Surrogate Expertise Indicators of Professional Financial Analysts

Article excerpt

The study of professional financial analysts' forecasts of earnings is important since: (1) the markets use these forecasts in making assessments of share price, (2) researchers use these estimates as surrogates for market earnings expectations, and (3) professional financial analysts generally overestimate earnings. The study of analyst expertise can help market participants, researchers and even managers to discount the effects of such overestimates. Further, since expert behavior is deemed to be desirable behavior (Bouwman and Bradley, 1997), identification of experts and their decision-making processes could improve the efficacy of training and education, as well as facilitate the development of expert systems.

The term "expertise" has been the source of much confusion. While it is difficult to define expertise, most researchers would agree that experts are presumed to make high quality decisions. Prior researchers have examined expertise on the basis of individual attributes, experience, knowledge, decision processes, and decision quality, yet a comprehensive model of expertise has proven elusive. It seems as though a parsimonious linear model of expertise might map psychological characteristics and cognitive processes to decision quality as a way to classify expertise and predict outcomes. However, even with such a model, it would be difficult to gather reliable data from experts along all of these dimensions. Hence, we often resort to the use of surrogate indicators of expertise.

The objective of this research is to identify and validate a surrogate measure of expertise for professional financial analysts that can be readily obtained from public sources. Hunton and McEwen (1997) examined experimentally various nonpublic experiential factors (e.g., age, years as financial analysts, years with firm) and cognitive factors (e.g., cognitive search strategy, time initially searching for information, time subsequently reviewing information) that might be used to classify financial analysts as relatively high or low expertise forecasters. Using an external measure of expertise provided by brokerage firm management, the researchers found that the analysts' cognitive search strategy distinguished high from low expertise analysts. They used a sophisticated retinal imaging system to classify the analysts' cognitive strategy. Since this technology is expensive, extremely difficult to obtain and complicated to use, we re-examine Hunton and McEwen's research findings in an attempt to identify and v alidate a parsimonious, publicly available surrogate indicator of expertise for financial analysts.

Using sixty "sell-side" financial analysts and a retinal imaging data collection method (called the Integrated Retinal Imaging System or IRIS), our findings suggest that experiential factors, commonly used as proxies for expertise (Affleck-Graves et al., 1990; Anderson, 1988; Biggs, 1984; Bouwman, 1982; Bouwman et al., 1987), are poor indicators of expertise, while membership on the All-America Research Team appears to be a reliable surrogate that is publicly available. These findings are consistent with Stickel (1992) who provides empirical evidence that membership on the All-America Team and accuracy are positively related. The current study extends Stickel's work by examining the relations between various surrogates for expertise, All-America team membership and accuracy. Unique to the current study is a factor-analytic, logistic approach that classifies accuracy by experiential factors, cognitive factors and membership on the All-America team.

The next section of this paper describes surrogates for analyst expertise that are found in the current literature. Subsequent sections provide the research design and report the results. The final section summarizes and concludes the findings of the study and indicates areas for future research.

SURROGATE EXPERTISE INDICATORS

It is generally assumed that higher levels of expertise involve more elaborate and complex cognitive processing of informational inputs which results in higher quality outputs (Yates, 1990). …