Analyst Long-Term Growth Forecasts, Accounting Fundamentals And Stock Returns by GersteinFIsher
We decompose consensus analyst long-term growth forecasts into a hard growth component that captures accounting information (asset and sales growth, profitability and equity dilution) and an orthogonal soft growth component. The soft component does not forecast future returns, and the hard component does forecast future returns, but in a perverse way. Specifically, stocks with accounting information indicating favorable long-term growth forecasts tend to realize negative future excess returns. This and other evidence we present is consistent with biased long-term growth forecasts generating stock mispricing.
Analyst Long-Term Growth Forecasts, Accounting Fundamentals And Stock Returns – Introduction
The Gordon growth model expresses a stock’s price as a function of its current dividends, a discount rate, and long-term growth expectations. Of the three relevant components of price, determining long-term growth expectations requires the most judgement and is the most likely to be subject to systematic mistakes. This paper analyzes potential errors in long-term growth expectations by examining the long-term consensus (mean) forecasts of earnings reported by sell-side analysts.2 Consistent with earlier work, we find evidence of systematic errors in the forecasts, as well as evidence that these errors are reflected in stock prices in ways that are consistent with various return anomalies discussed in the academic finance literature.
To better understand the biases in long-term growth forecasts we decompose the forecasts into what we call a hard component, which can be explained by accounting and choice variables, and a soft component, which is the residual. Elements of the hard component include accounting ratios that capture profitability and changes in sales, as well as choices that influence asset growth and equity dilution. As we show, both components of long-term growth are related to current stock prices, suggesting that either the forecasts or the rationale used by the forecasters influence stock prices.3 However, our evidence indicates that the forecasts of sell-side analysts are systematically biased, and that these biases may have influenced stock prices in ways that make their returns predictable.
The observed biases are linked to the hard component of the growth forecasts. In particular, the forecasts suggest that analysts believe profits are mean reverting, but profitability actually tends to be fairly persistent. The forecasts also indicate that analysts believe that high past sales growth is a good predictor of future earnings growth. However, we find that high sales growth is actually weakly negatively associated with future earnings growth. Endogenous firm decisions, such as the rate of asset growth, and the use of external financing, are associated with higher growth forecasts, but the relationship between these choices and actual earnings growth is actually negative. The soft component of the growth forecasts does in fact correctly predict actual growth, although in some tests the relationship is relatively weak.
The above evidence is consistent with the idea that the logic of mapping hard information to expected future growth rates may be leading investors astray. If this is the case, investors may be able to profit with trading strategies that buy stocks when the hard component of growth is unfavorable and sell when the hard component is favorable. Our evidence, which is consistent with other papers in the investment anomalies literature, indicates that this is indeed the case.
Our paper is not the first to describe biases in analyst long-term growth forecasts and relate these biases to abnormal stock returns.4 Previous research by Dechow and Sloan (1997), Chan, Karceski and Lakonishok (2003), La Porta (1996) and Sloan and Skinner (2002) find evidence that overly optimistic equity analyst forecasts contribute to the value premium and that growth stocks underperform when high expectations are not met. Copeland, Dolgoff, and Moel (2004) show that innovations in analyst long-term growth estimates are positively correlated with contemporaneous stock returns. A more recent paper by Da and Warachka (2011) conjectures that short-term earnings forecasts are much more accurate than the long-term forecasts and shows that a strategy that exploits differences between these forecasts generates excess returns.
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