Can Analysts Predict Breaks in Earnings Strings?

The Review of Accounting and Finance 18 (4), 613-632.

34 Pages Posted: 12 Jun 2019 Last revised: 26 Dec 2020

See all articles by Vadim S. Balashov

Vadim S. Balashov

Rutgers School of Business-Camden

Zhanel DeVides

Penn State - Abington

Date Written: May 25, 2019

Abstract

Purpose: This study investigates the behavior of sell-side analysts covering firms that are about to experience breaks in strings of consecutive quarterly earnings increases.

Design/methodology/approach: We estimate the likelihood of analysts predicting a break using logit regressions for 1.8M EPS forecasts issued by individual analysts from 1992 to 2017.

Findings: We find that analysts can predict breaks in earnings strings by issuing less favorable earnings estimates ahead of the break announcement. The probability of detecting a break is higher for longer and more severe breaks, for more skillful analysts, and for firms with richer information environment. We find that analysts’ warnings are heeded by investors and result in a less severe reaction to the break announcements.

Originality/value: Breaks in strings of earnings increases are situations when information asymmetry exists and could be mitigated by information intermediaries such as sell-side analysts. Therefore, it is important to examine whether analysts have any informational advantages or disadvantages over insiders and institutional investors in the quarters prior to breaks in strings and whether they communicate it to the market in a timely and accurate manner, thus reducing information asymmetry by “leveling the field” across the investment community.

Keywords: Sell-Side Analysts, Earnings Forecasts, Breaks, Strings of Earnings Increases

JEL Classification: G11, G14, G24, M41

Suggested Citation

Balashov, Vadim S. and DeVides, Zhanel, Can Analysts Predict Breaks in Earnings Strings? (May 25, 2019). The Review of Accounting and Finance 18 (4), 613-632., Available at SSRN: https://ssrn.com/abstract=3394312 or http://dx.doi.org/10.2139/ssrn.3394312

Vadim S. Balashov (Contact Author)

Rutgers School of Business-Camden ( email )

Camden, NJ 08102
United States
856-225-6706 (Phone)

Zhanel DeVides

Penn State - Abington ( email )

1600 Woodland Rd
Abington, PA 19001
United States
267-633-3325 (Phone)

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