This study examines how the business relationship between IPO underwriters and institutional investors affects the holding periods of IPO stocks allocated to institutional investors by underwriters. Studies such as Reuter (2006), Binay, Gatchev, and Prinsky (2007), and Chung and Kim (2015) have shown that the business relationship between underwriters and institutional investors affects underwriters’ initial allocation of IPO stocks. Chemmanur, Hu, and Huang (2010) argue that underwriters allocate more IPO stocks to institutional investors because institutional investors tend to hold stocks longer, especially for weaker post-issue demand IPOs, helping underwriters’ efforts to stabilize prices in the after-IPO market period. However, it is well known that on average, institutional investors sell initially allocated IPO stocks quite soon after they have been listed in the market. For example, Aggarwal (2003) and Boehmer, Boehmer, and Fishe (2006) report that in the U.S. market, flipping—that is, selling initially allocated IPO stocks within a couple of days after their listings —is common and it is mostly conducted by institutional investors. An analysis by the Financial Supervisory Service in Korea of 142 Korean IPOs listed in the Jan. 2008 to Sept. 2010 period found that on average, 64.7% of IPO shares were allocated to institutional investors, who sold 34.2% of them during the first trading day and 48% of them during the first 4 weeks after the listing. In addition, Dongyang Securities, examining 119 Korean IPOs from the Jan. 2010 to Jun. 2011 period, report that the return on IPO stocks falls rapidly after their listing, and the average closing price of the IPO stocks in one year falls to only 77% of their closing prices on the first trading day. They blame the institutional investors’ flipping of IPO stocks for the rapid price drop. It is thus evident that the length of time that institutional investors hold initially allocated IPOs in the after-market is an important issue for IPO firms and underwriters who wish to stabilize prices and protect their reputation capital. We therefore argue, based on the findings from prior studies, that the holding period can be affected by the business relationship between underwriters and institutional investors. To examine the effect of the business relationship between underwriters and institutional investors on institutional investors’ holding period of initially allocated IPOs, we use monthly mutual fund holding information and IPOs listed in the KOSDAQ market for the 2002 to 2012 period. To the best of our knowledge, this is the first study to examine the holding period of IPO stocks by institutional investors in Korea. Following Chung and Kim (2015), we measure the strength of a business relationship in three ways: (1) the total investment amount by each mutual fund in IPOs underwritten by each underwriter, (2) the number of participation by each mutual fund in IPOs underwritten by each underwriter, and (3) the average investment of each mutual fund in each IPO underwritten by an underwriter for the two years prior to the target IPO. We find that mutual funds sell, on average, about 48% of the IPO stocks within 30 calendar days of the listing. However, funds that have 1 (2)-year business relationships with the underwriter hold the IPO stocks, on average, 22.67 (17.12) calendar days longer than those without a relationship. We also show that the strength of the relationship matters: the stronger the relationship is, the longer the holding period. Tobit analyses confirm Aggarwal’s (2003) finding that institutional investors tend to sell more underpriced IPOs (hot IPOs) more quickly. We, however, show that when institutional investors have a strong business relationship with underwriters, they seem to hold hot IPOs longer than when they have a relatively weaker relationship. Combined with the results from Chung and Kim (2015), these results imply that underwriters allocate more favorable IPO stocks to institutional investors with whom they have business relationships, and institutional investors hold the IPO stocks longer, helping the underwriters stabilize prices in the after-IPO market. In addition, we find that funds that are affiliated with underwriters hold IPO stocks for shorter periods than unaffiliated funds. We also find that the business relationship has a negative effect on the holding periods of affiliated funds.

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The cost of capital is one of the key determinants of corporate investment, and according to the q theory framework, it has a negative effect on investment. However, there has been relatively little research on the relation between the cost of capital and corporate investment. Frank and Shen (2016) report the following findings: (1) the cost of capital estimated by asset pricing models such as the CAPM have a positive effect on investment; and (2) the implied cost of capital is negatively related to investment. They provide considerable insight into methods for estimating the cost of equity, but do not give a convincing explanation for their unexpected results. Building on their insight, we use a dataset of listed Korean firms to estimate a firm’s expected return using different measurement methods, and investigate whether the empirical result depends on how the cost of equity capital is measured. The most common approach to estimating a firm’s cost of equity is to calculate the expected return based on the CAPM. The implied cost of equity derived from stock prices and analyst forecasts could be an alternative measure of the expected return for a firm’s stock. In this study, we estimate the implied cost of equity by applying two different valuation models: the Gordon growth model and the abnormal earnings growth model developed by Ohlson and Juettner-Nauroth (2005). The sample dataset covers the 2002 to 2016 period. Following Frank and Shen (2016), we use a g-decomposed model in which investment is formulated as a function of marginal profitability and discount rate for a firm’s capital budgeting decision. According to the model based on the q theory, investment should be negatively related to the weighted average cost of capital, which represents the discount rate. Our findings, however, show that the CAPM-based estimate does not confirm the validity of the theoretical prediction. We consider the effect of macroeconomic factor such as the business cycle on the association between the CAPM-based estimates and corporate investment, as it is possible that firms make different investment decisions at different stages of the business cycle. We divide our sample period into an expansion period and a contraction period, and conduct the same analysis on the subsamples. We find that the positive impact of the CAPM estimate is only significant in the expansion period. We also find that the positive effect is large for the subset of relatively small firms that are listed on the Kosdaq market. The implied cost of capital-based analysis produces different results. We find that the implied cost of capital is negatively linked to investment, perhaps because the implied cost of equity capital (i.e., the return required by shareholders) better reflects investors’ expectations about the riskiness of firm-specific investments, and the information imbedded in the estimate can be useful for managers’ capital budgeting decisions. Next, we focus on the internal capital market effect, which occurs in the Korean business group. Following previous studies, we assume that firms that belong to business groups have easier access to internal capital markets. To examine whether the internal capital market plays a significant role in firms’ investment decisions, we categorize our sample firms into two subsamples: independent firms and group-affiliated firms. Our analyses of the subsamples suggest that the cost of capital can have different effects on firms’ investment expenditures depending on their business group affiliation. More specifically, we find that the negative relation between the weighted average cost of capital and investment is statistically insignificant for group-affiliated firms, whereas for independent firms, we find that investment is negatively affected by the weighted average cost of capital. This evidence is consistent with the notion that the investments of group-affiliated firms with easier access to internal capital markets are less affected by external financing cost than independent firms. In a further analysis, we also find that the investment of group-affiliated firms is negatively correlated with the debt component of the WACC, but not with its equity component. This implies that the internal capital market relieves the financing constraints of firms belonging to business groups, but it does not completely substitute for the external capital market. Finally, we use the expected return estimated by the Fama–French three-factor model as a proxy for the cost of equity. We find that the FF3-based estimate has a negative effect on investment, but the effect is not statistically significant for the whole sample, suggesting that the factor loadings of the Fama-French model can measure firm-specific risk more accurately than the CAPM beta, but they are insufficient to represent the unsystematic risk that individual firms are likely to face with respect to their investment decisions.

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Almost all prior studies on the Korean accrual anomaly focus on the stock market. In contrast to prior studies, our study examines whether or not the accrual anomaly exists in the Korean corporate bond market. Using the corporate bond data traded in the Korean market from 2000 to 2011, we find supporting evidence of the accrual anomaly in the Korean corporate bond market using three approaches: (1) hedge portfolio return test, (2) the Fama-French five factor (three equity market factors plus two bond market factors) time series regression test, and (3) the Fama-MacBeth cross sectional test. Next, we examine whether the unique Korean corporate governance system, chaebol, affects the magnitude of the accrual mispricing in the corporate bond market and find evidence that the accrual anomaly exists only for bonds issued by non-chaebol firms. This finding indicates that bond investors’ expectation about explicit or implicit subsidy from chaebol-affiliated subsidiaries dampens accrual mispricing of a chaebol firm. Taken together, our results suggest that investors in the Korean corporate bond market fixate on earnings, in particular, for non-chaebol firms.

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The essence of strategic asset allocation (SAA) is to derive the optimal asset allocation taking into account the diversification effect of each asset class under the same risk and return profile structure. The mean variance optimization method is useful when the structure of the risk and return of assets is homogeneous. However, due to the emergence of various alternative investments, methods for determining optimal asset allocation are the subject of controversy. We review the problems of the traditional SAA method that is used by pension funds and propose a new SAA method that considers multiple factors. We examine how to apply the new model to domestic pension funds. We provide a new asset allocation paradigm that provides a reasonable weighting for alternative investments and a basis for determining foreign currency hedge ratios for foreign investments. A review of domestic pension funds’ portfolios, assuming a similar target rate of returns and risk tolerance limits, identifies big differences in the weights given to alternative investments. The variation occurs because most pension funds predetermine the portion of the portfolio that will be alternative investments and then perform an SAA only for the remaining asset classes. As decisions on the weights of alternative investment consider the entire portfolio, it is necessary to improve the current asset-based SAA method that performs the allocation after excluding the alternative investment. Factor-based SAA can be a useful method for incorporating alternative investments into the asset allocation framework and can identify methodologies for integrating risk with existing assets such as stocks and bonds. There are four steps in factor-based SAA: selecting an asset mapping the asset class to each corresponding factor determining the target factor weight and re-mapping it to the asset class. In the first step, an appropriate factor must be selected to explain the payoff of each asset class. We use a principal component analysis to extract the common elements in all of the asset classes and then conduct a correlation analysis with the factor candidates to determine growth, inflation, real interest rates, credit, and FX. It is interesting to note that the inflation factor has a higher negative correlation with the other factors during an economic crisis, confirming that investing in the inflation factor during a crisis can effectively hedge a portfolio’s returns. Second, to estimate the factor coefficients for each asset using a multifactor model it is necessary to map the asset class to each corresponding factor. The estimated coefficients are then used as a link in determining the target factor exposure and to determine the optimal asset allocation. The current six asset classes are re-defined and divided into 17 asset classes based on the cash flow and risk characteristics of each asset class. In particular, we classify the alternative investments into equity and debt types. In this way, we can make the risk and profit structure of each asset class as homogeneous as possible. Third, the target factor weight is determined by selecting an appropriate factor exposure for the risk profile of the pension fund. The target factor weight should be consistent with the pension fund’s investment objectives and risk tolerance limits and is also a key element in determining the optimal allocation of investment assets. To determine the target factor exposure, we first derive a factor-based efficient frontier. Once the factor-based efficient frontier has been acquired, the optimal factor exposure with the highest Sharpe ratio is selected from the alternatives that meet the target returns and risk tolerances of the pension fund. In this case, the target factor exposure is based on the implied factor exposure obtained from the current portfolio’s starting point. Fourth, to remap to the asset class, we determine the asset class that meets the target factor weight. This process usually uses an optimization method. We set up a hypothetical portfolio of pension funds and analyze these portfolios. The implications of the case analysis are summarized as follows. First, to increase the Sharpe ratio, it is desirable to reduce the domestic equity investments and increase the portion of overseas investments. Second, the foreign exchange hedge policy of the pension fund is too conservative and should be expanded. Third, the impact of alternative investments on the portfolio’s risk-return profile varies by its composition, such that the weights should be distributed based on the correlation of factors and factor exposures. In addition, factor-based SAA allows monitoring of the exposure of investment assets to each of the corresponding risk factors at all of the times when effective risk monitoring for alternative investments is expected.

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