GenAI for Social Sciences: A Data-Driven-Robust-Control Approach to Corporate Finance

Title: GenAI for Social Sciences: A Data-Driven-Robust-Control Approach to Corporate Finance

Abstract: I overview non-text-based generative modeling (involving transformer-based reinforcement learning or the novel "panel trees") for portfolio management, test asset creation, and detecting heterogeneity groups (e.g., asset clusters with differential return predictability), before briefly introducing the concept of data-driven generative equilibrium for counterfactual analysis in economics. I then focus on how goal-oriented GenAI applies to corporate decision-making that entails complex, high-dimensional, and non-linear stochastic control during which managers learn and adapt via dynamic interactions with the market environment. In Campello, Cong, and Zhou (2024), 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 understand managerial preferences (through an extension involving inverse reinforcement learning and generative adversarial networks).

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Bio: Lin William Cong is the Rudd Family Endowed Chair Professor of Management and Professor of Finance at the Johnson Graduate School of Management at Cornell University, where he is the founding faculty director for the FinTech Initiative and the founder of the Digital Economy and Financial Technology Lab (DEFT Lab).Professor Cong’s research spans financial economics, information economics, AI for Finance, applied theory, FinTech, the digital economy, and entrepreneurship and innovation, and has been published in top academic journals and featured in BBC, Bloomberg, CNN, the Economist, Washington Post, etc.