Does Competition Improve Information Quality: Evidence from the Security Analyst Market

Chuqing Jin (Carnegie Mellon University)

January 27, 2023, 11:00–12:30

Auditorium 3

Job Market Seminar


This paper studies the effect of competition on the quality of information provided by experts. I estimate the incentives and the information structure of security analysts who compete to make earnings forecasts. Security analysts are rewarded for being more accurate than their peers, which creates competition. This reward for relative accuracy leads analysts to distort their forecasts to differentiate themselves, but also disciplines them to be less influenced by the prevailing incentive to report over-optimistic forecasts in the financial market. I structurally estimate a contest model with incomplete information that captures these two effects, disentangling the payoff for relative accuracy from the payoffs for optimism and absolute accuracy. Using the model, I conduct counterfactuals to evaluate policies that reduce the importance of relative accuracy in the current market, simulating their effect on the quality of information. I find that the disciplinary effect dominates: the reward for relative accuracy reduces individual and consensus forecast errors by 34.01% and 60.84% respectively, but at a cost of increasing individual and consensus forecast variances by 6.59% and 6.68% because of the distortionary effect. For each security, it is optimal to have moderate competition between the covering analysts, as competition generates more aggregate information but intensifies the distortionary effect.