• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.10a
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• pp.337-346
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• 1999

In this paper, we propose the rough set approach to extract trading rules able to discriminate between bullish and bearish markets in stock market. The rough set approach is very valuable to extract trading rules. First, it does not make any assumption about the distribution of the data. Second, it not only handles noise well, but also eliminates irrelevant factors. In addition, the rough set approach appropriate for detecting stock market timing because this approach does not generate the signal for trade when the pattern of market is uncertain. The experimental results are encouraging and prove the usefulness of the rough set approach for stock market analysis with respect to profitability.

• 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.

• Communications for Statistical Applications and Methods
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• v.8 no.2
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• pp.543-556
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• 2001

This study suggests integrated neural network modes for he stock price index forecasting using change-point detection. The basic concept of this proposed model is to obtain significant intervals occurred by change points, identify them as change-point groups, and reflect them in stock price index forecasting. The model is composed of three phases. The first phase is to detect successive structural changes in stock price index dataset. The second phase is to forecast change-point group with various data mining classifiers. The final phase is to forecast the stock price index with backpropagation neural networks. The proposed model is applied to the stock price index forecasting. This study then examines the predictability of integrated neural network models and compares the performance of data mining classifiers.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1999.08a
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• pp.78-87
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• 1999

A mathematical model for the stress free surface profile in Over-Round and Round-Oval grove rolling, which can be used effectively in the calculation of pass area, is presented. The new model has generality, simplicity and accuracy for practical usage. The stress free surface profile of an outgoing stock can be modeled when the maximum spread of it known a priori. The equation for the stress free surface profile is formulated from the linear interpolation of the radius of curvature of an incoming stock and that of roll groove to the axis direction. In developing the analytical model, the effect of rolling temperature and friction between roll and work piece(stock) were not considered since the geometry of roll groove and the incoming work piece were assumed a dominant factor which decides the stress free surface profile of the outgoing stock. A simulation with the analytical model developed also has been carried out to demonstrate the stress free surface profile of the outgoing stock.

• Journal of Korea Technical Association of The Pulp and Paper Industry
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• v.37 no.5 s.113
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• pp.63-69
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• 2005

This study was performed to introduce recently developed polyvinylamine as a wet-end process and paper strength improving aids. As a retention and drainage aids, high cationic charged polyvinylamine was more effective at the BCTMP and ONP stock condition than LBKP stock condition. As a dry tensile strength aid, dual system of polyvinylamine with anionic polyacrylamide was the best at the LBKP or ONP stock conditions. On the other hand, polyvinyl amine alone was better than dual system of polyvinylamine with anionic polyacrylamide at the BCTMP condition. As a wet tensile strength aid, polyvinylamine single system and dual system of polyvinylamine with anionic polyacrylamide were good at LBKP, BCTMP and ONP stock conditions. However, poly(aminoamide)-epichlorohydrin resin was good at LBKP and ONP stock conditions but efficiency of poly(aminoamide)-epichlorohydrin resin was remarkably decreased at BCTMP stock condition.

• Journal of Korea Technology Innovation Society
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• v.9 no.3
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• pp.606-625
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• 2006

This Study is to show the impact of IT capital stock accumulation on the total factor productivity in 9 industries during 1980 through 2000. We construct the If capital stock using input and output table provided by Bank of Korea (2000). Using sequence testing methodologies, we investigate the nonstationary characteristics of the relevant data and test the cointegration relationship between total factor productivity and IT capital stock. Over the past two decades, IT capital stock contributed between 0.19 to 0.07 percentage point per IT capital stock on total factor productivity. Our empirical results, therefore, do not support Solow's IT paradox in using the long period panel data case in Korea.

• The Journal of Information Technology
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• v.9 no.4
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• pp.75-85
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• 2006

This study is to test the influence of stock option granting information on the firm's performance. The important issue in stock option is that agent cost is the important determinant factor for the long term performance. The agent cost arises between the manager and shareholders. So many study are concentrated in diminishing the agent cost, and develop some substitute tools to measure the agent cost. The event study about stock option analyzes returns around event date at a time. Event study provides estimation periods and cumulative returns. Announcements about stock option are generally associated with positive abnormal returns in short term period, but not showing positive effect in long term period. It is important to investigate the responses of stocks to new information contained in the announcements of stock option. Therefore it is important to study the long term performance in the case of stock option. The event time portfolio approach exists the CAR model, BHAR model and WR model. And the calendar time portfolio approach has the 3 factor model, 4 factor model, CTAR model, and RATS model. This study is forced to develop and arrange two approach method in evaluating the performance, the event time portfolio approach and calendar time portfolio approach.

• The Journal of Information Technology
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• v.8 no.3
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• pp.57-71
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• 2005

This thesis is to explore the relaxation of restrictions at the acquisition of own stock, and to get the results from this system many countries' legislations were alluded as model cases for Korean system. In comparing with America, Japan, Europe(EC 2nd Commercial Law), and England, the final suggestion for Korean companies law as follows: The solution of problems which is derived from the acquisition at own stock in enterprise is to make the optimum situation for the economic development and stability of stock market. So, to solve these problems needs the relaxation of restrictions for this system and by the relaxation of restrictions can get the distribution of its profit to stockholder, and the compensation for employers and employees. Furthermore, through this system the company can achieve the protection against M&A and the supply of company funds. In conclusion the relaxation of restrictions at the acquisition of own stock is acknowledged the necessity, but the problems that would be followed must be necessarily minimized, and to do so, the legal system has to be molded for this purpose and the its procedure(that is, accounting deal of own stock, the fictitious dividend, and non-appliance of tendency control) has to be prior to the legal system.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.113-123
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• 2024

Stock market news sentiment analysis (SA) aims to identify the attitudes of the news of the stock on the official platforms toward companies' stocks. It supports making the right decision in investing or analysts' evaluation. However, the research on Arabic SA is limited compared to that on English SA due to the complexity and limited corpora of the Arabic language. This paper develops a model of sentiment classification to predict the polarity of Arabic stock news in microblogs. Also, it aims to extract the reasons which lead to polarity categorization as the main economic causes or aspects based on semantic unity. Therefore, this paper presents an Arabic SA approach based on the logistic regression model and the Bidirectional Encoder Representations from Transformers (BERT) model. The proposed model is used to classify articles as positive, negative, or neutral. It was trained on the basis of data collected from an official Saudi stock market article platform that was later preprocessed and labeled. Moreover, the economic reasons for the articles based on semantic unit, divided into seven economic aspects to highlight the polarity of the articles, were investigated. The supervised BERT model obtained 88% article classification accuracy based on SA, and the unsupervised mean Word2Vec encoder obtained 80% economic-aspect clustering accuracy. Predicting polarity classification on the Arabic stock market news and their economic reasons would provide valuable benefits to the stock SA field.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.11-21
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• 2021

In the last few years, there is growing interest in the field of simultaneous equation estimation in finance due to the endogeneity problem caused by measurement errors, simultaneity, or omitted variables. This study aims to discuss the endogeneity problem in corporate financing decisions and investigate the interrelationship of financial decision-making such as investment decision, dividend decision, and external financing decision in Pakistan Stock Exchange (PSX) using two-stage least squares (2SLS) and generalized method of moment (GMM) estimation. The Bruech-Pagan test shows that the data has no heteroskedasticity issue and 2SLS is a better approach in the context of this study as compared to the GMM approach, and internal instruments are also sufficiently strong and valid. The three financial decision-making attributes are not jointly determined, and the dividend is influenced by one-sided investment. In the emerging stock market context, external financing and investment are not inter-related and did not affect each other. The question of whether the simultaneous equation estimation can be useful in the context of the emerging stock markets and newly-growing firms remains unanswered. The inclusive evidence shows that the theoretical link in the emerging stock market is difficult to prove like in developed stock markets.