• Journal of Digital Convergence
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• v.18 no.7
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• pp.223-228
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• 2020

The stock price reflects people's psychology, and factors affecting the entire stock market include economic growth rate, economic rate, interest rate, trade balance, exchange rate, and currency. The domestic stock market is heavily influenced by the stock index of the United States and neighboring countries on the previous day, and the representative stock indexes are the Dow index, NASDAQ, and S & P500. Recently, research on stock price analysis using stock news has been actively conducted, and research is underway to predict the future based on past time series data through artificial intelligence-based analysis. However, even if the stock market is hit for a short period of time by the forecasting system, the market will no longer move according to the short-term strategy, and it will have to change anew. Therefore, this model monitored Samsung Electronics' stock data and news information through text mining, and presented a predictable model by showing the analyzed results.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.04a
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• pp.347-356
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• 2000

This paper compares four models of artificial neural networks (ANN) supported by genetic algorithms the prediction of stock price index. Previous research proposed many hybrid models of ANN and genetic algorithms(GA) in order to train the network, to select the feature subsets, and to optimize the network topologies. Most these studies, however, only used GA to improve a part of architectural factors of ANN. In this paper, GA simultaneously optimized multiple factors of ANN. Experimental results show that GA approach to simultaneous optimization for ANN (SOGANN3) outperforms the other approaches.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.172.6-172
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• 2001

In recent years, many attempts have been made to predict the behavior of bonds, currencies, stock, or other economic markets. Most previous experiments used multilayer perceptrons(MLP) for stock market forecasting, The Kospi 200 Index is modeled using different neural networks and fuzzy system predictions. In this paper, a multilayer perceptron architecture, a dynamic polynomial neural network(DPNN) and a fuzzy system are used to predict the Kospi 200 index. The results of prediction is compared with the root mean squared error(RMSE) and the scatter plot. The results show that the fuzzy system is performing slightly better than DPNN and MLP. We can develop the desired fuzzy system by learning methods ...

• 한국산학경영학회:학술대회논문집
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• 2006.12a
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• pp.107-120
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• 2006

This thesis examines the relationship between the trading volume and price return in the korean stock Index Futures until June 2005. First, the volume of KOSPI200 futures doesn't play a primary role with the clear explanation of return model. Second, an unexpected volume shocks are negatively associated with the return in case of the KOSPI200 futures, but it is a meaningless relation in the KOSDAQ50 futures. In the case of open interest, it's difficult to find any mean in a both futures. Third, The changes in the trading volumes by foreign investors are positively associated with the return and the volatility, but individuals and domestic commercial investors are negatively associated with the return. This empirical result seems that foreign investors are initiatively trading the korean stock index futures, individuals and domestic commercial investors follow the lead made by foreign investors.

• Journal of Intelligence and Information Systems
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• v.1 no.2
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• pp.103-121
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• 1995

This study proposes a artificial neural network method to predict the time to buy and sell the stocks listed on the Korea Composite Stock Price Index(KOSPI). Four types (NN1, NN2, NN3, NN4) of independent networks were developed to predict KOSPIs up/down direction after four weeks. These networks have a difference only in the length of learning period. NN5 - arithmetic average of four networks outputs - shows an higher accuracy than other network types and Multiple Linear Regression (MLR), and buying and selling simulation using systems outputs produces higher reture than buy-and-hold strategy.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.12
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• pp.1-7
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• 2021

The study examines the influence of COVID-19 on the stock market returns of Saudi Arabia. The data was analyzed through event study methodology using daily price data of Tadawul All Share Index (TASI). The study examines the behavior pattern of the Saudi Arabian stock market in different phases during the event period by selecting six-event windows with a range of 10 days. The results report a negative Abnormal Return (AR) of -0.003 on the event date, while the abnormal returns reversed the next day to 0.005 positively. The result of Cumulative Abnormal Return (CAR) is negative and significant at the 1 percent level in all the six-event windows starting from the event date to day 59 after the event for the TASI index. Even though the influence of the COVID-19 pandemic decreased after 30 days of the event date, it increased during the last ten days of the event window. The stock market volatility of Saudi Arabia increased during the post-event period compared to the pre-event period with a negative mean return of -0.326 and a greater standard deviation. In a conclusion, the study found a significant influence of the COVID-19 pandemic on the stock market returns of TASI.

• 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.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.1
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• pp.143-152
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• 2022

This paper examines the relationship between investor sentiment and the risk of a stock price crash at the firm level. Our dataset includes 131 firms listed on the Saudi stock exchange (Tadawul) from 2011 to 2019, as well as 953 firm-year observations. To evaluate crash risk, we employ two distinct proxies and propose an index for measuring firm-level sentiment which we use for the first time in our study. The average turnover rate, price-earnings ratio, and overnight return are the three sentiment proxies we utilize in our index. Our findings show that high levels of investor emotion increase managers' proclivity to withhold unfavorable news from investors, which aggravates the risk of a stock price crash. We undertake cross-sectional regressions by sector to ensure the robustness of our findings, and our findings are confirmed. After accounting for any endogeneity issues with the GMM technique, the results remain the same. Furthermore, we analyze the liquidity effect by dividing our sample into subsamples with better and worse liquidity and find that firms with worse liquidity have a considerably greater positive impact of investor mood. Overall, our findings help investors and regulators recognize the significance of this downside risk and how to manage it in the stock market.

• Journal of Korean Institute of Industrial Engineers
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• v.29 no.1
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• pp.14-20
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• 2003

The purpose of this paper is an attempt to analyze the dynamic relationship between KSE and KOSDAQ, two competing markets in Korean stock market, in the viewpoint of competition. Lotka-Volterra model, one of well-known competitive diffusion model, is adopted to represent the competitive situations of Korean stock market and it is estimated using daily empirical index data of KSE and KOSDAQ during 1997~2001. The results show that there existed a predator-prey relationship between two markets in which KSE acted as a predator right after the emergence of KOSDAQ. This interaction was altered to a symbiotic relationship and finally to the pure competition relationship. We also perform an equilibrium analysis of the estimated Lotka-Volterra equations and, as a result, it is found that there is a market index equilibrium point that would be stable in the latest relationship.

• Journal of the Korean Data and Information Science Society
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• v.21 no.6
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• pp.1337-1341
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• 2010

The paper investigates linkages between the Korea stock market and each of the major Asia-Pacific stock markets, namely those of the Japan, China, Australia, New-Zealand, We employs the Johansen technique to test for pairwise cointergration between the Korea stock market and each of the major Asia-Pacific stock markets. The major stock indices of the markets are used, from 1 September 2006 to 31 August 2010. The results from the test implies that the Korea market is not cointergrated with any of the major Asia-Pacific markets during the period. Our study implies that there are no long-run linkages between the Korea and any of the major Asia-Pacific stock markets.