A semiparametric graphical modelling approach for large-scale equity selection

Han Liu, John Mulvey*, Tianqi Zhao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1053-1067
Number of pages15
JournalQuantitative Finance
Issue number7
StatePublished - Jul 2 2016


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

ASJC Scopus subject areas

  • Finance
  • Economics, Econometrics and Finance(all)


Dive into the research topics of 'A semiparametric graphical modelling approach for large-scale equity selection'. Together they form a unique fingerprint.

Cite this