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The task we consider is portfolio construction in a speculative market, afundamental problem in modern finance. While various empirical works now existto explore deep learning in finance, the theory side is almost non-existent. Inthis work, we focus on developing a theoretical framework for understanding theuse of data augmentation for deep-learning-based approaches to quantitativefinance. The proposed theory clarifies the role and necessity of dataaugmentation for finance; moreover, our theory implies that a simple algorithmof injecting a random noise of strength $\sqrt{|r_{t-1}|}$ to the observedreturn $r_{t}$ is better than not injecting any noise and a few otherfinancially irrelevant data augmentation techniques.

Theoretically Motivated Data Augmentation and Regularization for Portfolio Construction