Wang, Yuanrong;
Aste, Tomaso;
(2022)
Dynamic portfolio optimization with inverse covariance clustering.
Expert Systems with Applications
, Article 118739. 10.1016/j.eswa.2022.118739.
(In press).
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Abstract
Market conditions change continuously. However, in portfolio's 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 maximum loss in extreme situations. This is shown to be consistent across markets, periods, optimization methods and selection of portfolio assets.
| Type: | Article |
|---|---|
| Title: | Dynamic portfolio optimization with inverse covariance clustering |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.eswa.2022.118739 |
| Publisher version: | https://doi.org/10.1016/j.eswa.2022.118739 |
| Language: | English |
| Additional information: | This work is licensed under an Attribution 4.0 International License (CC BY 4.0). |
| Keywords: | Dynamic Portfolio Optimization, Portfolio Management, Financial Market States, Market Regimes, Temporal Clustering, Information Filtering Networks, Covariance Structure |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10156110 |
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