Information Content of Option Prices: Comparing Analyst Forecasts to Option-Based Forecasts

Finance researchers keep producing increasingly complex and computationally-intensive models of stock returns. Separately, professional analysts forecast stock returns daily for their clients. Are the sophisticated methods of researchers achieving better forecasts or are we better off relying on the expertise of analysts on the ground? Do the two sets of actors even capture the same information? In this paper, I hypothesize that analyst forecasts and forecasts constructed using option prices will be different because they draw on different information sets. Using hypothesis tests and quantile regressions, I find that option-based forecasts are statistically significantly different from analyst forecasts at every level of the forecast distribution. Then, using cross-sectional regressions, I show that this difference originates in the distinct information sets used to create the forecasts: option based forecasts incorporate information about the probability of extreme events while analyst forecasts focus on information about firm and macroeconomic fundamentals.