New Determinants of Analysts’ Earnings Forecast Accuracy

Financial analysts play an important role for the capital market. By gathering, processing and distributing information to the market they function as information intermediaries and thereby help reducing the information asymmetry. Analysts provide information in their research reports and thereby help private and institutional investors forming expectations of a firm’s future business performance. Thus, information supplied by analysts, e.g. in form of earnings per share forecasts, can have substantial impact on investment decisions. Furthermore, analysts’ earnings forecasts are of interest to accounting researchers who use these forecasts as proxies for earnings expectations of the capital market. However, as there is a wide range of information supplied by different analysts it is essential to investors as well as to accounting researchers to identify analysts who provide the most precise forecasts.

In this chapter, I introduce a new variable to measure the forecast effort an analyst devotes when making earnings forecasts. The main idea of my measure is considering not only the forecast effort that can be derived from an analyst’s behavior for one specific firm, but also an analyst’s general behavior with respect to other firms. Thus, I label my new effort measure “general forecast effort” and provide empirical evidence that it can explain differences in analysts’ forecast accuracy. The following ideas and analyses largely base on my research study “How to measure Analyst Forecast Effort” which will be published as journal article in the European Accounting Review. I have presented this study at the 34th European Accounting Association Annual Congress in Rome (2011), at the American Accounting Association Annual Meeting in Denver (2011) and at the Accounting Seminar for doctoral students at the University of Cologne.

In this chapter, I investigate analysts’ issuance of so-called supplementary forecasts (e.g. cash flow or sales forecasts). I argue that issuing supplementary forecasts has an impact on the accuracy of the analyst’s respective earnings forecast and introduce a new variable to measure this effect. This new variable is based on the number of different kinds of supplementary forecasts an analyst issues besides her earnings forecast. From empirical analyses I find that the measure can explain differences in analysts’ earnings forecast accuracy.

Analysts’ earnings forecasts are an important element in the capital market. Investors use analysts’ forecasts as a basis for their investment decisions and accounting researchers employ earnings forecasts in their models to proxy for the market’s earnings expectations. Thus, investors as well as researchers seek to identify accurate forecasts to raise their expected returns or improve their models. Accordingly, the accounting literature provides great insights into analysts’ forecasts and conducts empirical studies to identify characteristics that help finding accurate analysts; i.e. the literature provides determinants of analysts’ earnings forecast accuracy. These determinants comprise e.g. an analyst’s prior performance, her reputation and experience or the size of her employer.