Sustainability, Accuracy, Fairness, and Explainability (SAFE) Machine Learning in Quantitative Trading
Traditional and deep ML methods are evaluated using SAFE framework to enhance quantitative trading and risk management in IBM stocks.
What it examines
This study explores how advanced machine learning and deep learning models can improve trading strategies by predicting stock prices. It compares simple models with complex neural networks using IBM stock data, and introduces the SAFE framework to measure model reliability, fairness, and transparency, helping in effective risk management.
What it concludes
Deep learning models, especially GRU, LSTM, and RNN, outperformed traditional methods and buy-and-hold strategies, generating better profits and risk management. The SAFE framework clarified model decisions. This research can be applied to design more reliable trading systems and improve financial decision-making.
Evidence objects
Deep learning models including GRU, LSTM, RNN, and CNN remarkably outperform classical logistic regression and SVM, generating superior IBM stock forecasts and trading strategies using twenty years of historical data.
key_findings bullet 1 · key_findings · validation V0
A GRU-based strategy achieved highest profits, lower drawdowns, while the study presented an eight-step methodology and SAFE framework for evaluating sustainability, accuracy, fairness, and explainability, setting benchmarks in risk-managed trading.
key_findings bullet 2 · key_findings · validation V0
The research introduced metrics including Rank Graduation Accuracy, Robustness, and Fairness for quantitative insights, yet challenges such as high false positions and limited external factors persist, emphasizing balanced, interpretable AI.
key_findings bullet 3 · key_findings · validation V0
This paper introduces the innovative $\mathrm{SAFE}$ framework, which integrates sustainability, accuracy, fairness, and explainability in quantitative trading. It offers a novel comparison of traditional $ML$ and deep learning methods through rigorous backtesting for transparent AI. Although it employs established techniques like $RNN$, $LSTM$, and \$logistic regression\$, its approach is original.
key_findings bullet 4 · key_findings · validation V0
Raw abstract and provenance
- … networks with conventional mathematical and statistical … by mathematical models rooted in established financial … learning algorithms for financial time series analysis. …
Source row: 1884 · abstract type: snippet