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AlphaManager: A Data-Driven-Robust-Control Approach to Corporate Finance
Abstract
Corporate decision-making involves high-dimensional, non-linear stochastic control under managerial learning and dynamic interactions with the economic environment. We introduce an AI-assisted, data-driven-robust-control (DDRC) framework to complement theory, reduced-form models, and structural estimations in corporate finance research. We do so with an emphasis on explaining and predicting firm outcomes empirically, and offering policy recommendations for flexible business objectives. Specifically, we build a predictive environment module through supervised deep learning and add a policy module through deep reinforcement learning that goes beyond hypothesis testing on historical data or simulations. 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 identifies important managerial decisions while offering effective policy recommendations adaptive to market evolution and feedback. We document rich heterogeneity in model prediction performance, ambiguity, and policy efficacy in the cross section of U.S. public firms and across time regimes. AlphaManager recommendations improve managerial performance by over 10% historically and have implications on management horizons. Critically, our DDRC approach informs where theory and causal analysis in corporate finance research should focus, and admits the incorporation of fragmented knowledge through ambiguity-guided transfer learning.
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