Risky Advice and Reputational Bias

TL;DR

This paper models expert advice under reputational incentives, showing how reputation affects risk-taking, advice cutoff levels, and success rates, with implications for financial analysts' behavior and monitoring regimes.

Contribution

It introduces a recursive, belief-based equilibrium model of advice under reputation, revealing how reputation influences risk decisions and provides a way to implement targeted experimentation rates.

Findings

High-reputation analysts make fewer risky calls.

Higher signal precision lowers the advice cutoff.

Reputation increases the likelihood of successful recommendations.

Abstract

We study expert advice under reputational incentives, with sell-side equity research as the lead application. A long-lived analyst receives a continuous private signal about a binary payoff and recommends a risky (Buy) or safe action. Recommendations and outcomes are public, and clients' implementation effort depends on current reputation. In a recursive, belief-based equilibrium: (i) advice follows a cutoff in the signal; (ii) under a simple diagnosticity asymmetry, the cutoff is (weakly) increasing in reputation (reputational conservatism); and (iii) comparative statics are transparent - higher signal precision or a higher success prior lowers the cutoff, whereas stronger career concerns raise it. A success-contingent bonus implements any target experimentation rate via a closed-form mapping. The model predicts that high-reputation analysts make fewer risky calls yet attain higher conditional hit rates, and it clarifies how committee thresholds and monitoring regimes shift behavior.