• The Journal of Asian Finance, Economics and Business
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• v.7 no.4
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• pp.21-28
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• 2020

The objectives of this study are threefold: 1) to identify the concepts of earnings, stock return and liquidity risks on public shareholding industrial companies listed in the Amman Stock Exchange, 2) to investigate the relationship between earnings, stock return, strength and direction of this relationship, and 3) to find out the effect of liquidity risks at stock return and the effect of liquidity risks on the relationship between earnings and stock return on Jordanian public shareholding industrial companies. To achieve the objectives, an analytical descriptive approach was used. As the data on the public shareholding industrial companies listed in the Amman Stock Exchange were accredited by 52 companies for the period between 2014-2019, data validation tests and their suitability for analysis were considered. A linear regression test was used to test the study hypotheses on the statistical analysis program. The results show that there is a positive and significant correlation at significance level between the earnings and stock return. The results of the study also showed that there is a statistically significant negative effect at significance level of liquidity risk on stock return. In addition, it was demonstrated that liquidity risks have significant negative effects on the relationship between earnings and stock returns.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.3
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• pp.195-202
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• 2022

This study investigates whether institutional investors increase or decrease the volatility of stock returns in the Thai stock market. For the purpose we used the data from SETSMART, a database provided by the Stock Exchange of Thailand (SET). Our sample is a balanced panel data covering 3,160 firm-year observations from 316 nonfinancial firms listed on the SET from 2011 to 2020. We analyze the link between institutional holdings and the volatility of stock returns by the pooled Ordinary Least Squares (OLS) model, the fixed effects model, and the random-effects model. In particular, we regress the stock return volatility on institutional ownership while controlling for firm size, financial leverage, growth opportunities, and stock turnover and accounting for industry effects and year effects. Our results indicate institutional investors' positive and significant influence on the volatility of the stock returns. Additionally, we performed the dynamic Generalized Method of Moment (GMM) estimator to alleviate concerns of possible endogeneity. The result still shows a positive impact of institutional investors on the volatility in stock returns. Overall, the findings of this study suggest that an increase in the volatility of stock returns in the Thai stock market may stem from a higher proportion of equity held by the institutional investors.

• Journal of Information Technology Services
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• v.13 no.3
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• pp.221-233
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• 2014

Because people's interest of the stock market has been increased with the development of economy, a lot of studies have been going to predict fluctuation of stock prices. Latterly many studies have been made using scientific and technological method among the various forecasting method, and also data using for study are becoming diverse. So, in this paper we propose stock prices prediction models using sentiment analysis and machine learning based on news articles and SNS data to improve the accuracy of prediction of stock prices. Stock prices prediction models that we propose are generated through the four-step process that contain data collection, sentiment dictionary construction, sentiment analysis, and machine learning. The data have been collected to target newspapers related to economy in the case of news article and to target twitter in the case of SNS data. Sentiment dictionary was built using news articles among the collected data, and we utilize it to process sentiment analysis. In machine learning phase, we generate prediction models using various techniques of classification and the data that was made through sentiment analysis. After generating prediction models, we conducted 10-fold cross-validation to measure the performance of they. The experimental result showed that accuracy is over 80% in a number of ways and F1 score is closer to 0.8. The result can be seen as significantly enhanced result compared with conventional researches utilizing opinion mining or data mining techniques.

• The Journal of Bigdata
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• v.4 no.2
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• pp.1-12
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• 2019

As Artificial Intelligence (AI) technology develops, it is applied to various fields such as image, voice, and text. AI has shown fine results in certain areas. Researchers have tried to predict the stock market by utilizing artificial intelligence as well. Predicting the stock market is known as one of the difficult problems since the stock market is affected by various factors such as economy and politics. In the field of AI, there are attempts to predict the ups and downs of stock price by studying stock price patterns using various machine learning techniques. This study suggest a way of predicting stock price patterns based on the Convolutional Neural Network(CNN) among machine learning techniques. CNN uses neural networks to classify images by extracting features from images through convolutional layers. Therefore, this study tries to classify candlestick images made by stock data in order to predict patterns. This study has two objectives. The first one referred as Case 1 is to predict the patterns with the images made by the same-day stock price data. The second one referred as Case 2 is to predict the next day stock price patterns with the images produced by the daily stock price data. In Case 1, data augmentation methods - random modification and Gaussian noise - are applied to generate more training data, and the generated images are put into the model to fit. Given that deep learning requires a large amount of data, this study suggests a method of data augmentation for candlestick images. Also, this study compares the accuracies of the images with Gaussian noise and different classification problems. All data in this study is collected through OpenAPI provided by DaiShin Securities. Case 1 has five different labels depending on patterns. The patterns are up with up closing, up with down closing, down with up closing, down with down closing, and staying. The images in Case 1 are created by removing the last candle(-1candle), the last two candles(-2candles), and the last three candles(-3candles) from 60 minutes, 30 minutes, 10 minutes, and 5 minutes candle charts. 60 minutes candle chart means one candle in the image has 60 minutes of information containing an open price, high price, low price, close price. Case 2 has two labels that are up and down. This study for Case 2 has generated for 60 minutes, 30 minutes, 10 minutes, and 5minutes candle charts without removing any candle. Considering the stock data, moving the candles in the images is suggested, instead of existing data augmentation techniques. How much the candles are moved is defined as the modified value. The average difference of closing prices between candles was 0.0029. Therefore, in this study, 0.003, 0.002, 0.001, 0.00025 are used for the modified value. The number of images was doubled after data augmentation. When it comes to Gaussian Noise, the mean value was 0, and the value of variance was 0.01. For both Case 1 and Case 2, the model is based on VGG-Net16 that has 16 layers. As a result, 10 minutes -1candle showed the best accuracy among 60 minutes, 30 minutes, 10 minutes, 5minutes candle charts. Thus, 10 minutes images were utilized for the rest of the experiment in Case 1. The three candles removed from the images were selected for data augmentation and application of Gaussian noise. 10 minutes -3candle resulted in 79.72% accuracy. The accuracy of the images with 0.00025 modified value and 100% changed candles was 79.92%. Applying Gaussian noise helped the accuracy to be 80.98%. According to the outcomes of Case 2, 60minutes candle charts could predict patterns of tomorrow by 82.60%. To sum up, this study is expected to contribute to further studies on the prediction of stock price patterns using images. This research provides a possible method for data augmentation of stock data.

• 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.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.157-165
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• 2022

As the stock market is affected by various circumstances including economic and political variables, predicting the stock market is considered a still open problem. When combined with corporate financial statement data analysis, which is used as fundamental analysis, and technical analysis with a short data generation cycle, there is a problem that the time domain does not match. Our proposed method, LSTE the operating profit and market outlook of a petrochemical company and estimates the sales and operating profit of the company, it was possible to solve the above-mentioned problems and improve the accuracy of stock price prediction. Extensive experiments on real-world stock data show that our method outperforms the 8.58% relative improvements on average w.r.t. accuracy.

• 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.

• Management & Information Systems Review
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• v.26
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• pp.293-309
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• 2008

This study examined the market efficiency of korea stock market by comparing variance ratios(VR) of stock groups which is sorted by market capitalization. We compute variance ratios of KOSPI large capitalization, midium capitalization, and small capitalization for 546 trading days from 2006/01/02 to 2008/04/15. For our study, we also use high frequency data that is; intra-day 1 minute data. The characteristics of variance ratios of stock groups by market capitalization as follows: From 1 to 5 minute interval, variance ratios of three stock group increase far from zero(0). The longer time interval, the more variance ratios decrease, but only large capitalization converge on around zero. This means that the market of large capitalization is more efficient compare to other stock groups. The entire sample period can be divided two sub-period because the impact of sub prime crisis arised from U.S.A. influences Korea stock market. Before sub prime crisis, the VRs of mid cap and small cap do not converge on around zero except large cap although the time interval is longer. After sub prime crisis, the VRs of three stock groups decrease when time interval is longer, but only large cap converge on around zero. We conclude that large cap is more efficient than other stock groups in Korea Stock Market.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.4
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• pp.191-196
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• 2022

From several socioeconomic perspectives, the present health crisis can be connected to the 2008 financial and economic catastrophe. Governments worldwide are working hard to keep the markets in check, as evidence suggests that the health crisis may soon become an economic crisis. This paper aims to analyze the effect of COVID-19 on the selected stock market. Using a panel of daily COVID-19 confirmed cases and deaths and the stock market from 22 developing countries, we exploit an oil price as a shock to the stock market and examine the effect of COVID-19 on the slowdown of the stock market. We find a negative and significant impact of COVID-19 on the stock market in the first stage till April. However, there is no net influence on the stock market downturn when we extend the period. However, further study suggests that the outbreak's negative influence on the selected stock market has diminished and has begun to decline as of mid-April. As a result of the COVID-19 effect on the chosen stock, our findings imply that the government in the chosen market should consider a regulatory mechanism to reduce the stock market slowdown induced by the pandemic COVID-19.

• Journal of the Korea Society of Computer and Information
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• v.21 no.6
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• pp.9-19
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• 2016

In finance literature, stock liquidity showing how stocks can be cashed out in the market has received rich attentions from both academicians and practitioners. The reasons are plenty. First, it is known that stock liquidity affects significantly asset pricing. Second, macroeconomic announcements influence liquidity in the stock market. Therefore, stock liquidity itself affects investors' decision and managers' decision as well. Though there exist a great deal of literature about stock liquidity in finance literature, it is quite clear that there are no studies attempting to investigate the stock liquidity issue as one of decision making problems. In finance literature, most of stock liquidity studies had dealt with limited views such as how much it influences stock price, which variables are associated with describing the stock liquidity significantly, etc. However, this paper posits that stock liquidity issue may become a serious decision-making problem, and then be handled by using data mining techniques to estimate its future extent with statistical validity. In this sense, we collected financial data set from a number of manufacturing companies listed in KRX (Korea Exchange) during the period of 2010 to 2013. The reason why we selected dataset from 2010 was to avoid the after-shocks of financial crisis that occurred in 2008. We used Fn-GuidPro system to gather total 5,700 financial data set. Stock liquidity measure was computed by the procedures proposed by Amihud (2002) which is known to show best metrics for showing relationship with daily return. We applied five data mining techniques (or classifiers) such as Bayesian network, support vector machine (SVM), decision tree, neural network, and ensemble method. Bayesian networks include GBN (General Bayesian Network), NBN (Naive BN), TAN (Tree Augmented NBN). Decision tree uses CART and C4.5. Regression result was used as a benchmarking performance. Ensemble method uses two types-integration of two classifiers, and three classifiers. Ensemble method is based on voting for the sake of integrating classifiers. Among the single classifiers, CART showed best performance with 48.2%, compared with 37.18% by regression. Among the ensemble methods, the result from integrating TAN, CART, and SVM was best with 49.25%. Through the additional analysis in individual industries, those relatively stabilized industries like electronic appliances, wholesale & retailing, woods, leather-bags-shoes showed better performance over 50%.