• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.1-17
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• 2019

Because stock price forecasting is an important issue both academically and practically, research in stock price prediction has been actively conducted. The stock price forecasting research is classified into using structured data and using unstructured data. With structured data such as historical stock price and financial statements, past studies usually used technical analysis approach and fundamental analysis. In the big data era, the amount of information has rapidly increased, and the artificial intelligence methodology that can find meaning by quantifying string information, which is an unstructured data that takes up a large amount of information, has developed rapidly. With these developments, many attempts with unstructured data are being made to predict stock prices through online news by applying text mining to stock price forecasts. The stock price prediction methodology adopted in many papers is to forecast stock prices with the news of the target companies to be forecasted. However, according to previous research, not only news of a target company affects its stock price, but news of companies that are related to the company can also affect the stock price. However, finding a highly relevant company is not easy because of the market-wide impact and random signs. Thus, existing studies have found highly relevant companies based primarily on pre-determined international industry classification standards. However, according to recent research, global industry classification standard has different homogeneity within the sectors, and it leads to a limitation that forecasting stock prices by taking them all together without considering only relevant companies can adversely affect predictive performance. To overcome the limitation, we first used random matrix theory with text mining for stock prediction. Wherever the dimension of data is large, the classical limit theorems are no longer suitable, because the statistical efficiency will be reduced. Therefore, a simple correlation analysis in the financial market does not mean the true correlation. To solve the issue, we adopt random matrix theory, which is mainly used in econophysics, to remove market-wide effects and random signals and find a true correlation between companies. With the true correlation, we perform cluster analysis to find relevant companies. Also, based on the clustering analysis, we used multiple kernel learning algorithm, which is an ensemble of support vector machine to incorporate the effects of the target firm and its relevant firms simultaneously. Each kernel was assigned to predict stock prices with features of financial news of the target firm and its relevant firms. The results of this study are as follows. The results of this paper are as follows. (1) Following the existing research flow, we confirmed that it is an effective way to forecast stock prices using news from relevant companies. (2) When looking for a relevant company, looking for it in the wrong way can lower AI prediction performance. (3) The proposed approach with random matrix theory shows better performance than previous studies if cluster analysis is performed based on the true correlation by removing market-wide effects and random signals. The contribution of this study is as follows. First, this study shows that random matrix theory, which is used mainly in economic physics, can be combined with artificial intelligence to produce good methodologies. This suggests that it is important not only to develop AI algorithms but also to adopt physics theory. This extends the existing research that presented the methodology by integrating artificial intelligence with complex system theory through transfer entropy. Second, this study stressed that finding the right companies in the stock market is an important issue. This suggests that it is not only important to study artificial intelligence algorithms, but how to theoretically adjust the input values. Third, we confirmed that firms classified as Global Industrial Classification Standard (GICS) might have low relevance and suggested it is necessary to theoretically define the relevance rather than simply finding it in the GICS.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.3
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• pp.319-326
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• 2022

This study investigates the specific and conclusive reasons why a company issues bonus shares, as well as the rationale and the best timing for bonus share issuance. The study examines Carlsberg's annual reports from 1988 to 2004 to evaluate the factors that influence bonus share payments and timing. Examine supporting evidence from other businesses as well. An analysis of Carlsberg Brewery Malaysia Berhad's bonus shares granted from its inception to 2004 found that the announcement of bonus shares would increase the company's share price. As a result, the findings suggest that bonus shares are issued to correct market asymmetry. This research supports the idea that issuing bonus shares would increase the stock price, resulting in increased liquidity. Hence, companies issue bonus shares to boost their liquidity and to convey private positive information to their shareholders. This research adds to the literature by focusing on the timing and key features of bonus share issuing. It implies that dividend policy should be customized to market imperfections. As a result, dividend policies would differ significantly between organizations based on the weights each of the imperfections has on the firm and shareholders.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.7
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• pp.433-446
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• 2021

This study aims to identify the elasticities and casualties of financial performance and determinants of the mining and extractive companies listed in Jordan's stock market over the 2005-2018 period. The conceptual framework is based on the Resource-Based View theory and Arbitrage Pricing theory is used to describe the relationship between the external environment and the financial performance of the companies. Profitability ratio (return on assets) is utilized as a proxy of financial performance measurement. Meantime, the company's characteristics, macroeconomic variables, and non-economic factors are utilized as independent factors. Data sources are panel data set for mining and extractive companies over the above period. Fully Modified Ordinary Least Square (FMOLS), Dynamic Ordinary Least Squares (DOLS), and Pooled Mean Group (PMG) methods are applied. The empirical findings indicated that company size, sales growth, financial leverage, liquidity, and GDP growth were the critical determinants of mining and extractive companies' financial performance in the Amman Stock Exchange. Thus, the findings conclude that company characteristics and GDP growth mainly drive financial performance. Moreover, the findings reveal that a bidirectional causal elasticity exists between GDP and financial leverage and return on assets (ROA). Sound financial performance can be obtained by paying more attention to GDP growth and firms' characteristics.

• The Journal of Asian Finance, Economics and Business
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• v.6 no.4
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• pp.37-43
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• 2019

The research aims to study the relationship between export performance and stock return of Vietnamese fishery companies. To conduct this study, quarterly data was collected for period from 2010-2018 of 13 fishery companies listing in Ho Chi Minh Stock Exchange (HOSE) and Ha Noi Stock Exchange (HNX). The export performance was measured by export intensity, export growth and export market coverage. In addition, interest rate, exchange rate, GDP, firm size, profitability, and financial leverage were considered as the control variables in the research model. Panel data analysis with Generalized Least Squares model was employed to estimate the predictive regression. The findings indicated that export intensity and export growth have a significant and positive relationship with stock returns. However, export market coverage has not a significant relationship with stock return at the 0.05 level. Profitability, financial leverage, and exchange rate have a positive relationship, while interest rate and GDP have no relation to stock return at the 0.05 significance level. The findings imply that investors should consider the export intensity instead of export growth and export market coverage as selecting stock of fishery exports firms to invest; managers should increase export intensity to increase company's stock price or firm market value.

• Journal of Distribution Science
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• v.21 no.9
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• pp.103-114
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• 2023

Purpose: This paper aims to assess psychological heuristics' effectiveness on cumulative returns after significant stock price changes. Specifically, it compares availability and anchoring heuristics' empirical validity due to conflicting stock return predictions. Research Design, Data, and Methodology: This paper analyzes stock price changes of Korean distribution industry stocks in the KOSPI market from January 2004 to July 2022, where daily fluctuations exceed 10%. It evaluates availability heuristics using daily KOSPI index changes and tests anchoring heuristics using 52-week high and low stock prices as reference points. Results: As a result of the empirical analysis, stock price reversals did not consistently appear alongside changes in the daily KOSPI index. By contrast, stock price drifts consistently appeared around the 52-week highest stock price and 52-week lowest stock price. The result of the multiple regression analysis which controlled for both company-specific and event-specific variables supported the anchoring heuristics. Conclusions: For stocks related to the Korean distribution industry in the KOSPI market, the anchoring heuristics theory provides a consistent explanation for stock returns after large-scale stock price fluctuations that initially appear to be random movements.

• Journal of Distribution Science
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• v.21 no.12
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• pp.83-89
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• 2023

Purpose: Research on spin-off and treasury stock is necessary because the market has realized that this can be utilized for major shareholder private interest. Considering the unique characteristic of a spin-off and treasury stock in the Korean stock market, this study contributes to the literature by examining the effects on shareholder value in the Korean distribution industry. Research design, data, and methodology: The present study investigates literature, analyst reports, and news articles to examine the spin-off process and analyze how treasury stock magic happens. Results: Setting the exchange ratio favoring Spin-Co in the spin-off is the leading cause for reducing the minor shareholders' value. Moreover, treating treasury stock as an asset is also problematic, allowing the allocation of Spin-Co shares. This leads to an increase in the major shareholder controls of Spin-Co without any contribution from the major shareholders. Therefore, the exchange ratio should be calculated reasonably, and treasury stock from the stock repurchase should be treated as stock retirement. Conclusion: By analyzing the spin-off and how treasury stock magic occurs, this study provides recommendations to improve shareholder value. Moreover, it contributes to the maturation of the Korean capital market by promoting a discussion on the revision of spin-off and treasury stock.

• Proceedings of the Korean Fiber Society Conference
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• 2003.10a
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• pp.53-54
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• 2003

Fukusuke applied for beginning "Civil affairs reproduction procedure" on June 21, 2003. It′s bankrupt. After obtaining the data of "Commodity Attribute, Sales, Sates Cost, Stock, Ordinary Profit, And Special Loss" based on the financial statement at the time of the bankruptcy, the influence of the production method improves checking. They are compared with a similar apparel company. The production method is close with the distribution circumstances. As a result, it searches for the limit of "Mass-Sail system" in fiber ＆ fashion product by "Make To Stock(MTS) Production Method".

• Proceedings of the Safety Management and Science Conference
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• 2005.11a
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• pp.194-198
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• 2005

Inventory information system is providing the benefits of smoother demand, lower inventories(work in process, safety stock) and reduced costs. This study focused on improvement of safety level inventory efficiency by inventory information system. The results indicated that inventory information system allowed the company to serve its customers more surely and efficiently.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.5
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• pp.19-29
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• 2020

The paper examines the impact of information about cash flow from operating activities of firms listed on Vietnam's stock market to the decision making of individual investors. Data were collected from interviews with 160 individual investors about their investment decisions based on information on profit growth and cash flow growth from operating activities. T-test was conducted to research on Vietnam's stock market - a market considered as information that is not really public, transparent and ineffective. The research results show that: (1) investors do not care about cash flow from operating activities when making investment decisions if the company's profits grow positively, (2) information about cash flow from operating activities only affects the decisions of individual investors once profit growth is negative, and (3) conflicting information between profit growth and cash flow growth from business activities significantly affects the confidence and comfort of investors in Vietnam's stock market when they make investment decisions. Then, the study points out the mistake of investors when making investment decisions, and offers recommendations to investors when making investment decisions, not only concerned with profit growth, but also paying special attention to cash flow growth, especially cash flow from the company's business operations.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.5
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• pp.111-118
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• 2022

The purpose of this research is to evaluate the quality of non-financial information disclosure by companies listed on the Vietnamese stock exchange. In 2019, 140 annual reports from 140 companies listed on the Vietnam Stock Exchange were included in the research sample. The remaining 134 reports were eligible study after removing those that lacked essential data. Using the statistical software SPSS version 25 and Excel office software, the study has selected the data processing method and the disproportionate disclosure index method to evaluate the quality of non-financial information disclosure of companies. The findings of the study demonstrate that companies listed on the Vietnam stock exchange are particularly interested in giving non-financial information to financial statement consumers as required by law, although the level of disclosure is still inadequate. The findings also illustrate the varying levels of non-financial information disclosure by category of information, as well as substantial disparities between them (general information about the company, environmental and social information, corporate governance information, etc.). The findings of the study show that the majority of Vietnam's publicly traded enterprises are less interested in reporting environmental information.