---
title: "Measuring overconfidence from what executives say"
description: "CEO and managerial overconfidence appear on 98 recent papers, mostly measured from option-exercise behaviour or press coverage, as in the 2000s. Only 6 of them carry a text, language or speech keyword, and 4 of those are in AI journals. Can a language model score overconfidence from earnings calls, and does it predict the same decisions?"
url: https://scholar4ai.com/ideas/measuring-overconfidence-from-executive-speech
markdown_url: https://scholar4ai.com/ideas/measuring-overconfidence-from-executive-speech.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["finance", "management", "ai"]
keywords: ["CEO overconfidence", "managerial overconfidence"]
---

# Measuring overconfidence from what executives say

CEO and managerial overconfidence appear on 98 recent papers, mostly measured from option-exercise behaviour or press coverage, as in the 2000s. Only 6 of them carry a text, language or speech keyword, and 4 of those are in AI journals. Can a language model score overconfidence from earnings calls, and does it predict the same decisions?

## What prompted this

Since October 2025, 98 papers carry an overconfidence keyword (44 in finance journals, 24 in AI, 18 in economics): "overconfidence" on 51, "CEO overconfidence" on 22, "managerial overconfidence" on 15. Only 6 of the 98 also carry a text, language, machine-learning, speech, tone or linguistic keyword, and 4 of those 6 are in AI journals.

## Questions

- Does a language model's reading of CEOs' answers on earnings calls (certainty, dismissal of risks, forecast ranges) agree with the option-based measure, and where do they disagree?
- Which measure better predicts overinvestment, overpriced acquisitions and later restatements?
- Can the text measure be computed for private firms and non-US firms, where option data do not exist?

## Data that could help

Earnings call transcripts, option exercise data (ExecuComp), deal and investment data (Compustat, SDC).

## Next step

Score 1,000 earnings calls with a language model for overconfidence and compare the scores with the option-based measure for the same CEOs.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 23 – Oct 26* | Papers, last 36 months |
|---|---:|---:|---|---:|
| CEO overconfidence | 0 | – | 0, 0, 0, 0, 0, 0, 2, 3, 2, 1, 1, 3, 0, 2, 1, 1, 2, 1, 0, 2, 0, 3, 0, 3, 2, 0, 0, 1, 4, 3, 4, 1, 2, 3, 0, 2, 0 | 49 |
| managerial overconfidence | 0 | – | 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 2, 2, 1, 1, 3, 4, 3, 0, 1, 2, 2, 1, 1, 0, 0, 1, 1, 2, 5, 0, 3, 1, 0, 1, 1, 0 | 42 |

## Cite this page

Scholar4AI. (2026). Measuring overconfidence from what executives say. Journal Research Rankings. Retrieved October 23, 2026, from https://scholar4ai.com/ideas/measuring-overconfidence-from-executive-speech

```bibtex
@misc{scholar4ai-ideas-measuring-overconfidence-from-executive-speech,
  author = {{Scholar4AI}},
  title = {Measuring overconfidence from what executives say},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/ideas/measuring-overconfidence-from-executive-speech}},
  urldate = {2026-10-23},
  note = {Journal Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).