Generative Adversarial Networks in Asset Pricing

Student: Turyshev Arsenii

Supervisor: Svetlana Bryzgalova

Faculty: International College of Economics and Finance

Educational Programme: Financial Economics (Master)

Year of Graduation: 2020

This paper focuses on the asset pricing method introduced by Chen, Pelger, and J. Zhu (2019), which is based on an adversarial SDF estimation via generative adversarial networks (GANs), a powerful computer science technique. We propose an elastic net regularization that penalizes short positions. The proposed solution effectively reduces short positions and improves the stability of the model making the method more feasible in real-world applications.

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