A Comparative Study of Supervised Machine Learning Algorithms for Stock Market Trend Prediction

Abstract

Impact of many factors on the stock prices makes the stock prediction a difficult and highly complicated task. In this paper, machine learning techniques have been applied for the stock price prediction in order to overcome such difficulties. In the implemented work, five models have been developed and their performances are compared in predicting the stock market trends. These models are based on five supervised learning techniques i.e., Support Vector Machine (SVM), Random Forest, K-Nearest Neighbor (KNN), Naive Bayes, and Softmax. The experimental results show that Random Forest algorithm performs the best for large datasets and Naive Bayesian Classifier is the best for small datasets. The results also reveal that reduction in the number of technical indicators reduces the accuracies of each algorithm.