An index tracking model with stratified sampling and optimal allocation |
| |
Authors: | Meihua Wang Fengmin Xu Yu‐Hong Dai |
| |
Institution: | 1. School of Economics and Management, Xidian University, Xi'an 710071, China;2. School of Economics and Finance, Xi'an Jiaotong University, Xi'an 710071, China;3. Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China |
| |
Abstract: | This paper investigates the portfolio strategy problem for passive fund management. We propose a novel portfolio strategy that combines the existing stratified strategy and optimized sampling strategy. The proposed method enables one to include adequate practical information in portfolio decision making, and promotes better out‐of‐sample performance. A mixed‐integer program model is built that captures the stratification information, the cardinality requirement, and other practical constraints. The corresponding model is able to forecast and generate optimal tracking portfolios with high performance, especially in out‐of‐sample time period. As mixed‐integer program is a well‐known NP‐hard problem, to tackle the computational challenge, we propose a stratified hybrid genetic algorithm, in which a novel crossover operator is introduced. To evaluate the proposed strategy and algorithm, we conduct numerical tests on real data sets collected from China Stock Exchange Markets. The experimental results show that the algorithm runs efficiently and the portfolio strategy performs significantly better than other existing strategies. |
| |
Keywords: | index tracking out‐of‐sample performance stratified sampling stratified hybrid genetic algorithm s‐rar crossover |
|
|