Summary
This chapter discusses the application of an index tracking technique to mutual fund replication problems. By using a tracking error (TE) minimization method and two tactical rebalancing strategies (i.e. the calendar based strategy and the tolerance triggered strategy), a multiperiod fund tracking model is developed that replicates S&P 500 mutual fund returns. The impact of excess returns and loss aversion on overall tracking performance is also discussed in two extended cases of the original TE optimization. An evolutionary method, Differential Evolution, is used for optimizing the asset weights. According to the experiment results, it is found that the proposed model replicates the first two moments of the fund returns by using only five equities. The TE optimization strategy under loss aversion with tolerance triggered rebalancing dominates other combinations studied with regard to tracking ability and cost efficiency.
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Zhang, J., Maringer, D. (2010). Index Mutual Fund Replication. In: Brabazon, A., O’Neill, M., Maringer, D.G. (eds) Natural Computing in Computational Finance. Studies in Computational Intelligence, vol 293. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13950-5_7
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DOI: https://doi.org/10.1007/978-3-642-13950-5_7
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