Papers › Theoretically Motivated Data Augmentation and Regularization for Portfolio Construction
Theoretically Motivated Data Augmentation and Regularization for Portfolio Construction
Liu Ziyin, Kentaro Minami, Kentaro Imajo
The 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 implies that a simple algorithm of injecting a random noise of strength √(|rₜ₋₁|) to the observed return rₜ is better than not injecting any noise and a few other financially irrelevant data augmentation techniques.
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