Large portfolio allocation based on high-dimensional regression and Kendall’s Tau

AbstractIn financial investments, portfolio allocation is always one of the most fundamental and challenging tasks. This paper proposes a robust portfolio optimization approach, extending the application of classical mean-variance (M-VAR) method for high-dimensional situations. The yielded assets return of the proposed method can be enhanced to some extent. It is called Kendall's tau unconstrained shrinkage regression for M-VAR method (KUSR-MV). By some representative empirical studies, it is shown to have a more robust estimation of high-dimensional portfolio allocation compared to its competitors. Besides, its Sharpe ratio can be improved while the risk constraint can be well remained.Keywords: Kendall's taumean-variancerisk constraintSharpe ratioshrinkage estimation Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work is supported by Sichuan Natural Science Foundation [Grant: 2022NSFSC1850], the New Interdisciplinary Training Fund [Grant: 2682023JX004] and the Fundamental Research Funds for the Central Universities [Grant:2682020ZT113, 2682022CX049, 2682021ZTPY044 and 2682021ZTPY078].