Improvement methods for stock market prediction using financial news articles

Dang, Minh Thanh; Duong, Duc

doi:10.1109/nics.2016.7725636

Cited by 29 publications

(16 citation statements)

References 11 publications

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“…For these reasons, many researchers (Bollen et al, 2011;Li et al, 2014) have attempted to apply SA to stock prediction in order to increase prediction accuracy by using financial news (Dang and Duong, 2016;Jeong et al, 2018;Nam and Seong, 2019), and social media (Mittal and Goel, 2012;Nguyen et al, 2015;Jeong et al, 2018).…”

Section: Sentiment Analysismentioning

confidence: 99%

A Survey on Machine Learning for Stock Price Prediction: Algorithms and Techniques

Obthong

Tantisantiwong

Jeamwatthanachai

et al. 2020

Proceedings of the 2nd International Conference on Finance, Economics, Management and IT Business

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Stock market trading is an activity in which investors need fast and accurate information to make effective decisions. Since many stocks are traded on a stock exchange, numerous factors influence the decision-making process. Moreover, the behaviour of stock prices is uncertain and hard to predict. For these reasons, stock price prediction is an important process and a challenging one. This leads to the research of finding the most effective prediction model that generates the most accurate prediction with the lowest error percentage. This paper reviews studies on machine learning techniques and algorithm employed to improve the accuracy of stock price prediction.

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Section: Sentiment Analysismentioning

confidence: 99%

A Survey on Machine Learning for Stock Price Prediction: Algorithms and Techniques

Obthong

Tantisantiwong

Jeamwatthanachai

et al. 2020

Proceedings of the 2nd International Conference on Finance, Economics, Management and IT Business

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“…Fundamental analysis focuses on studying the competitor, markets, and business [7,11]. Technical analysis focuses on historical prices analysis, which leads to determining the upcoming results of stock prices [11,15,18]. Text mining is a technique that involves extracting valuable information and pattern [12][13][14].…”

Section: Related Studiesmentioning

confidence: 99%

“…According to Gaikward et al, [12] information or textual data can be presented in four forms namely Term Based Method (TBM), Phrase Based Method (PBM), Concept Based Method (CBM) and Pattern Taxonomy Method (PTM). Most of the researches in stock market using bags of word (BoW), which is the TBM to represent the features [5,7,10,18]. Though so there are other studies which have used combination of words as the feature set [22][23], and this representation is hardly found in stock market financial news.…”

Section: Related Studiesmentioning

confidence: 99%

Analytics of stock market prices based on machine learning algorithms

Samad

Mutalib

Abdul-Rahman

2019

IJEECS

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This study focuses on the use of machine learning algorithms to analyse financial news on stock market prices. Stock market prediction is a challenging task because the market is known to be very volatile and dynamic. Investors face these kinds of problems as they do not properly understand which stock product to subscribe or when to sell the product with an optimum profit. Analyzing the information individually or manually is a tedious task as many aspects have to be considered. Five different companies from Bursa Malaysia namely CIMB, Sime Darby, Axiata, Maybank and Petronas were chosen in this study. Two sets of experiments were performed based on different data types. The first experiment employs textual data involving 6368 articles, extracted from financial news that have been classified into positive or negative using Support Vector Machine (SVM) algorithm. Bags of words and bags of combination words are extracted as the features for the first experiment. The second experiment employs the numeric data type extracted from historical data involving 5321 records to predict whether the stock price is going up (positive) or down (negative) using Random Forest algorithm. The Rain Forest algorithm gives better accuracy in comparison with SVM algorithm with 99% and 68% accuracy respectively. The results demonstrate the complexities of the textual-based data and demand better feature extraction technique.

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“…Although many algorithms have achieved good results in certain aspects, there are many parameter configurations and data selection problems in the use of machine learning, which is still an important area of research. On the other hand, in the fundamental analysis [16][17][18], people mainly use natural language processing to analyze the company's financial news and financial statements to predict the future stock price trend.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Prediction Method for Stock Price Using LSTM and Ensemble EMD

Yang

Xiao

2020

Complexity

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The stock market is a chaotic, complex, and dynamic financial market. The prediction of future stock prices is a concern and controversial research issue for researchers. More and more analysis and prediction methods are proposed by researchers. We proposed a hybrid method for the prediction of future stock prices using LSTM and ensemble EMD in this paper. We use comprehensive EMD to decompose the complex original stock price time series into several subsequences which are smoother, more regular and stable than the original time series. Then, we use the LSTM method to train and predict each subsequence. Finally, we obtained the prediction values of the original stock price time series by fused the prediction values of several subsequences. In the experiment, we selected five data to fully test the performance of the method. The comparison results with the other four prediction methods show that the predicted values show higher accuracy. The hybrid prediction method we proposed is effective and accurate in future stock price prediction. Hence, the hybrid prediction method has practical application and reference value.

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Improvement methods for stock market prediction using financial news articles

Cited by 29 publications

References 11 publications

A Survey on Machine Learning for Stock Price Prediction: Algorithms and Techniques

A Survey on Machine Learning for Stock Price Prediction: Algorithms and Techniques

Analytics of stock market prices based on machine learning algorithms

A Hybrid Prediction Method for Stock Price Using LSTM and Ensemble EMD

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