2025/04/05 by Anastasis Kratsios, Xiaofei Shi, Kratsios, Anastasis +5
Computer Science · Decision Sciences · Economics, Econometrics and Finance · #68T07 #68T30 #91-08 #91-10 #91B50 #91B69 #91G15 #91G60 #93E35 #Complex Systems and Time Series Analysis #FOS: Computer and information sciences #FOS: Economics and business #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG) #Mathematical Finance (q-fin.MF) #Pricing of Securities (q-fin.PR) #Stock Market Forecasting Methods
paper · pdf · doi:10.48550/arxiv.2504.04300
openalex publication_date 2025/04/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We present a general computational framework for solving continuous-time financial market equilibria under minimal modeling assumptions while incorporating realistic financial frictions, such as trading costs, and supporting multiple interacting agents. Inspired by generative adversarial networks (GANs), our approach employs a novel generative deep reinforcement learning framework with a decoupling feedback system embedded in the adversarial training loop, which we term as the reinforcement link. This architecture stabilizes the training dynamics by incorporating feedback from the discriminator. Our theoretically guided feedback mechanism enables the decoupling of the equilibrium system, overcoming challenges that hinder conventional numerical algorithms. Experimentally, our algorithm not only learns but also provides testable predictions on how asset returns and volatilities emerge from the endogenous trading behavior of market participants, where traditional analytical methods fall short. The design of our model is further supported by an approximation guarantee.