Man vs. Machine Learning: The Term Structure of Earnings Expectations and Conditional Biases

71 Pages Posted: 21 Sep 2020

See all articles by Jules H. van Binsbergen

Jules H. van Binsbergen

University of Pennsylvania - The Wharton School; National Bureau of Economic Research (NBER)

Xiao Han

University of Edinburgh Business School

Alejandro Lopez-Lira

BI Norwegian Business School; University of Pennsylvania - Finance Department; University of Pennsylvania - The Wharton School

Multiple version iconThere are 2 versions of this paper

Date Written: September 2020

Abstract

We use machine learning to construct a statistically optimal and unbiased benchmark for firms' earnings expectations. We show that analyst expectations are on average biased upwards, and that this bias exhibits substantial time-series and cross-sectional variation. On average, the bias increases in the forecast horizon, and analysts revise their expectations downwards as earnings announcement dates approach. We find that analysts' biases are associated with negative cross-sectional return predictability, and the short legs of many anomalies consist of firms for which the analysts' forecasts are excessively optimistic relative to our benchmark. Managers of companies with the greatest upward biased earnings forecasts are more likely to issue stocks.

Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

Suggested Citation

van Binsbergen, Jules H. and Han, Xiao and Lopez Lira, Alejandro, Man vs. Machine Learning: The Term Structure of Earnings Expectations and Conditional Biases (September 2020). NBER Working Paper No. w27843, Available at SSRN: https://ssrn.com/abstract=3696210

Jules H. Van Binsbergen (Contact Author)

University of Pennsylvania - The Wharton School ( email )

3641 Locust Walk
Philadelphia, PA 19104-6365
United States

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

HOME PAGE: http://www.nber.org/people/jules_vanbinsbergen

Xiao Han

University of Edinburgh Business School ( email )

29 Buccleuch Place
Edinburgh
United Kingdom

HOME PAGE: http://sites.google.com/site/xiaohanfinance/home

Alejandro Lopez Lira

BI Norwegian Business School ( email )

Nydalsveien 37
Oslo, 0442
Norway

University of Pennsylvania - Finance Department ( email )

The Wharton School
3620 Locust Walk
Philadelphia, PA 19104
United States

University of Pennsylvania - The Wharton School ( email )

3641 Locust Walk
Philadelphia, PA 19104-6365
United States

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
12
Abstract Views
266
PlumX Metrics