Optimal Aggregation of Money Supply Forecasts: Accuracy, Profitability and Market Efficiency




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    • Associate Professor of Finance, New York University Graduate School of Business Administration, and Assistant Professor of Finance, Baruch College, City University of New York. This research was done during 1981–2 while Stephen Figlewski was a Batterymarch Fellow. We would like to thank Roy Radner and Roger Klein for many helpful discussions and Michael Brennan for excellent editorial suggestions.


We present a general procedure for aggregating expert forecasts which exploits regularities in the structure of information within the forecaster population. Specific information structures lead to aggregation methods which adjust for additive bias, differences in individual accuracy, and correlation among forecasts. As an application, we construct composite predictions of the weekly change in the money supply from forecasts made by twenty major securities dealers, for which high positive correlation is found to be a significant characteristic. Due to instability in the information structure, our methods cannot improve on the accuracy of a simple average in this case. However, they do capture information about the correlation among money supply forecasts which is not fully impounded in short-term interest rates. Forecasts from our models accurately predict the direction of price changes for Treasury bills and Treasury bill futures after a money supply announcement.