Improved stock price forecasting by streamlining indicators: an approach via feature selection and classification

Accurately predicting changes in stock prices is a complex and challenging task due to the multitude of factors influencing the stock market. Stock market analysts commonly rely on indicators for forecasting, but the interpretation of these indicators is often complicated and can result in inaccurate predictions. To enhance the precision of stock price forecasting, we propose a novel approach that incorporates feature selection algorithms and classification techniques. In fact, by identifying the most impactful indicators affecting each stock's price, the process of predicting stock prices will be significantly simplified. We conducted experimental tests on stock data from multiple companies listed in the Tehran Stock Exchange, spanning 2008 to 2021. Our findings demonstrate that reducing the number of features and indicators can significantly enhance the accuracy of stock price predictions in specific scenarios.