and are relatively less accurate in forecasting growth, unemployment, and inflation. His findings indicated to him that some forecasters are “optimizing the market value of their reputations” instead of “acting to minimize mean squared error.”
David Laster, Paul Bennett, and In Sun Geoum reviewed U.S. real GDP forecast behavior of economists participating in Blue Chip Economic Indicator surveys in the 1976 to 1995 period.26 They conclude that “forecasters' deviations from the consensus are related to the types of firms for which they work” and illustrate that “professional forecasting has a strong strategic component.” Studying mean absolute deviation (MAD) from the consensus, they report that “independent forecasters with firms bearing their own names tend to make unconventional forecasts.” (See Table 1.5.)
Table 1.5 Average Forecast Deviations from Consensus GDP Growth Forecasts 1977–1996
Source: David Laster, Paul Bennett, and In Sum Geoum, “Rational Basis in Macroeconomic Forecasts,” Federal Reserve Bank of New York Research Papers, July 1996.
Consensus Copycats
Sticking with the consensus forecast is the opposite of going out on a limb. One motivation for doing so is that there's safety in numbers. Some forecasters might think that they'll never be fired when they are wrong if so many others also were incorrect. But is it wise to copy the consensus?
When the economics profession is criticized for inaccurate forecasts, the judgments reflect comparisons of actual outcomes to consensus forecasts. But the reason for copying consensus economic forecasts is that the consensus average of all forecasts usually outperforms any individual forecaster.27
Why? Because when we consider all forecasts, we incorporate more input information than is represented in any individual pronouncement. Also, the averaging process can iron out individual forecasters' biases. In addition to their relative accuracy, consensus economic forecasts also have the advantage of often being free of charge and time-saving. However, relying on consensus forecasts does have some important drawbacks:
• First, consensus economic growth forecasts almost always fail at critical turning points in the business cycle – especially when heading into a recession.28 For companies and investors, not being prepared for a recession can often turn success into failure.
• Second, if one's objective is to outperform one's peers, sticking with the consensus is not the recipe for such relative success. Students of forecasting approach this issue from the perspective of herding. Consider the results of one study of the characteristics of competing forecasters in the Blue Chip Survey. Two-time winners of the top forecaster award herd to the consensus the least amount, while the most herding is done by nonwinners.29
Although 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.
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