Lin William Cong is the Rudd Family Professor of Management (endowed faculty chair by the Rudd Family Foundation) and a Tenured Professor of Finance at the Johnson Graduate School of Management at Cornell University SC Johnson College of Business.
Website: https://www.linwilliamcong.com/
Paper: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4590323
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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