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.
翻译:我们所考虑的任务是在投机市场中进行投资组合的建设,这是现代金融的一个基本问题。虽然现在存在各种经验性工作来探索金融的深层学习,但理论方面几乎不存在。在这项工作中,我们侧重于开发一个理论框架,以了解如何利用数据增加来采用基于深层学习的量化融资方法。拟议的理论澄清了数据增加对金融的作用和必要性;此外,我们的理论暗示,向观察到的回报美元注入随机强度噪音的简单算法比不注入任何噪音和少数其他与财务无关的数据增加技术好。