Income Classification Shifting and Financial Analysts’ Forecasts

Shanshan Pan (), Michael Lacina and Haeyoung Shin ()
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Shanshan Pan: University of Houston-Clear Lake, 2700 Bay Area Boulevard, Houston, TX 77058, USA
Michael Lacina: University of Houston-Clear Lake, 2700 Bay Area Boulevard, Houston, TX 77058, USA
Haeyoung Shin: University of Houston-Clear Lake, 2700 Bay Area Boulevard, Houston, TX 77058, USA

Review of Pacific Basin Financial Markets and Policies (RPBFMP), 2019, vol. 22, issue 02, 1-48

Abstract: Income classification shifting involves misclassifying core expenses into non-core items to boost core earnings. Managers engage in classification shifting because they believe they can manage the perceptions of investors and financial analysts. We examine analysts’ earnings forecasts to determine whether analysts can identify classification shifting ex post and how they respond to shifted income statement components. Analysts play a role as information intermediaries between firms and investors. We find that analysts respond less to increased core earnings from classification shifting. However, analysts fail to gauge the full impact of classification shifting, leading to more optimistically biased and less accurate forecasts.

Keywords: Income classification shifting; analysts’ earnings forecasts; forecast revisions; forecast bias; forecast accuracy (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1142/S0219091519500103

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