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Machine learning and asset management: clustering for portfolio construction
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Resumo(s)
This research investigates how machine learning can be applied to
portfolio management and the results of a related experimental asset
allocation. Clustering aims at minimizing inter-clustering
similarities, therefore translating in potentially higher
diversification benefits, one of the goals of portfolio managers. The
chosen allocation strategy of this research is K-Means clustering on
prices with the 100 stocks with largest capitalization in the S&P500
index, fully backtested and measured as of performance. The
strategy yields interesting out-of-sample explanatory power, with
good results over the considered period, although confirming the
difficulty for portfolio managers to consistently deliver high
abnormal returns.
Descrição
Palavras-chave
Machine learning Asset management Clustering Portfolio