Sustainability, Accuracy, Fairness, and Explainability (SAFE) Machine Learning in Quantitative Trading

The paper investigates the application of advanced machine learning (ML) methodologies, with a particular emphasis on state-of-the-art deep learning models, to predict financial market dynamics and maximize profitability through algorithmic trading strategies. The study compares the predictive capabilities and behavioral characteristics of traditional machine learning approaches, such as logistic regression and support vector machines, with those of highly sophisticated deep learning architectures, including Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Gated Recurrent Units (GRUs). The findings underscore the fundamental distinctions between these methodologies, with deeply trained models exhibiting markedly different predictive behaviors and performance, particularly in capturing complex temporal patterns within financial data. A cornerstone of the paper is the introduction and rigorous analysis of a framework to evaluate models, by means of the SAFE framework (Sustainability, Accuracy, Fairness, and Explainability). The framework is designed to address the opacity of black-box ML models by systematically evaluating their behavior across a set of critical dimensions. It also demonstrates how models’ predictive outputs align with the observed data, thereby reinforcing their reliability and robustness. The paper leverages historical stock price data from International Business Machines Corporation (IBM). The dataset is partitioned into a training phase during which the models are calibrated, and a validation phase, used to evaluate the predictive performance of the generated trading signals. The study addresses two primary machine learning tasks: regression and classification. Classical models are utilized for classification tasks, with their outputs directly interpreted as trading signals, while advanced deep learning models are employed for regression, with predictions of future stock prices further processed into actionable trading strategies. To evaluate the effectiveness of each strategy, rigorous backtesting is conducted, incorporating visual representations such as equity curves to assess profitability and key risk metrics like maximum drawdown for risk management. Supplementary performance indicators, including hit rates and the incidence of false positions, are analyzed alongside the equity curves to provide a holistic assessment of each model’s performance. This comprehensive evaluation not only highlights the superiority of cutting-edge deep learning models in predicting financial market trends but also demonstrates the pivotal role of the SAFE framework in ensuring that machine learning models remain trustworthy, interpretable, and aligned with ethical considerations.