In Too Deep: Guenzel (2025)

What this is. The paper’s core results, the conceptual framework formalizing sunk cost effects on divestiture decisions, and the identification strategy: enough to know what it found and how, without reading all 54 pages. To replicate or extend it, read the full source at the original.

TL;DR

This paper provides the first cleanly identified field evidence that sunk costs distort corporate investment decisions. In fixed-exchange-ratio (Fixed Shares) stock mergers, the final dollar acquisition cost is unknown at agreement signing. Aggregate market fluctuations between merger agreement and completion create plausibly exogenous variation in acquisition costs. Higher quasi-random acquisition costs strongly predict that acquiring firms hold on to the acquired business rather than divesting it: an interquartile cost increase reduces annual divestiture rates by 8% to 9%. Placebo tests using post-completion market fluctuations find no effect, supporting the sunk cost interpretation. The effect is concentrated in firm-years when the acquiring CEO (who personally incurred the cost) is still in office, and in financially unconstrained firms. Mechanism tests rule out learning, investment budget constraints, and CEO entrenchment as primary explanations; the evidence is most consistent with managerial behavioral frictions generated by sunk cost thinking.

Core results

Magnitudes and significance are as reported; \*\*/\*\*\* = 5%/1%. Locators point into the source PDF.

#	Result	Locator	Magnitude
R1	Quasi-random acquisition cost increases reduce divestiture rates by ~8%: the main sunk cost effect, robust to controls	Table III, col. (1), p. 1617	Cox coefficient on delta-C = -0.065 (z=-2.77\*\*\*); interquartile cost increase (1.28 pp of market cap) reduces divestiture rate 8%
R2	Effect robust to time-varying controls (acquirer prior-year return, industry distress indicator): coefficient barely changes	Table III, col. (2), p. 1617	Coefficient -0.068 (z=-2.89\*\*\*); with time-varying interactions up to -0.077 (col. 4); interquartile effect 8-9.4%
R3	Placebo: post-completion market fluctuations do NOT predict divestiture rates: only pre-completion (sunk) cost variation matters	Table IV (Panel A), p. 1621	All five placebo coefficients between 0.009 and 0.011, z-stats 0.30-0.40, all insignificant
R4	Within-divestiture sample (divested-only) replicates: sunk cost effect holds without case-control matching	Table V, p. 1622	Coefficient -0.070 to -0.065 (z=-2.29 to -2.50\*\*); interquartile effect 8.0-8.1%
R5	Financial constraints dampen the effect: distortions ~13% for unconstrained firms, ~6% for constrained	Table VII (Panel A), p. 1626	Unconstrained: -0.113 to -0.106 (z=-3.42 to -3.94\*\*\*); Constrained: -0.047 to -0.046 (insignificant)
R6	Effect is CEO-specific: concentrated in firm-years when the acquiring CEO is still at the helm; 30-50% lower once that CEO departs	Table VIII, col. (2), p. 1628	Acquiring CEO: -0.105 (z=-2.51\*\*); New CEO: -0.060 (z=-1.65, insignificant); New CEO base effect +0.575\*\*\*
R7	Concentrated in diversifying acquisitions (a proxy for inferior deal quality): no significant effect for same-industry deals	Table X, p. 1633	Diversifying: -0.068 to -0.079 (z=-2.89 to -3.20\*\*\*); Same-industry: -0.011 to -0.035 (all insignificant)

Overall (paper’s conclusion). Quasi-random increases in acquisition costs cause firms to hold on to acquired businesses longer, consistent with managers taking sunk costs into account when deciding whether to divest. The mechanism appears to be an intrapersonal, CEO-specific behavioral friction: the effect disappears when the CEO who made the costly acquisition is replaced, extending the observation by Weisbach (1995) that divestitures are more likely after CEO changes (here shown to reflect a sunk cost channel rather than general strategy shifts). Financial constraints partially counteract the distortion, consistent with constraints limiting Malmendier and Tate (2005)-style behavioral investment distortions. Same-industry deals are unaffected, whereas diversifying acquisitions (a proxy for inferior deal quality flagged by Kaplan and Weisbach (1992)) concentrate the distortions. These patterns are most consistent with sunk costs generating psychological frictions in managerial decision making (Staw and Hoang (1995)), and are difficult to reconcile with CEO learning, investment budgets, or CEO entrenchment as the primary driver. Contemporaneous work by Cronqvist and Pely (2024) concludes that many divestitures are “corrections of failure,” consistent with the efficiency costs of delay documented here.

Theory / model

The paper has no structural model estimated by moments. It begins with a simple three-period reduced-form conceptual framework (Section I.A, pp. 1599-1601, Figure 1) that formalizes the prediction being tested.

Setup. At $t=0$, a manager buys an asset at total cost $\bar{C} = C + \Delta C$, where $C$ is known at the investment decision and $\Delta C$ is a mean-zero random variable realized between $t=0$ and $t=1$. At $t=1$, the manager decides to keep or divest the asset. The asset’s market price (divestiture value) is $P$, the firm-specific interim cash flow (synergy) is $X$, and the long-run payoff if kept is $Z$. A divestiture yields $P = Z$ (competitive buyer market). The manager has potentially nonstandard preferences.

Standard manager. For $\kappa = 0$, the manager at $t=1$ solves:

$$\max_{d_1 \in \{0,1\}} \; (1-d_1)(X + Z) + d_1 P \tag{Result 1, p. 1601}$$

where $d_1 = 1$ denotes divestiture. The standard manager divests if and only if $X + Z < P$. With $P = Z$, this reduces to $X < 0$: the divestiture decision is independent of the realized cost shock $\Delta C$.

Sunk cost manager. For $\kappa > 0$, the manager incurs a disutility from divesting that is increasing in the total cost $\bar{C}$. The manager solves (p. 1600):

$$\max_{d_1 \in \{0,1\}} \; (1-d_1)(X + Z) + d_1 \!\!\underbrace{\!\!\left(P - \kappa\bar{C}\right)}_{\text{net of sunk cost disutility}} \tag{Result 2, p. 1601}$$

The sunk cost manager divests if and only if $X < -\kappa(\bar{C}) = -\kappa(C + \Delta C)$. A larger realized cost shock $\Delta C$ raises the threshold and makes divestiture less likely. This is the testable prediction: the probability of divestiture at $t=1$ is decreasing in $\Delta C$ for a sunk cost manager ($\kappa > 0$) and independent of $\Delta C$ for a standard manager ($\kappa = 0$).

The Internet Appendix extends the framework to a prospect-theory setting (Kahneman and Tversky (1979), Thaler (1980)) showing that diminishing sensitivity to losses generates the same prediction: a higher cost shock codes as a larger loss domain for the manager, making continued holding relatively more attractive.

Method

The paper applies three estimators to a hand-collected dataset of Fixed Shares M&A deals.

Cox (1972) proportional hazards model (main estimator). The primary specification models the hazard of divestiture as (Section III.E, p. 1616, equation 4):

$$h(t \mid \mathbf{X}_i) = h_0(t) \exp(\boldsymbol{\delta}' \mathbf{X}_i) \tag{4}$$

where $t$ is survival time (years since acquisition), $h_0(t)$ is the unspecified baseline hazard, and $\mathbf{X}_i$ includes the main variable of interest $\Delta C_i$, deal- and firm-level controls, acquirer and target industry fixed effects, and acquisition year fixed effects. The model treats nondivested acquisitions as right-censored (censoring date: December 15, 2018, or acquirer takeover date). Time-varying covariates are accommodated by reshaping data into one-year-long sub-spells. Proportional hazards assumption is tested via Schoenfeld (1982) residuals; some control variables require time-interaction corrections.

Logit and stratified hazard models. Robustness to using a logit model instead of the hazard model (Efron (1988), Jenter and Kanaan (2015)), and to stratified Cox (1972) models. These produce qualitatively identical results (Internet Appendix, Section IV.A).

Two-stage control function approach. To address concerns about endogenous acquisition cost changes, the paper also implements a two-stage approach (Wooldridge (2015)): in the first stage, regress the actual cost change on the market-induced component and controls; in the second stage, include the residual from the first stage in the hazard model to control for endogeneity. Results are stable (Internet Appendix Table IA.VII).

This paper builds on survival-analysis (the Cox proportional hazards model) and panel-regression primitives, and uses a natural-experiment identification design: market fluctuations during the binding merger agreement period shift acquisition costs quasi-randomly across deals in the same year, analogous to instrumental-variables logic.

Empirical specifications

Acquisition cost change construction (pp. 1610-1611, equations 1 and 1’). The endogenous change in acquisition cost induced by the acquirer’s own stock price movements is (equation 1):

$$\Delta C_i^{Acq} = \Delta R_i^{Acq} \times \%\text{stock}_i \times \frac{\text{Deal Value}_i}{\text{Market Cap}_i^{Acq}} \tag{1}$$

where $\%\text{stock}_i \in (0,1]$ is the fraction of merger consideration paid in stock, relative deal value is the deal value at agreement relative to the acquirer’s pre-announcement market capitalization, and $\Delta R_i^{Acq} = \sum_{t=\tau_1+2}^{\tau_2} R_{i,t}^{Acq}$ is the cumulative daily acquirer return during the transaction period (merger agreement date $\tau_1$ to completion date $\tau_2$).

To isolate exogenous variation, the acquirer’s daily return is replaced by the daily market return, adjusted for expected market appreciation and the acquirer’s industry beta (equation 2’):

$$\Delta R_i = \sum_{t=\tau_1+2}^{\tau_2} \hat{\beta}_{i,\tau_1}\!\left(R_t^{Mkt} - E_{\tau_1}\!\left[R_t^{Mkt}\right]\right) \tag{2'}$$

The market-driven cost change used as the main variable of interest is then (equation 1’):

$$\Delta C_i = \Delta R_i \times \%\text{stock}_i \times \frac{\text{Deal Value}_i}{\text{Market Cap}_i^{Acq}} \tag{1'}$$

Main estimating equation (p. 1611, equation 3):

$$\Pr(\text{Divestiture}_{i,t}) = \alpha + \kappa\,\Delta C_i + \boldsymbol{\delta}'\mathbf{X}_{i,t} + \nu_{j(\text{Acq})} + \nu_{j(\text{Tar})} + \mu_{t_0} + \varepsilon_{i,t} \tag{3}$$

where $i$ denotes an acquisition, $t$ is years elapsed since acquisition, $t_0$ is the acquisition (calendar) year, $\text{Divestiture}_{i,t}$ is an indicator for the year of divestiture, $\Delta C_i$ is the market-induced cost change (equation 1’), $\nu_{j(\text{Acq})}$ and $\nu_{j(\text{Tar})}$ are acquirer and target industry fixed effects, and $\mu_{t_0}$ are acquisition year fixed effects. The null hypothesis (no sunk cost effects) is $\kappa = 0$. Standard errors are clustered by quarter of acquisition (treatment is assigned by market fluctuations between merger agreement and completion, which cluster within calendar quarters).

Identifying variation. Market fluctuations between merger agreement and completion shift acquisition costs across deals in the same year (same $\mu_{t_0}$) but in different calendar quarters: Table II confirms that the market return during the transaction period (i) strongly predicts firm returns (Panel A, F-statistic above 70), and (ii) is unpredictable from deal and firm characteristics (Panel B, joint F-statistic for 8 covariates = 0.56, p-value = 0.81). This validates the “as good as randomly assigned” assumption conditional on acquisition year.

Key robustness tests. Placebo using post-completion market fluctuations (Table IV, pp. 1621-1622): coefficients insignificant across all 10 specifications. Fixed Dollar placebo (Table VI, p. 1624): the hypothetical cost change for Fixed Dollar deals (which do not have exchange-ratio-induced cost variation) is insignificant while the actual Fixed Shares effect remains. Results are robust to financial constraint controls, alternative clustering, alternative time specifications, and gradual removal of high-withdrawal-probability observations.

Datasets used

Dataset	Role in paper	Wiki page
SDC Platinum M&A database	Starting universe of domestic acquisitions by US public acquirers 1980-2016; deal characteristics and divestiture flags	SDC Platinum (licensed)
Nexis (LexisNexis) news search	Divestiture identification: systematic search for newspaper articles and news wires for acquisitions not flagged by SDC	No page yet
SEC EDGAR filings (10-K, 10-Q, 8-K, S-4, Exhibit 21)	Hand-collected merger agreement terms (exchange ratio type, deal terms); divestiture verification	SEC EDGAR
CRSP monthly and daily returns	Acquirer stock returns during transaction period; acquirer market capitalization; beta estimation; control variables	WRDS / CRSP (licensed)
Compustat	Acquirer financial characteristics; industry market-to-book; leverage; financial constraint construction	WRDS / Compustat (licensed)
Execucomp	CEO tenure and compensation data for ~50% of acquirers; CEO change dates	WRDS / Execucomp (licensed)
SEC filings, BoardEx, Bloomberg, Capital IQ, Who’s Who	Hand-collected CEO education and biographical data for CEO sophistication tests	No page yet

Sample: US public acquirers, acquisitions 1980-2016, divestitures tracked through December 2018. Main sample: 558 Fixed Shares acquisitions (279 divested), 4,461 firm-year observations.

When to read the full paper

Use the original if you are: replicating the sunk cost hazard model (the Internet Appendix contains the full identification validity tests, Schoenfeld residual analysis, and the two-stage control function approach); designing an identification strategy for sunk cost effects in other investment contexts (R&D, VC, financial intermediation); studying CEO-level behavioral mechanisms in M&A decision making; or examining efficiency costs of delayed divestitures (Section V.D, counterfactual analysis). The locators above point to the exact tables.

Attribution and rights

Source: peer-reviewed, The Journal of Finance 80(3), June 2025. This distillation was extracted by an LLM on 2026-06-06 and is not human-verified or independently reproduced. The article is paywalled (Wiley/American Finance Association). Extract-only; no PDF hosted.

Guenzel, Marius. “In Too Deep: The Effect of Sunk Costs on Corporate Investment.” The Journal of Finance 80, no. 3 (June 2025): 1593-1646. DOI: 10.1111/jofi.13430. © 2025 the American Finance Association. Distilled by the Institute for Automated Research (extract-only, not reproduced).