The value of publicly available predicted earnings surprises

Abstract

This paper demonstrates how to collect and manage free predicted earnings surprises available in the public domain. The predicted earnings surprises we collect are expected to be more accurate than the corresponding consensus estimates and other predicted earnings, but have not been studied in the academic literature until very recently. We find a number of unexpected and problematic idiosyncrasies with the source of the data and the predicted earnings surprises themselves. The data are hard to work with, perhaps by design, and contain both big and small extreme values that are unexpected given their origin. It is unclear how these observations are selected for public release. After the data science exercise of managing and merging the predicted earnings surprises with other freely available public information (specifically ticker symbols and return data), we examine the predicted earnings surprises and investigate how the predicted earnings surprises affect short-term stock prices. We find evidence of a linear association between the predicted earnings surprises and subsequent short-term returns, although the significance is driven by extreme outliers. Most importantly, we use the predicted earnings surprises to form short-term trading strategies. The most profitable trading strategy that exploits the predicted earnings surprises is a contrarian trading strategy.