earnings rather than current earnings, using aggregate time-series data for the Standard and Poor’s composite Stock Price Index. We use firm-level panel data and test hypothesis that Korean firm’s dividend decisions also depend more on permanent earnings rather than transitory earnings. We find that the permanent earning has a significantly positive relationship with dividend and the coefficient estimate of permanent earnings is larger than that of transitory earnings. Future more, we use cash flows as earnings variable and find the similar results. There is a significantly positive relationship between permanent cash flows and dividends. And the marginal effect of permanent cash flows on dividends is significantly larger than that of transitory cash flows on them. These empirical results show that Korean firms take into consideration of permanence of earnings more in making their dividend decisions.

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The challenging market condition for insurance carriers has become intense so that insurance companies look for all the feasible options for survival including consolidation with other insurance carriers, agent and broker networks and even non-insurance financial institutions. Using event study methodology, our study investigates whether the mergers and acquisitions of US insurers create value and there is strategic advantage for insurance carriers to consider cross-industry consolidations. We analyze all within-border mergers and acquisitions during 2003~2012 where either or both of the acquirer and target are US insurance carrier. We find that insurance mergers and acquisitions have positive valuation effects on acquiring firms, especially for the cross-industry mergers and acquisitions.

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This study examines the relationship between the Northeast Asian and U.S. markets with particular attention placed on the global financial crisis period. For this purpose, the paper employs dynamic approaches including DCC-MGARCH, BEKK and Risk Decomposition models to ensure the robustness of empirical findings. The results are as follows. First, The Northeast Asian stock market remains relatively independent from the U.S. market movements during the sample period. Second, the regional market shows an increasing trend of joint integration with the U.S. market. Third, an increased integration is found to be only unique to the crisis period. We find no evidence to support the findings of previous empirical studies which suggest the increased level of integration since the GFC.

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Most venture investments in Korea are from venture funds supported by limited partners (LPs), and thus LPs always want to know the main determinants of the fund performance and how well their funds are operated. General partners (GPs) who want to successfully receive the investments for their venture funds from LPs are also very interested in the same issues. This study measures the performance of Korean venture funds using the public market equivalent (PME), internal rate of return (IRR), and multiple measures. The features and determinants of each performance measure are then analyzed using actual Korean venture fund data. Few studies have addressed these issues, due to limited access to and a shortage of Korean venture fund data. This study is the first to use actual venture fund data to investigate the performance and its determinants. To accurately measure the performance of venture funds, the Net Asset Value problem must be addressed. However, this study is free from that issue because the sample only contains fully liquidated venture funds. The PME measure (average 1.07) shows that on average, Korean venture funds performed slightly better than the Korea Composite Stock Price Index market from 1992 to 2010. This result may appear disappointing, but it is too early to draw conclusions, as our sample only includesthe funds formed until the early 2000s. The IRR is the most volatile ofthe three measures (its standard deviation is 10.2 times the average), because it is influenced by vintage years and market situations, such as fund liquidation periods. The average is much higher than the median for all three of the measures, suggesting that some larger funds perform considerably well. We analyze the determinants of fund performance based on three factors—fund characteristics, fund operator characteristics, and market situation characteristics—and find not only that each performance measure has different determinants, but also that the latter’s explanatory power is higher for the PME measure than for the other measures. We also find that the PME and multiple measures exhibit similar patterns, whereas the IRR is biased as a performance measure. The determinants that explain the PME measure well are fund size, fund operation period, fund cash inflow and outflow, and the number of new fund formations. The determinants that best explain multiple measures are fund size and fund cash inflow and outflow. The best determinants for the IRR measure are fund size (negative relation), fund operation period, fund cash inflow, number of liquidated funds, and vintage year. For the PME and multiple measures, fund size has a U-shaped relation with fund performance, whereas it has a negative relation with the IRR measure. The fact that the funds smaller than $3 million perform well, especially for the IRR measure, is a unique feature of Korean venture funds not found in Harris et al. (2014), who studied U.S. venture funds. This suggests that the IRR can be highly biased for smaller funds. Remarkably, the shorter the fund’s operation period, the better the fund performance in the case of the PME measure, but not in the case of the multiple measures. The performance differences among the investment fields stand out for the PME and multiple measures (especially the latter), but are not found for the IRR. For the PME and multiple measures, IT-specialized funds exhibit the best performance, and bio-specialized funds the worst. General funds without specialized investment fields only outperform specialized funds for the multiple measure. Funds with more than two cash flowsperform better than those with only one cash flow for all three of the measures. However, the IRR only shows a statistically significant result for cash inflows (liquidation), indicating that it is biased toward fund liquidation. There is no significant difference in performance between corporate venture capital (CVC)and non-CVC, between financial and non-financial venture capital (VC), and between individual and non-individual VC for all of the three measures. The number of new funds during the fund formation period has a significantly negative influence on the PME measure, and the number of liquidation funds during the fund liquidation period negatively influences theIRR measure. Because the average operating period of Korean venture funds is five-and-half years, the data used for this study only cover the venture funds mainly formed before 2005. With more fund data from the 1980s, 1990s, and 2000s, as in the U.S., it would be possible to obtain more meaningful results. It is probable that we willunderstand more about the Korean venture fund industry once we reach the mid-2010s and have access to the performance results of venture funds formed during the 2000s, which was a booming period for the Korean venture capital industry.

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Risk aversion and time preference are fundamental variables when people make financial decisions, but there are numerous opinions for how to estimate them. Researchers frequently estimate the demand of financial products with an ordinary survey, where subjects provide self-perceptive information. Given that behavioral financial questions are included in the simple questionnaire, the subjects may misunderstand such questions, or may not be able to completely understand the importance of the concepts driving them. Therefore, subjective bias could be implicitly problematic in many surveys, rendering their results untrustworthy. Although researchers can draw their conclusions based on the data collected from the survey, the economic results may be distorted or prove insignificant. This study takes the behavioral and experimental economics explored in Kahneman and Tversky (1979) and applies those theories to a financial product, such as a single payment annuity. This is a relatively new product in Korea’s aging population, thus demand for it has not been analyzed in the Korean context, despite the popularity of such products in the U.S. (Mitchell and Utkus, 2003). Following the method of eliciting risk aversion proposed by Holt and Laury (2002), we develop a new measure of risk aversion that is modified using the quantile normalization (Bolstad et al., 2003), and use the measure to check for subjective bias when choosing an annuity product. Time preference is also very critical in financial decisions. We propose a time preference measure that follows Coller and Williams (1999) and use it as an objective measure of the subject’s time preference. We design an experiment in which the subjects are guided by the instruction and payoff schemes, and follow several sessions. The participants choose a lottery from a pair of lotteries whose payoffs and probabilities are different, revealing the participants’ risk aversion preferences. In the next experiment on time preference, the participants choose a lottery from a pool of several featuring different interest rate payoffs with higher and lower payment spans, depending on the interest rates. During the experiment, the participants’ choices reveal their time preferences for our analysis. In addition to the results of the aforementioned experiments, we collect the participants’ background information, such as financial status, financial literacy, and intelligence. Given the dataset, we can then find that the subjective answers from the survey are not significantly related to the financial decision on single payment annuity. This allows us to verify that the subjective bias exists, and confirm that self-perceptive surveys are not trustworthy in choosing financial products. We also compare the self-perceptive answers and the experimental measures to test which methods are more reliable and significant. The results show that neither approach significantly determines time preferences and risk aversion in some model specifications. We construct the bias variables of risk aversion, time preference, life expectancy, financial literacy, and intelligence using the differences between the subjective answers and experimental results. Thus, when we execute econometric models with the bias variables of interest, we discover that the biased self-perceptive and experimental measures of the time preference, risk aversion, life expectancy, financial literacy, and pension literacy are significant determinants of the demand for single payment annuity. Therefore, the preferential and behavioral variables are critical in choosing financial products such as annuities, rather than the self-perceptive answers in the survey. We also run a Heckman two-step regression of the premium of the single payment annuity and find that financial status and portfolio are the most crucial determinants, regarding the moneywise incentive. Finally, the time preference and risk aversion experiments we design reveal that behavioral and preferential biases are critical in choosing financial products. Therefore, we conclude that researchers should take bias variables seriously when they estimate the demand for financial products, even after controlling for other variables.

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We investigate the effectiveness of the original PIN model (Easley, Kiefer, O’Hara, and Paperman, 1996) and five variants of the adjusted PIN model (Duarte and Young, 2009) in the Korean stock market. Throughout the series of likelihood-ratio fitness tests, we find that the unrestricted version of the adjusted PIN model fits best in the Korean stock market data.

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The closing price of KOSPI200 on Nov 11, 2010, the expiration day for options, was determined at 247.51 points following a severe fall of 7.11 points, which caused great disorder in the Korean financial market. The Korean Supervisory Service and the Korea Exchange investigated the event and discovered that Deutsche Bank had submitted a huge sales order for 2.44 trillion Won of 199 stocks simultaneously, on condition of holding a large amount of KOSPI200 put options. Deutsche Bank was later indicted for the cross-market manipulation based on the Financial Investment Services and Capital Markets Act (hereafter “Capital Market Act”). In this paper, we investigate the cross-market manipulation using index arbitrage in determining stock closing prices on expiration days. We deal withtwo aspects in relation to this issue. First, in terms of law, we explain the various types of cross-market manipulation regulated by the Capital Markets Act. Specifically, we discuss the concept of “existence of purpose” to satisfy the requirement for cross-market manipulation action. Second, in terms of finance, the hypotheses to verify the “existence of purpose” on the cross-market manipulation are established and tested empirically. This empirical test is also related to Kumar and Seppi’s (1992) model, in which the manipulator earns a positive expected profit by holding a futures position and then manipulating the spot price used to compute the cash settlement on the expiration day. In empirical analysis, we identify that expiration effects exist in the Korean market, consistent with previous studies. The volatility and trading volume of the closing price on expiration days are distinctly higher than those on non-expiration days and those of non-KOSPI200 stocks. Then we establish the first hypothesis that if expiration-day effects are caused by index arbitrage activity, the effects are stronger in stocks where the trading volume for index arbitrage is higher. The results show that the portfolio with a greater transaction ratio of index arbitrage has higher volatility and trading volume on expiration days. The differences are statistically significant. The regression analysis using volatility and trading volume on expiration days as the independent variable and index arbitrage as the dependent variable also shows that volatility and trading volume on expiration days are positively correlated with index arbitrage. These results support our first hypothesis that index arbitrage are an important source of expiration-day effects. However, the positive relationship between the expiration-day effect and index arbitrage does not necessarily imply that cross-market manipulation activity exists in the market and affects the closing price on expiration days. Normal index arbitrage can also cause an increase in volatility and trading volume due to its tremendous trading size. To disentangle these two possibilities we use the order submission data of closing call auctions and establish the second hypothesis. Our second hypothesis is that a cross-market manipulator will submit a distinctly low-priced limit sale order or distinctly high-priced limit purchase order, both of which lack economic rationality, to move the closing price as much as possible on expiration days. According to the Kumar and Seppi (1992) model, the cross-market manipulator can earn a positive expected profit by manipulating the spot price used to compute the cash settlement on the expiration day because the profit from the futures position exceeds the loss from the spot position. Analysis of the index arbitrage order data in the closing call auctions on expiration days shows a significant increase in distinctly low-priced limit sale orders and distinctly high-priced limit purchase orders. The limit orders are also concentrated in the last minute of the closing call auction. Finally we test a third hypothesis to confirm the price impact of cross-market manipulation. Our third hypothesis is that orders for index arbitrage have a greater effect on price than other normal orders if there is cross-market manipulation on expiration days, because the manipulators try to maximize the price impact of their orders to maximize their profits in the futures position. The results show that the price impact of index arbitrage orders is larger than that of normal orders. Overall, these results imply that there exist index arbitrage orders issued for the purposes of cross-market manipulation on expiration days in Korea. The findings are also consistent with Kumar and Seppi’s (1992) prediction that cross-market manipulators try to manipulate the spot price used in cash settlement on the expiration day.

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