Paper: arXiv 2409.17933

Abstract

We create a firm-level ChatGPT investment score, based on conference calls, that measures managers’ anticipated changes in capital expenditures. We validate the score with interpretable textual content and its strong correlation with CFO survey responses. The investment score predicts future capital expenditure for up to nine quarters, controlling for Tobin’s $q$ and other determinants, implying the investment score provides incremental information about firms’ future investment opportunities. The investment score also separately forecasts future total, intangible, and R&D investments. Consistent with theoretical predictions, high-investment-score firms experience significant positive short-term returns upon disclosure, and negative long-run future abnormal returns. We demonstrate ChatGPT’s applicability to measure other policies, such as dividends and employment.

Complexity vs Empirical Score

  • Math Complexity: 4.5/10
  • Empirical Rigor: 8.5/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper’s mathematical complexity is moderate, relying on established theoretical frameworks like q-theory and standard regression models, without dense derivations. Empirical rigor is high, evidenced by extensive backtesting on real-world data (74k transcripts), validation against CFO surveys, and robustness checks.

Research Flowchart

  flowchart TD
  A["Research Goal: Measure ChatGPT's Value in<br>Firm-Level Investment Policy Forecasting"] --> B["Methodology: Create Firm-Level<br>ChatGPT Investment Score"]
  B --> C["Data Source: Earnings<br>Conference Call Transcripts"]
  C --> D["Computation: Prompt ChatGPT to<br>Quantify Managerial Sentiment on<br>Future Capital Expenditures"]
  D --> E["Validation & Outcomes"]
  E --> F["Primary Outcome: Score predicts<br>future CapEx up to 9 quarters<br>(controlling for Tobin's q)"]
  E --> G["Secondary Outcomes: Predicts total,<br>intangible, & R&D investments"]
  E --> H["Market Reaction: Short-term positive<br>abnormal returns, Long-term negative<br>abnormal returns"]