How do managers' non-responses during earnings calls affect analyst forecasts

TL;DR

This study investigates how managers' non-responses during earnings calls influence analyst forecast accuracy and market volatility, revealing that higher non-response rates lead to increased uncertainty and trading costs, especially during COVID-19.

Contribution

The paper introduces a novel NLP-based measure of non-responses using ChatGPT-4 and LLaMA 3.3, linking non-responses to analyst forecast errors and market dynamics.

Findings

Higher non-response rates increase forecast errors and dispersion.

Non-responses are associated with greater market volatility and trading volume.

Effects are amplified during COVID-19 and for certain firm characteristics.

Abstract

This paper examines the impact of managers' non-responses (NORs) during quarterly earnings calls on analyst forecast behavior by developing a novel measure of NORs using two large language models: ChatGPT-4 and LLaMA 3.3. We adopt a three step prompting approach including identification, classification, and evaluation, to extract NORs from earnings call transcripts of S&P 500 firms. We find that a higher incidence of NORs is significantly associated with greater analyst forecast errors, dispersion, and uncertainty. These effects are more pronounced among firms with high institutional ownership, greater R&D expenditures, operations across multiple industries, and earnings calls held during the COVID-19 period. Further analysis shows that NORs are followed by greater post-earnings announcement drift, higher return volatility, increased trading volume, and wider bid-ask spreads, suggesting that NORs raise information processing costs and exacerbate uncertainty. Overall, our findings indicate that managers' non-responses during earnings calls impair the information environment for analysts and investors.