Inside the Crystal Ball. Harris Maury

it may be sensible for less skilled or infrequent forecasters to mimic the consensus, they often don't. Laster, Bennett, and Geoum also find that nonwinners in the Blue Chip survey herd more often to the forecasts of winners than to the consensus forecast.

      Success Factors: Why Some Forecasters Excel

      Both successful and unsuccessful forecast histories offer valuable lessons. In subsequent chapters we review many of both. What follows here is an example of what one can learn from studying relatively successful forecasters of U.S. Federal Reserve monetary policy – a key input to all financial forecasting.

      European Central Bank (ECB) economists analyzed the accuracy and characteristics of forecasters seeking to predict to what extent the Federal Reserve System, in its Federal Open Market Committee (FOMC) meetings between February 1999 and September 2005, would alter the Federal funds rate target.30 Specifically, they studied how forecasters' accuracy was related to their education, professional experience, type of employer, and geographic location relative to Washington, D.C., where the FOMC meets. Here's what they learned:

      • Education matters, but you don't need a PhD to be a relatively accurate forecaster of Fed actions. Forecasters with a master's degree were more accurate than those with other degrees. However, having a PhD was not associated with superior accuracy.

      • A forecaster's geographic location and local environment influence monetary policy forecast accuracy. Prognosticators working in regions where local economic circumstances – inflation and job growth – deviated most from the national conditions influencing U.S. monetary policy recorded larger errors than others. This finding reminds us that forecasters should ask themselves if their everyday environment is conditioning their judgment. My experience is that investors and analysts in relatively depressed U.S. regions are sometimes too pessimistic about overall U.S. economic conditions, while residents of comparatively strong regions can be too optimistic.

      • In forecasting, specialized knowledge of institutional behavior complements statistical skills. Individuals who had worked for the Federal Reserve Board of Governors recorded relatively fewer errors in forecasting Fed policy. During the period studied, monetary policy forecasters often estimated statistical “reaction functions,” attempting to assign numerical values to actions the Fed had taken in the past in response to various economic statistics (such as inflation and unemployment). However, the Fed can be influenced by variables that are not easily quantifiable. Moreover, the Fed's response to economic statistics can be altered over time by changes in its internal policy making procedures and FOMC membership.