Opening the Black Box of Financial AI with CLEAR-Trade: A CLass-Enhanced Attentive Response Approach for Explaining and Visualizing Deep Learning-Driven Stock Market Prediction

Abstract

Deep learning has been shown to outperform traditional machine
learning algorithms across a wide range of problem domains. However,
current deep learning algorithms have been criticized as uninterpretable
"black-boxes" which cannot explain their decision making
processes. This is a major shortcoming that prevents the widespread
application of deep learning to domains with regulatory
processes such as finance. As such, industries such as finance
have to rely on traditional models like decision trees that are much
more interpretable but less effective than deep learning for complex
problems. In this paper, we propose CLEAR-Trade, a novel
financial AI visualization framework for deep learning-driven stock
market prediction that mitigates the interpretability issue of deep
learning methods. In particular, CLEAR-Trade provides a effective
way to visualize and explain decisions made by deep stock market
prediction models. We show the efficacy of CLEAR-Trade in enhancing
the interpretability of stock market prediction by conducting
experiments based on S&P 500 stock index prediction. The results
demonstrate that CLEAR-Trade can provide significant insight
into the decision-making process of deep learning-driven financial
models, particularly for regulatory processes, thus improving their
potential uptake in the financial industry.