This website uses cookies.

By clicking the "Accept" button or continuing to browse our site, you agree to first-party and session-only cookies being stored on your device to enhance site navigation and analyze site performance and traffic. For more information on our use of cookies, please see our Privacy Policy.

  1. Home
  2. ASSA Annual Meeting
  3. 2025
  4. Preliminary Program
  5. Artificial Intelligence and Finance
« Back to Results

Artificial Intelligence and Finance

Paper Session

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

San Francisco Marriott Marquis
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 on Workforce Dynamics and Firm Value

Mark Chen
,
Georgia State University
Joanna Wang
,
Peking University

Abstract

This paper studies the employment effects of Artificial Intelligence (AI) innovation and the consequences of these effects for corporate valuation. We apply state-of-the-art techniques in deep learning and large language models (LLMs) to identify and categorize AI-related innovations from among the millions of U.S. patents granted during 2007-2023. Using microdata on individual workers’ labor-market transitions and roles within the firm, we examine whether different types of AI innovation cause labor augmentation and/or labor displacement. We find that most categories of AI innovation, including those related to perception, prediction, or engagement, have augmenting effects on innovating firms’ skilled labor. AI innovations tied to engagement cause greater employment growth in pre-existing (“core”) skilled roles, while innovations related to inference or creativity drive more employment growth in entirely new roles. We also document that most types of AI patents have displacement effects on unskilled workers. In general, skilled-labor-augmenting AI patents add significant value to an innovating firm, but only if the patents do not systematically displace the innovator’s unskilled workers. Overall, our findings suggest that firms can benefit greatly from AI that augments skilled labor without displacing other segments of the workforce.

Dissecting Corporate Culture Using Generative AI – Insights from Analyst Reports

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 represents one of the first efforts to delve into the role of corporate culture in shareholder value creation from sell-side equity analysts’ vantage point. Applying generative AI (ChatGPT) to 2.4 million analyst reports over the period 2000 2020, we explore the antecedents and consequences of corporate culture. Specifically, we employ a causal relation extraction approach that automatically finds triples in the form of (culture, relationship, cause or consequence) from analyst reports. The aggregation of these triples constitutes a knowledge graph that represents culture as a node within an interconnected network of firm characteristics, corporate events, and business outcomes. The graph shows that business strategy, management team, and mergers and acquisitions are major drivers of cultural changes, and market share and growth, profitability, and innovation are direct outcomes of firms with a strong culture. Guided by these extracted causal relations, we empirically evaluate a number of new causes and consequences of culture and find supporting evidence. Moreover, we show that positive or future-oriented discussions of culture in analyst reports are positively and significantly associated with analysts’ stock recommendations and target prices. We conclude that analyst reports offer insights into the mechanisms through which culture affects business outcomes and that analysts’ research on culture contributes to the observed culture-firm value link.

Discussant(s)
Alejandro Lopez Lira
,
University of Florida
Anastassia Fedyk
,
University of California-Berkeley
Paola Sapienza
,
Northwestern University
JEL Classifications
  • G1 - General Financial Markets
  • O3 - Innovation; Research and Development; Technological Change; Intellectual Property Rights