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

46 Pages Posted: 31 Oct 2023 Last revised: 22 Apr 2024

See all articles by Murillo Campello

Murillo Campello

Cornell University - Samuel Curtis Johnson Graduate School of Management; National Bureau of Economic Research (NBER)

Lin William Cong

Cornell University - Samuel Curtis Johnson Graduate School of Management; National Bureau of Economic Research (NBER)

Luofeng Zhou

New York University (NYU) - Leonard N. Stern School of Business

Date Written: December 2, 2023

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 any business objectives. Specifically, we build a predictive environment module through supervised neural networks 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 also document the rich heterogeneity in model prediction performance, ambiguity, and policy efficacy in the cross section of U.S. public firms and across time regimes. Critically, our DDRC approach distinguishes between scenarios where theory and causal identification are important from situations where predictive models trained on historical observations suffice. It informs where corporate finance research should focus, allowing for the incorporation of fragmented knowledge through ambiguity-guided transfer learning.

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

Suggested Citation

Campello, Murillo and Cong, Lin and Zhou, Luofeng, AlphaManager: A Data-Driven-Robust-Control Approach to Corporate Finance (December 2, 2023). Available at SSRN: https://ssrn.com/abstract=4590323 or http://dx.doi.org/10.2139/ssrn.4590323

Murillo Campello

Cornell University - Samuel Curtis Johnson Graduate School of Management ( email )

114 East Avenue
369 Sage Hall
Ithaca, NY 14853
United States

HOME PAGE: http://www.johnson.cornell.edu/Faculty-And-Research/Profile.aspx?id=mnc35

National Bureau of Economic Research (NBER) ( email )

1050 Massachusetts Avenue
Cambridge, MA 02138

Lin Cong (Contact Author)

Cornell University - Samuel Curtis Johnson Graduate School of Management ( email )

Ithaca, NY 14853
United States

HOME PAGE: http://www.linwilliamcong.com/

National Bureau of Economic Research (NBER) ( email )

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Luofeng Zhou

New York University (NYU) - Leonard N. Stern School of Business ( email )

44 West 4th Street
Suite 9-160
New York, NY NY 10012
United States

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