Stock trend prediction using news sentiment analysis

TL;DR

This paper explores predicting stock trends by analyzing news sentiment, demonstrating that machine learning models like RF and SVM can achieve over 80% accuracy in forecasting stock movements based on news polarity.

Contribution

It introduces a sentiment classification approach for stock prediction using news articles and compares the performance of RF, SVM, and Naive Bayes models.

Findings

RF and SVM outperform Naive Bayes in accuracy

Prediction accuracy exceeds 80%

Model improves over random labeling by 30%

Abstract

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%.