Predicting Sell-Side Analysts' Relative Earnings Forecast Accuracy When It Matters Most

37 Pages Posted: 17 May 2017  

Niklas Blümke

University of Cologne

Dieter Hess

University of Cologne - Department of Corporate Finance; University of Cologne - Centre for Financial Research (CFR)

Alexander Stolz

University of Cologne

Date Written: May 16, 2017

Abstract

We introduce a novel framework to predict the relative accuracy of sell-side analysts’ annual earnings forecasts out-of-sample. Prior studies only evaluate forecasts shortly before the corresponding earnings release. In contrast, our study is the first to provide long-term predictions which are of particular value for both investors and academics. Overall, we show that analysts classified as superior outperform their inferior counterparts by 8.4 percent, on average. The prediction performance is even more pronounced for longer-term forecasts and for firms with high dispersion of analysts’ forecasts, that is, when the identification of superior forecasts matters most. Moreover, we challenge the conclusion of existing literature that characteristics reflecting an analyst’s skill set are not helpful to obtain better predictions. In particular, when evaluating forecasts which draw on similar information sets, we find that a model based on analyst characteristics outperforms a model focusing simply on the forecast horizon, for example.

Keywords: Equity Analysts, Earnings Forecasts, Accuracy Prediction

JEL Classification: G12, G14

Suggested Citation

Blümke, Niklas and Hess, Dieter and Stolz, Alexander, Predicting Sell-Side Analysts' Relative Earnings Forecast Accuracy When It Matters Most (May 16, 2017). Available at SSRN: https://ssrn.com/abstract=2969075 or http://dx.doi.org/10.2139/ssrn.2969075

Niklas Blümke

University of Cologne ( email )

Albertus-Magnus-Platz
Cologne, 50923
Germany

Dieter Hess (Contact Author)

University of Cologne - Department of Corporate Finance ( email )

Corporate Finance Seminar
Albertus-Magnus-Platz
D-50923 Cologne
Germany
+49 221 470 7876 (Phone)
+49 221 470 7466 (Fax)

HOME PAGE: http://cf.uni-koeln.de/

University of Cologne - Centre for Financial Research (CFR)

Germany

Alexander Stolz

University of Cologne ( email )

Albertus-Magnus-Platz
Cologne, 50923
Germany

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