• Proceedings of the Safety Management and Science Conference
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• 2002.11a
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• pp.321-339
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• 2002

The result of variance decomposition through yield of Treasury of 30 year maturity of USA, S&P 500 index, stock price of KEPCO has 76.12% of impulse of KEPCO stock price at short-term horizon, but they have 51.40% at long-term horizon. After one year, they occupy 13.65%, and 33.25%. So their effects are increased. By the way, S&P 500 index and yield of Treasury of 30 year maturity of USA have relatively more effect for forecast of stock price oi KEPCO at short-term & long-term. The yield of Treasury of 30 year maturity of USA more than S&P 500 index have more effect for stock price of KEPCO. It is why. That foreign investors through fall of stock price of USA invest for emerging market is less than movement for emerging market of hedge funds through effect of fall of yield of Treasury of 30 year maturity of USA, according to relative effects for stock price of Korea companies. The result of variance decomposition through won/dollar foreign exchange rate, yield of corporate bond of 3 year maturity, Korea Stock Price index(KOSPI), stock price of KEPCO has 81.33% of impulse of KEPCO stock price at short-term horizon, but they have 41.73% at long-term horizon. After one year, they occupy 23.57% and 34.70%. So their effects are increased. By the way, KOSPI and won/dollar foreign exchange rate have relatively more effect for forecast of stock price of KEPCO at short-term & long-term. The won/dollar foreign exchange rate more than KOSPI have more effect for stock price of KEPCO. It is why. The recovery of economic condition through improvement of company revenue causes of rising of KOSPI. But, if persistence of low interest rate continues, fall of won/dollar foreign exchange rate will be more aggravated. And it will give positive effect for stock price of KEPCO. This gives more positive effect at two main reason. Firstly, through fall of won/dollar foreign exchange rate and rising of credit rating of Korea will be followed. Therefore, foreign investors will invest more funds to Korea. Secondly, inflow of foreign investment funds through profit of won/dollar foreign exchange rate and stock investment will be occurred. If appreciation of won against dollar is forecasted, foreign investors will buy won. Through this won, investors will do investment. Won/dollar foreign exchange rate is affected through external factors of yen/dollar foreign exchange rate, etc. Therefore, the exclusion of instable factors for foreign investors through rising of credit rating of Korea is necessary things.

• Journal of Convergence for Information Technology
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• v.11 no.1
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• pp.28-33
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• 2021

Since the stock price is a measure of the future value of the company, when analyzing the stock price, the company's growth potential, such as sales and profits, is considered and invested in stocks. In order to set the criteria for selecting stocks, institutional investors look at current industry trends and macroeconomic indicators, first select relevant fields that can grow, then select related companies, analyze them, set a target price, then buy, and sell when the target price is reached. Stock trading is carried out in the same way. However, general individual investors do not have any knowledge of investment, and invest in items recommended by experts or acquaintances without analysis of financial statements or growth potential of the company, which is lower in terms of return than institutional investors and foreign investors. Therefore, in this study, we propose a research method to select undervalued stocks by analyzing ROE, an indicator that considers the growth potential of a company, such as sales and profits, and predict the stock price flow of the selected stock through deep learning algorithms. This study is conducted to help with investment.

• Korean Management Science Review
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• v.28 no.1
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• pp.53-60
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• 2011

Time series analysis methods have been traditionally used in stock price prediction. However, most of the existing methods represent some methodological limitations in reflecting influence from external factors that affect the fluctuation of stock prices, such as oil prices, exchange rates, money interest rates, and the stock price indexes of other countries. To overcome the limitations, we propose a network based method incorporating the relations between the individual company stock prices and the external factors by using a graph-based semi-supervised learning algorithm. For verifying the significance of the proposed method, it was applied to the prediction problems of company stock prices listed in the KOSPI from January 2007 to August 2008.

• Journal of KIISE:Computer Systems and Theory
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• v.37 no.6
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• pp.354-367
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• 2010

There are many methods for analyzing patterns in time series data. Although stock data represents a time series, there are few studies on stock pattern analysis and prediction. Since people believe that stock price changes randomly we cannot predict stock prices using a scientific method. In this paper, we measured the degree of the randomness of stock prices using Kolmogorov complexity, and we showed that there is a strong correlation between the degree and the accuracy of stock price prediction using our semi-global alignment method. We transformed the stock price data to quantized string sequences. Then we measured randomness of stock prices using Kolmogorov complexity of the string sequences. We use KOSPI 690 stock data during 28 years for our experiments and to evaluate our methodology. When a high Kolmogorov complexity, the stock price cannot be predicted, when a low complexity, the stock price can be predicted, but the prediction ratio of stock price changes of interest to investors, is 12% prediction ratio for short-term predictions and a 54% prediction ratio for long-term predictions.

• Korean Journal of Construction Engineering and Management
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• v.20 no.5
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• pp.3-11
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• 2019

This study is designed to examine the stock price of construction firms which are affected by the deregulation of new apartment sales price. As empirical methodology, it uses the traditional event study analysis to test the influence of the deregulation of new apartment sales price and the regression analysis to test which variables are related. The results of this study are summarized as follows : First, the cumulative abnormal return of stock is positive when government announced the deregulation of new apartment sales price. The cumulative abnormal return of stock for 21 trading day before -10 to +10 day is 25.51% which is significant different from zero at 1 percent level. This result suggests that the deregulation of new apartment sales price conveys good information to stock market that the firms performance will be good in the future. Second, in the regression analysis this study shows that the cumulative abnormal return of stock is related to firm's profit margin ratio.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.1113-1119
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• 2021

This study aims to determine the impact of the news coverage of the COVID-19 pandemic on the composite stocks' movement (IHSG) in Indonesia. This study used secondary data of daily time series with an observation range of March 2020-June 2020. This study used three main variables, namely, COVID-19 news, the daily price of a composite stock market index (IHSG), and interest rate. This study clarifies pandemic news into two forms to facilitate quantitative analysis, namely, good news and bad news. Both pandemic news conditions, which have been clarified, are then processed into the index and reprocessed along with two other variables using vector autoregressive (VAR). The results showed that the good news have a dominant effect on developing the composite stock price index (IHSG) in Indonesia during the COVID-19 pandemic. Although the good news dominates the composite stock price index (IHSG) movement in Indonesia, the bad news must also be anticipated. By implementing a series of macroeconomic policies that follow the conditions of the composite stock price index (IHSG) movements on the stock exchange floor, the bad news response can decrease the potential for a decline in investor confidence, so that the financial system's macroeconomic stability is maintained.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1201-1210
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• 2019

Stock price is characterized as being mutable, non-linear and stochastic. These key characteristics are known to have a direct influence on the stock markets globally. Given that the stock price data often contain both linear and non-linear patterns, no single model can be adequate in modelling and predicting time series data. The autoregressive integrated moving average (ARIMA) model cannot deal with non-linear relationships, however, it provides an accurate and effective way to process autocorrelation and non-stationary data in time series forecasting. On the other hand, the neural network provides an effective prediction of non-linear sequences. As a result, in this study, we used a hybrid ARIMA and neural network model to forecast the monthly closing price of the Shanghai composite index and Shenzhen component index.

• Journal of Digital Convergence
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• v.14 no.6
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• pp.157-165
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• 2016

The purpose of this study was to analyze the effect of the shipping industry real economy index on the stock prices of domestic shipping companies. The parameters used in this analysis were the stock price of H Company in South Korea and shipping industry real economy indices including BDI, CCFI and HRCI. The period analysis was from 2012 to 2015. The weekly data for four years of the stock price index of shipping companies, BDI, CCFI, and HRCI were used. The effects of CCFI and HRCI on the stock price index of domestic shipping companies were analyzed using the VAR model, and the effects of BDI on the stock price index of domestic shipping companies were analyzed using the VECM model. The VAR model analysis results showed that CCFI and HRCI had negative effects on the stock price index, and the VECM model analysis results showed that BDI also had a negative effect on the stock price index.

• Asia-Pacific Journal of Business
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• v.14 no.3
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• pp.187-204
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• 2023

Purpose - This study deals with the manager's overconfidence and stock price delay, and verified whether the stock price delay phenomenon changes as the overconfidence increases. Design/methodology/approach - Manager overconfidence means that managers have over confidence in their positions or abilities, and was measured according to Schrand and Zechman (2012). Stock price delay is a phenomenon in which information of company is not immediately reflected in the stock price, but is reflected over time, and was measured by the method suggested in a study by Hou and Moskowitz (2005). The analysis subjects used in this study are companies listed on the KOSPI market between 2011 and 2019, and the final sample is 5,509 company-years. Findings - As a result of the verification, it was shown that the stock price delay decreased as the manager's overconfidence increased, and this effect was amplified as the foreign shareholder's share ratio increased and the number of follow-up financial analysts increased. This means that as the manager's overconfidence increases, he actively provides high-quality information to the capital market. In addition, as a result of subdividing the manager's overconfidence into the investment and capital raising aspects, the capital raising aspect has a significant effect on reducing stock delays. This can be interpreted as the fact that managers with overconfident tendencies have a greater incentive to satisfy investors' information needs. Research implications or Originality - In previous studies, the characteristics of managers with strong overconfidence have both positive and negative aspects. The results of this study are significant in that they clearly demonstrated the positive aspect through the market variable of stock price delay, and it is expected to help capital market stakeholders understand the characteristics of managers with a strong propensity for overconfidence.

• Journal of the Korea Convergence Society
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• v.12 no.7
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• pp.31-36
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• 2021

As the global economy stagnated due to the Corona 19 virus from Wuhan, China, most countries, including the US Federal Reserve System, introduced policies to boost the economy by increasing the amount of money. Most of the stock investors tend to invest only by listening to the recommendations of famous YouTubers or acquaintances without analyzing the financial statements of the company, so there is a high possibility of the loss of stock investments. Therefore, in this research, I have used artificial intelligence deep learning techniques developed under the existing automatic trading conditions to analyze and predict macro-indicators that affect stock prices, giving weights on individual stock price predictions through correlations that affect stock prices. In addition, since stock prices react sensitively to real-time stock market news, a more accurate stock price prediction is made by reflecting the weight to the stock price predicted by artificial intelligence through stock market news text mining, providing stock investors with the basis for deciding to make a proper stock investment.