The Language of Letting Go: Text-Label Co-Production and the Limits of Computational Inference in CEO Departure Data
Year: 2026
Type: preprint
Abstract: Can textual descriptions of chief executive departures recover whether an exit was forced, and what does such recoverability establish when text and label share a production process? We conduct a leakage-aware computational audit of an open database of 5,516 coded CEO departures in S&P 1500 firms from 2000 through 2018, whose narrative field consists of coder-constructed summaries rather than firm announcements. Among 5,365 discretionary departures with usable text, forced cases are substantially more negative than other exits (Cohen's d = − .89), and dominant-topic membership is associated with forced status (Cramer's V = .41 in the four-topic model, .56 in a seven-topic sensitivity model). A row-random benchmark produces ROC-AUC = .891. Keeping each firm's observations in a single validation fold and learning all text transformations from training data lowers the figure to .860 (95% cluster-bootstrap CI [.849, .872]); it remains .838 after direct label and coder-meta cues are masked and .824 under a strict topic-only specification. A model trained on 2000–2013 cases reaches .886 in 2014–2018. High-dimensional TF-IDF improves ranking (.937) but yields compressed, poorly calibrated probabilities. These results establish robust within-database recoverability, not evidence that firms' impression management leaks the truth. We develop text-label co-production as a measurable extension of familiar criterion-contamination and annotation-artifact problems, and propose provenance validity as the standard that connects a text's origin to the inferences it can support. The caution applies wherever coders both label events and write the narratives that describe them; testing firm communication directly requires a source-separated design.
Source: Research Square
Authors: Murat Günsel, Erkut Altındağ, Ayşe Günsel
Institutions: Doğuş University, Kocaeli Üniversitesi
Language: English
Subfield: General Social Sciences
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7225833199