Stock Prediction by Integrating Sentiment Scores of Financial News and MLP-Regressor: A Machine Learning Approach

Abstract

The stock market is highly volatile as it depends on political, financial, environmental, and various internal and external factors along with historical stock data. Such information is available to people through microblogs and news and predicting stock price merely on historical data is hard. The high volatility emphasizes the importance to check the effect of external factors on the stock market. In this paper, a machine learning model is proposed where the financial news is used along with historical stock price data to predict upcoming stock prices. The paper has used three algorithms to calculate various sentiment scores and used them in different combinations to understand the impact of financial news on stock price as well the impact of each sentiment scoring algorithm. Experiments have been conducted on ten-year historical stock price data as well as financial news of four different companies from different sectors to predict the next day and next week's stock trends and accuracy metrics were checked for a period of 10, 30, and 100 days. The proposed model can achieve the highest accuracy of 0.90 for both trend and future trends for a period of 10 days. Experiments have also been performed to check the difficulty in predicting some stocks. It was found that Tata Motors an automobile company stock prediction has maximum MAPE and hence deviates more from actual prediction as compared to others.