• Industry Promotion Research
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• v.9 no.2
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• pp.29-35
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• 2024

Recently, the Korean financial market has implemented a ban on unleveraged short selling, and leveraged short selling, which involves selling borrowed securities, is called general short selling. This study sought to come up with improvement measures to revitalize short selling from the perspective of individual investors. Short selling refers to selling stocks you do not own in the stock market, predicting that the stock price of the stock will fall, and borrowing stocks to sell them. Based on the results of this study, the short selling market's growth and improvement plans are as follows. First, a plan must be developed to expand short selling opportunities for individual investors. In the domestic short selling market, including KOSPI and KOSDAQ, foreign and institutional participants account for more than 95% of the market, and individual investors are very small. Therefore, its expansion is inevitable. Second, monitoring and punishment for unfair short selling transactions must be strengthened. Representative improvement measures that can minimize the side effects of short selling include strengthening monitoring of unfair trading and short selling, and raising the level of punishment. In addition, measures must be taken to further increase the level of punishment for short selling related to unfair transactions. Third, the short selling reporting and disclosure system needs to be improved. In the case of Korea, short selling transactions are not yet as active as in developed countries, but there is a need to expand the disclosure system to strengthen market transparency in preparation for future short selling transactions becoming more active. In conclusion, it is reported that if short selling regulations are excessively strengthened, losses may occur in terms of price efficiency and market liquidity, which may ultimately have a negative impact on the market. Therefore, policies related to short selling must be made while taking into account the positive aspects of regulatory effects and the negative impact on the market.

• Journal of the Korean Data and Information Science Society
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• v.16 no.3
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• pp.567-580
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• 2005

It is usually assumed that asset returns in the stock market are normally distributed. However, analyses of real data show that the distribution tends to be skewed and to have heavier tails than those of the normal distribution. In this paper, we investigate the method of estimating the value at risk(VaR) of stock returns. The VaR is computed by using the transformation and back-transformation method. The analysis of KOSPI and KOSDAQ data shows that the proposed estimation outperformed that under the normal assumption.

• Korean Management Science Review
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• v.29 no.1
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• pp.57-71
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• 2012

In this paper, we deal with a sector investment strategy by implementing the black-litterman model that incorporates expert evaluation and sector rotation momentum. Expert evaluation analyzes the relative performance of the industry sector compared with the market, while sector rotation momentum reflects the price impact of significant sector anomaly. In addition, we consider the portfolio impact of sector cardinality and weight constraints within the context of mean-variance portfolio optimization. Finally, we demonstrate the empirical viability of the proposed sector investment strategy with KOSPI 200 data.

• Journal of the Korean Operations Research and Management Science Society
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• v.39 no.1
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• pp.113-127
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• 2014

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 utility defined in terms of mean and variance by managing the constraint set and the objective function in an iterative manner. A nonlinear programming algorithm was developed and tested with promising results against Korean stock market data sets.

• Communications for Statistical Applications and Methods
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• v.31 no.5
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• pp.585-599
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• 2024

Options pricing remains a critical aspect of finance, dominated by traditional models such as Black-Scholes and binomial tree. However, as market dynamics become more complex, numerical methods such as Monte Carlo simulation are accommodating uncertainty and offering promising alternatives. In this paper, we examine how effective different options pricing methods, from traditional models to machine learning algorithms, are at predicting KOSPI200 option prices and maximizing investment returns. Using a dataset of 2023, we compare the performance of models over different time frames and highlight the strengths and limitations of each model. In particular, we find that machine learning models are not as good at predicting prices as traditional models but are adept at identifying undervalued options and producing significant returns. Our findings challenge existing assumptions about the relationship between forecast accuracy and investment profitability and highlight the potential of advanced methods in exploring dynamic financial environments.

• Journal of Venture Innovation
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• v.2 no.2
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• pp.95-115
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• 2019

In the context of today's business environment, not only is the nation or company's credit rating considered very important in our recent society, but it is also becoming important in international transactions. Likewise, at this point of time when the importance and reliability of credit evaluation are becoming important at home and abroad, this study analyzes financial ratios related to corporate profitability, safety, activity, financial growth, and profit growth to study the impact of financial indicators on enterprise value and corporate taxes on credit evaluation. To proceed with this, the financial ratio of 465 companies of KOSPI securities listed in 2017 was calculated and the impact of enterprise value and corporate taxes on credit evaluation was analyzed. Especially, this further study tried to derive a reliable and consistent conclusion by analyzing the financial data of KOSPI securities listed companies for eight years from 2011, which is the first year of K-IFRS introduction, to 2018. Research has shown that the significance levels among variables that show the profitability, safety, activity, financial growth, and profit growth of each financial ratio were significant at the 99% level, except for the profit growth. Validation of the research hypothesis found that while the profitability of KOSPI-listed companies significantly affects corporate value and income tax, indicators such as safety ratio and growth ratio do not significantly affect corporate value and income tax. Activity ratio resulted in significant effects on the value of enterprise value but not significant impacts on income taxes. In addition, it was found that the enterprise value has a significant effect on the company's credit and corporate income taxes, and that corporate income taxes also have a significant effect on the corporate credit evaluation, and this also shows that there is a mediating function of corporate tax. And as a result of further study, when looking at the financial ratio for eight years from 2011 to 2018, it was found that two variables, KARA and LTAX, are significant at a 1% significant level to KISC, whereas LEVE variables is not significant to KISC. The limitation of this study is that credit rating score and financial score cannot be said to be reliable indicators that investors in the capital market can normally obtain, compared to ranking criteria for corporate bonds or corporate bills directly related to capital procurement costs of enterprise. Above all, it is necessary to develop credit rating score and financial score reflecting financial indicators such as business cash flow or net assets market value and non-financial indicators such as industry growth potential or production efficiency.

• The Journal of the Korea Contents Association
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• v.18 no.4
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• pp.444-453
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• 2018

The study addresses the analysis on the financial determinants of corporate research and development (R&D) expenditure in finance. Overall level of R&D spending was estimated as one of the top-tier on a global basis and a majority of the expenditure was invested by large domestic firms in private sector. Consequently, financial factors that influence R&D intensity were empirically tested in the first hypothesis by using conditional quantile regression model for firms listed in KOSPI stock market in the post-era of the global financial turmoil. Firms in the groups of high- and low-R&D intensity were statistically compared to detect financial differences in the second hypothesis which was accompanied by the test of multi-logit model that included firms without R&D outlay. Concerning the results of the hypothesis tests, R&D spending of the prior fiscal year, firm size, business risk and advertising expense overall showed statistically significant impacts to determine the level. As an extended study of [1] that had examined financial factors of R&D intensity at the macro-level, the results of the present study are anticipated to contribute to maximizing shareholders' wealth in advance or emerging capital markets, when applied to find an optimal level of R&D expenditure.

• The KIPS Transactions:PartB
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• v.14B no.7
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• pp.531-540
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• 2007

In this research, we proposed a two-phase hybrid stock price forecasting model with cointegration tests and artificial neural networks. Using not only the related stocks to the target stock but also the past information as input features in neural networks, the new model showed an improved performance in forecasting than that of the usual neural networks. Firstly in order to extract stocks which have long run relationships with the target stock, we made use of Johansen's cointegration test. In stock market, some stocks are apt to vary similarly and these phenomenon can be very informative to forecast the target stock. Johansen's cointegration test provides whether variables are related and whether the relationship is statistically significant. Secondly, we learned the model which includes lagged variables of the target and related stocks in addition to other characteristics of them. Although former research usually did not incorporate those variables, it is well known that most economic time series data are depend on its past value. Also, it is common in econometric literatures to consider lagged values as dependent variables. We implemented a price direction forecasting system for KOSPI index to examine the performance of the proposed model. As the result, our model had 11.29% higher forecasting accuracy on average than the model learned without cointegration test and also showed 10.59% higher on average than the model which randomly selected stocks to make the size of the feature set same as that of the proposed model.

• Land and Housing Review
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• v.4 no.1
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• pp.43-54
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• 2013

This This paper investigates the variation of the factors to determinate housing price in Seoul metropolitan area after sub-prime financial crisis, in Korea, using a VAR model. The model includes housing price and housing rent (Jeonse) in Seoul metropolitan area from 1999 to 2011, and uses interest rate, real GDP, KOSPI, Producer Price Index and practices to impulse response and variance decomposition analysis to grasp the dynamic relation between a variable of macro economy and and a variable of housing price. Data is classified to 2 groups before and after the 3rd quater of 2008, when sub-prime crisis occurred; one is from the 1st quater of 1999 to the 3rd quater of 2008, and the other is from the 2nd quater of 1999 and the 4th quater of 2011. As a result, comparing before and after sub-prime crisis, housing price is more influenced by its own variation or Jeonse price's variation instead of interest rate and KOSPI. Both before and after sub-prime financial crisis, Jeonse price is also influenced by its own variation and housing price. While after sub-prime financial crisis, influences of Producer Price Index, KOSPI and interest rate were weakened, influence of real GDP is expanded. As housing price and housing rent are more influenced by real economy factors such as GDP, its own variation than before sub-prime financial crisis, the recent trend that the house prices is declined is difficult to be converted, considering domestic economic recession and uncertainty, continued by Europe financial crisis. In the future to activate the housing business, it ia necessary to promote purchasing power rather than relaxation of financial and supply regulation.

• Journal of Industrial Convergence
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• v.18 no.2
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• pp.19-26
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• 2020

This study analyzed the status and trend of gender wage gap among listed companies in KOPSI market over the 2000-2017 period. The main results of the study are as follows. First, the gender employment gap index for KOSPI listed companies stood at 39.81 in 2017, with 40 women per 100 men being employed. Although the absolute value of the proportion of female employment remains low, it has not only been higher than 33.74 in 2000 but has also increased steadily in recent years. In terms of the number of years of service, the average number of male employees in 2017 was 9.9 years, compared with 6.9 years for female employees, and the gender tenure gap decreased over the past few years. Finally, The gender wage gap index increased from 60.57 in 2000 to 67.87 in 2017. In addition, there are slight variations in the size of the company or industry, but consistent results have shown that the gender wage gap decreases in recent years. The findings suggest that our society is developing in a way that reduces the gender employment gap and the gender wage gap.