AlphaManager: A Data-Driven-Robust-Control Approach to Corporate Finance

Abstract

Corporate decision-making entails complex, high-dimensional, and non-linear stochastic control during which managers learn and adapt via dynamic interactions with the market environment. We propose a data-driven-robust-control (DDRC) framework to complement traditional theory, reduced-form models, and structural estimations in corporate finance research, emphasizing both empirical explanation and prediction of firm outcomes while delivering policy recommendations for a variety of business objectives. Specifically, we develop a predictive environment module using supervised deep learning and integrate a decision-making module based on generative deep reinforcement learning. By incorporating model ambiguity and robust control techniques, our framework not only better explains and predicts corporate outcomes in- and out-of-sample but also prescribes key managerial actions that significantly outperform historical ones. We document rich heterogeneity in model ambiguity, prediction performance, and policy efficacy in the cross section of U.S. public firms and over time. Importantly, DDRC helps delineate where theory and causal analysis should concentrate, integrate fragmented prior knowledge (e.g., via transfer learning), and reveal managerial preferences (through an extension involving inverse reinforcement learning).

Keywords: AI/ML, Ambiguity, Big Data, Deep Learning, Managerial Decision, Offline Reinforcement Learning, Stochastic Control, Transfer Learning.