Dynamic portfolio optimization with inverse covariance clustering

Wang, Yuanrong and Aste, Tomaso (2023) Dynamic portfolio optimization with inverse covariance clustering. Expert Systems With Applications, 213. ISSN 0957-4174

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Abstract

Market conditions change continuously. However, in portfolio investment strategies, it is hard to account for this intrinsic non-stationarity. In this paper, we propose to address this issue by using the Inverse Covariance Clustering (ICC) method to identify inherent market states and then integrate such states into a dynamic portfolio optimization process. Extensive experiments across three different markets, NASDAQ, FTSE and HS300, over a period of ten years, demonstrate the advantages of our proposed algorithm, termed Inverse Covariance Clustering-Portfolio Optimization (ICC-PO). The core of the ICC-PO methodology concerns the identification and clustering of market states from the analytics of past data and the forecasting of the future market state. It is therefore agnostic to the specific portfolio optimization method of choice. By applying the same portfolio optimization technique on a ICC temporal cluster, instead of the whole train period, we show that one can generate portfolios with substantially higher Sharpe Ratios, which are statistically more robust and resilient with great reductions in the maximum loss in extreme situations. This is shown to be consistent across markets, periods, optimization methods and selection of portfolio assets.

Item Type:	Article
Additional Information:	© 2022 The Authors
Divisions:	Systemic Risk Centre
Subjects:	H Social Sciences > HB Economic Theory
JEL classification:	G - Financial Economics > G1 - General Financial Markets > G10 - General
Date Deposited:	04 Jan 2023 15:30
Last Modified:	18 Nov 2024 08:21
URI:	http://eprints.lse.ac.uk/id/eprint/117701

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