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There are both academic and practical needs for more accuracy. For us in academia, wider research on business performance and financial models draws upon valuations and the estimation of cost of equity. Unreliable forecasts undermine other findings. Analysts frequently cite long-term earnings growth prospects as a key justification for their stock recommendations or target prices. A popular valuation ratio, the price-to-earnings-to-growth (PEG) ratio, requires a measure of expected long-term earnings growth as its key ingredient. In these applications, the quality of long-term earnings growth forecasts has direct consequences on the quality of valuation outcomes. The commonly used long-term growth proxy – LTG issued by analysts – is well known for its drawbacks: highly upwardly biased, inaccurate, and to fail to fully incorporate public information. The poor quality of LTG inevitably affects the valuation applications relying on it.

The key idea of our research is to pooling information from multiple sources to produce better long-term growth prediction. The idea is intuitive: information sources that are not perfectly correlated will jointly contribute to the prediction, leading to an outcome superior to that of each individual source alone. Predicting long-term growth could particularly benefit from multiple information sources because long-term growth is highly uncertain and is affected by many factors, which any single information source is unlikely to cover entirely. With our simple analytical model we used three sources of predictive information: analyst reports, financial statements and stock prices.

The Availability Of An Alternative To The - Often Excessively Optimistic - Long-Term Growth Forecast Approach Used By Analysts Is Long Overdue.
The Availability Of An Alternative To The – Often Excessively Optimistic – Long-Term Growth Forecast Approach Used By Analysts Is Long Overdue.

We examine the effect of pooling predictive information of long-term growth in a large panel of firms over 26 years. The first stage of the research was to evaluate different predictors of growth and to identify an unbiased prediction specification with the highest accuracy. We evaluated the prediction accuracy and bias of a comprehensive list of candidates of growth predictors, including LTG, past growth, the forward earnings-to-price ratio (FEP), past returns, other price/return-based predictors, and financial statement variables such as capital expenditure, R&D, external financing, and dividend payouts.  We find a simple specification of four predictors: LTG, past earnings growth, the forward earnings-to-price ratio and past returns produces the unbiased and most accurate long-term growth prediction.

The second stage of the research was to demonstrate the economic consequences of improving the growth prediction. We tested the usefulness of the improved long-term growth prediction G*, obtained from the first stage, in three principal applications: to construct trading strategies, to predict future equity value, and to estimate cost of equity. In the first application, we find that a trading strategy based on G* yields higher hedge returns than a strategy based on LTG. The superior profitability of the G* strategy is robust even after we control for LTG and common risk factors. In the second application, we find that the predicted one-year-ahead equity values based on G* are more accurate and less biased than those based on LTG. In the third application, we find that the estimated cost of equity has a better quality when G* substitutes LTG in the estimation: the estimated cost of equity based on G* positively correlates with realized returns over the majority of the sample period, whereas the estimates based on LTG do so over less than half of the period. Taken as a whole, these findings indicate that our improved growth prediction produces significant economic consequences in valuation and investment applications.

Valuation and investment applications demand expected long-term earnings growth as a crucial input. Our work demonstrates that it’s possible to effectively improve the prediction of long-term earnings growth by extracting information from multiple sources, and that such an improvement leads to economically significant consequences in valuation and investment applications.

There are some caveats. The study described here has been exploratory and empirical due to the lack of theory for fully understanding growth in general. Our research design have also restricted the sample to firms that have survived for the long-term and operate successfully, so it would be fair to say the results only apply to this subset of firms.

Dr Zhan Gao, Lancaster University Management School,