When Does a Generalist CEO Create Shareholder Value? The Effect of Managerial Challenge: Gelman, Fralich, Bitektine & Zahraei (2026)

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TL;DR

Gelman, Fralich, Bitektine and Zahraei study whether investors react positively to generalist CEO experience at new CEO announcements, and why prior studies found no such reaction. Using 1095 CEO turnovers in S&P 1500 firms from 2000 to 2015 and the General Ability Index (GAI) of Custodio, Ferreira and Matos (2013), they find no significant average CAR response to CEO experience. However, when the firm is complex (large in scale and scope of operations) or has performed poorly prior to the CEO change, investors react positively to higher GAI. One standard deviation of experience is associated with CAR increases corresponding to about 0.85% of market capitalization for a complex firm and 0.82% for a poorly performing firm; in firms facing at least one challenge (roughly two-thirds of the sample), the estimate is 0.68%. CEO compensation, by contrast, carries a positive and stable experience premium (7.3% per SD) regardless of firm challenge, consistent with a CEO job-market model where the outside option is priced against average-firm challenge rather than the specific hiring firm.

Core results

#	Result	Locator	Magnitude as reported
R1	Base effect of CEO generalist experience (GAI) on 5-day announcement CARs: no significant average effect	Table 4, col 1, p. 11	GAI coeff = 0.00252 (se = 0.00248), not significant
R2	Firm complexity positively moderates the GAI-CAR relation (linear interaction)	Table 4, col 2, p. 11	GAI × Complexity index = 0.00405** (se = 0.00183)
R3	High-complexity firms show positive investor reaction to CEO experience; low-complexity firms do not	Table 4, col 3, p. 11	GAI × High complexity = 0.00900** (se = 0.00372); F-test split-sample p = 0.0433
R4	Prior firm performance negatively moderates the GAI-CAR effect (higher performance, smaller GAI benefit)	Table 4, col 4, p. 11	GAI × Performance index = −0.00654*** (se = 0.00241); F-test split-sample p = 0.0028
R5	Low-performing firms: CEO experience raises CARs by 0.82%; high-performing firms: no significant effect	Table 4, col 5, p. 11	GAI × Low performance = 0.00874*** (se = 0.00329); GAI × High performance = −0.00439 (not sig)
R6	Any-challenge firms: one SD in GAI raises CARs by 0.68%; Double Challenge shows the largest effect	Table 4, cols 7-8, pp. 11-12	GAI × Any challenge = 0.00727*** (se = 0.00281); GAI × Double challenge = 0.0162*** (se = 0.00545); vs No Challenge p = 0.0031
R7	CEO compensation premium for generalist experience is positive and unmoderated by firm challenge	Table 5, col 1, p. 13	GAI = 0.0728** (se = 0.0307); 7.3% per SD; no significant moderation by complexity or performance
R8	GAI Surprise robustness: challenge firms show positive CARs; no-challenge firms show negative CARs	Table 6, col 8, p. 15	GAI_SURP × Any challenge = 0.00743** (se = 0.00289); GAI_SURP × No challenge = −0.00738* (se = 0.00442)
R9	Long-term ROA: complexity positively moderates the effect of residual CEO experience on operational performance	Table 7, col 2, p. 16	GAI_SURP × Complexity index = 0.00592** (se = 0.00252)
R10	Poor prior performance and lower residual firm efficiency predict appointments of higher-GAI CEOs	Table 3, cols 2 and 4, p. 10	Performance index = −0.126*** (se = 0.0386); Residual firm efficiency = −1.173*** (se = 0.369)
R11	One-challenge firms have higher experience-related CARs than no-challenge firms	Table 4, col 7, p. 11	GAI × One challenge = 0.00503* (se = 0.00305); One vs. No Challenge p = 0.0159
R12	Double-challenge firms show the largest experience-related CAR coefficient	Table 4, col 7, p. 11	GAI × Double challenge = 0.0162*** (se = 0.00545); Double vs. One Challenge p = 0.0605
R13	For long-term Tobin’s Q, GAI Surprise interacts positively with prior performance, a weak exception to the broader challenge pattern	Table 8, cols 6 and 8, p. 17	GAI_SURP × Performance index = 0.0688* (se = 0.0366), col 6; 0.0690* (se = 0.0366), col 8
R14	Long-term Tobin’s Q shows no significant GAI Surprise main effect or Any Challenge interaction	Table 8, cols 1 and 8, p. 17	GAI_SURP = −0.0154 (se = 0.0287), not significant; GAI_SURP × Any challenge = 0.00435 (se = 0.0325), not significant
R15	Higher-GAI CEOs have a higher baseline hazard of succession	Table 9, col 1, p. 19	GAI = 0.114*** (se = 0.0435)
R16	Complexity alignment weakly lowers CEO succession hazard in the split specification, but not in the full model	Table 9, cols 2 and 6, p. 19	GAI × Complexity index = −0.0647* (se = 0.0369), col 2; −0.0597 (se = 0.0367), col 6, not significant
R17	The GAI–succession-hazard association is stronger at higher prior performance; the paper interprets poorly performing, high-GAI matches as having longer CEO presence	Table 9, cols 4 and 6, p. 19	GAI × Performance index = 0.0964** (se = 0.0407), col 4; 0.0967** (se = 0.0407), col 6
R18	Low-complexity firms show no significant GAI-CAR relation	Table 4, col 3, p. 11	GAI × Low complexity = −0.000122 (se = 0.00297), not significant
R19	GAI Surprise reproduces the high-complexity and low-performance CAR effects	Table 6, cols 3 and 5, p. 15	GAI_SURP × High complexity = 0.00860** (se = 0.00390); GAI_SURP × Low performance = 0.00813** (se = 0.00338); split tests p = 0.0602 and p = 0.0077
R20	GAI Surprise leaves the pooled average CAR effect insignificant	Table 6, col 1, p. 15	GAI_SURP = 0.00208 (se = 0.00260), not significant

Overall. Higher generalist CEO experience is associated with stronger investor reactions when the firm faces at least one dimension of managerial challenge (complexity or prior poor performance), and with negative reactions in firms without such challenge in the GAI Surprise specifications. CEO compensation does not share this conditionality: boards pay a consistent premium for experience irrespective of firm challenge, consistent with the theoretical prediction that CEO outside options are tied to market-average challenge, not the specific firm. Betzer, van den Bongard and Limbach (2020)‘s absence of a broad investor reaction is explained by pooling challenge and non-challenge firms where the effects partially cancel.

Theory / model

The paper develops a two-round CEO job-market model (Appendix A, pp. 21-22) grounded in the competitive assignment literature of Gabaix and Landier (2008), Pan (2017), and Tervio (2008). The model has limited market participation: only one CEO candidate enters per period and faces one firm in round one, with probability $\pi$ the candidate also meets a second firm in round two.

Total surplus. The production function assumes complementarity between firm challenge $c_j$ and CEO generalist experience $m_i$, so total surplus from a match is:

T(c_j, m_i) = c_j \cdot m_i \tag{A1}

Investor (residual) surplus is the difference between total surplus and CEO compensation $p$:

v(c_j, m_i) = c_j \cdot m_i - p \tag{A2}

If no first-round deal is reached, a second-round firm can hire the candidate and pay the entire match surplus (Appendix A, p. 21):

p_2 = T'(c_k, m_i) = c_k \cdot m_i \tag{A3}

CEO outside option and round-1 compensation. The paper assumes $p_0 \leq C_{\min} m_i$. If round two occurs, the CEO faces firm $k$ with challenge $c_k \sim U[C_{\min}, C_{\max}]$ and receives pay equal to total surplus. The expected round-2 compensation, conditional on entering round two, is:

E[p_2 \mid \text{Round two}] = E[c_k] \cdot m_i = \frac{C_{\min} + C_{\max}}{2} \cdot m_i \tag{A4}

The CEO’s outside option in round one is the probability-weighted average of round-two pay and current-position pay $p_0$:

\text{outside\_option} = \pi \cdot \frac{C_{\min} + C_{\max}}{2} \cdot m_i + (1-\pi) \cdot p_0 \tag{A5}

Because firms match the outside option exactly (they have stronger bargaining power in round one), first-round CEO compensation is:

p_1 = \pi \cdot \frac{C_{\min} + C_{\max}}{2} \cdot m_i + (1-\pi) \cdot p_0 \tag{A6}

Taking the derivative with respect to experience shows that CEO compensation is independent of the hiring firm’s specific challenge level $c_j$:

\frac{\partial p_1}{\partial m_i} = \pi \cdot \frac{C_{\min} + C_{\max}}{2} \tag{A7}

Investor surplus and the threshold. Substituting (A6) into (A2) yields investor surplus:

v(c_j, m_i) = c_j \cdot m_i - \left\{ \pi \cdot \frac{C_{\min} + C_{\max}}{2} \cdot m_i + (1-\pi) \cdot p_0 \right\} \tag{A8}

subject to the firm participation constraint (investor surplus is non-negative):

c_j \cdot m_i \geq \pi \cdot \frac{C_{\min} + C_{\max}}{2} \cdot m_i + (1-\pi) \cdot p_0 \tag{A9}

The marginal effect of CEO experience on investor surplus is:

\frac{\partial v(c_j, m_i)}{\partial m_i} = c_j - \pi \cdot \frac{C_{\max} + C_{\min}}{2} \tag{A10}

This is positive if and only if $c_j > \pi(C_{\max} + C_{\min})/2$: investor surplus is increasing in CEO experience only when the firm’s challenge level exceeds the threshold. For firms below the threshold, the CEO captures the full increment in total surplus as compensation, leaving investors no better off with a more experienced hire.

Hypotheses (pp. 4-5). Drawing on the managerial challenge concept of Hambrick et al. (2005) and the firm complexity literature, the model yields four empirical predictions:

• H1: Firm complexity positively moderates the GAI effect on investor reaction to a new CEO.
• H2: CEO experience has a positive effect on investor reaction when the firm is sufficiently complex.
• H3: Prior firm performance negatively moderates the GAI effect on investor reaction.
• H4: CEO experience has a positive effect on investor reaction when prior performance is sufficiently poor.

Method

General Ability Index. Following Custodio, Ferreira and Matos (2013), CEO generalist experience is measured by GAI, the first principal component of five standardized career-breadth variables (p. 6):

\text{GAI}_{it} = 0.494 \cdot \#\_\text{positions} + 0.585 \cdot \#\_\text{firms} + 0.508 \cdot \#\_\text{industries} + 0.316 \cdot \text{CEO\_Exp\_Dummy} + 0.238 \cdot \text{Cong\_Exp\_Dummy} \tag{1}

where all inputs are standardized. Number of positions, firms, and industries come from BoardEx merged with Execucomp; conglomerate experience from Worldscope. The final GAI is also standardized.

Complexity Index. Firm complexity captures the scale and scope dimensions of Chandler (1994)‘s analysis: the average of standardized log(number of employees) (from Compustat/Execucomp) and number of product segments (from Worldscope, counting non-zero-sales segments in the CEO appointment year). High complexity = upper tercile; low complexity = lower two terciles (p. 6-7).

Performance Index. Firm performance is isolated from industry and firm-characteristics effects using two components (p. 7): (1) CAPM Alpha (Jensen, 1969) estimated on monthly CRSP returns over the three years before the CEO announcement; (2) Residual Firm Efficiency from Demerjian, Lev and McVay (2012), the residual of a regression of firm efficiency on firm characteristics, measured in the calendar year before the CEO announcement. The Performance Index is the average of these two standardized measures. Low performance = below-median index in the year of turnover.

GAI Surprise. To partially address endogeneity from non-random CEO-firm matching, the paper constructs GAI Surprise as the OLS residual from regressing incoming CEO GAI on Complexity, Performance, firm controls, and departing CEO compensation (Table 3, col 4, pp. 8-10). This isolates the portion of incoming CEO experience not predicted by observable firm characteristics and the departing CEO’s pay, which may reflect unobserved challenge.

Empirical specifications

Announcement CAR regression (primary, Table 4, p. 11). The dependent variable is the Carhart (1997) 4-factor cumulative abnormal return over a 5-day window [-2, 2] centered on the new CEO announcement date, estimated using a [-255, -46] pre-announcement window. The baseline regression and interaction specifications are:

$\text{CAR}_{22,it} = \alpha + \beta_1 \text{GAI}_{it} + \beta_2 \text{GAI}_{it} \times \text{Challenge}_{it} + \beta_3 \text{Challenge}_{it} + \gamma' X_{it} + \delta_t + \delta_s + \varepsilon_{it}$

where $\text{Challenge}_{it}$ is either the Complexity Index (col 2, linear) or the Performance Index (col 4, linear), or piecewise high/low dummies (cols 3, 5), or intersection dummies for “No Challenge” (high performance × low complexity), “One Challenge” (low performance × low complexity or high performance × high complexity), “Double Challenge” (low performance × high complexity), and “Any Challenge” (One + Double, cols 7-8). Controls $X_{it}$ include prior 3-year sales growth, log assets, prior 3-year firm efficiency, complexity, performance, previous-year stock return and volatility, Fasttrack, MaleY0, Insider, Forced, Unclassified, and CEO age dummies. Year and 2-digit SIC industry fixed effects are included throughout; standard errors are clustered by firm.

Table 3 estimates the CEO sorting equation, with incoming-CEO GAI as the dependent variable; the specification used to construct GAI Surprise is column 4 (p. 10):

$\text{GAI}_{it} = \alpha + \beta_1 \text{Complexity}_{it} + \beta_2 \text{Performance}_{it} + \gamma' X_{it} + \delta_t + \delta_{s(1)} + \varepsilon_{it}$

The columns replace the two indices with their scale/scope and performance components, then add departing-CEO compensation and GAI controls. Controls include sales growth, lagged assets, prior firm efficiency and returns, turnover indicators, and CEO characteristics. Year and one-digit SIC fixed effects are included, with standard errors clustered by firm; column 4 has 961 observations. The residual from this fitted model is GAI Surprise, used in Table 6 and the long-term outcome tests.

For Table 4, the split-sample specifications estimate group-specific experience slopes, with No, One, and Double Challenge categories as defined above; Any Challenge pools the latter two (Table 4, cols. 3, 5, 7-8, pp. 11-12):

$\text{CAR}_{22,it} = \alpha + \sum_{g \in \mathcal{G}} \beta_g (\text{GAI}_{it} \times \mathbf{1}\{\text{Challenge}_{it}=g\}) + \sum_{g \in \mathcal{G}} \theta_g \mathbf{1}\{\text{Challenge}_{it}=g\} + \gamma' X_{it} + \delta_t + \delta_{s(2)} + \varepsilon_{it}$

Table 4 uses 1,095 CEO turnovers, the controls listed above, year and two-digit SIC fixed effects, and firm-clustered standard errors. The threshold splits are upper-tercile complexity and below-median performance; No Challenge is high performance/low complexity, Double Challenge is low performance/high complexity, One Challenge contains the other two combinations, and Any Challenge is the complement of No Challenge.

Compensation regression (Table 5, p. 13). Log total CEO compensation (TDC1 from Execucomp, first calendar year for which Execucomp reports the executive as CEO) is regressed on GAI and the same challenge interactions with identical controls. This tests whether firm challenge moderates the compensation-experience relation, as it does for CARs; the prediction is that it does not.

$\log(\text{Comp}_{it}) = \alpha + \beta_1 \text{GAI}_{it} + \beta_2 (\text{GAI}_{it} \times \text{Challenge}_{it}) + \beta_3 \text{Challenge}_{it} + \gamma' X_{it} + \delta_t + \delta_{s(2)} + \varepsilon_{it}$

Table 5 has 941 observations in the baseline and challenge specifications, the same control set, year and two-digit SIC fixed effects, and firm-clustered standard errors (Table 5, pp. 13-14).

GAI Surprise robustness (Table 6, p. 15). GAI Surprise replaces GAI as the main explanatory variable, replicating all challenge-interaction specifications with the same control set. The purpose is to show that results hold when the experience measure isolates the component not predicted by observable firm and departing-CEO characteristics.

$\text{CAR}_{22,it} = \alpha + \beta_1 \text{GAI\_SURP}_{it} + \beta_2 (\text{GAI\_SURP}_{it} \times \text{Challenge}_{it}) + \beta_3 \text{Challenge}_{it} + \gamma' X_{it} + \delta_t + \delta_{s(2)} + \varepsilon_{it}$

Table 6 uses 944 observations, year and two-digit SIC fixed effects, the Table 4 controls, and standard errors clustered by firm (Table 6, p. 15).

Long-term performance (Tables 7-8, p. 16-17). Industry-adjusted ROA ($\bar{\Omega}\_{\text{IND\_ADJ\_ROA}}_{t+1:t+3}$) and Tobin’s Q, averaged over years t+1 to t+3 after appointment, are the outcome variables. Lagged industry-adjusted performance ($\Omega\_{\text{IND\_ADJ\_ROA}}_{t-1}$) controls for mean reversion. GAI Surprise is used as the explanatory variable to address selection; the same challenge-interaction structure as Table 4 applies. Year and sector fixed effects; firm-clustered standard errors. A propensity-score-matched sample (matched on complexity, performance, firm controls, year and industry) is used as a further robustness check (Internet Appendix 7).

$\overline{\text{IND\_ADJ\_Y}}_{i,t+1:t+3} = \alpha + \beta_1 \text{GAI\_SURP}_{it} + \beta_2 (\text{GAI\_SURP}_{it} \times \text{Challenge}_{it}) + \beta_3 \text{Challenge}_{it} + \rho\,\text{IND\_ADJ\_Y}_{i,t-1} + \gamma' X_{it} + \delta_t + \delta_s + \varepsilon_{it}$

Here $Y$ is ROA in Table 7 or Tobin’s Q in Table 8; the samples contain 890 and 891 observations, respectively, with the Table 4 controls, year and sector fixed effects, and firm-clustered standard errors (pp. 16-17).

CEO tenure (Cox hazard model, Table 9, p. 19). The hazard of CEO succession is modeled as:

$h(t) = h_0(t) \exp\!\left(\beta_1 \text{GAI}_{it} + \beta_2 \text{GAI}_{it} \times \text{Challenge}_{it} + \gamma' X_{it}\right)$

using CEO departure dates from Execucomp as of June 30, 2024. The sample has 1,086 CEO spells. Year and 2-digit SIC dummies and the same controls as Table 4 are included; standard errors are reported in parentheses (Table 9, p. 19). A higher (lower) coefficient means shorter (longer) expected CEO tenure.

Datasets used

Dataset	Role in paper	Wiki page
CRSP (via WRDS)	Stock prices for 4-factor Carhart CAR estimation; stock return and volatility controls	WRDS
Compustat (via WRDS)	Firm financial characteristics (assets, sales growth, firm size)	WRDS
Execucomp (via WRDS)	CEO identification; compensation (TDC1); GAI components (positions, prior CEO role)	WRDS
Worldscope	Number of product segments (scope dimension of Complexity Index); conglomerate experience for GAI	Worldscope
BoardEx	CEO career history (positions, firms, industries) for GAI construction; merged with Execucomp and Worldscope	no page yet
Factiva	News articles used to classify CEO departure reason as exogenous, forced, or unclassified per Eisfeldt and Kuhnen (2013)	no page yet
Demerjian, Lev and McVay (2012) data	Residual firm efficiency scores (managerial ability measure); from public data supplement at faculty.washington.edu/pdemeri	no page yet

Sample: 1095 CEO turnovers in S&P 1500-listed firms, January 2000-December 2015 (after excluding interim CEO appointments, financial firms, and turnovers without media announcement data). Long-term ROA/Tobin’s Q analyses use 890-891 observations; CEO tenure analysis uses 1086 observations.

When to read the full paper

Read the original when: studying CEO succession and whether CEO human capital creates value differentially by firm type; examining whether competitive CEO assignment models with market frictions can explain the compensation-investor-reaction disconnect; interested in the GAI measure of Custodio, Ferreira and Matos (2013) and its interaction with firm characteristics. The main CAR results are in Table 4 (pp. 11-12); compensation results in Table 5 (pp. 13-14); the identification robustness using GAI Surprise in Table 6 (p. 15); and long-term performance and CEO tenure in Tables 7-9 (pp. 16-19). The toy model and threshold derivation are in Appendix A (pp. 21-22).

Attribution and rights

This article is open access under a Creative Commons Attribution (CC BY 4.0) license. Published by Elsevier B.V.

Gelman, S., Fralich, R., Bitektine, A., & Zahraei, S. (2026). When does a generalist CEO create shareholder value? The effect of managerial challenge. Journal of Corporate Finance, 97, 102917. https://doi.org/10.1016/j.jcorpfin.2025.102917

Machine-distilled by paper-distiller (claude-sonnet-4-6), 2026-06-26. Not human-verified; not reproduced. Extraction role: extracted only.