What Data Augmentation Do We Need for Deep-Learning-Based Finance?
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The main task we consider is portfolio construction in a speculative market, a fundamental problem in modern finance. While various empirical works now exist to explore deep learning in finance, the theory side is almost non-existent. In this work, we focus on developing a theoretical framework for understanding the use of data augmentation for deep-learning-based approaches to quantitative finance. The proposed theory clarifies the role and necessity of data augmentation for finance; moreover, our theory motivates a simple algorithm of injecting a random noise of strength √(|r_t-1|) to the observed return r_t. This algorithm is shown to work well in practice.
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