Current Trends in Financial Forecasting

Predicting the prices of stocks is a complex challenge that has attracted the interest of both investors and computer scientists. This review paper delves into the realm Artificial Intelligence and Machine Learning techniques employed in the past few years to construct effective models for predicting stock prices. It categorizes a range of linear and non-linear machine learning algorithms, neural network architectures, data sources, and commonly utilized metrics for assessing research outcomes. The paper also examines the methods of implementation and assesses their reproducibility. Furthermore, it provides a summary of technical papers and offers concise descriptions of each one. The overarching aim of reviewing these technical papers is to explore the potential for retail investors to generate profits through day trading in the stock market using relatively modest computational resources at their disposal.