A Comparison of Machine Learning Methods for Forecasting Dow Jones Stock Index

Stock market forecasting is a challenging and attractive topic for researchers and investors, helping them test their new methods and improve stock returns. Especially in the time of financial crisis, these methods gain popularity. The algorithmic solutions based on machine learning are used widely among investors, starting from amateur ones up to leading hedge funds, improving their investment strategies. This paper made an extensive analysis and comparison of several machine learning algorithms to predict the Dow Jones stock index movement. The input features for the algorithms will be some other financial indices, commodity prices and technical indicators. The algorithms such as decision tree, logistic regression, neural networks, support vector machine, random forest, and AdaBoost have exploited for comparison purposes. The data preprocessing step used a few normalization and data transformation techniques to investigate their influence on the predictions. In the end, we presented a few ways of tuning hyperparameters by metaheuristics such as genetic algorithm, differential evolution, and immunological algorithm.