Combining Forecasts with Missing Data: Making Use of Portfolio Theory

In this work we propose the construction of optimized forecast-portfolios where analysts are thought of as “assets” with specific characteristics that may be combined in portfolios. The analysts’ forecasts were made about the German stock market index DAX on a 6-month horizon as provided by the ZEW Financial Market Survey. A Differential Evolution algorithm is applied that is flexible enough to work with the holey structure of the survey dataset, as it allows for the introduction of a weights-shifting scheme that prevents the exclusion of analysts with missing data and the resulting loss of information. The method is implemented for each of three objective functions that translate ideas from financial management into the forecast-portfolio framework: the mean–variance, the Value-at-Risk, and an asymmetric target strategy. With a backtest we show that weighting several individual forecasts with a forecast-portfolio can indeed improve the forecast quality. Copyright Springer Science+Business Media New York 2014