A Comparative Study on Different Machine Learning Algorithms for Predictive Analysis of Stock Prices

Stock price prediction is a vital part of the life of any person involved in the financial market. Predicting trends is a topic that is increasingly gaining popularity among researchers, investors, and traders alike. However, numerous external factors affect the volatility of stock prices, thereby making the prediction extremely complicated. Being able to make accurate predictions is of paramount importance to maximize the profitability of trading in stocks. Machine learning algorithms can train and improve their performance individually and autonomously. This characteristic is beneficial in the financial domain, where large amounts of historical data must be understood. There are some underlying patterns in the historical data that humans may be unable to identify, and this is where machine learning models become very relevant. This paper provides a comparative analysis between three machine learning algorithms—linear regression, support vector machines (SVM), and random forest as tools to perform predictions on stock prices. These algorithms all use a set of eleven technical indicators as input features to the models and perform technical analysis to make the predictions. The paper also provides the result of incorporating sentiment analysis into the prediction models.