AI in Corporate Governance: Can Machines Recover Corporate Purpose?

Boris Nikolov, Norman Schuerhoff and Sam Wagner
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Boris Nikolov: University of Lausanne; Swiss Finance Institute; European Corporate Governance Institute (ECGI)
Sam Wagner: University of Lausanne

No 25-23, Swiss Finance Institute Research Paper Series from Swiss Finance Institute

Abstract: A key question in automating governance is whether machines can recover the corporate objective. We develop a corporate recovery theorem that establishes when this is possible and provide a practical framework for its application. Training a machine on a large dataset of firms' investment and financial decisions, we find that most neoclassical models fail to explain the data since the machine learns from managers to underestimate the shadow cost of capital. This bias persists even after accounting for financial frictions, intangible intensity, behavioral factors, and ESG. We develop an alignment measure that shows why managerial alignment with shareholder-value remains imperfect and how to debias managerial decisions.

Keywords: Corporate Purpose; Inverse Reinforcement Learning (search for similar items in EconPapers)
JEL-codes: D22 G30 L21 (search for similar items in EconPapers)
Pages: 70 pages
Date: 2025-03
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Persistent link: https://EconPapers.repec.org/RePEc:chf:rpseri:rp2523

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