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.

本文提出了 CLEAR-Trade，一个新的金融 AI 可视化框架，用于深度学习驱动的股票市场预测，以解决深度学习方法的可解释性问题并提高其在金融行业中的应用。通过对 S＆P 500 股票指数预测的实验，结果表明，CLEAR-Trade 可以为解释深度学习驱动的金融模型做出的决策提供重要洞见，并提高其在监管流程方面的潜在应用。

使用 CLEAR-Trade 揭示金融 AI 的黑匣子：一种基于类别增强的关注响应方法，用于解释和可视化深度学习驱动的股市预测

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