Analyst Forecast Bundling Intensity and Earnings Surprise

53 Pages Posted: 29 May 2024

See all articles by Mary E. Barth

Mary E. Barth

Stanford University - Graduate School of Business

Wayne R. Landsman

University of North Carolina Kenan-Flagler Business School

Junyoung Jeong

University of North Carolina (UNC) at Chapel Hill - Accounting Area

Sean Wang

Southern Methodist University (SMU) - Accounting Department

Date Written: May 24, 2024

Abstract

We find analysts convey information about a firm’s earnings without fully revising their earnings forecast by increasing bundling intensity, which is the extent to which an analyst report that has an earnings forecast revision includes also price target and/or recommendation revisions with the same sign as the earnings forecast revision. We develop a firm-level measure of bundling intensity, BF_Score, and find it is an economically meaningful predictor of analyst-based earnings surprises. The surprises reflect bias in consensus earnings forecasts related to information analysts convey through bundling intensity. Analysts’ use of bundling and the predictive power of BF_Score are higher when macroeconomic uncertainty is higher, which is when analysts’ incentives to avoid bold earnings forecast revisions are greater. Additionally, firms with higher BF_Score are more likely to report earnings that barely meet or beat the consensus forecast. This finding suggests analysts make more beatable earnings forecasts to curry favor with management by bundling rather than reflecting all the positive news in higher earnings forecasts. Adjusting analyst-based earnings surprises for the implications of BF_Score results in a distribution of earnings surprises that more closely resembles a normal distribution. Notably, the adjustments reduce the well-known kink asymmetry around zero for consensus analyst forecast-based earnings surprises by 66%, and markedly reduce skewness and kurtosis. Prior research attributes the kink primarily to earnings management. Instead, our findings suggest the kink reflects predictable analyst-based earnings surprises, and highlight the need for research utilizing consensus analyst earnings forecasts and analyst-based earnings surprises to account for these biases.

Keywords: Financial Analysts, Earnings Announcements, Financial Accounting, Capital Markets, Earnings Forecasts, Analyst Forecast Error, Earnings Surprises

JEL Classification: G1, G10, M41

Suggested Citation

Barth, Mary E. and Landsman, Wayne R. and Jeong, Junyoung and Wang, Sean, Analyst Forecast Bundling Intensity and Earnings Surprise (May 24, 2024). Available at SSRN: https://ssrn.com/abstract=4839739 or http://dx.doi.org/10.2139/ssrn.4839739

Mary E. Barth

Stanford University - Graduate School of Business ( email )

655 Knight Way
Stanford, CA 94305-5015
United States
650-723-9040 (Phone)
650-725-0468 (Fax)

Wayne R. Landsman

University of North Carolina Kenan-Flagler Business School ( email )

McColl Building
Chapel Hill, NC 27599-3490
United States
919-962-3221 (Phone)
919-962-4727 (Fax)

Junyoung Jeong

University of North Carolina (UNC) at Chapel Hill - Accounting Area ( email )

McColl Building
Chapel Hill, NC 27599-3490
United States

Sean Wang (Contact Author)

Southern Methodist University (SMU) - Accounting Department ( email )

United States
2147682858 (Phone)

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