ABSTRACT
Enhanced-index-funds have attracted considerable attention from investors over the last decade, which aims at outperforming a benchmark index while maintaining a similar risk level. In this article, we investigate an enhanced indexation methodology using Conditional Value-at-Risk (CVaR). In particular, we adopt CVaR of excess returns as risk measurement subject to cardinality constraint for controlling the tracking portfolio scale precisely and tunable short-selling constraints for adjusting the margin of each risky asset adaptively within the budget of short-selling. As the resulted model is a mixed 0–1 binary program, we propose an improved hybrid heuristic method, where a customized relax-round-polish is embedded to improve the quality of the iterative population. Computational results on five standard data sets from OR-library show that our proposed method is generally superior to the naive portfolio strategy and the CVaR-LASSO method in terms of the out-of-sample excess return, Sharpe ratio and maximum drawdown of the portfolio.
Disclosure statement
No potential conflict of interest was reported by the authors.
Notes
1 The cardinality constraint is with prescribed in order to restrict the scale of the constructed portfolio.
2 The enhanced indexation model is to add an regularized term (i.e. norm) to a trade-off objective function between TE and ER. See model (2.3) of Zhao et al. (Citation2019) for details.
3 The AQP method is designed to solve a tractable quadratic subproblem and an regularized subproblem with closed-form solution alternately. See the iterative process in Section 3 of Zhao et al. (Citation2019) for details.
4 See Algorithm 2 of the subsection 'Improved hybrid heuristic method' for details.
5 See https://www.ibm.com/analytics/cplex-optimizer for a detailed introduction.