An index tracking model with stratified sampling and optimal allocation. (16th October 2017)

• Record Type: Journal Article
• Authors: Wang, Meihua; Xu, Fengmin; Dai, Yu‐Hong
• Is Part Of: Applied stochastic models in business and industry. Volume 34:Number 2(2018)
• Page Start: 144
• Page End: 157
• Publication Date: 2017-10-16
• Subjects: index tracking -- out‐of‐sample performance -- stratified sampling -- stratified hybrid genetic algorithm -- s‐rar crossover; Stochastic analysis -- Periodicals; Stochastic processes -- Periodicals; Business mathematics -- Periodicals; Finance -- Mathematical models -- Periodicals; Industrial management -- Mathematical models -- Periodicals; 338.00151923
• DOI: 10.1002/asmb.2287
• Languages: English
• ISSNs: 1524-1904

Abstract

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.