A sparse enhanced indexation model with chance and cardinality constraints

Fengmin Xu (), Meihua Wang (), Yu-Hong Dai () and Dachuan Xu ()
Additional contact information
Fengmin Xu: Xi’an Jiaotong University
Meihua Wang: Xidian University
Yu-Hong Dai: Chinese Academy of Sciences
Dachuan Xu: Beijing University of Technology

Journal of Global Optimization, 2018, vol. 70, issue 1, No 2, 5-25

Abstract: Abstract Enhanced indexation aims to construct a portfolio to track and outperform the performance of a stock market index by employing both passive and active fund management strategies. This paper presents a novel sparse enhanced indexation model with chance and cardinality constraints. Its goal is to maximize the excess return that can be attained with a high probability, while the model allows a fund manger to limit the number of stocks in the portfolio and specify the maximum tolerable relative market risk. In particular, we model the asset returns as random variables and estimate their probability distributions by the Capital Asset Pricing Model or Fama-French 3-factor model, and measure the relative market risk with the coherent semideviation risk function. We deal with the chance constraint via distributionally robust approach and present a second-order cone programming and a semidefinite programming safe approximation for the model under different sets of potential distribution functions. A hybrid genetic algorithm is applied to solve the NP-hard problem. Numerical tests are conducted on the real data sets from major international stock markets, including USA, UK, Germany and China. The results demonstrate that the proposed model and the method can efficiently solve the enhanced indexation problem and our approach can generally achieve sparse tracking portfolios with good out-of-sample excess returns and high robustness.

Keywords: Enhanced indexation; Chance constraint; Mixed integer programming; Distributionally robust approach (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (6)

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DOI: 10.1007/s10898-017-0513-1

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