Stock Market Prediction using Financial News Articles on Ho Chi Minh Stock Exchange

Duong, Duc; Nguyen, Toan T.; Dang, Minh Thanh

doi:10.1145/2857546.2857619

Cited by 19 publications

(4 citation statements)

References 9 publications

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“…Moreover, its lowest accuracy is 86.66%, and the highest is 96.94% (for TPB ticker). This result is better than similar studies such as Kim [ 75 ], Kara et al [ 76 ], Patel et al [ 77 ], and Duong et al [ 78 ]. One success in the SVM model comes from its model estimation method.…”

Section: Discussioncontrasting

confidence: 54%

Is It Possible to Earn Abnormal Return in an Inefficient Market? An Approach Based on Machine Learning in Stock Trading

Khoa

Huynh

2021

Computational Intelligence and Neuroscience

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Risk management and stock investment decision-making is an essential topic for investors and fund managers, especially in the context of the COVID-19 pandemic. The problem becomes easier if the market is efficient, where stock prices fully reflect potential risk. Nevertheless, if the market is not efficient, investors may have an opportunity to find an effective investment method. Vietnam is one of the emerging markets; the efficiency is still weak. Thus, there will be an opportunity for astute investors. This study aims to test the weak-form efficient market and provide a modern approach to investors’ decision-making. To achieve that aim, this study uses historical data of stocks in the VN-Index and VN30 portfolio to buy and sell within a one-day period under the rolling window approach to test the Ho Chi Minh City Stock Exchange (HoSE) through a runs test and to perform stock trading using the support vector machine (SVM) and logistic regression. The buying/selling of stocks is guided by the forecasted outcomes (increase/decrease) of logistic regression and SVM. This study adjusted the return rate in proportion to the risks and compared it with index investments of VN-Index and VN30 to evaluate investment efficiency. The test results dismissed the weak-form efficient-market hypothesis, which opens up many opportunities for short-term traders. This study’s primary contribution is to provide a stock trading strategy for short-term investors to maximize trading profits. Because logistic regression and SVM have proven effective trading methods, investors can use them to achieve abnormal returns.

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Section: Discussioncontrasting

confidence: 54%

Is It Possible to Earn Abnormal Return in an Inefficient Market? An Approach Based on Machine Learning in Stock Trading

Khoa

Huynh

2021

Computational Intelligence and Neuroscience

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show abstract

“…Moreover, the lowest accuracy is 86.66%, and the highest is 96.94% (the ticker TPB). This result is better than previous similar studies such as Kim [34], Kara et al [35], Patel et al [36], Duong et al [37]. While both the logistic regression and SVM are pretty stable, the accuracy of the ANN model highly fluctuates among different stocks, ranging from 43.3% to 87.14% and reaching an average of 60.03%.…”

Section: Discussioncontrasting

confidence: 45%

Forecasting stock price movement direction by machine learning algorithm

Khoa

Huynh

2022

IJECE

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<p><span lang="EN-US">Forecasting stock price movement direction (SPMD) is an essential issue for short-term investors and a hot topic for researchers. It is a real challenge concerning the efficient market hypothesis that historical data would not be helpful in forecasting because it is already reflected in prices. Some commonly-used classical methods are based on statistics and econometric models. However, forecasting becomes more complicated when the variables in the model are all nonstationary, and the relationships between the variables are sometimes very weak or simultaneous. The continuous development of powerful algorithms features in machine learning and artificial intelligence has opened a promising new direction. This study compares the predictive ability of three forecasting models, including <a name="_Hlk106797328"></a>support vector machine (SVM), artificial neural networks (ANN), and logistic regression. The data used is those of the stocks in the VN30 basket with a holding period of one day. With the rolling window method, this study got a highly predictive SVM with an average accuracy of 92.48%.</span></p>

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“…In a study specific to stock market informational efficiency within the HOSE, Duong et al (2016) examined the importance of financial news in making stock market predictions specific to the HOSE. The authors found that financial news related to stock prices was significant in predicting trends in the VN30-Index as news influences investors and stock market behavior.…”

Section: Literature Reviewmentioning

confidence: 99%

The Impact of the Introduction of Index Futures on the Daily Returns Anomaly in the Ho Chi Minh Stock Exchange

Truong

Friday

2021

IJFS

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This study investigated the impact of the introduction of the VN30-Index futures contract on the daily returns anomaly for the Ho Chi Minh Stock Exchange (HOSE). Daily returns of the VN30-Index for the period 6 February 2012 through 31 December 2019 are used in this study to ascertain the new VN30-Index futures contract influence on the day-of-the-week anomaly observed in the HOSE. To test this effect, ordinary least square (OLS), generalized autoregressive conditional heteroskedasticity [GARCH (1,1)] and exponential generalized autoregressive conditional heteroskedasticity [EGARCH (1,1)] regression models were employed. The empirical results obtained from the models support the presence of the day-of-the-week effect for the HOSE during the study period. Specifically, a negative effect was observed for Monday. However, the analysis revealed that the day-of-the-week effect was only present in stock returns for the pre-index futures period, not for the post-index futures period. These findings suggest that the introduction of the VN30-Index futures contract had a significant impact on the daily returns anomaly in Vietnam’s HOSE, providing evidence that the introduction of the index futures contract facilitated market efficiency.

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Stock Market Prediction using Financial News Articles on Ho Chi Minh Stock Exchange

Cited by 19 publications

References 9 publications

Is It Possible to Earn Abnormal Return in an Inefficient Market? An Approach Based on Machine Learning in Stock Trading

Is It Possible to Earn Abnormal Return in an Inefficient Market? An Approach Based on Machine Learning in Stock Trading

Forecasting stock price movement direction by machine learning algorithm

The Impact of the Introduction of Index Futures on the Daily Returns Anomaly in the Ho Chi Minh Stock Exchange

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