• Journal of the Korea Convergence Society
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• v.12 no.6
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• pp.9-16
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• 2021

In the past, there have been various studies on predicting the stock market by machine learning techniques using stock price data and financial big data. As stock index ETFs that can be traded through HTS and MTS are created, research on predicting stock indices has recently attracted attention. In this paper, machine learning models for KOSPI's up and down predictions are implemented separately. These models are optimized through a grid search of their control parameters. In addition, a hybrid machine learning model that combines individual models is proposed to improve the precision and increase the ETF trading return. The performance of the predictiion models is evaluated by the accuracy and the precision that determines the ETF trading return. The accuracy and precision of the hybrid up prediction model are 72.1 % and 63.8 %, and those of the down prediction model are 79.8% and 64.3%. The precision of the hybrid down prediction model is improved by at least 14.3 % and at most 20.5 %. The hybrid up and down prediction models show an ETF trading return of 10.49%, and 25.91%, respectively. Trading inverse×2 and leverage ETF can increase the return by 1.5 to 2 times. Further research on a down prediction machine learning model is expected to increase the rate of return.

• Asia-Pacific Journal of Business
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• v.11 no.3
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• pp.95-106
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• 2020

Purpose - This study examines the information effect of the disclosure of new office investments on the Korean stock market and investigates determinant of performance of sample firms. Design/methodology/approach - The sample consists of companies listed on the Korean Exchange that announced investments in new office construction for eleven-years from January 2007 to December 2017. It analyzes excess return using event study methodology and studies the determinants of abnormal return with multiple regression analysis. Findings - We find that abnormal returns of the short and long window are positive on average and statistically significant. In particular, CAR of high growth subsample is a larger positive return than that of the low one both short and long window. Difference in abnormal returns by investment size is observed only in short time window. But there is not observed difference by cash holding level. Research implications or Originality - This finding is able to be added to the evidence of the theory of corporate value maximization academically. Moreover, it shows the possibility that building a new office can have a positive effect on corporate value. It is expected to help investors make decisions because it can provide useful information to market participants in practice.

• Journal of Service Research and Studies
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• v.10 no.4
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• pp.71-88
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• 2020

CB(Convertible bond) is mezzanine security that have the characteristics of bonds and stocks. From the perspective of investors, the purpose of the research is to empirically investigate the degree of investment efficiency of CB and to suggest efficient investment plans. The research method investigated the maturity interest rate, conversion price, and conversion date for CB, and then linked it with daily stock price fluctuations after the conversion date to determine the degree of investment efficiency and stock conversion effect of CB. As a result of the study, it was analyzed that the ratio of the conversion price exceeded days was only about 1/4 of the conversion date, so the investment efficiency was low. The conversion day yield was -6.3% on average and the maturity day yield was -5.2% on average, showing a minus return on average, which was calculated differently from investor expectations. It was analyzed that the number of stocks with a minus conversion day is 2.4 times greater than the number of plus stocks and 3.7 times more than the number of plus stocks with a minus maturity return, so the expected return on stock conversion of CB is low. The research contribution was derived from the problem that the expected rate of return of CB is not high, and it is that the investor's point of view when purchasing CB was established.

• The Korean Journal of Financial Management
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• v.26 no.3
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• pp.199-226
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• 2009

We examine the intertemporal relation between risk and return in the Korean stock market and foreign exchange market based on the two factor ICAPM framework. The standard GARCH model and the GJR(1993) model are employed to estimate conditional variances of the stock returns and foreign exchange rates. The covariance between the rates of stock returns and changes in the exchange rates are estimated by the constant conditional correlation model of Bollerslev(1990) and the dynamic conditional correlation model of Engle(2002). The multivariate GARCH in mean model and quasi-maximum likelihood estimation method, consequently, are applied to investigate riskreturn relation jointly. We find that the estimated coefficient of relative risk aversion is negative and statistically significant in the post-financial crisis sample period in the Korean stock market. We also show that the expected stock returns are negatively related to the dynamic covariance with foreign exchange rates. Both estimated parameters of conditional variance and covariance in the foreign exchange market, however, are not statistically significant. The GJR model is better than the standard GARCH model to estimate the conditional variances. In addition, the dynamic conditional correlation model has higher explanatory power than the constant correlation model. The empirical results of this study suggest following two points to investors and risk managers in hedging and diversifying strategies for their portfolios in the Korean stock market: first, the variability of foreign exchange rates should be considered, and second, time-varying correlation between stock returns and changes in foreign exchange rates supposed to be considered.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.9
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• pp.1-8
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• 2022

This paper studies market efficiency from a weak form aspect using opening and closing prices of the Shanghai stock exchange composite index (SSEC) and Shenzhen stock exchange composite index (SZSEC) under the expected return theory. Classical methods (autocorrelation and runs test) are used to examine the features of stock returns, and little evidence against mutual independence of returns is found. We predict daily returns of SSEC and SZSEC with AR(p) and VAR(p) models (in this paper, p = 5 is taken as a one-week lag) and perform a virtual experiment on two indexes based on the predicted value of daily returns from AR(p) or VAR(p) model. From the results of AR(p) and VAR(p) for two indexes, we attempt to find out how the market efficiency level changes when the information from the other market is under consideration as we check the market efficiency level in one market. We find that SSEC in 2014-2016 and SZSEC in 2015-2016 are inefficient from the result of autocorrelation, that SSEC in 2016 and SZSEC in 2013 are not efficient from the result of runs test, that the stock market is efficient except 2005, 2009, 2010 and 2017 in SSEC and 2005, 2016 and 2017 in SZSEC and that SSEC is more influenced by SZSEC but SSEC influences SZSEC less from the result of the virtual experiment.

• Journal of Korea Society of Industrial Information Systems
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• v.21 no.5
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• pp.73-83
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• 2016

NAVER provides search frequency data of search terms via its DataLab service (http://datalab.naver.com/). Using this data, this paper examines the relation between the search frequency of firm's name and its future stock returns. Our results show that the search frequency of firm's name is a new investor attention measure, which is different from previously explored attention measures such as extreme returns, turnover, etc. Firms that go through higher search frequency this week tend to have higher returns in the next week. We do not find return reversal in the long run for the firms with higher search frequency. Furthermore, the extent to which search frequency affects stock returns becomes more pronounced following market-wide attention grabbing events. Our results indicate that search frequency incorporates information for future stock returns.

• The Korean Journal of Financial Management
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• v.24 no.4
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• pp.75-106
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• 2007

In this study, it is analyzed whether oil price plays a major role in the pricing return on Koran stock market and examined why the covariance risk between oil and return on stock is different in each industry. Firstly, this study explores whether the expected rate of return on stock is pricing due to global oil price factors as a function of risk premium by using a two-factor APT. Also, it is examined whether spill-over effects of oil price volatility affect the beta risk to oil price. Considering the asymmetry of oil price volatility, we use the GJR model. As a result, it shows that oil price is an independent pricing factor and oil price volatility transmits to stock return in only electricity and electrical equipment. Secondly, the two step-analyzing process is introduced to find why the covariance between oil price factor and stock return is different in each industry. The first step is to study whether beta risk exists in each industry by using two proxy variables like size and liquidity as control variables. The second step is to grasp the systematic relationship between the difference of liquidity and size and beta to oil price factor by using the panel-data model which can be analyzed efficiently using the cross-sectional data formed with time series. Through the analysis, we can argue that oil price factor is an independent pricing factor in only electricity and electrical equipment having the greatest market capitalization, and know that beta risk to oil price factor is a proxy of size in the other industries. According to the result of panel-data model, it is argued that the beta to oil price factor augments when market capitalization increases and this fact supports the first assertion. In conclusion, the expected rate of return of electricity and electrical equipment works as a function of risk premium to market portfolio and oil price, and the reason to make beta risk power differentiated in each industry attributes to the size.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.117-129
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• 2015

As the Internet becomes ubiquitous, a large volume of information is posted on the Internet with exponential growth every day. Accordingly, it is not unusual that investors in stock markets gather and compile firm-specific or market-wide information through online searches. Importantly, it becomes easier for investors to acquire value-relevant information for their investment decision with the help of powerful search tools on the Internet. Our study examines whether or not the Internet helps investors assess a firm's value better by using firm-level data over long periods spanning from January 2004 to December 2013. To this end, we construct weekly-based search volume for information technology (IT) services firms on the Internet. We limit our focus to IT firms since they are often equipped with intangible assets and relatively less recognized to the public which makes them hard-to measure. To obtain the information on those firms, investors are more likely to consult the Internet and use the information to appreciate the firms more accurately and eventually improve their investment decisions. Prior studies have shown that changes in search volumes can reflect the various aspects of the complex human behaviors and forecast near-term values of economic indicators, including automobile sales, unemployment claims, and etc. Moreover, search volume of firm names or stock ticker symbols has been used as a direct proxy of individual investors' attention in financial markets since, different from indirect measures such as turnover and extreme returns, they can reveal and quantify the interest of investors in an objective way. Following this line of research, this study aims to gauge whether the information retrieved from the Internet is value relevant in assessing a firm. We also use search volume for analysis but, distinguished from prior studies, explore its impact on return comovements with market returns. Given that a firm's returns tend to comove with market returns excessively when investors are less informed about the firm, we empirically test the value of information by examining the association between Internet searches and the extent to which a firm's returns comove. Our results show that Internet searches are negatively associated with return comovements as expected. When sample is split by the size of firms, the impact of Internet searches on return comovements is shown to be greater for large firms than small ones. Interestingly, we find a greater impact of Internet searches on return comovements for years from 2009 to 2013 than earlier years possibly due to more aggressive and informative exploit of Internet searches in obtaining financial information. We also complement our analyses by examining the association between return volatility and Internet search volumes. If Internet searches capture investors' attention associated with a change in firm-specific fundamentals such as new product releases, stock splits and so on, a firm's return volatility is likely to increase while search results can provide value-relevant information to investors. Our results suggest that in general, an increase in the volume of Internet searches is not positively associated with return volatility. However, we find a positive association between Internet searches and return volatility when the sample is limited to larger firms. A stronger result from larger firms implies that investors still pay less attention to the information obtained from Internet searches for small firms while the information is value relevant in assessing stock values. However, we do find any systematic differences in the magnitude of Internet searches impact on return volatility by time periods. Taken together, our results shed new light on the value of information searched from the Internet in assessing stock values. Given the informational role of the Internet in stock markets, we believe the results would guide investors to exploit Internet search tools to be better informed, as a result improving their investment decisions.

• Journal of Digital Convergence
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• v.13 no.11
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• pp.45-55
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• 2015

This study used for KOSPI, KOSDAQ, entertainment culture and digital contents index that is related to cultural contents industry. There was investigated the each stock price index and return trends for a total 597 weeks to July 2015 from March 2004. They looked the content-related stocks about investment worth to comparative analysis the return, volatility, correlation, synchronization phenomena etc. of each stock index. When we saw the growth potential of the cultural contents industry forward, looked forward to the investment possibility of related stocks. Analysis Result cultural content related stocks showed a higher rate after the last 2008 global financial crisis. Recent as high interest in the cultural contents industry, we could see that the investment merit increases slowly. In the future, the cultural content industry is expected to continue to evolve. The increase of investments value in the cultural content related businesses is much expectation.

• The Korean Journal of Financial Management
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• v.24 no.3
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• pp.153-186
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• 2007

It is well known that the distributional properties of financial asset returns exhibit fatter-tails and skewer-mean than the assumption of normal distribution. The correct assumption of return distribution might improve the estimated performance of the Value-at-Risk(VaR) models in financial markets. In this paper, we estimate and compare the VaR performance using the RiskMetrics, GARCH and FIGARCH models based on the normal and skewed-Student-t distributions in two daily returns of the Korean Composite Stock Index(KOSPI) and Korean Won-US Dollar(KRW-USD) exchange rate. We also perform the expected shortfall to assess the size of expected loss in terms of the estimation of the empirical failure rate. From the results of empirical VaR analysis, it is found that the presence of long memory in the volatility of sample returns is not an important in estimating an accurate VaR performance. However, it is more important to consider a model with skewed-Student-t distribution innovation in determining better VaR. In short, the appropriate assumption of return distribution provides more accurate VaR models for the portfolio managers and investors.