A semiparametric graphical modelling approach for large-scale equity selection

作者：Liu Han; Mulvey John^*; Zhao Tianqi

来源：Quantitative Finance, 2016, 16(7): 1053-1067.

DOI：10.1080/14697688.2015.1101149

• Equity selection
• Machine learning
• Graphical model
• Rebalancing gains
• Semiparametric methods
• Elliptical copula
• Stability selection
• Markowitz strategy

We propose a new stock selection strategy that exploits rebalancing returns and improves portfolio performance. To effectively harvest rebalancing gains, we apply ideas from elliptical-copula graphical modelling and stability inference to select stocks that are as independent as possible. The proposed elliptical-copula graphical model has a latent Gaussian representation; its structure can be effectively inferred using the regularized rank-based estimators. The resulting algorithm is computationally efficient and scales to large data-sets. To show the efficacy of the proposed method, we apply it to conduct equity selection based on a 16-year health care stock data-set and a large 34-year stock data-set. Empirical tests show that the proposed method is superior to alternative strategies including a principal component analysis-based approach and the classical Markowitz strategy based on the traditional buy-and-hold assumption.

• 出版日期2016-7