A semiparametric graphical modelling approach for large-scale equity selection

Han Liu, John Mulvey, Tianqi Zhao

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

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
Volume16
Issue number7
DOIs
StatePublished - Jul 2 2016

All Science Journal Classification (ASJC) codes

  • Finance
  • Economics, Econometrics and Finance(all)

Keywords

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

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