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.
[1]
Rob Fergus,et al.
Visualizing and Understanding Convolutional Networks
,
2013,
ECCV.
[2]
Carlos Guestrin,et al.
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
,
2016,
ArXiv.
[3]
Alexander Wong,et al.
Explaining the Unexplained: A CLass-Enhanced Attentive Response (CLEAR) Approach to Understanding Deep Neural Networks
,
2017,
2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[4]
J. DiCarlo,et al.
Using goal-driven deep learning models to understand sensory cortex
,
2016,
Nature Neuroscience.
[5]
Abhishek Das,et al.
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
,
2016,
2017 IEEE International Conference on Computer Vision (ICCV).
[6]
Geoffrey E. Hinton,et al.
Deep Learning
,
2015,
Nature.