• Management & Information Systems Review
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• v.33 no.1
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• pp.83-97
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• 2014

This study examined the effects institutional block ownership on the stock market liquidity in Korean Stock Market. The two measures of institutional block ownership are used. They are the percentage of a stock owned by institutional blockholder and the number of institutional blockholder that own the stock. This study used the Amihud(2002) illiquidity measure to measure stock market liquidity. The results are as fellows. First, this study showed that the number of institutional blockholder is significantly negatively correlated with the Amihud(2002) illiquidity measure in the analysis which is used the whole data. But we found no a consistent results between the number of institutional blockholder and the Amihud(2002) illiquidity measure in the grouped institutional blockholder's number analysis. This indicates that the effects institutional blockholder on market liquidity is not simple. Second, this study showed that the percentage of a stock owned by institutional blockholder are negatively related with Amihud(2002) illiquidity measure, especially revealed statistically significant in the group 3(11.71%~17.38%) and group 4(7.45%~11.65%). This results suggest that the institutional blockholder have positive effect on the market liquidity in the group 3 and 4. Third, the significance of the percentage of institutional block ownership and the number of institutional block ownership in explaining illiquidity are more showed in the term of the global financial crisis(2008) than the before and the after of the global financial crisis.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.10
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• pp.441-450
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• 2014

The stock market is constantly changing and sometimes the stock prices unaccountably plummet or surge. So, the stock market is recognized as a complex system and the change on the stock prices is unpredictable. Recently, many researchers try to understand the stock market as the network among individual stocks and to find a clue about the change of the stock prices from big data being created in real time from Internet. We focus on the correlation between the stock prices and the human interactions in Internet especially in the stock message boards. To uncover this correlation, we collected and investigated the articles concerning with 57 target companies, members of KOSPI200. From the analysis result, we found that there is no significant correlation between the stock prices and the article volume, but the strength of correlation between the article volume and the stock prices is relevant to the stock return. We propose a new method for recommending stock portfolio base on the result of our analysis. According to the simulated investment test using the article data from the stock message boards in 'Daum' portal site, the returns of our portfolio is about 1.55% per month, which is about 0.72% and 1.21% higher than that of the Markowitz's efficient portfolio and that of the KOSPI average respectively. Also, the case using the data from 'Naver' portal site, the stock returns of our proposed portfolio is about 0.90%, which is 0.35%, 0.40%, and 0.58% higher than those of our previous portfolio, Markowitz's efficient portfolio, and KOSPI average respectively. This study presents that collective human behavior on Internet stock message board can be much helpful to understand the stock market and the correlation between the stock price and the collective human behavior can be used to invest in stocks.

• Asia-Pacific Journal of Business
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• v.13 no.4
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• pp.79-89
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• 2022

Purpose - The purpose of this study was to examine the impact of ESG rating changes of companies listed in Korean Stock Exchange on stock returns. Design/methodology/approach - This study collected prices and ESG ratings of all the companies listed on the Korea Composite Stock Price Index. Based on yearly change of ESG ratings we grouped companies as 2 portfolios(upgrade and downgrade) and calculated portfolios' return. Findings - First, the difference in returns between upgraded and downgraded portfolios is small and statistically insignificant. Second, however, in the COVID-19 period (2020 ~ 2021), the upgraded portfolio outperforms the downgraded portfolio by 0.7 percentage points per month. The difference in returns between upgraded and downgraded portfolios is statistically significant after controlling for the Carhart four factors. Lastly, there are much higher volatility when the ESG rating changes are made of companies with low levels of ESG ratings. Research implications or Originality - This study is the first to examine the impact of ESG rating changes on stock returns in Korea. Furthermore, the findings can serve as a reference for managers who want to control a firm's risk by ESG rating changes. Practically, asset managers can use the findings to construct portfolios that are less risky or more profitable than the market portfolio.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.103-128
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• 2021

Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.

• Management & Information Systems Review
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• v.39 no.2
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• pp.17-37
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• 2020

The purpose of this study is to investigate the global spillover effects through the existence of linear and nonlinear causal relationships between the US, European and BRIC financial markets after the period from the introduction of the Euro, the financial crisis and the subsequent EU debt crisis in 2007~2010. Although the global spillover effects of the financial crisis are well described, the nature of the volatility effects and the spread mechanisms between the US, Europe and BRIC stock markets have not been systematically examined. A stepwise filtering methodology was introduced to investigate the dynamic linear and nonlinear causality, which included a vector autoregressive regression model and a multivariate GARCH model. The sample in this paper includes the post-Euro period, and also includes the financial crisis and the Eurozone financial and sovereign crisis. The empirical results can have many implications for the efficiency of the BRIC stock market. These results not only affect the predictability of this market, but can also be useful in future research to quantify the process of financial integration in the market. The interdependence between the United States, Europe and the BRIC can reveal significant implications for financial market regulation, hedging and trading strategies. And the findings show that the BRIC has been integrated internationally since the sub-prime and financial crisis erupted in the United States, and the spillover effects have become more specific and remarkable. Furthermore, there is no consistent evidence supporting the decoupling phenomenon. Some nonlinear causality persists even after filtering during the investigation period. Although the tail distribution dependence and higher moments may be significant factors for the remaining interdependencies, this can be largely explained by the simple volatility spillover effects in nonlinear causality.

• Smart Media Journal
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• v.11 no.1
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• pp.38-45
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• 2022

A lot of research is being going until this day in order to obtain stable profit in the stock market. Trading algorithms are widely used, accounting for over 80% of the trading volume of the US stock market. Despite a lot of research, there is no trading algorithm that always shows good performance. In other words, there is no guarantee that an algorithm that performed well in the past will perform well in the future. The reason is that there are many factors that affect the stock price and there are uncertainties about the future. Therefore, in this paper, we propose a model using TimeGAN that predicts future returns well and selects algorithms that are expected to have high returns based on past records of the returns of algorithms. We use TimeGAN becasue it is probabilistic, whereas LSTM method predicts future time series data is deterministic. The advantage of TimeGAN probabilistic prediction is that it can reflect uncertainty about the future. As an experimental result, the method proposed in this paper achieves a high return with little volatility and shows superior results compared to many comparison algorithms.

• The Korean Journal of Financial Management
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• v.24 no.3
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• pp.91-131
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• 2007

In the paper, the effects of sidecar on index arbitrage trading and non-index arbitrage trading in the Korean stock market are examined. The analyses of return, volatility, and liquidity dynamics illustrate that there are no distinct differences for index arbitrage group and non-index arbitrage group surrounding the sidecar events. For further analysis, we construct pseudo-sidecar sample and analyse the effects of the actual sidecar and pseudo-sidecar on arbitrage sample and non-index arbitrage sample. The result of analysis using pseudo-sidecar shows that the differences between index arbitrage group and non-index arbitrage group are larger in pseudo-sidecar sample than in actual sidecar sample. This means that former results can be explained by temporary order clustering in one side before and after the event. Sidecar has little effect on non-index arbitrage group, however, it has relatively large effect on arbitrage group. These results imply that it needs to redesign the sidecar system of the Korean stock market which applies for all program trading including arbitrage and non-index arbitrage trading.

• The Journal of Society for e-Business Studies
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• v.24 no.4
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• pp.109-118
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• 2019

As a result of the United States (U.S) -China trade conflict, the recent instability of the stock market has led many people to invest in Bitcoin, a commodity that many previous studies have interpreted as a safe asset. However, recent Bitcoin market price fluctuations suggest that the asset's stability stems from speculative purchasing trends. Therefore, classifying the characteristics of Bitcoin assets can be an important reference point in analyzing relevant accounting information. To determine whether Bitcoin is a safe asset, this study analyzed the correlation between Bitcoin and economic indicators to verify whether gold and Bitcoin responded similarly in time series analyses. These show that the regression explanatory power between the price of gold and bitcoin is low, thus no relation between the two assets could be drawn. Additionally, the Granger causality analyses of six individual economic variables and Bitcoin did not establish any notable causality. This can be interpreted that short-term price fluctuations have a significant impact on the nature of Bitcoin as an asset.

• The Korean Journal of Financial Management
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• v.27 no.2
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• pp.169-204
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• 2010

This paper examines the dynamic relationship between fund flow and market risk at the aggregate level and explores whether sudden sharp changes in fund flow (fund run) can cause a systemic risk in the Korean financial markets. We use daily and weekly data and regression and VAR analysis. Main results of the paper are as follows: First, in the stock market, a concurrent and a lagged unexpected fund flows have a positive relationship with market volatility. A positive shock in fund flow predicts an increase in stock market volatility. In the bond market, an unexpected fund flow has a negative relationship with the default risk premium, but a positive relationship with the term premium. And an unexpected fund flow of the money market fund has a negative relationship with the liquidy risk, but the explanatory power is very low. Second, for examining whether changes in fund flow induce a systemic risk, we construct a spillover index based on the forecast error variance decomposition of VAR model. A spillover index represents that how much the shock in fund flow can explain the change of market risk in a market. In general, explanatory powers from spillover indexes are so fluctuant and low. In the stock market, the impact of shocks in fund flow on market risk is relatively high and persistent during the period from the end of 2007 to 2008, which is the subprime-mortgage crisis period. In bond market, since the end of 2008, the impact of shocks in fund flow spreads to default risk continually, while in the money market, such a systematic effect doesn't take place. The persistent patterns of spillover effect appearing around a certain period in the stock market and the bond market suggest that the shock to the unexpected fund flow may increase the market risk and can be a cause of systemic risk in the financial markets. However, summarizing the results of regression and VAR model analysis, and considering the very low explanatory power of spillover index analysis, we can conclude that changes in fund flow have a very limited power in explaining changes in market risk and it is not very likely to induce the systemic risk by a fund run in the Korean financial markets.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.8
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• pp.25-31
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• 2020

The purpose of this paper is to examine a financial distress premium in the emerging market. A risk-return trade-off of negative book equity (NBE) and distress firms is empirically analyzed using data from the Stock Exchange of Thailand. This research employs Ohlson's (1980) bankruptcy model as a measurement of distress risk. The results indicate that distress firms outperform solvent firms in the Thai market and deny distress anomaly often found in the developed market. Fama-Frech (1993) three-factor model and Carhart (1997) four-factor model verify the existence of a distress premium in the Thai capital market. Risk-seeking investors demand greater compensation for bearing risks of distress firms' going concern. This paper provides fresh evidence that default risk is a significant explanatory factor in pricing stocks in the emerging market. Also, this study sheds light on the role of NBE firms in asset pricing. Most studies eliminate NBE firms from their sample. However, NBE firms yield superior average cross-sectional returns, albeit with higher volatility. Investors are rewarded with distress risks associated with NBE firms. The outperformance of NBE firms is statistically significant when compared to the overall market. The NBE premium disappears when factoring size, value, and momentum in time-series analysis.