Creating Controversy in Proxy Voting Advice: Malenko, Malenko & Spatt (2025)

What this is. The paper’s core results, the information-design model, and the Bayesian persuasion method it builds on, with the defining equations: enough to know what it found and how, without reading all 52 pages. To replicate or extend it, read the full source at the original.

TL;DR

A profit-maximizing proxy advisory firm sells research reports to institutional investors and issues public vote recommendations. The paper shows that the advisor’s optimal strategy combines two distinct elements: a fully informative (unbiased) private research report for subscribers, and a partially informative, asymmetrically biased public vote recommendation that favors the a priori unlikely alternative too often. By recommending against the likely outcome more often than its probability warrants, the advisor induces close, contentious shareholder votes, raising the value of its advice and thereby the willingness to pay for the research report. The paper calls this “creating controversy.” The model is cast as an information design problem following Rayo and Segal (2010) and Kamenica and Gentzkow (2011). The result rationalizes the proxy advisory industry’s one-size-fits-all approach, explains the rubber-stamping pattern documented in Malenko and Shen (2016) (positive ISS recommendations receive 93% average support on say-on-pay), explains why negative recommendations generate dispersed votes, and suggests that the active voting behavior of large institutional investors studied by Iliev and Lowry (2015) is consistent with rational adjustment for recommendation bias.

Core results

Magnitudes and significance are as reported. Locators point into the source PDF.

#	Result	Locator	Magnitude
R1	Optimal public recommendation is binary and partially informative: the advisor recommends against the a priori likely alternative too often, inducing a controversial posterior near 0.5	Proposition 4, pp. 2325-2326	When mu >= mu_0(q), recommendation s = 0 is given with probability (1-mu)/(1-mu_0(q)) > Pr(theta=0) = 1-mu; more often than warranted by the true probability the proposal is value-decreasing
R2	Maximizing the average probability of a split vote is the objective: controversial recommendations raise the split-vote probability, increasing subscribers’ willingness to pay for the report	Eq. (12)-(14), pp. 2321-2322; Figure 3, p. 2323	Concavification of Pr(Piv|q, mu_s) over mu_s, subject to the Bayes plausibility constraint, yields the advisor’s optimal policy; for q = 0.01 and mu = 0.8, partially informative recommendation raises split-vote probability above that of any other policy
R3	Near mu = 0.5, optimal recommendation is uninformative: close votes are already likely without intervention, so information design has no benefit	Proposition 5, p. 2327; Section III.E, p. 2334	ISS board-declassification positive recommendations: 100% of cases 2010-2019 (zero negative), yet shareholder support averages 73.7%, consistent with uninformative design prediction
R4	Subscriber demand rises with controversy: the fee the advisor can charge equals V(q, S) H^{-1}(1-q); a higher average split-vote probability V raises willingness to pay	Proposition 3, eq. (11), p. 2321; eq. (9)-(10), p. 2320	Shareholder i subscribes iff v_i >= f/V(q, S); the controversy mechanism converts a higher split-vote probability directly into higher revenue per subscriber
R5	Fully informative private research report is optimal: noise in the report weakly lowers subscribers’ willingness to pay	Proposition 8, p. 2330	Any signal that induces a posterior of exactly 1/2 for the subscriber is worthless; the advisor can always combine signals to eliminate such posteriors, and the remaining signals achieve full informativeness
R6	Rubber-stamping of pro-prior recommendations, dispersion under anti-prior recommendations: ISS say-on-pay positive recommendations receive 93% average support with no failures; negative recommendations receive 69% average support with 11% failure probability	Section III.G, p. 2335; Table IA.I (Internet Appendix)	About 500 close votes per year (40%-60% support) occur when ISS recommends against management; close votes are extremely rare when ISS agrees with management
R7	Ban on recommendations has ambiguous welfare effects: it removes the bias but reduces information for nonsubscribers; whether the net effect is positive depends on the distribution of shareholder valuations v_i	Proposition A1, p. 2343; Example A1, p. 2344	For v_H = 5: ban raises correct-decision probability from 92.5% to 95.65% (positive effect dominates); for v_H = 7: ban lowers it to 90.3% < 92.5% (negative effect dominates)

Overall (paper’s conclusion). Proxy advisors have a structural incentive to design biased vote recommendations that create controversy, because close votes increase the relevance and value of their research reports. This incentive is inherent to the core business model of selling information and is independent of any consulting conflict of interest. At the same time, research reports remain fully informative and valuable to subscribers. Proxy advisors’ recommendations should therefore not be treated as the appropriate benchmark for evaluating institutional investors’ voting behavior; the votes of large, engaged asset managers are a more suitable reference.

Theory / model

Setup (Section I, pp. 2311-2316)

A firm has $N \geq 3$ (odd) shareholders, each owning one share. A proposal is approved if at least $\frac{N+1}{2}$ shareholders vote in favor. Let $d \in \{0,1\}$ denote the decision (1 = approve). The proposal’s payoff to shareholder $i$ is (p. 2312, eq. 1-2):

$$u_i(d, \theta) = v_i \cdot u(d, \theta), \qquad u(1, \theta) = \begin{cases} 1 & \text{if } \theta = 1 \\ -1 & \text{if } \theta = 0 \end{cases}, \quad u(0, \theta) = 0 \tag{1-2}$$

where $\theta \in \{0,1\}$ is the unknown state and $v_i \geq 0$ is shareholder $i$‘s concern (drawn i.i.d. from distribution $H(\cdot)$ on $[\underline{v}, \bar{v}]$). The prior is $\Pr(\theta=1) = \mu \in (0,1)$.

Information structure (Section I.B, pp. 2312-2313)

The proxy advisor designs two signals. The private signal (research report) $\mathcal{R} = (R, \{\phi(\cdot|\theta)\}_{\theta \in \{0,1\}})$ is available only to subscribers. The public signal (vote recommendation) $\mathcal{S} = (S, \{\gamma(\cdot|r)\}_{r \in R})$ maps the report realization $r$ to a public recommendation $s \in S$, observable by all shareholders. The paper shows (Proposition 8) that the optimal private signal is fully informative: $R = \{0,1\}$ and $r = \theta$, so subscribers learn the state with certainty.

Voting equilibrium (Section II.A, pp. 2316-2319)

Given recommendation $s$ (inducing posterior $\mu_s = \Pr(\theta=1|s)$) and fraction $q$ of subscribers, a nonsubscribing shareholder’s equilibrium probability of voting “for” is (Proposition 2, eq. 7, p. 2318):

$$\pi(q, \mu_s) = \frac{z_s(1-2q) - 1 + \sqrt{(z_s-1)^2 + 4q^2 z_s}}{2(z_s-1)(1-q)}, \qquad z_s \equiv \left(\frac{\mu_s}{1-\mu_s}\right)^{\frac{2}{N-1}} \tag{7}$$

for $\mu_s \neq \frac{1}{2}$. Subscribers vote according to the state: $a_i = \theta$. Nonsubscribers condition their vote not only on $\mu_s$ but also on the information revealed by the event of being pivotal (“strategic voting”).

The value of the report to shareholder $i$, conditional on recommendation $s$, is $\frac{v_i}{2} \Pr(\text{Piv}|q, \mu_s)$, where (eqs. 8-10, p. 2320):

$$V(q, \mathcal{S}) = \frac{1}{2} \sum_{s \in S} \Pr(\text{Piv}|q, \mu_s) \tau_s, \qquad \tau_s \equiv \mu \gamma(s|1) + (1-\mu)\gamma(s|0) \tag{10}$$

is the average (pre-recommendation) probability of a shareholder being pivotal, weighted by the frequency of each recommendation.

Method

The paper solves the advisor’s problem by Bayesian persuasion (Kamenica and Gentzkow (2011)) applied to a multi-agent, multi-audience setting. The advisor maximizes expected profit $N q f$, where $q$ is the equilibrium fraction of subscribers and the optimal fee (eq. 11, p. 2321) is:

$$f = V(q, \mathcal{S}) \cdot H^{-1}(1-q) \tag{11}$$

Substituting into the profit expression, the advisor solves (eq. 12, 15, pp. 2321-2328):

$$\max_{q, \mathcal{S}} \; q H^{-1}(1-q) \left( \sum_{s \in S} \Pr(\text{Piv}|q, \mu_s) \tau_s \right) \tag{12}$$

subject to the Bayes plausibility constraint:

$$\sum_{s \in S} \mu_s \tau_s = \mu \tag{13}$$

The key step is the concavification of the function $\Pr(\text{Piv}|q, \cdot)$ over posterior beliefs. For a given $q$, the optimal public recommendation design is found by taking the concave closure $P(q, \mu_s)$ of $\Pr(\text{Piv}|q, \mu_s)$ (Figure 3, p. 2323). Because $\Pr(\text{Piv}|q, \mu_s)$ is (by Lemma 1, p. 2322) strictly convex near $\mu_s = 0$ and $\mu_s = 1$ (for small $q$), and strictly concave near $\mu_s = 1/2$, the concave closure is achieved by a binary recommendation that places mass at $\mu_0 \in (0, 1/2)$ (the controversial posterior) and $\mu_1 = 1$ (full certainty in the likely direction), or symmetrically.

The method builds on the principal-agent framework for the advisor’s optimization, and the bayesian-persuasion technique for solving the optimal information design. The timeline-consistent property (p. 2313) distinguishes this paper from most Bayesian persuasion models: because the advisor maximizes ex ante profits and has no stake in the vote outcome, the optimal policy is dynamically consistent.

Empirical specifications

This is a pure-theory paper. The paper does not estimate any econometric specification. Section III (pp. 2330-2336) presents anecdotal and survey evidence to corroborate the mechanism, drawing on:

• Ertimur, Ferri, and Oesch (2013, 2018): variability in ISS research-report severity when negative recommendations are issued; shareholders less likely to vote against management when the report conveys less severe concerns.
• Case studies: the 2024 Tesla say-on-pay vote (ISS issued a negative recommendation but the report was more positive; Vanguard and BlackRock voted for, consistent with large-v_i shareholders voting on the report rather than the recommendation; p. 2331).
• ISS board declassification data: zero negative ISS recommendations on shareholder proposals to declassify boards from 2010 to 2019, yet average support of 73.7%, consistent with Proposition 5 (uninformative recommendation near mu = 0.5; p. 2334).
• ISS say-on-pay voting outcomes: 93% average support with zero failures on positive recommendations; 69% average support and 11% failure rate on negative recommendations (Table IA.I; p. 2335), consistent with rubber-stamping (Proposition 4) and close-vote prediction.
• Hayne and Vance (2019) interview evidence: proxy advisor employees confirm that maintaining a consistent proportion of negative recommendations is viewed as a way to stay relevant (Section III.F, p. 2334-2335).

Datasets used

Dataset	Role in paper	Wiki page
ISS Voting Analytics (say-on-pay and director elections, 2011-2019)	Empirical corroboration of rubber-stamping and close-vote patterns (Section III.G)	No page yet
ISS board-declassification recommendations (2010-2019)	Corroboration of uninformative-recommendation prediction (Section III.E)	No page yet
Ertimur, Ferri, and Oesch (2013, 2018) hand-collected ISS reports	Content of research reports vs. recommendations (Section III.A)	No page yet

The paper has no original data collection. Empirical illustrations use published sources and aggregate statistics from the literature.

When to read the full paper

Use the original if you are: building a structural model of information intermediaries in financial markets; studying proxy advisory regulation (the ban-on-recommendations analysis in Appendix A); analyzing the information content of voting recommendations vs. research reports; or extending the model to costly information acquisition, ideological shareholders, or multi-firm settings (Internet Appendix Sections IV.C, IV.F). The locators above point to the exact propositions and figures.

Attribution and rights

Source: peer-reviewed, The Journal of Finance 80(4). This distillation was extracted by an LLM on 2026-06-05 and is not human-verified or independently reproduced. The CC BY-NC-ND 4.0 licence permits sharing with attribution but does not permit adaptations or commercial use; the verbatim PDF is not hosted here.

Malenko, Andrey, Nadya Malenko, and Chester Spatt. “Creating Controversy in Proxy Voting Advice.” The Journal of Finance 80, no. 4 (August 2025): 2303-2354. DOI: 10.1111/jofi.13438. © 2025 The Author(s). Licensed under CC BY-NC-ND 4.0. This page is a distillation by the Institute for Automated Research: core results extracted and re-expressed; extract-only.