Stock trend prediction using news sentiment analysis
By J. Kalyani, Prof.Divya Bharathi, P. Jyothi. 2016
129 citations recorded by the source corpus.
Efficient Market Hypothesis is the popular theory about stock prediction. With its failure much research has been carried in the area of prediction of stocks. This project is about taking non quantifiable data such as financial news articles about a company and predicting its future stock trend with news sentiment classification. Assuming that news articles have impact on stock market, this is an attempt to study relationship between news and stock trend. To show this, we created three different classification models which depict polarity of news articles being positive or negative. Observations show that RF and SVM perform well in all types of testing. Na\"ive Bayes gives good result but not compared to the other two. Experiments are conducted to evaluate various aspects of the proposed model and encouraging results are obtained in all of the experiments. The accuracy of the prediction model is more than 80% and in comparison with news random labeling with 50% of accuracy; the model has increased the accuracy by 30%.
Subfields: Artificial Intelligence, Management Science and Operations Research
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Citations in the corpus
- Forecasting Economics and Financial Time Series: ARIMA vs. LSTM (2018, background): “2001; Huck, 2009; Xiong, 2015] and measuring the impact of incorporating news for selected companies [Siah and Myers, Joshi et al. 2013; Ding et al. 2015].”