Abstract
This chapter presents a new asset allocation model which combines a clustering technique with traditional asset allocation methods to improve portfolio Sharpe ratios and portfolio weights stability. The approach identifies optimal clustering patterns in different cluster number cases by using a population-based evolutionary method, namely Differential Evolution. Traditional asset allocations are used to compute the portfolio weights with the clustering. According to the experiment results, it is found that clustering contributes to higher Sharpe ratios and lower portfolio instability than that without clustering. Market practitioners may employ the clustering technique to improve portfolio weights stability and risk-adjusted returns, or for other optimization purposes while distributing the asset weights.
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Zhang, J., Maringer, D. (2010). A Clustering Application in Portfolio Management. In: Ao, SI., Gelman, L. (eds) Electronic Engineering and Computing Technology. Lecture Notes in Electrical Engineering, vol 60. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-8776-8_27
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DOI: https://doi.org/10.1007/978-90-481-8776-8_27
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