• The Journal of Asian Finance, Economics and Business
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• 제8권8호
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• pp.399-407
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• 2021

Stock movement is difficult to predict because it has dynamic characteristics and is influenced by many factors. Even so, there are some approaches to predict stock price movements, namely technical analysis, fundamental analysis, and sentiment analysis. Many researches have tried to predict stock price movement by utilizing these analysis techniques. However, the results obtained are varied and inconsistent depending on the variables and object used. This is because stock price movement is influenced by a variety of factors, and it is likely that those studies did not cover all of them. One of which is that no research considers the use of fundamental analysis in terms of currency exchange rates and the use of foreign stock price index movement related to the technical analysis. This research aims to predict stock price movements in Indonesia based on sentiment analysis, technical analysis, and fundamental analysis using Support Vector Machine. The result obtained has a prediction accuracy rate of 65,33% on an average. The inclusion of currency exchange rate and foreign stock price index movement as a predictor in this research which can increase average prediction accuracy rate by 11.78% compared to the prediction without using these two variables which only results in average prediction accuracy rate of 53.55%.

• International Journal of Computer Science & Network Security
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• 제23권4호
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• pp.166-171
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• 2023

The Stock price analysis is an increasing concern in a financial time series. The purpose of the study is to analyze the price parameters of date, high, low, and news feed about the stock exchange price. Long short term memory (LSTM) is a cutting-edge technology used for predicting the data based on time series. LSTM performs well in executing large sequence of data. This paper presents the Long Short Term Memory Model has used to analyze the stock price ranges of 10 days and 20 days by exponential moving average. The proposed approach gives better performance using technical indicators of stock price with an accuracy of 82.6% and cross entropy of 71%.

• 한국컴퓨터정보학회논문지
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• 제22권11호
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• pp.111-116
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• 2017

This paper proposes a data mining approach to predicting stock price direction. Stock market fluctuates due to many factors. Therefore, predicting stock price direction has become an important issue in the field of stock market analysis. However, in literature, there are few studies applying data mining approaches to predicting the stock price direction. To contribute to literature, this paper proposes comparing single classifiers and ensemble classifiers. Single classifiers include logistic regression, decision tree, neural network, and support vector machine. Ensemble classifiers we consider are adaboost, random forest, bagging, stacking, and vote. For the sake of experiments, we garnered dataset from Korea Stock Exchange (KRX) ranging from 2008 to 2015. Data mining experiments using WEKA revealed that random forest, one of ensemble classifiers, shows best results in terms of metrics such as AUC (area under the ROC curve) and accuracy.

• The Journal of Asian Finance, Economics and Business
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• 제5권4호
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• pp.67-72
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• 2018

The purpose of this study is to investigate the effect of quality of management discussion and analysis (MD&A) disclosure on stock price crash risk. The MD&A can be seen to reflect the management's intention on public announcement and reveals directly what the management says to communicate with outside investors. A firm's high-quality MD&A implies the management's commitment to communicating with the market, not allowing the managers to have incentives to hoard unfavorable news, which if revealed to the public, may lead to downward stock price corrections, damaging corporate values. The high-quality MD&A is, thus, likely to reduce the stock price crash risk. We use a logistic regression to test whether MD&A influences crash risk using listed companies in the Korean Stock Exchange (KSE) stock market between 2010 and 2013. Findings of the empirical test show that the higher the quality of MD&A, the less likely crash risk appears, implying that the MD&A disclosed adequately can be one of the factors mitigating firm's stock price crash risk. This study has implications as it presents the MD&A disclosure as a factor influencing stock price crash risk and suggests voluntary disclosure as well as mandatory disclosure acts as a variable that explains the risk of stock price crash.

• The Journal of Asian Finance, Economics and Business
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• 제4권2호
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• pp.19-23
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• 2017

It is undisputable that crude oil and its price fluctuations are major components that affect most of the countries' economies. Recent studies have demonstrated that beside the impact that crude oil price fluctuations have on common macroeconomic indicators like gross domestic product (GDP), inflation rates, exchange rates, unemployment rate, etc., it also has a strong influence on stock markets and their performance. This relationship has been examined in a number of settings, but it is yet to be unraveled in the Omani context. Accordingly, the main purpose of this study is to examine the possible effect of the oil price fluctuations on stock price movements. The study applies Toda and Yamamoto's (1995) Granger non-causality test on the daily Oman stock index (Muscat Securities Market Index) and oil prices between the period of 2 January 2003 and 13 March 2016. The results indicated that the oil price fluctuations have a significant impact on stock index movements. However, the stock price movements do not have a significant impact on oil prices. These findings have significant implications not only for the Omani economy but also for the economy of similar countries, particularly in the Gulf Cooperation Council (GCC) countries. The latter should carefully consider their policies and strategies regarding crude oil production and the generated income allocation as it might potentially affect the financial markets performance in these countries.

• Journal of East Asia Management
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• 제3권1호
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• pp.41-55
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• 2022

This study investigates whether foreign investors' behavior is involved in firm-specific investor sentiment. Because the mixed role of foreign investors on investor sentiment formation seems to exist in the Korean stock market, it needs to examine the moderate or incremental effect of foreign investors on the stock price crash risk which is due to investor sentiment. The analysis results using Korea Stock Exchanges - listed firms for the period of 2011-2019 show the increased future stock price crash risk which is attributable to high investor sentiment is mitigated for firms with the high foreign ownership, indicating the moderate effect. This study expands the literature on the foreign investors' behavior in the Korean stock market, by showing foreign investors are not involved in firm-specific investor sentiment, which improves market's efficiency in the Korean stock market. Also, the paper is valuable to the academic and practice field in that the findings shed light on the foreign investors' mitigating role in stock price crashes in the behavioral finance perspective.

• 대한안전경영과학회:학술대회논문집
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• 대한안전경영과학회 2005년도 추계학술대회
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• pp.537-557
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• 2005

The estimate on volatility of stock price is related with optimum of portfolio and Important for allocation of capital asset. If the volatility of stock price is varied according to macroeconomic variables on monetary policy and industrial production, it will assist capital asset to allocate. This paper is related with stock market volatilities on macroeconomic variables in U.S. and Europe, Korea. And, it Is pertain to vary in time of this variables. Thus, this paper is related with volatilities of monetary and physical macroeconomic variables on basis of statistics. And, it is ranged front capital investment to portfolio allocation. Also, this paper takes out of sample forecast and study more after this. In case Germany, France, Italy and the Netherlands, the relative importance of monetary policy and Industrial production Is different from these countries. In case Italy and the Netherlands, monetary policy is primary factor at stabilizing for volatility of stock price. In case Korea, increasing monetary policy and industrial production is positively affected stock market. It is that the positive effect of stock price is caused by mollifying monetary policy and economic growth. Specially, this conclusion is similar to US. In Korea, gradual increase in monetary and industrial production is necessary to stability of stock market. It is different to previous results on basis of increasing stock price of money in long period.

• The Journal of Asian Finance, Economics and Business
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• 제7권11호
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• pp.107-115
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• 2020

This study is concerned with the relationship between firm's ownership structure and the co-movement of the stock return with the market return. Four different types of firm ownership, including managerial ownership, state ownership, foreign ownership, and concentrated ownership, are among the main features of the company's governance mechanism and have been separately documemented in the previous research to understand their impact on stock price synchronicity. We constructed the regression model, using stock price synchronicity as the dependent variable and the above four components of ownership structure as explanantory variables. The pooled OLS, the fixed effects model, and the random effects are employed to investigate the outcome of the study. Data used in the reserch are of public firms listed on the Ho Chi Minh City Stock Exchange (HOSE) during the five-year period term from 2015 to 2019. The data sample contains 235 companies from 10 industries with 1135 observations. The results revealed by the fixed effects model, the large ownership and the managerial ownership are found to have adverse effect on the stock price synchronicity, whereas the foreign ownership model is revealed to have positive influence on the stock return co-movement. The effect of the state ownership on the stock price synchronicity is not confirmed.

• 아태비즈니스연구
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• 제10권4호
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• pp.77-86
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• 2019

The purpose of this study is to examine how the characteristics of family firms affect stock price crash risk. Prior studies argued that the opacity of information due to agency problem causes a plunge in stock prices. The governance characteristics of family firms can increase information opacity which leads to crash risk. Therefore, this study verifies whether family firms have a high possibility of stock price crash risk. We use a logistic regression model to test the relationship between family firms and stock price crash risk using listed firms listed on the Korean Stock Exchange during the fiscal years 2011 through 2017. The family firm is defined as the case where the controlling shareholder is the chief executive officer or the registered executive. If the controlling shareholder's share is less than 5%, it is not considered a family business. We found that family firms are more likely to experience a plunge in stock prices. This supports the hypothesis of this study that passive information disclosure behavior and information opacity of family firms increase stock price crash risk.

• Communications for Statistical Applications and Methods
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• 제8권2호
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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.