Based on the research into computational learning theory and pattern recognition, a new subject of computer science was born: machine learning. Continued usage of the Capital Asset Pricing Model (CAPM) based on information from the US equities markets allows for accurate price projections. Training on time series data for the whole stock universe and external macroeconomic factors allows the applicable Machine Learning models to significantly outperform the CAPM on out-of-sample (OOS) test data. Scores for categorization models varied widely; among the machine and deep learning models examined, the long short-term memory (LSTM) model showed greater accuracy. In most directional evaluation indicators, the experimental results show that traditional ML algorithms perform better. Furthermore, all ML algorithms are vulnerable to fluctuations in transaction cost, which can negatively affect trading performance. Yet, the effects of both explicit and implicit transaction costs on market activity are distinct. This research is important because it allows us to determine which algorithm is most lucrative across various markets.

Recognition, Machine, Learning, Accuracy, Risk,