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Artificial Intelligence and Finance

Paper Session

Friday, Jan. 3, 2025 8:00 AM - 10:00 AM (PST)

San Francisco Marriott Marquis, Foothill C
Hosted By: Association of Financial Economists
  • Chair: Lemma W. Senbet, University of Maryland

Trading Volume Alpha

Ruslan Goyenko
,
McGill University
Bryan Kelly
,
Yale University, AQR and NBER
Tobias Moskowitz
,
Yale University, AQR and NBER
Yinan Su
,
Johns Hopkins University
Chao Zhang
,
University of Oxford

Abstract

Portfolio optimization chiefly focuses on risk and return prediction, yet implementation costs also play a critical role. Predicting trading costs is challenging, however, since costs depend endogenously on trade size and trader identity, thus impeding a generic solution. We focus on a key, yet general, component of trading costs that abstracts from these challenges – trading volume. Individual stock trading volume is highly predictable, especially with machine learning. We model the economic benefits of predicting stock volume through a portfolio framework that trades off portfolio tracking error versus net-of-cost performance – translating volume prediction into net-of-cost portfolio alpha. We find the benefits of predicting individual stock volume to be substantial, and potentially as large as those from stock return prediction.

Displacement or Augmentation? The Effects of AI Innovation on Workforce Dynamics and Firm Value

Mark Chen
,
Georgia State University
Joanna Wang
,
Peking University

Abstract

This paper studies the effects of Artificial Intelligence (AI) innovation on firm-level employment dynamics and corporate valuation. Applying state-of-the-art large language models (LLMs) and Generative AI to U.S. patent data during 2007-2023, we identify AI-related innovations in seven key functional areas. Using microdata on individual workers’ skills and job transitions, we find that AI innovations related to engagement, learning, or creativity augment human labor, but those related to perception displace it. Augmenting AI innovations raise firm-level productivity, while displacing AI innovations lower operating costs. We also find that augmenting (displacing) AI innovations yield more (less) positive valuation effects when the innovating firm has better access to prospective hires (higher costs of terminating employees). Overall, our findings suggest that AI innovations can bring large potential value gains to innovating firms, but how much of those gains are realized depends critically on what frictions are present in the external labor market.

Dissecting Corporate Culture Using Generative AI

Kai Li
,
University of British Columbia
Feng Mai
,
University of Iowa
Rui Shen
,
Chinese University of Hong Kong
Chelsea Yang
,
University of British Columbia
Tengfei Zhang
,
Rutgers University

Abstract

This paper conducts the first large-scale study of how analysts and corporate insiders differ in their assessment of corporate culture and quantifies the economic implications of these differences. We employ generative AI to analyze analyst reports, earnings call transcripts, and employee reviews, and organize extracted information into a knowledge graph that links a culture type to its perceived causes and effects. We document systematic differences between analysts’ perspectives on culture and those of executives and employees. We further show that analysts’ culture analyses are incorporated into stock recommendations and target prices, and that investors react to a report’s coverage of culture. Our findings suggest that analysts’ perspectives on corporate culture contain value-relevant information not captured by insiders' views.

Discussant(s)
Gustavo Schwenkler
,
Santa Clara University
Anastassia Fedyk
,
University of California-Berkeley
Baozhang Yang
,
Georgia State University
JEL Classifications
  • G1 - General Financial Markets
  • O3 - Innovation; Research and Development; Technological Change; Intellectual Property Rights