Tactical Asset Allocation mit Genetischen Algorithmen

Manuel Ammann and Christian Zenkner

Swiss Journal of Economics and Statistics (SJES), 2003, vol. 139, issue I, 1-40

Abstract: In this study of tactical asset allocation, we use a genetic algorithm to implement a market timing strategy. The algorithm makes a daily decision whether to invest in the market index or in a riskless asset. The market index is represented by the S&P500 Composite Index, the riskless asset by a 3-month T-Bill. The decision of the genetic algorithm is based on fundamental macroeconomic variables. The association of fundamental variables with a set of operators creates a space of possible strategies from which the genetic algorithm attempts to select the optimal solution. To test its performance, we apply the genetic algorithm to different time periods of in-sample and out-of-sample data using rolling return estimates. In total, 39 different timing strategies are tested over the time period of 1980-2000. On a risk-adjusted basis, we observe a moderate outperformance for the timing strategy suggested by the algorithm compared to a passive index strategy. The forecasting power of the algorithm is higher during times of high volatility and pronounced changes in the return series. Moreover, the algorithm is more successful in forecasting long-term return patterns than short-term fluctuations.

Date: 2003
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