Theoretically Motivated Data Augmentation and Regularization for Portfolio Construction

Liu Ziyin, Kentaro Minami and Kentaro Imajo

Papers from arXiv.org

Abstract: 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 $\sqrt{|r_{t-1}|}$ to the observed return $r_{t}$ is better than not injecting any noise and a few other financially irrelevant data augmentation techniques.

Date: 2021-06, Revised 2022-12
New Economics Papers: this item is included in nep-cmp and nep-fmk
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2106.04114

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