We create a market-wide measure of dispersion in options investors' expectations by aggregating across all stocks the dispersion in trading volume across moneynesses (DISP). DISP exhibits strong negative predictive power for future market returns and its information content is not subsumed by...

In recent studies, the analysts' consensus forecasts are widely used as a proxy for the unobservable market's consensus expectation of future earnings. As prior studies indicate, the analysts' consensus forecasts measure the true underlying construct with errors and the errors may vary...

Using a unique survey dataset, the author studies how financial market experts form their stock market expectations. He documents a strong disagreement among experts about how important macroeconomic and financial variables are related to stock returns. The results of an analysis of the...

Using a unique survey dataset, I study how financial market experts form their stock market expectations. I document a strong disagreement among experts about how important macroeconomic and financial variables are related to stock returns. The results of an analysis of the relationships between...

Movements in expected returns (ER) can cause a bias in measured autocorrelations, and the resulting spurious component is positive for infrequent regime shifts. We demonstrate this point analytically and investigate its empirical prevalence. In a key contribution, we use shifts in ex ante ER...

We introduce a new measure of stock misevaluation, 𝑄, which is consistent with the Gordon growth model for firm valuation. In our empirical application, we use 𝑄 to relate analyst forecasts to stock returns and measure the profitability of investment strategies that rely on information in...

I provide evidence that investors systematically overweight analyst forecasts by demonstrating that prices do not fully reflect the predictable component of analyst forecast errors. This evidence conflicts with conclusions in prior research relying on traditional approaches to predicting analyst...

This paper reviews research that uses big data and/or machine learning methods to provide insight relevant for equity valuation. Given the huge volume of research in this area, the review focuses on studies that either use or inform on accounting variables. The article concludes by providing...

We analyze three different mechanical models to forecast earnings and compare their forecasts with those of analysts. Moreover, we evaluate implied cost of capital (ICC) estimates that are based on these forecasts. With our analyses we answer three open questions in the literature. 1) Do model...

The enterprise multiple (EM) effect has been documented across global stock markets. EM is a robust predictor of expected average returns and generates a stronger value effect than traditional value metrics. We find evidence the EM effect is primarily attributable to mispricing and cannot be...