Dynamic portfolio optimization with inverse covariance clustering

Yuanrong Wang and Tomaso Aste

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

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.

Keywords: covariance structure; dynamic portfolio optimization; financial market states; information filtering networks; market regimes; portfolio management; temporal clustering (search for similar items in EconPapers)
JEL-codes: G10 (search for similar items in EconPapers)
Date: 2023-03-01
New Economics Papers: this item is included in nep-rmg
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Citations: View citations in EconPapers (2)

Published in Expert Systems With Applications, 1, March, 2023, 213. ISSN: 0957-4174

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