• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2014.10a
• /
• pp.708-710
• /
• 2014

최근 금융 분야에서는 빅 데이터를 이용하여 주가예측 모형을 만들어내고 있으며, 특히 금융 시계열 자료의 변동성 집중 현상을 금융 빅 데이터를 이용하여 분석함으로써 세계 주식시장의 동조화 현상을 분석하고 있다. 본 논문에서는 한국과 중국의 일별 주가지수수익률과 일중 주가지수수익률을 이용하여 이들 2개 국가의 대표적인 주가지수 시계열 데이터에 변동성 집중 현상이 존재하는지를 보다 세밀하게 추적하여 양국 주식시장의 동조화 현상을 분석한다. 분석 결과, 한국의 KOSPI와 중국의 Shanghai 종합주가지수의 지수수익률 시계열 자료는 단위근이 존재하지 않으며, 변동성 집중 현상을 보이는 것으로 나타났다. 또한 한국보다는 중국 주식시장의 변동성 집중현상이 보다 강하게 나타나며, 이러한 현상은 일중 주가지수수익률 시계열 자료에서 보다 두드러지게 나타났다.

• Journal of Intelligence and Information Systems
• /
• v.27 no.1
• /
• pp.65-82
• /
• 2021

Stock market investors are generally split into foreign investors, institutional investors, and individual investors. Compared to individual investor groups, professional investor groups such as foreign investors have an advantage in information and financial power and, as a result, foreign investors are known to show good investment performance among market participants. The purpose of this study is to propose an investment strategy that combines investor-specific transaction information and machine learning, and to analyze the portfolio investment performance of the proposed model using actual stock price and investor-specific transaction data. The Korea Exchange offers daily information on the volume of purchase and sale of each investor to securities firms. We developed a data collection program in C# programming language using an API provided by Daishin Securities Cybosplus, and collected 151 out of 200 KOSPI stocks with daily opening price, closing price and investor-specific net purchase data from January 2, 2007 to July 31, 2017. The self-organizing map model is an artificial neural network that performs clustering by unsupervised learning and has been introduced by Teuvo Kohonen since 1984. We implement competition among intra-surface artificial neurons, and all connections are non-recursive artificial neural networks that go from bottom to top. It can also be expanded to multiple layers, although many fault layers are commonly used. Linear functions are used by active functions of artificial nerve cells, and learning rules use Instar rules as well as general competitive learning. The core of the backpropagation model is the model that performs classification by supervised learning as an artificial neural network. We grouped and transformed investor-specific transaction volume data to learn backpropagation models through the self-organizing map model of artificial neural networks. As a result of the estimation of verification data through training, the portfolios were rebalanced monthly. For performance analysis, a passive portfolio was designated and the KOSPI 200 and KOSPI index returns for proxies on market returns were also obtained. Performance analysis was conducted using the equally-weighted portfolio return, compound interest rate, annual return, Maximum Draw Down, standard deviation, and Sharpe Ratio. Buy and hold returns of the top 10 market capitalization stocks are designated as a benchmark. Buy and hold strategy is the best strategy under the efficient market hypothesis. The prediction rate of learning data using backpropagation model was significantly high at 96.61%, while the prediction rate of verification data was also relatively high in the results of the 57.1% verification data. The performance evaluation of self-organizing map grouping can be determined as a result of a backpropagation model. This is because if the grouping results of the self-organizing map model had been poor, the learning results of the backpropagation model would have been poor. In this way, the performance assessment of machine learning is judged to be better learned than previous studies. Our portfolio doubled the return on the benchmark and performed better than the market returns on the KOSPI and KOSPI 200 indexes. In contrast to the benchmark, the MDD and standard deviation for portfolio risk indicators also showed better results. The Sharpe Ratio performed higher than benchmarks and stock market indexes. Through this, we presented the direction of portfolio composition program using machine learning and investor-specific transaction information and showed that it can be used to develop programs for real stock investment. The return is the result of monthly portfolio composition and asset rebalancing to the same proportion. Better outcomes are predicted when forming a monthly portfolio if the system is enforced by rebalancing the suggested stocks continuously without selling and re-buying it. Therefore, real transactions appear to be relevant.

• Korean Business Review
• /
• v.18 no.1
• /
• pp.43-57
• /
• 2005

This paper investigate to test the validity of the basic assumptions of strategic trading models with the broker and informed trader using daily closing data of KOSPI 200 stock index futures for the year 2001-2003. Major results are summarized as follows: (i) For these years, while foreign investors and brokerage companies traded for the directions consistent with the model, brokerage companies and individual investors traded for inconsistent directions. (ii) Cross correlation function (CCF) analysis shows no systematic dependency in the trading between all three participants(foreign investor, brokerage companies and individual investors) for these years. (iii) Chi-square validity test for the 30 days of the largest unexpected trading volume shows some systematic dependency in the trading between three participants for these years. Finally, some limitations of this paper and direction for further research were suggested.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.19 no.2
• /
• pp.125-139
• /
• 2024

The corporate logo symbolizes the company's value, goals and vision as a visual symbol representing the company. It serves as a communication tool for companies by conveying different messages depending on design and color. As demands for ESG management have recently increased, companies have begun to implicitly demonstrate values such as environmental protection and corporate transparency through logos. Companies use logos as a strategy to visually emphasize the value they pursue and project the desired image as a signal. In this process, stakeholders who see the logo experience cognitive bias. Therefore, this study seeks to find out that ESG value can be indirectly communicated by the design of corporate logos, which can also affect a company's ESG evaluation. In addition, this study will examine the moderate effect that logos expect to encounter a greater bias effect as the companies actively include ESG-related issues in corporate disclosure data. This study conducted an analysis of 617 KOSPI-listed companies using ESG evaluation data from 2020 to 2022. The analysis confirmed the significant relation of the type of logo and ESG disclosure on ESG evaluation but found partially moderate effect of ESG disclosure.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.18 no.2
• /
• pp.149-157
• /
• 2017

This study investigated one of the contemporary financial issues that is still being debated among governmental policy makers, corporate managers, and investors in the domestic capital market. We attempted to identify the most optimal level of cash holdings for firms during the most updated fiscal years (from 2011 to 2015). The study utilized empirical methodologies, such as ANCOVA and RANCOVA, with respect to the 'inter-' and 'intra-industry' analyses for KOSPI-listed firms. Regarding the first hypothesis testing for inter-industry influence, we revealed with statistical significance that there were differences; however, there were only 3 pronounced industries among the 25 industries sampled in this study. Regarding the second hypothesis, only a few (i.e. two) industries showed no statistically significant intra-industry influence. Based on our results, most KOSPI-listed firms still seem to be searching for their optimal levels of cash reserves. Hence, we can anticipate that the value maximization as a corporate goal can be achieved after adjusting the current levels of their cash holdings according to the optimal points.

• The Korean Journal of Applied Statistics
• /
• v.24 no.5
• /
• pp.833-845
• /
• 2011

This paper conducts a statistical analysis of extreme values for both daily log-returns and daily negative log-returns, which are computed using a collection of KOSPI data from January 3, 1998 to August 31, 2011. The Poisson-GPD model is used as a statistical analysis model for extreme values and the maximum likelihood method is applied for the estimation of parameters and extreme quantiles. To the Poisson-GPD model is also added the Bayesian method that assumes the usual noninformative prior distribution for the parameters, where the Markov chain Monte Carlo method is applied for the estimation of parameters and extreme quantiles. According to this analysis, both the maximum likelihood method and the Bayesian method form the same conclusion that the distribution of the log-returns has a shorter right tail than the normal distribution, but that the distribution of the negative log-returns has a heavier right tail than the normal distribution. An advantage of using the Bayesian method in extreme value analysis is that there is nothing to worry about the classical asymptotic properties of the maximum likelihood estimators even when the regularity conditions are not satisfied, and that in prediction it is effective to reflect the uncertainties from both the parameters and a future observation.

• The Korean Journal of Financial Studies
• /
• v.5 no.1
• /
• pp.191-212
• /
• 1999

1997년에 우리 나라는 외환충격으로 인한 금융위기 속에서 시장가격이 급격하게 변동하였다. 이로 인해 차익거래를 가능하게 하는 차입과 대출이 크게 제약되었고, 이것은 시장간 균형관계에 중요한 영향을 줄 수 있다. 이에 이러한 금융위기에서도 주요 시장간의 균형관계가 유지되었는지를 검정하는 것이 이 연구의 목적이다. 분석자료로 KOSPI 200 현물 종가 및 선물 결제가격, 연간 회사채 수익률, 양도성 예금 연간이자율, 기준환율의 일일 자료를 사용하였다. 1996년 5월 3일부터 1998년 5월 21일까지의 기간을 외환충격에 의한 금융위기 전, 중, 후의 3단계로 구분하여 각 단계별로 백터오차수정모형 분석과 충격반응분석을 하였다. 금융위기 이전인 제1단계에서는 5개 내생변수간의 균형관계가 존재하였다. 금융위기가 급속하게 진행된 제2단계에서는 균형관계가 존재하지 않았다. 그러나 주가지수, 주가지수 선물가격 및 기준환율 변수를 내생변수로 하고, 나머지 변수를 외생변수로 분석한 경우에는 균형관계가 존재하였다. 금융위기 진정단계인 제3단계에서는 5개 내생변수간의 균형관계가 성립하였다.

• Journal of Intelligence and Information Systems
• /
• v.23 no.2
• /
• pp.107-122
• /
• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.

• Journal of Venture Innovation
• /
• v.2 no.2
• /
• pp.95-115
• /
• 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 Korean Journal of Applied Statistics
• /
• v.23 no.4
• /
• pp.651-668
• /
• 2010

Various estimators of two risk measures of a specific financial portfolio, Value-at-Risk and Expected Shortfall, are compared for each case of 1-day and 10-day horizons. We use the Korea Composite Stock Price Index data of 20-year period including the year 2008 of the global financial crisis. Indexes of five foreign stock markets are also used for the empirical comparison study. The estimator considering both the heavy tail of loss distribution and the conditional heteroscedasticity of time series is of main concern, while other standard and new estimators are considered too. We investigate which estimator is best for the Korean stock market and which one shows the best overall performance.