(PDF) Evaluating the Performance of Machine Learning Algorithms in Financial Market Forecasting: A Comprehensive Survey

With increasing competition and pace in the financial markets, robust forecasting methods are becoming more and more valuable to investors. While machine learning algorithms offer a proven way of modeling non-linearities in time series, their advantages against common stochastic models in the domain of financial market prediction are largely based on limited empirical results. The same holds true for determining advantages of certain machine learning architectures against others. This study surveys more than 150 related articles on applying machine learning to financial market forecasting. Based on a comprehensive literature review, we build a table across seven main parameters describing the experiments conducted in these studies.