Geography and Hedge Fund Activism: Faleye (2026)

What this is. The paper’s core results, the competing hypotheses it tests (information vs. familiarity), and the regression specifications with their key findings: enough to understand what the paper found and how, without reading all 23 pages. To replicate or extend it, read the full source at the original.

TL;DR

The paper asks whether geographical proximity between a hedge fund and a potential target affects activism targeting and returns. Using 2,106 activism events involving 693 U.S. hedge funds from 2000 to 2014, Faleye finds that activist funds disproportionately target firms located closer to their headquarters: targets are 43.1% more likely than matched controls to be within 100 km of the fund. Despite this proximity preference, activism returns are lower for nearer targets: mean CAR[-10,+10] is 3.84% for below-median-distance targets versus 6.89% for those farther away. Differences in activism costs, target selection, and employee wealth transfers by proximity do not explain the return gap, so the results align with familiarity bias in hedge fund targeting decisions. Additional tests find a stronger proximity preference among small targets. Continuous and median-distance estimates are larger for openly hostile campaigns, though local-firm effects are similar across hostile and non-hostile campaigns. Significant passive Schedule 13G investments also show a proximity preference and lower returns for local investees, although the preference is weaker than for activism campaigns.

Core results

Magnitudes and significance are as reported; \*/\*\*/\*\*\* = 10%/5%/1%. Locators point into the source PDF.

#	Result	Locator	Magnitude
R1	Activist hedge funds prefer geographically closer targets: distance is negative and significant in probit predicting targeting likelihood	Table 1, col (1), p. 5	Distance coeff = -0.0225\*\*\* (SE 0.009); 1-SD decrease in distance raises targeting probability by 7.4% relative to unconditional mean
R2	Firms within 100 km are significantly more likely to be activism targets	Table 1, col (3), p. 5	Local firm (<=100 km) coeff = 0.1977\*\*\* (SE 0.035); targets 19.8% more likely to be local to the hedge fund
R3	Activism returns are lower for closer targets in univariate comparisons	Table 2, Panel B, p. 6	Mean CAR[-10,+10]: 3.84% (below-median distance) vs 6.89% (above-median); within 100 km: 4.36% vs beyond 100 km: 5.53%
R4	Lower returns for closer targets hold in multivariate OLS controlling for target characteristics	Table 3, Panel A, cols (3)-(4), p. 7	Nearer firm = -0.0307\*\*\* (SE 0.009); local firm = -0.0291\*\* (SE 0.014); continuous distance: 1-SD decrease = 1.2 pp decline in CAR (21.9% relative to full-sample mean)
R5	Employment reductions post-activism are similar for proximate and distant targets: wealth-transfer explanation ruled out	Table 6, Panels A-B, p. 12	Target x post-activism = -0.9816\*\*\* for full sample; Chow test nearer vs farther: p=0.951; no statistically significant difference by proximity
R6	Proximity preference is significantly stronger among small targets than large ones, consistent with the mere-exposure channel being stronger for locally covered small firms	Table 9, Panel C, p. 16	Small firms: local firm = 0.2517\*\*\*, 28.9% more likely to be local target; large: 0.1240\*\*, only 7.7%; Chow test p=0.046
R7	Continuous and median-distance estimates suggest stronger proximity effects for hostile campaigns, but local-firm effects are similar across hostility groups	Table 11, Panels A-C, p. 18	Distance coeff: hostile = -0.0402\*\* (SE 0.019) vs non-hostile = -0.0172\* (SE 0.009), Chow p=0.258; nearer-firm coeffs = 0.1567\*\*\* vs 0.0665\*\*\*, Chow p=0.093; local-firm coeffs = 0.1998\*\* vs 0.2006\*\*\*, Chow p=0.993
R8	Passive 13G investments show a weaker proximity preference than activism, as well as lower announcement returns for local investees	Tables 13-14, pp. 20-21	9.9% of 13G investees vs 8.2% of matched controls are local; local-firm coefficient = 0.0945\*\* (SE 0.039) in Table 13. Table 14 local CAR coefficient = -0.0334\*\*\* (SE 0.012), 3.3 pp lower return
R9	Matched target firms are more likely than controls to be within 100 km of an activist fund	Section 3, p. 5	14.6% of targets versus 10.2% of controls are within 100 km; targets are 43.1% more likely to be local
R10	Returns around active 13D campaigns exceed those around prior passive 13G filings by the same hedge funds	Section 4.1, p. 6	For 262 events with both filings, mean CAR[-10,+10] is 2.55% around the initial 13G and 5.11% around the subsequent 13D; 13G switchers’ activism returns are statistically indistinguishable from other activists
R11	Estimated campaign costs are too small to explain the lower returns on nearby targets	Section 5.1, p. 8	Local-target return gap implies $2.29 million lower dollar returns; estimated average cost of hedge-fund campaigns is $1.27 million
R12	Tests of target selection on firm size and Tobin’s q do not explain the lower returns for nearby targets	Table 4, Panel A-B, p. 9	Local-firm coefficient = 0.1240\*\* for large firms and 0.1759\*\*\* for high-q firms; the paper finds the effects weaker or statistically similar to low-q firms
R13	Activism raises DB pension underfunding, but the increase does not differ significantly by proximity	Table 7, Panels A-C, p. 13	Target x post = 0.0384\*\*\* (SE 0.011), 3.84 pp (14.5%); Chow p = 0.966 for nearer/farther and 0.448 for local/distant
R14	Activism lowers employer contributions to DC plans, with no significant proximity difference	Table 8, Panels A-C, p. 15	Target x post = -0.9296\*\*\* (SE 0.281), about $0.93m annually; Chow p = 0.502 and 0.869
R15	Nearby-target CAR reductions are larger for small than large firms, but the difference is not statistically significant	Table 10, p. 17	A 1-SD distance decrease lowers CAR by 1.2 pp for small targets and 0.9 pp for large; nearer coefficients = -0.0325\*\* and -0.0193\*; Chow p = 0.405
R16	Hostile campaigns earn higher announcement CARs, while proximity-return differences across hostile and non-hostile events are insignificant	Section 5.4.2, p. 18; Table 12	Hostile mean CAR[-10,+10] = 6.56% versus 5.08% non-hostile; proximity Chow p-values = 0.889, 0.969, 0.659
R17	Passive 13G proximity patterns are stronger for small firms than large firms	Section 5.4.3, p. 19 (untabulated results)	Local 13G investment probability is 10.5% higher for small firms versus 2.0% for large; local-firm CAR gaps are -4.3 pp and -2.4 pp, respectively
R18	Proximity preference persists across campaign order, while lower returns are detected for first and fourth-plus events	Section 5.5, p. 20	Proximity preference appears at first, second, third, and fourth-plus events, weakening from the third; CAR is significantly lower for closer targets in first and fourth-plus campaigns, not second or third

Overall (paper’s conclusion). Economic factors including activism costs, target selection effects, and reduced employee wealth transfers at nearby firms do not explain why hedge funds earn lower returns on geographically closer targets. Results are consistent with familiarity bias: hedge funds disproportionately target closer firms while realizing suboptimally lower returns from such activism, suggesting that even sophisticated investors are not immune to distortions created by a bias toward the familiar. This extends the hedge fund geography literature, where Teo (2009) documents that funds with regional investment focus outperform funds without a physical presence in the region, but at the portfolio level rather than the activist-targeting level.

Theory / model

The paper has no formal economic model. It evaluates two competing hypotheses for why geographical proximity matters in hedge fund activism:

Information hypothesis. Proximity reduces the cost of obtaining localized “soft” information about potential targets, including local media coverage, interactions with employees and managers, relationships with suppliers and clients, and the ability to directly observe target facilities. If proximity provides an information advantage, activism returns should be higher (not lower) for closer targets. Brav et al. (2008) establish that most activism events are ones where the hedge fund believes the target is undervalued and that better monitoring, advising, and communication can unlock value; these actions are easier and less costly for closer entities, which is the kernel of the information hypothesis.

Familiarity hypothesis. Proximity reflects the familiarity bias, a cognitive bias arising from the “mere exposure” effect first identified by Zajonc (1968). Repeated exposure to a stimulus creates affective preference for it even without substantive informational content. Huberman (2001) shows this bias is pervasive among investors, including the tendency of individuals to hold shares in their regional Bell Operating Company. A related bias is the “illusion of control” (Langer, 1975): hedge fund managers may overestimate their ability to influence outcomes at nearby firms because they perceive them as more familiar and hence easier to control. This predicts a proximity preference in targeting but lower (suboptimal) returns for closer targets because target selection is, by definition, biased. To distinguish the two hypotheses from rational wealth-transfer explanations, the paper tests whether wealth transfers from employees explain the return gap. Agrawal and Lim (2022) document significant post-activism pension underfunding; the paper tests and rules out this as the driver of lower proximity returns, finding that such wealth transfers are equally present for proximate and distant targets.

The two hypotheses have opposite predictions for activism returns: the information hypothesis predicts higher returns for closer targets; the familiarity hypothesis predicts lower returns. The empirical analysis is organized around distinguishing these predictions and ruling out rational economic alternatives.

Method

The paper applies standard empirical methods from the corporate governance and behavioral finance literatures; it proposes no new estimator. The headline empirical exercises are:

Targeting propensity model. For each of 2,040 events with a unique targeting fiscal year, up to five control firms are selected from never-targeted Compustat firms in the same two-digit SIC industry. The propensity score uses log market capitalization, market-to-book ratio, three-year average sales growth, institutional ownership, and industry and year fixed effects. Nearest-score matches must be within 0.2 standard deviations of the target’s score; matching is with replacement. This builds on matching to construct a counterfactual set of potential targets.

Probit regressions. Targeting likelihood is estimated via probit (probit-regression) over the 1,794-target matched sample with 3,679 unique control firms. The model includes year and two-digit SIC code industry fixed effects; standard errors are clustered at the firm level.

Activism return OLS. For the 2,060-event return subsample, CAR[-10,+10] is regressed on proximity measures and controls via OLS (panel-regression). In Table 3 Panel A, column (1) uses year and industry fixed effects and columns (2)-(4) use year and hedge-fund fixed effects. Panel B uses hedge-fund-by-year fixed effects for funds with three or more campaigns in the same year. Standard errors are clustered at the firm level.

Difference-in-differences (employment/pensions). For the wealth-transfer tests in Section 5.3, a DiD estimator (difference-in-differences) is applied. Each target is matched (propensity score, no replacement) to a control firm in the same two-digit SIC industry. Treated and control firms are compared over the [-5, +5] year window around the activism event.

Empirical specifications

Targeting probit (Table 1). The dependent variable is an indicator equal to 1 for the target in its targeting fiscal year and 0 for that target in other years and for matched controls in all years. The proximity variable in column (1) is distance in thousands of kilometers; columns (2)-(3) use indicator variables for below-median and within-100-km distance, respectively. Equation estimated:

$$\text{Pr}(\text{Target}_{it} = 1) = \Phi\!\left(\alpha + \beta_1 \text{Proximity}_{it} + \beta_2' X_{it} + \gamma_t + \delta_j\right)$$

where $\Phi$ is the standard normal CDF, $\text{Proximity}_{it}$ is one of three distance measures, $X_{it}$ is a vector of firm controls (size, Tobin’s q, excess return, ROA, capital expenditures, cash holdings, institutional ownership, R&D, total debt, sales growth, SG&A, dividends, dividend yield), $\gamma_t$ are year fixed effects, and $\delta_j$ are industry fixed effects. Sample: 87,134 observations (1,794 targets + 3,679 control firms x multiple years).

Propensity-score matching (Section 3, p. 4). The paper estimates this targeting score over the 2,040-event matching pool:

$$\Pr(\text{Target}_{it}=1)=\Phi\!\left(\alpha+\theta_1\text{Size}_{it}+\theta_2\text{MarketBook}_{it}+\theta_3\text{SalesGrowth3yr}_{it}+\theta_4\text{InstitutionalOwnership}_{it}+\gamma_t+\delta_j\right)$$

Up to five never-targeted firms in the same two-digit SIC industry are retained when their score is nearest to the target’s and within 0.2 standard deviations. The resulting targeting panel has 1,794 targets and 3,679 unique controls. The matching-score model includes year and industry fixed effects; the main Table 1 targeting probit uses the broader set of controls listed above.

Event-study market model (Section 4.1, p. 6). For each event, the value-weighted CRSP market return is the market factor and the estimation window is trading days [-301,-46], with at least 70 observations. Abnormal returns are summed within each event window:

$$R_{it}=\alpha_i+\beta_i R_{mt}+\varepsilon_{it},\qquad AR_{it}=R_{it}-\hat{\alpha}_i-\hat{\beta}_iR_{mt},\qquad CAR_i[a,b]=\sum_{t=a}^{b}AR_{it}$$

The full announcement-return sample contains 2,060 events after excluding concurrent target news and insufficient market-model windows.

Activism return OLS (Table 3). CAR[-10,+10] is the dependent variable (p. 7). The key coefficients are on the proximity measures:

$$\text{CAR}_{i}[-10,+10] = \alpha + \beta_1 \text{Proximity}_{i} + \beta_2' X_i + \gamma_t + \delta_j + \varepsilon_i$$

This equation displays the baseline Panel A column (1) fixed effects; columns with alternative effects use the specifications described below. Controls $X_i$ include market cap, Tobin’s q, cash holdings, total debt, dividend yield, ROA, dissident ownership, board representation, and a wolf-pack indicator. In Panel A, column (1) uses year and two-digit SIC industry fixed effects and columns (2)-(4) use year and hedge-fund fixed effects. Panel B uses hedge-fund-by-year fixed effects for funds with three or more campaigns in the same year. Standard errors are clustered at the firm level. The main return sample contains 2,060 events (individual columns lose some observations to missing data; e.g., Panel A N=2,056 and Panel B N=652). The continuous distance coefficient in column (1) is 0.0082\*\*\* (SE 0.003); the nearer-firm indicator in column (3) is -0.0307\*\*\*.

DiD employment regression (equation 1, p. 10). The paper’s only numbered equation. The dependent variable $Y_{it}$ is employment in thousands. The estimation window is [-5, +5] years relative to the activism fiscal year:

$$Y_{it} = \beta_0 + \beta_1 \text{Post}_{it} + \beta_2 \left(\text{Target}_i \times \text{Post}_{it}\right) + \gamma \text{Controls}_{it} + \varphi \text{Year}_t + \delta \text{Firm}_i + \varepsilon_{it} \tag{1}$$

where $\text{Post}_{it}$ equals 1 for post-activism fiscal years, $\text{Target}_i$ equals 1 for target firms (0 for matched controls), and $\delta \text{Firm}_i$ are firm fixed effects. The coefficient of interest is $\beta_2$, capturing the differential change in employment for target firms relative to controls after activism. $\beta_2 = -0.9816$\*\*\* (SE 0.189) in the full sample (Table 6 Panel A), implying a 982-employee reduction (14.6% of mean). Panels B and C split the sample by proximity; Chow tests find no statistically significant difference (p-values 0.951 and 0.916).

Hostile and non-hostile campaign probits (Table 11). The probit specification mirrors Table 1, estimated separately for 402 hostile and 1,704 non-hostile campaigns. Coefficients on the continuous distance variable: hostile = -0.0402\*\* vs non-hostile = -0.0172\*.

Return regressions by size and hostility (Tables 10 and 12, pp. 17, 18). These are the Table 3 CAR specification estimated separately for small/large targets and hostile/non-hostile campaigns, with year and two-digit SIC industry fixed effects and firm-clustered standard errors. Table 10 uses 1,021 small-target and 1,024 large-target events; Table 12 uses 383 hostile and 1,657 non-hostile events. Chow tests compare the proximity coefficients across each pair of samples.

Employee wealth-transfer outcomes (Tables 6-8, pp. 12-15). Equation (1) is estimated separately with employment, DB pension underfunding, and DC employer contributions as the dependent variable. Each regression uses the relevant matched target-control sample over event years [-5,+5], year and firm fixed effects, and firm-clustered standard errors. Employment uses 2,066 events; DB underfunding has 502 events for 384 targets; DC contributions have 1,001 events for 769 targets. Tables 7-8 report proximity subgroup estimates and Chow tests.

Passive-investment targeting probit (Table 13, p. 20). This uses the target-selection probit with SC13G investee as the dependent variable, year and two-digit SIC industry fixed effects, and firm-clustered standard errors (137,104 observations). The same Table 14 CAR regression design uses 4,582 13G announcement events, year and industry fixed effects, and firm-clustered standard errors.

Persistence across campaign order (Section 5.5, p. 20). The paper estimates separate targeting and CAR regressions for campaigns one, two, three, and four-plus. It reports the significance pattern in the text but does not tabulate coefficients, fixed effects, or standard-error details for these regressions.

Pre-trend regressions (Table 5, p. 11). The dependent variable is the change in employment, DB underfunding, or DC contributions over years [-2,-1]. OLS includes target status, outcome-specific controls, year and two-digit SIC industry fixed effects, and firm-clustered standard errors:

$$\Delta Y_{i,-2:-1}=\alpha+\beta\text{Target}_i+\gamma'X_i+\phi_t+\delta_j+\varepsilon_i$$

For the post-event DiD specifications in Tables 6-8, equation (1) is estimated separately for each outcome and for the full, nearer/farther, and local/distant samples. The window is [-5,+5], with year and firm fixed effects and firm-clustered standard errors. Table 6 includes 2,066 events; Table 7 includes 502 events (384 targets); Table 8 includes 1,001 events (769 targets).

Datasets used

Dataset	Role in paper	Wiki page
Boyson and Pichler (2019) activism sample	Base sample of 2,206 hedge fund activism events (2000-2014), from which 2,106 events in the 48 contiguous US states are retained	No page yet
SEC EDGAR (Schedule 13D/13G filings)	Identify hedge fund and target ZIP codes; define passive 13G investments; locate fund headquarters over time	SEC EDGAR
CRSP (via WRDS)	Market model for CAR/BHAR estimation; stock returns; value-weighted market portfolio (estimation window [-301, -46])	WRDS / CRSP (licensed)
Compustat (via WRDS)	Firm characteristics: market cap, Tobin’s q, ROA, sales growth, capital expenditures, cash holdings, R&D, total debt, SG&A, dividends; DB pension data (Compustat Pension files)	WRDS / Compustat (licensed)
DOL Private Pension Plan Research Files (Form 5500)	Employer contributions to defined contribution (DC) pension plans, matched to Compustat via EIN; 1,001 events for 769 unique targets	No page yet

Sample scope: 2,106 activism events, 693 U.S. hedge funds, 2000-2014; return tests use 2,060 events; 13G tests use 5,568 filings.

When to read the full paper

Use the original if you are: studying behavioral biases in institutional investors with a focus on hedge fund targeting; evaluating whether geographical proximity is an economic or behavioral factor in activism; replicating the DiD employment or pension tests (the paper matches each event to one control firm without replacement, so the exact match set matters for robustness); or extending the analysis to other dimensions of familiarity (language, industry familiarity, social networks). Locators above point directly to the tables.

Attribution and rights

Source: peer-reviewed, Journal of Financial Markets 77 (2026) 101005. This distillation was first extracted by an LLM on 2026-06-25 and augmented on 2026-10-04; it is not human-verified or independently reproduced. The CC BY-NC-ND 4.0 licence permits reuse for non-commercial purposes with attribution and no derivatives; the verbatim PDF is not hosted in this batch.

Faleye, Olubunmi. “Does Familiarity Breed Activism? Geography and Hedge Fund Activism.” Journal of Financial Markets 77 (2026): 101005. DOI: 10.1016/j.finmar.2025.101005. © 2025 The Author. Published by Elsevier B.V. under CC BY-NC-ND 4.0. This page is a distillation by the Institute for Automated Research: core results summarized and re-expressed; not a substitute for the original.