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

TL;DR

This paper introduces CLEAR-Trade, a visualization framework that enhances the interpretability of deep learning models for stock market prediction, addressing the black-box issue and facilitating regulatory compliance in finance.

Contribution

The paper presents a novel visualization approach, CLEAR-Trade, specifically designed to explain and visualize deep learning decisions in financial stock prediction models.

Findings

CLEAR-Trade improves interpretability of deep stock prediction models.

Experiments on S&P 500 demonstrate effective decision visualization.

Enhanced interpretability aids regulatory acceptance of AI models.

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.