Dynamic Portfolio Optimization with Inverse Covariance Clustering

TL;DR

This paper introduces ICC-PO, a dynamic portfolio optimization method that uses inverse covariance clustering to identify market states, leading to more robust portfolios with higher Sharpe Ratios across various markets and conditions.

Contribution

The paper presents a novel approach combining inverse covariance clustering with portfolio optimization, improving robustness and performance by accounting for market non-stationarity.

Findings

Portfolios using ICC-PO have higher Sharpe Ratios.

ICC-PO portfolios are more robust and resilient.

Method is effective across different markets and optimization techniques.

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.