What Data Augmentation Do We Need for Deep-Learning-Based Finance?

• URL: http://arxiv.org/abs/2106.04114v1
• Date: Tue, 8 Jun 2021 05:26:58 GMT
• Title: What Data Augmentation Do We Need for Deep-Learning-Based Finance?
• Authors: Liu Ziyin, Kentaro Minami, Kentaro Imajo
• Abstract summary: 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. This algorithm is shown to work well in practice.
• Score: 2.470815298095903
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 $\sqrt{|r_{t-1}|}$ to the observed return $r_{t}$. This algorithm is shown to work well in practice.

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