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

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

Visualizing the Periods of Stock Prices Using Non-Harmonic Analysis of the NASDAQ Composite Index Since 1985

Abstract: The prediction of stock prices is studied extensively, because of the demand from private investors and financial institutions. However, long-term prediction is difficult due to the large number of factors that affect the real market. Previous research has focused on the fluctuation patterns and fluctuation periodicity of stock prices. We have likewise focused on the periodicity of stock prices. We have used a new high-resolution frequency analysis (non-harmonic analysis) method can solve the previous problem of the frequency resolution being low. As a consequence, we have succeeded in visualizing the various periodicities of stock prices. The periodicity fluctuates gently in many periods, but we have confirmed that it fluctuated violently in periods when a sudden event occurred. We expect that this experimental result in combination with previous research will help increase predictive accuracy and will aid long-term prediction

Visualization of Relations Between Financial Indices Using Multidimensional Scalling

This paper applies Multidimensional scaling techniques for visualizing possible time-varying correlations between twenty five stock market values. The method is useful for observing stable or emerging clusters of stock markets with similar behavior. The graphs may also guide the construction of multivariate econometric models.N/