Abstract
We show analysts’ own earnings forecasts predict error in their own forecasts of earnings at other horizons, which we argue provides a measure of the extent to which analysts inefficiently use information. We construct our measure by exploiting two sources of variation in analysts’ incentives: (i) more recent forecasts have greater salience at the time of the earnings release so accuracy incentives are higher (lower) at shorter (longer) forecast horizons and (ii) analysts have greater incentives for optimism (pessimism) at longer (shorter) horizons. Consistent with these incentives affecting the incorporation of information into forecasts, we document (i) current year forecasts underweight (overweight) information in shorter (longer) horizon forecasts and (ii) the mis-weighting is more pronounced when recent news is negative—when analysts have greater (weaker) incentives to incorporate the news into shorter (longer) horizon forecasts. Finally, returns tests suggest that forecasts adjusted for the inefficiency we document better represent market expectations of earnings.
Highlights
Predicting earnings is a central function of accounting research
While this evidence fueled the use of analyst forecasts as proxies for market expectations, subsequent studies have documented a range of predictable errors related to publicly available information (e.g., DeBondt and Thaler 1990; Lys and Sohn 1990; Abarbanell 1991; Mendenhall 1991; Abarbanell and Bernard 1992; Easterwood and Nutt 1999; So 2013)
We propose a novel measure of forecast inefficiency: the extent to which analysts’ earnings forecasts predict error in their own earnings forecasts at other horizons
Summary
Predicting earnings is a central function of accounting research. Early studies on the properties of earnings relied on time-series models to predict earnings Share price target forecasts, which are often based off a discounted cash flow model, differ significantly from the market’s expectation of cash flows (e.g., Bradshaw et al 2013) We expect this optimism (or pessimism) will largely be reflected in longer horizon earnings expectations, resulting in overweighted information. After incorporating the information in both past returns and share price target optimism, analysts’ own forecasts explain 11.3% of the current year’s forecast error and the incremental predictability from firm characteristics is only 2.2%. We construct our regression model using two simple tradeoffs that affect accuracy incentives (e.g., Chen and Jiang 2006; Bagnoli et al 2008), so we believe our findings are most consistent with incentive-based explanations This does not rule out behavioral explanations, such as difficulty increasing with horizon or analysts better understanding the implications of information for shorter horizon earnings. These explanations are potentially related—if long-horizon forecast accuracy is sufficiently unimportant to investors, analysts may not have sufficiently strong incentives to learn how information maps into long-horizon earnings
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