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Deep learning has been shown to outperform traditional machinelearning 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 makingprocesses. This is a major shortcoming that prevents the widespreadapplication of deep learning to domains with regulatoryprocesses such as finance. As such, industries such as financehave to rely on traditional models like decision trees that are muchmore interpretable but less effective than deep learning for complexproblems. In this paper, we propose CLEAR-Trade, a novelfinancial AI visualization framework for deep learning-driven stockmarket prediction that mitigates the interpretability issue of deeplearning methods. In particular, CLEAR-Trade provides a effectiveway to visualize and explain decisions made by deep stock marketprediction models. We show the efficacy of CLEAR-Trade in enhancingthe interpretability of stock market prediction by conductingexperiments based on Su0026P 500 stock index prediction. The resultsdemonstrate that CLEAR-Trade can provide significant insightinto the decision-making process of deep learning-driven financialmodels, particularly for regulatory processes, thus improving theirpotential uptake in the financial industry.

journal of computational vision and imaging systems

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