Tone complexity and analyst forecast behaviors: evidence from earnings conference calls

Abstract

Purpose

Both the SEC (Securities and Exchange Commission) and the popular press have routinely criticized firms for the complexity of their financial disclosures. This study aims to investigate how financial analysts respond to the tone complexity of firm disclosures.

Design/methodology/approach

Using approximately 20,000 earnings conference call transcripts of S&P 1,500 firms between 2005 and 2015, the authors first calculate the abnormal negative tone, the measure of tone complexity; then use such tone measure in econometric models to examine analyst forecast behavior. The authors also test the robustness of the results under different model specifications, tone word lists and alternative tone measure calculations.

Findings

Consistent with the notion that analysts respond to the information demand from investors and incur more costs and effort to analyze firm disclosure when the tone is more complex, the authors find that higher tone complexity is positively and significantly associated with more analyst following, longer report duration, more forecast revisions, larger forecast error and larger forecast dispersion. In addition, the authors find that tone complexity has a long-term impact on analyst following but has a limited long-term impact on analyst report duration, analyst revision, forecast error and dispersion.

Originality/value

This study complements existing literature by highlighting the information role of financial analysts and by providing evidence that analysts incorporate the management tone disclosed during conference calls to adjust their forecasting behaviors. The results can be used by policymakers as evidence and support for further improving firm communication from a new dimension of disclosure tone.

Citation

Kwon, K., Zhou, M., Wang, T., Cheng, X. and Qiao, Z. (2023), "Tone complexity and analyst forecast behaviors: evidence from earnings conference calls", Asian Review of Accounting, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/ARA-01-2023-0009