Do Activists Align with Larger Mutual Funds?

Manish Jha

REVIEW 4 major objections 5 minor 18 references

Do Activists Align with Larger Mutual Funds?

T0 review · 4 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read This paper claims that activists design their proxy campaigns to align with the preferences of institutions holding larger stakes in the target company, and that such alignment raises attention, votes, and success.

desk verdict A genuinely new text-based measure and a robust holdings-alignment correlation, with a causal claim that outruns the identification. read the letter →

arxiv 2411.16553 v1 pith:NPRQ4CFT submitted 2024-11-25 q-fin.CP q-fin.GN

Manish Jha This is my paper · ORCID

classification q-fin.CPq-fin.GN

keywords hedgefundactivismshareholderpreferencesproxyconteststextanalysismachinelearningmutualvotinginstitutionalinvestorsAlignscore

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper claims that activist hedge funds design their proxy campaigns around the preferences of the institutions that own the largest stakes in the target firm. It estimates each institution's preferences from how it voted on shareholder proposals in the prior two years, then measures how closely the activist's written communications match those preferences using a text-based score called Align. The central finding is that a one-standard-deviation increase in an institution's stake is associated with a 0.4 to 0.7 percentage point rise in alignment, and that campaigns aligning better with large holders draw more attention, more votes, and a higher chance of success. If true, this means persuasion in proxy fights is aimed at a small set of powerful mutual funds, and activist language is a strategic tool rather than just rhetoric.

What carries the argument

The central object is Align, a score between 0 and 1 that measures how close a proxy communication is to an institution's revealed preferences. It is built in two steps: a Support Vector Regression is trained on each institution's votes against management on shareholder proposals in the two years before the fight, with up to five-word phrases as features, so every phrase receives a coefficient; then the frequencies of those phrases in the activist's proxy filings are combined with those coefficients, $\mathrm{Align}_{p,i} = \alpha_i + \beta_i \cdot \mathbf{x}_p$, to predict the chance that the institution supports the activist. A holdings-weighted aggregate version, AgAlign, weights each institution's Align by its stake in the target, and this aggregate is what links alignment to campaign success.

What would settle it

A direct test would be to estimate Align from pre-fight shareholder-proposal voting for the universe of proxy contests with disclosed votes, then compare it with the votes actually cast; if Align has no predictive power for institutions whose proxy-contest voting diverges from their shareholder-proposal voting, or if the holdings-alignment gradient reverses sign in that subsample, the central claim would be refuted.

Watch

Extended reading notes

Core claim

The paper's central claim is that activists tailor their campaign communications to the preferences of institutions holding larger stakes in the target. The author constructs Align, a 0-to-1 measure of how close a proxy communication is to an institution's revealed preferences, by training a support vector regression on each institution's votes against management on shareholder proposals with matched proposal text, then applying the resulting phrase coefficients to the activist's communications. The paper finds that a one-standard-deviation increase in target ownership (about 0.63 percentage points, versus a mean of 0.09%) is associated with a 0.4 to 0.7 percentage point increase in Align; an institution owning ten percent of the target sees average alignment of 65%, compared with 46% for institutions holding under 0.01%. Better-aligned campaigns also receive more attention (a 23% increase in EDGAR views per standard deviation), more actual votes (a three-percentage-point increase in institution support per standard deviation), and are 9.4 percentage points more likely to succeed when mutual funds hold above-average stakes. The author argues that the direction runs from activist tailoring to support, using six-month-lagged holdings, merger shocks to holdings, and a Russell reconstitution case study.

Load-bearing premise

The paper's load-bearing premise is that preferences revealed in institutions' votes on routine shareholder proposals transfer to contested proxy fights, so that the Align score built from proposal texts measures true alignment in campaigns; the validation sample of actual proxy-fight votes is small (1,457 institution-fight records from 199 fights).

Editorial extensions

If this is right

Activist proxy communications are a measurable strategic input: any campaign's text can be scored against institution preferences using public SEC filings.
Alignment predicts actual votes, not just attention: a one-standard-deviation rise in Align is associated with a three-percentage-point increase in institutions' activist support.
The success effect is conditional on mutual fund ownership: alignment raises win probability by 9.4 percentage points only when mutual funds hold above-average stakes, with no detectable effect otherwise.
Institutions pay attention selectively: for similar holdings, funds access proxy filings more often when the text aligns with their revealed preferences.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A testable extension the author does not run: applying the same Align construction to plentiful say-on-pay or ESG shareholder votes could reveal whether the holdings-alignment gradient holds outside the small proxy-fight sample.
If the mechanism is genuine, activists' issue selection should track the proxy guidelines of their largest mutual fund holders, so future campaign themes could in principle be predicted from those public guidelines.
The concentration of the success effect in above-average-ownership fights implies that in diffusely held targets, catering to big funds is cheaper but less decisive, so activists may rely on other persuasion channels such as media or direct board negotiations, which this paper does not measure.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

First thing to know: this is a real empirical contribution, not a buzzword paper. It builds a phrase-level measure of institution preferences from full shareholder proposal texts via SVR, then shows that proxy communications align more with institutions holding larger stakes in the target. That correlation is robust to proxy-fight and institution fixed effects, six-month lagged holdings, subsamples, and a manual proposal-type measure. I believe the descriptive result. The paper is also careful to use outcome variables—attention, votes, success—that are measured separately from the voting data used to construct Align, so the circularity concern that often plagues text-based measures does not really land here.

The genuinely new piece is the measure itself: nobody else has pulled full proposal text and used supervised ML to recover institution-level phrase weights, then applied those weights to activist communications. The validation against proxy voting guidelines in the appendix is a nice touch and gives me more confidence that the SVR coefficients capture something real. The attention result using EDGAR logs is clever and the magnitudes are plausible.

Now the soft spots, in proportion. The transfer assumption is the biggest one: SVR is trained on precatory, often one-paragraph shareholder proposals and applied to adversarial, multi-issue proxy solicitation materials. The paper acknowledges this and offers Table 6 as validation, but that validation covers only 199 fights that actually went to a vote—less than half the main sample. That is enough to show the measure has predictive content in a selected subsample, but not enough to rule out systematic bias in the full sample. It is a reason to treat coefficient magnitudes cautiously, not a reason to dismiss the paper.

The causal language is stronger than the identification. The phrase "activists design campaigns" is repeated throughout, but the exogenous variation is eleven fund mergers and one Russell reconstitution case study. The lagged-holdings result helps rule out the most obvious reverse-causality story, and the author is candid about not being able to separate selection from tailoring. Still, I would want the conclusion to say "associated with" rather than "design."

One technical issue the paper does not address: Align is a generated regressor, and the standard errors in the attention and vote regressions do not appear to account for first-stage estimation error. That is fixable with bootstrap or a correction, and probably does not change the qualitative findings, but a referee should ask for it.

Who is this for? Corporate governance and activism researchers, definitely. It deserves a serious referee. I would send it out.

Referee Report

4 major / 5 minor

Summary. The paper studies whether activist hedge funds tailor their proxy-fight communications to the preferences of the institutional investors that hold large stakes in the target company. The author constructs a text-based measure, Align, by training a support vector regression on mutual funds' votes on shareholder proposals and applying the phrase coefficients to proxy communications. Using a sample of 522 proxy fights from 2004 to 2019, the paper reports that alignment increases with an institution's stake in the target, and that better-aligned campaigns attract more attention from institutions, receive more actual votes, and are more likely to succeed when mutual fund ownership is high. The paper also presents robustness checks using lagged holdings, a small set of merger-based shocks, a Russell index reconstitution case study, and a non-machine-learning proposal-type measure.

Significance. If the central claim holds, the paper makes a useful contribution to the activism literature by moving beyond target and activist characteristics and quantifying how campaign language is tailored to the preferences of large shareholders. The paper has several genuine strengths: the Align measure is validated against actual institution votes in a subsample (Table 6); the manual proposal-type measure in Section 6.3 provides an independent non-machine-learning check; and the main alignment-holdings correlation is robust to proxy-fight and institution fixed effects and to six-month lagged holdings. The paper is also transparent about limitations, including the training/prediction domain shift and the lack of counterfactual data. However, the causal interpretation of the main result rests on very limited exogenous variation, and the invested-only subsample does not robustly reproduce the headline coefficient with both fixed effects.

major comments (4)

[§3.1–3.2, Table 6, Appendix C.4] The Align measure is trained on shareholder proposal texts and applied to proxy communications, a domain shift that the paper explicitly acknowledges in Appendix C.4. The validation against actual votes in Table 6 is limited to 1,457 institution-fight records from 199 fights that reached a ballot, a selected subsample of the 522 fights in Table 4. If SVR prediction error is correlated with holdings—for example, because larger institutions have more training observations or more systematic voting patterns—the coefficient in Table 4 could partly reflect measurement bias rather than true tailoring. The paper should report whether SVR prediction errors are orthogonal to holdings in the full sample, or provide a validation covering a broader set of fights, before the central claim can be regarded as established.
[§6.2] The causal interpretation in the abstract and in Section 6—that activists 'design' campaigns around the preferences of large shareholders—relies on very weak exogenous variation: 11 fund-merger proxy fights in Table 9 and one Russell reconstitution case study involving three institutions in Figure 5. The merger specification yields an Acquired×Post coefficient that is only marginally significant in the baseline (0.157, t=1.79), and the Russell evidence is a single case with no inferential power. These designs cannot rule out time trends or activist selection of merger periods. The paper should either substantially soften the causal language to 'suggest' or 'are consistent with,' or provide a stronger identification strategy.
[§4 vs. Internet Appendix C.2, Table 14] The main holdings-alignment result is not robust in the subsample of institutions that actually hold target shares when both proxy-fight and institution fixed effects are included: Table 14, column (4), reports a coefficient of 0.0065 with t=1.64, which is not significant at conventional levels. The paper's statement that the appendix 'shows that the results hold' for the invested subsample is therefore overstated. Because 81% of the main sample has zero holdings (Table 3), the Table 4 result may be driven largely by the contrast between invested and non-invested institutions rather than by the size of the stake. This sensitivity should be discussed and the invested-only specification should be displayed as a main robustness table.
[§5.3, Table 7] The reported 9.4 percentage-point effect for above-average-ownership fights is computed incorrectly. For OwnDum=1, the marginal effect of a one-standard-deviation increase in AgAlign is γ+β = −0.0218 + 0.111 = 0.0892, not −0.0218 + 0.0048 + 0.111 = 0.094; the ownership-dummy main effect is not part of the marginal effect of AgAlign. The text and conclusion should report approximately 8.9 percentage points (or the correct interaction effect) and should ideally provide a confidence interval. This is a small but real arithmetic error in a headline number.

minor comments (5)

[§3.3] The observation count is inconsistent: the text reports 66,836 observations and 12,582 with non-zero holdings, while Table 3 and Table 4 report 66,432 observations; please reconcile these numbers.
[Figure 5] Panel (a) of Figure 5 labels the target as 'Leap Water,' while the text and panel (b) refer to 'Leap Wireless'; this typo should be corrected.
[References] The citation 'Li, Patel, and Ramani Li et al.' is malformed and appears to merge authors and a subsequent citation; this entry should be fixed.
[§2.2, §3.3] The paper reports 533 proxy fights in Section 2.2 but the main analysis sample in Section 3.3 contains 522 fights; the source of the difference (for example, institutions failing the 100-proposal voting threshold) should be stated explicitly.
[§5.2, Table 6] The validation sample of 1,457 institution-fight records from 199 fights is described as covering less than 40% of the 522 fights; the paper should state this explicitly when discussing the generalizability of the validation.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: Align is estimated from shareholder-proposal voting and proxy-communication text; holdings, attention, votes, and success come from separate data sources and are not used to fit the measure.

full rationale

The paper's derivation chain uses separate data sources at each stage. Section 3.1 trains an SVR on institutions' votes on shareholder proposals (Eq. 1) to estimate phrase coefficients; Section 3.2 applies those coefficients to proxy-communication text to form Align (Eq. 2). Neither the institution's holdings in the target nor the outcome variables (EDGAR views, actual proxy-fight votes, success) enter the SVR training. Consequently, the Table 4 regression of Align on holdings is not an identity or a fitted-input prediction: holdings are not features and are not used to construct Align. Table 6 validates Align against realized proxy-fight votes in a separate subsample, and the appendix tests SVR coefficients against proxy-guideline text, providing external grounding beyond the fitted values. The attention results in Table 5 use SEC EDGAR server logs, which are independent of the proposal-vote training data; the success results in Table 7 use CapitalIQ outcomes, also independent. Self-citations such as Gormley, Jha, and Wang (2024) and Gormley and Jha (2024) are used for data-filtering conventions and literature support, not as uniqueness theorems or as the load-bearing justification for the central estimate. The paper itself flags the domain gap between training on routine shareholder proposals and predicting proxy-contest support in Internet Appendix C.4, but that is a validity and generalizability concern rather than a construction-based circularity, because Align is not defined in terms of holdings or outcomes, and the prediction is tested against actual votes. No equation in the paper defines the target outcome in terms of the fitted Align, and no parameter is fit to the outcome it is later said to predict.

Assumptions & free parameters 7 free parameters · 5 assumptions · 2 invented entities

The central claim rests on the transferability of proposal-vote preferences to proxy contests, the accuracy of holdings and attention measurement, and the validity of SVR as a preference extractor. The alignment measure is a new construct but is validated externally. No physical entities are postulated.

free parameters (7)

SVR inverse regularization parameter, c = 0.0001 (selected via grid search)
Chosen by three-fold cross-validation on proposal voting data to minimize prediction error; the paper shows robustness to alternative values in Internet Appendix D.2.
SVR epsilon-insensitive zone = 0.001
Chosen by the author to balance computational cost and economic significance; not part of the reported robustness checks.
n-gram length = up to 5-word phrases
Author choice for feature extraction; robustness to simpler n-gram sets is shown in Internet Appendix Figure 12.
phrase frequency thresholds = 1% to 70%
Phrases appearing in less than 1% or more than 70% of proposals are dropped; low thresholds are not varied in robustness tests.
minimum voting observations = 100 proposals per institution
Institutions with fewer than 100 votes in the two-year training window are excluded; robustness shown in Internet Appendix Figure 12.
training lookback window = 2 years
SVR is trained on proposals from the two years before a proxy fight; robustness to 1-3 years shown in Internet Appendix Figure 12.
ownership dummy cutoff in success regression = 14.3% (sample average)
Used to interact with aggregate alignment in the success regression; robustness to cutoffs from 12% to 18% shown in Internet Appendix Figure 11.

assumptions (5)

domain assumption Institutions' voting on shareholder proposals reveals their preferences in proxy contests.
SVR is trained on shareholder proposal votes and applied to proxy communications; if institutions weigh different factors in proxy fights, Align is mismeasured. Table 6 provides some validation but only for a selected sample.
domain assumption The mapping from EDGAR log IP addresses to institutions is accurate.
Attention is measured by counting views from IP3 blocks assigned to institutions using a December 2016 snapshot from Digital Element. Misallocation could bias the attention regressions.
domain assumption CRSP mutual fund holdings aggregated manually to the institution level reflect true ownership in targets.
The author manually matches funds to parent institutions; errors in aggregation could affect the holdings-alignment correlation.
domain assumption Proxy communications text is written before observing SVR scores and thus is not mechanically generated by the model.
The paper interprets alignment as activists designing messages; if activists somehow used the same model in writing, this would be circular. There is no evidence of such use.
standard math Support vector regression provides a valid mapping from phrase counts to voting propensity.
SVR is a standard high-dimensional regression method; the paper cross-validates hyperparameters and shows out-of-sample prediction improves over the mean.

invented entities (2)

Align score independent evidence
purpose: A continuous measure of the predicted alignment between a proxy communication and an institution's preferences, derived from SVR phrase coefficients.
Validated against actual institution support in proxy votes (Table 6) and against proxy voting guideline texts (Table 13), providing outside-the-main-analysis evidence.
Aggregate alignment (AgAlign)
purpose: Holdings-weighted average of Align across institutions for a proxy fight, used to predict fight success.
It is a deterministic function of Align and holdings, not an independent construct; its validity depends entirely on Align and holdings.

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Cite this review

Pith. "Pith review of Do Activists Align with Larger Mutual Funds?." pith.science (2026). https://pith.science/paper/NPRQ4CFT

@misc{pith2026241116553,
  author       = {Pith},
  title        = {Pith review of: Do Activists Align with Larger Mutual Funds?},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NPRQ4CFT}},
  note         = {Machine review of arXiv:2411.16553}
}

read the original abstract

This paper demonstrates that hedge funds tend to design their activist campaigns to align with the preferences and ideologies of institutions holding large stakes in the target company. I estimate these preferences by analyzing the institutions' previous proxy voting behavior. The results reveal that activists benefit from this approach. Campaigns with a stronger positive correlation between the preferences of larger institutions and activist communications attract more shareholder attention, receive more votes, and are more likely to succeed.

Figures

Figures reproduced from arXiv: 2411.16553 by the authors.

**Figure 1.** Alignment is positively associated with holdings The figure plots the average proxy communications’ alignment with institution preferences for holdings between 0 to 10%. The alignment is based on the institution’s voting patterns on shareholder proposals in the two years before the proxy fight. Holding represents the institution’s ownership in the target stock as a percentage of the target’s market cap. Holdings ar… view at source ↗

**Figure 2.** Institutions conduct more research about proxy fights that are well-aligned. This figure plots the number of times institutions accessed proxy communications filings on the SEC.gov server, averaged at each half percentage point holdings. The data for institutions’ access of SEC filings is available from DERA. The period considered for each proxy fight spans the date the proxy fight begins to 30 days after the proxy … view at source ↗

**Figure 3.** Distribution of proxy fight outcomes remains persistent. The stacked area plots the outcome of proxy fights over the 2004–2019 period. Proxy fights are assigned to the year when they began, i.e., the earliest date of SEC filings pertaining to the proxy fights. The information on proxy fights’ outcomes is collected from S&P CapitalIQ. 2004 2006 2008 2010 2012 2014 2016 2018 0% 20% 40% 60% 80% 100% Successful Settled … view at source ↗

Figures from the paper (9 more)

**Figure 4.** Figure 4: Proxy fights that are aligned well with the larger shareholders are more likely to win. This figure plots the proxy communications’ aggregate alignment, averaged for each year based on the proxy fight’s outcome. The sample includes proxy fights with at least 14.3% (the…

**Figure 5.** Figure 5: Alignment follows increased investments after Russell reconstitution The figure plots holdings and alignment for the proxy fight between Pentwater Capital (activist) and Leap Wireless (target) in 2011, before and after Leap Wireless assignment into the Russell 2000 in…

**Figure 6.** Figure 6: Institutions that own significant voting power vary across proxy fights. Figure (a) plots institutions with the largest stock ownership in the targets at the initiation of proxy fights over the 2004–2019 period. The holdings data is gathered from CRSP, and aggregated …

**Figure 7.** Figure 7: The SVR’s inverse regularization parameter that minimizes out-of-sample errors. The figure plots the percent of SVR runs for which an inverse regularization parameter (or c) reduces the out-of-sample mean absolute and mean squared error. The run sample includes 25 rand…

**Figure 8.** Figure 8: SVR coefficients follow proxy voting choices. Figure (a) plots SVR coefficients for “simple majority vote” calculated on December 31st of each year for BlackRock, Fidelity, and Vanguard. The calculations are based on the institution’s proxy voting choices in sharehold…

**Figure 9.** Figure 9: SVR coefficients of “call special meet” follow Morgan Stanley’s proxy voting guidelines. This figure plots the number of times the phrase “call special meet” is used in Morgan Stanley’s proxy voting guidelines and subsequent SVR coefficients. The SVR coefficients are …

**Figure 10.** Figure 10: Activists use phrases that will increase proxy communications’ alignment with larger shareholders’ preferences. The bar chart shows the marginal increase in proxy communications’ alignment with institution preferences, if the activist uses one more instance of the ph…

**Figure 11.** Figure 11: The positive association between proxy fight’s aggregate alignment and activist’s success holds for changing ownership dummy cutoff. This figure plots β coefficient with 95% confidence interval for regression of proxy fight outcome on institution holdings weighted pr…

**Figure 12.** Figure 12: The positive association between institution holdings and proxy communications’ alignment is robust to changing SVR parameters. This figure plots β coefficient with 95% confidence interval for regression of institution’s textbased likelihood of supporting activists …

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Reference graph

Works this paper leans on

18 extracted references · 18 canonical work pages

[1]

Alchian, A. A. and H. Demsetz (1972). Production, Information Costs, and Economic Organization.The American Economic Review 62(5), 777–795. Alexander, C. R., M. A. Chen, D. J. Seppi, and C. S. Spatt (2010, November). Interim news and the role of proxy voting advice. Review of Financial Studies 23(12), 4419–4454. Appel, I., T . A. Gormley, and D. B. Keim (...

work page 1972
[3]

FrontFour Capital Group LLC Form DFAN14A, number 0000921895-13-000151 | SEC Filings API

FrontFour (2013). FrontFour Capital Group LLC Form DFAN14A, number 0000921895-13-000151 | SEC Filings API. https: //sec-api.com/filing/acc/0000921895-13-000151. Gantchev, N. (2012, August). The costs of shareholder activism: Evidence from a sequential decision model. Journal of Financial Economics

work page arXiv 2013
[6]

In 2006 and 2010, no proxy fights above the cutoff holding were unsuccessful or with- drawn

The shade of the bubble represents the outcome of the proxy fight. In 2006 and 2010, no proxy fights above the cutoff holding were unsuccessful or with- drawn. 2006 2008 2010 2012 2014 2016 2018 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9Proxy communications' aggregate alignment (averaged) Unsuccessful or Withdrawn Successful or Settled 38 Figure 5: Alignment f...

work page 2006
[13]

To make sure that I have credible IP3 blocks, I go back quarterly from December 2016 and see what fraction of holdings do institution access through the EDGAR server

However, institutions sometimes change their underlying technology infrastructure and, in that process, register for different IP3 blocks. To make sure that I have credible IP3 blocks, I go back quarterly from December 2016 and see what fraction of holdings do institution access through the EDGAR server. I use CRSP mutual fund data to get institution hold...

work page 2016
[14]

I identify proxy fight documents based on the accession number of the filing in log files and SEC’ s index files

Subsequently, I match valid IP3 blocks from the organization lookup table with IP3 from EDGAR log files. I identify proxy fight documents based on the accession number of the filing in log files and SEC’ s index files. To measure the number of times an institution accessed proxy fight re- lated filings, I aggregate views for proxy fight documents during t...

work page 2013
[16]

experience board

Count is the number of times a phrase appeared in the institution’ s proxy guidelines text filed in year t. Since I use a two-year training period for SVR, I relate phrase counts from the proxy guidelines document to the SVR coefficients calculated 57 at the end of next year.δi ×t shows institution cross time level fixed effect and the errors,εn,i ,t , ar...

work page 2019
[17]

simple majority vote

The coefficients are significant for changing parameters on either side of the respective cutoffs. Thus, the text-based voting pre- diction is rooted in institutions’ proxy guidelines and is insensitive to changing parameters. [Figure 11 about here.] [Figure 12 about here.] 62 Figure 6: Institutions that own significant voting power vary across proxy figh...

work page 2004
[18]

climate change

The phrases are stripped of cases, punctuation, stop-words, and noun/verb forms. The coef- ficients are multiplied by 10,000. The coefficients indicate the marginal increase in the proxy communications’ alignment with institution preferences if it contains one more instance of the phrase. For example, a coefficient of 0.008 for BlackRock indicates that Bl...

work page 2008

Show all 18 references

[19]

δ f ×t represents institution cross year fixed effect

Count is the number of times the phrase appeared in the institution’ s proxy guidelines text filed in year t. δ f ×t represents institution cross year fixed effect. (1), (2), and (3) show results for all the 9,832 phrases described in Section 3.1 for each institution. The inst...

2004
[52]

The Board of Directors recommends

The list of largest shareholders contains fifty unique mutual fund institutions. It seems like the usual suspects such as BlackRock, Fidelity, and Vanguard are the major shareholders in all the proxy fights; and activists have to simply align communications with them irrespect...

2003
[107]

Gao, X., T . J. Wong, L. Xia, and G. Yu (2021, 01). Network-Induced Agency Conflicts in Delegated Portfolio Man- agement. The Accounting Review 96(1), 171–198. Gillan, S. L. and L. T . Starks (2000, August). Corporate governance proposals and shareholder activism: The role of ...

2003
[157]

Schoenfeld, J. (2020). Contracts between firms and shareholders. Journal of Accounting Research 58(2), 383–427. Schwartz-Ziv, M. and E. Volkova (2024). Is blockholder diversity detrimental? Management Science. Sims, C. A. (2003, April). Implications of rational inattention. Jo...

2020
[493]

32 Malenko, N. and Y. Shen (2016, December). The Role of Proxy Advisory Firms: Evidence from a Regression- Discontinuity Design. The Review of Financial Studies 29(12), 3394–3427. Manela, A. and A. Moreira (2017). News implied volatility and disaster concerns. Journal of Finan...

2017
[1407]

Ostergaard, and I

Norli, O., C. Ostergaard, and I. Schindele (2014, October). Liquidity and shareholder activism. The Review of Financial Studies 28(2), 486–520. NPR (2017). Why bill ackman sees activist investing as a moral crusade. https://www.nprillinois.org/post/why- bill-ackman-sees-activi...

2017
[2016]

To assign IP3 blocks to institutions, I use a procedure similar to Iliev et al

I use regular expressions, such as (.*blackrock.*) for Black- Rock Financial Management, to get IPv4 associated with institutions. To assign IP3 blocks to institutions, I use a procedure similar to Iliev et al. (2021). If an institution owns all or a subset of the IP3 address,...

2021
[2017]

The IP addresses in the dataset are in version 4 (IPv4) format, which defines an IP address as a 32-bit number separated into four 8-bit numbers

EDGAR log file data set includes information on the visitor’ s IP address, date, timestamp, CIK, and filing document’ s accession number. The IP addresses in the dataset are in version 4 (IPv4) format, which defines an IP address as a 32-bit number separated into four 8-bit nu...

2017
[2018]

in their proxy guidelines or letter

The institutions are willing to vote against the management recommendations on these issues thus making the coefficient pos- itive. BlackRock, which has been vocal about climate change, does show a higher coefficient for the phrase compared to others. "in their proxy guideline...

2021
[2615]

Kedia, S., L. T . Starks, and X. Wang (2021). Institutional investors and hedge fund activism.The Review of Corpo- rate Finance Studies 10(1), 1–43. Klein, A. and E. Zur (2009). Entrepreneurial Shareholder Activism: Hedge Funds and Other Private Investors.The Journal of Financ...

2021

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.

[1] [1]

Alchian, A. A. and H. Demsetz (1972). Production, Information Costs, and Economic Organization.The American Economic Review 62(5), 777–795. Alexander, C. R., M. A. Chen, D. J. Seppi, and C. S. Spatt (2010, November). Interim news and the role of proxy voting advice. Review of Financial Studies 23(12), 4419–4454. Appel, I., T . A. Gormley, and D. B. Keim (...

work page 1972

[2] [3]

FrontFour Capital Group LLC Form DFAN14A, number 0000921895-13-000151 | SEC Filings API

FrontFour (2013). FrontFour Capital Group LLC Form DFAN14A, number 0000921895-13-000151 | SEC Filings API. https: //sec-api.com/filing/acc/0000921895-13-000151. Gantchev, N. (2012, August). The costs of shareholder activism: Evidence from a sequential decision model. Journal of Financial Economics

work page arXiv 2013

[3] [6]

In 2006 and 2010, no proxy fights above the cutoff holding were unsuccessful or with- drawn

The shade of the bubble represents the outcome of the proxy fight. In 2006 and 2010, no proxy fights above the cutoff holding were unsuccessful or with- drawn. 2006 2008 2010 2012 2014 2016 2018 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9Proxy communications' aggregate alignment (averaged) Unsuccessful or Withdrawn Successful or Settled 38 Figure 5: Alignment f...

work page 2006

[4] [13]

To make sure that I have credible IP3 blocks, I go back quarterly from December 2016 and see what fraction of holdings do institution access through the EDGAR server

However, institutions sometimes change their underlying technology infrastructure and, in that process, register for different IP3 blocks. To make sure that I have credible IP3 blocks, I go back quarterly from December 2016 and see what fraction of holdings do institution access through the EDGAR server. I use CRSP mutual fund data to get institution hold...

work page 2016

[5] [14]

I identify proxy fight documents based on the accession number of the filing in log files and SEC’ s index files

Subsequently, I match valid IP3 blocks from the organization lookup table with IP3 from EDGAR log files. I identify proxy fight documents based on the accession number of the filing in log files and SEC’ s index files. To measure the number of times an institution accessed proxy fight re- lated filings, I aggregate views for proxy fight documents during t...

work page 2013

[6] [16]

experience board

Count is the number of times a phrase appeared in the institution’ s proxy guidelines text filed in year t. Since I use a two-year training period for SVR, I relate phrase counts from the proxy guidelines document to the SVR coefficients calculated 57 at the end of next year.δi ×t shows institution cross time level fixed effect and the errors,εn,i ,t , ar...

work page 2019

[7] [17]

simple majority vote

The coefficients are significant for changing parameters on either side of the respective cutoffs. Thus, the text-based voting pre- diction is rooted in institutions’ proxy guidelines and is insensitive to changing parameters. [Figure 11 about here.] [Figure 12 about here.] 62 Figure 6: Institutions that own significant voting power vary across proxy figh...

work page 2004

[8] [18]

climate change

The phrases are stripped of cases, punctuation, stop-words, and noun/verb forms. The coef- ficients are multiplied by 10,000. The coefficients indicate the marginal increase in the proxy communications’ alignment with institution preferences if it contains one more instance of the phrase. For example, a coefficient of 0.008 for BlackRock indicates that Bl...

work page 2008