We examine the predictability of 299 capital market anomalies enhanced by 30 machine learning approaches and over 250 models in a dataset with more than 500 million firm-month-anomaly observations. We find significant monthly (out-of-sample) returns of around 1.8-2.0%, and over 80% of the models...

We examine the role of concurrent information in the striking increase in investor response to earnings announcements from 2001 to 2016, as measured by return variability and volume following Beaver (1968). We find management guidance, analyst forecasts, and disaggregated financial statement...

This paper reveals that in addition to fundamental factors, the 52-week high price and recent investor sentiment play an important role in analysts' target price formation. Analysts' forecasts of short-term earnings and long-term earnings growth are shown to be important explanatory variables...

I estimate a theory-based behavioral momentum using analysts' predictable underreaction (APU) as a proxy for newswatchers underreaction. The results show that APU strongly predicts analysts' errors and, more importantly, stock returns. A long-short strategy based on APU generates a...

We examine the out-of-sample performance of 240 stock market anomalies enhanced by 49 machine learning algorithms and over 260 individually trained models across an international data sample of nearly 1.9 billion stock-month-anomaly observations from 1980 to 2019. We demonstrate significant...

This paper considers the impact of UK practices with respect to the measurement and disclosure of intangible assets, focusing on RD activities. We first update prior UK work relating RD activities to market prices. Second, given the clearly identified role of disclosure outside of the financial...

This paper extends the study of Herrmann and Thomas (2005) on granularity in analyst forecasts at multiples of nickels and finds that forecasts at multiples of nickels are more optimistic, and induce weaker market responses. Granularity in analyst forecasts combined with managers’ incentive to...

In this study we examine changes in the precision and the commonality of information contained in individual analysts' earnings forecasts, focusing on changes around earnings announcements. Using the empirical proxies suggested by the Barron et al. (1998) model that are based on the...

We define a delayed disclosure ratio (DD) as the fraction of 10-Q financial statement items that are withheld at the earlier quarterly earnings announcement. We find that higher DD firms have a greater delay in investor and analyst response to earnings surprises: (i) the fraction of total market...

Researchers in accounting have recently provided evidence of a striking increase in the usefulness of earnings announcements based on stock market price and volume reactions (Beaver et al., 2018; Barron et al., 2018). Price reactions, however, are unable to capture investor disagreement and volume...