• The Korean Journal of Financial Management
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• v.26 no.4
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• pp.103-140
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• 2009

This paper investigates various channels through which liquidity can affect stock returns and examines whether behavioral explanation for liquidity risk is reasonable. First, we examine whether liquidity level (average liquidity) plays a significant role in determining asset returns. The result is consistent with the hypothesis that a stock with higher average illiquidity will have a higher expected return. Second, we focus on the argument that liquidity has a non-diversifiable systematic component. If systemic liquidity has a different impact across individual securities, a stock that is more sensitive to systematic liquidity will have a higher expected return. The results of various tests are inconsistent with each other, not completely supporting the argument. Finally, the intra-market tests in Korea support the behavioral explanation for the liquidity premium, and the effect is stronger in the liquidity level than in the liquidity beta related to systematic liquidity.

• Journal of the Korean Operations Research and Management Science Society
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• v.34 no.4
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• pp.153-163
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• 2009

Unlike the mean-variance approach, the stochastic dominance approach is to form a portfolio that stochastically dominates a predetermined benchmark portfolio such as KOSPI. This study is to search a set of portfolio weights for the first-order stochastic dominance with maximum expected return by managing the constraint set and the objective function separately. A nonlinear programming algorithm was developed and tested with promising results against Korean stock market data sets.

• Journal of the Korea Society of Computer and Information
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• v.21 no.11
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• pp.151-158
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• 2016

This study analyzes the stock market reaction of disclosure of technological information using events which are collected in the Korean stock market for the thirteen-year period between January 2002 and December 2014. We find that abnormal return on the disclosure day of full sample firms is positive and statistically significant. However, abnormal return of high R&D intensity subsample is a larger positive number than that of the low one. Using a longer window, it shows that low R&D intensity negatively decreases the long term performance after the adoption of new technological information. The empirical evidence of the studying is expected to serve as a good judging guide-line for the investors.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2007.11a
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• pp.134-137
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• 2007

Unlike the mean-variance approach, the stochastic dominance approach is to form a portfolio that stochastically dominates a predetermined benchmark portfolio such as KOSPI. This study is to search a set of portfolio weights for the first degree stochastic dominance with maximum expected return by managing the constraint set and the objective function separately. An algorithm was developed and tested with promising results against Korean stock market data sets.

• Journal of the Korean Operations Research and Management Science Society
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• v.38 no.4
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• pp.35-52
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• 2013

Markowitz's portfolio selection model is used to construct an optimal portfolio which has minimum variance, while satisfying a minimum required expected return. The model uses estimators based on analysis of historical data to estimate the returns, standard deviations, and correlation coefficients of individual stocks being considered for investment. However, due to the inaccuracies involved in estimations, the true optimality of a portfolio constructed using the model is questionable. To investigate the effect of estimation inaccuracy on actual portfolio performance, we study the changes in a portfolio's realized return and standard deviation as the accuracy of the estimations for each stock's return, standard deviation, and correlation coefficient is increased. Furthermore, we empirically analyze the portfolio's performance by comparing it with the performance of active mutual funds that are being traded in the Korean stock market and the KOSPI benchmark index, in terms of portfolio returns, standard deviations of returns, and Sharpe ratios. Our results suggest that, among the three input parameters, the accuracy of the estimated returns of individual stocks has the largest effect on performance, while the accuracy of the estimates of the standard deviation of each stock's returns and the correlation coefficient between different stocks have smaller effects. In addition, it is shown that even a small increase in the accuracy of the estimated return of individual stocks improves the portfolio's performance substantially, suggesting that Markowitz's model can be more effectively applied in real-life investments with just an incremental effort to increase estimation accuracy.

• The Journal of the Korea Contents Association
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• v.21 no.11
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• pp.320-332
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• 2021

This paper investigates the influence of stock-level left-tail risk, which is defined using Value-at-Risk(VaR) estimates of past one-year daily stock returns, in the expected stock returns in the Korean stock market. Our results are summarized as follows: First, monthly-constructed zero-cost portfolios that buy (shortsell) the highest (lowest) left-tail risk decile in the previous month exhibit an average monthly return (called left-tail risk premium) of -2.29%. Second, Fama-MacBeth cross-sectional regressions suggest that left-tail risk in the previous month shows significant and negative explanatory power over return in this month, after controlling for various firm characteristics such as firm size, B/M, market beta, liquidity, maximum daily return, idiosyncratic volatility, and skewness. Third, the stocks with larger recent month loss have lower returns in the next month. Fourth, the magnitude of left-tail risk premium is negatively related with lagged market-level volatility. These results support the hypothesis from a perspective of behavioral finance that the overpricing of stocks with left-tail risk is attributed to the investors' underreaction to it.

• Journal of Industrial Convergence
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• v.2 no.2
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• pp.127-147
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• 2004

This paper examines the time-series relations among expected return, risk, and book-to-market(B/M) at the portfolio level. The time-series analysis is a natural alternative to cross-sectional regressions. An alternative feature of the time-series regressions is that they focus on changes in expected returns, not on average returns. Using the time-series analysis, we can directly test whether the three-factor model explains time-varying expected returns better than the characteristic-based model. These results should help distinguish between the risk and mispricing stories. We find that B/M is strongly associated with changes in risk, as measured by the Fama and French(1993) three-factor model. After controlling for changes in risk, B/M contains little additional information about expected returns. The evidence suggests that the three-factor model explains time-varying expected returns better than the characteristic-based model.

• Journal of the Korean Operations Research and Management Science Society
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• v.38 no.2
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• pp.75-93
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• 2013

In applying Markowitz's portfolio selection model to the stock market, we developed a comprehensive investment decision-making framework including key inputs for portfolio theory (i.e., individual stocks' expected rate of return and covariance) and minimum required expected return. For estimating the key inputs of our decision-making framework, we utilized an exponentially weighted moving average (EWMA) which places more emphasis on recent data than the conventional simple moving average (SMA). We empirically analyzed the investment results of the decision-making framework with the same 15 stocks in Samsung Group Funds found in the Korean stock market between 2007 and 2011. This five-year investment horizon is marked by global financial crises including the U.S. subprime mortgage crisis, the collapse of Lehman Brothers, and the European sovereign-debt crisis. We measure portfolio performance in terms of rate of return, standard deviation of returns, and Sharpe ratio. Results are compared with the following benchmarks : 1) KOSPI, 2) Samsung Group Funds, 3) Talmudic portfolio based on the na$\ddot{i}$ve 1/N rule, and 4) Markowitz's model with SMA. We performed sensitivity analyses on all the input parameters that are necessary for designing an investment decision-making framework : smoothing constant for EWMA, minimum required expected return for the portfolio, and portfolio rebalancing period. In conclusion, appropriate use of the comprehensive investment decision-making framework based on the Markowitz's model integrated with EWMA proves to achieve outstanding performance compared to the benchmarks.

• Advanced Industrial SCIence
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• v.2 no.3
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• pp.22-28
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• 2023

In this study, we conducted research on the automated trading of U.S. stocks through a volatility strategy using the Fear and Greed index. Volatility in the stock market is a common phenomenon that can lead to fluctuations in stock prices. Investors can capitalize on this volatility by implementing a strategy based on it, involving the buying and selling of stocks based on their expected level of volatility. The goal of this thesis is to investigate the effectiveness of the volatility strategy in generating profits in the stock market.This study employs a quantitative research methodology using secondary data from the stock market. The dataset comprises daily stock prices and daily volatility measures for the S&P 500 index stocks. Over a five-year period spanning from 2016 to 2020, the stocks were listed on the New York Stock Exchange (NYSE). The strategy involves purchasing stocks from the low volatility group and selling stocks from the high volatility group. The results indicate that the volatility strategy yields positive returns, with an average annual return of 9.2%, compared to the benchmark return of 7.5% for the sample period. Furthermore, the findings demonstrate that the strategy outperforms the benchmark return in four out of the five years within the sample period. Particularly noteworthy is the strategy's performance during periods of high market volatility, such as the COVID-19 pandemic in 2020, where it generated a return of 14.6%, as opposed to the benchmark return of 5.5%.

• Management & Information Systems Review
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• v.29 no.2
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• pp.1-25
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• 2010

This study attempted to empirically test the determinants of stock returns in Korean stock market applying multi-factor model proposed by Haugen and Baker(1996). Regression models were developed using 16 variables related to liquidity, risk, historical price, price level, and profitability as independent variables and 690 stock monthly returns as dependent variable. For the statistical analysis, the data were collected from the Kis Value database and the tests of forecasting power in this study minimized various possible bias discussed in the literature as possible. The statistical results indicated that: 1) Liquidity, one-month excess return, three-month excess return, PER, ROE, and volatility of total return affect stock returns simultaneously. 2) Liquidity, one-month excess return, three-month excess return, six-month excess return, PSR, PBR, ROE, and EPS have an antecedent influence on stock returns. Meanwhile, realized returns of decile portfolios increase in proportion to predicted returns. This results supported previous study by Haugen and Baker(1996) and indicated that firm-characteristic model can better predict stock returns than CAPM. 3) The firm-characteristic model has better predictive power than Fama-French three-factor model, which indicates that a portfolio constructed based on this model can achieve excess return. This study found that expected return factor models are accurate, which is consistent with other countries' results. There exists a surprising degree of commonality in the factors that are most important in determining the expected returns among different stocks.