High-dimensional index tracking based on the adaptive elastic net

Shu, Lianjie1; Shi, Fangquan1; Tian, Guoliang2

2020-04-18

10.1080/14697688.2020.1737328

QUANTITATIVE FINANCE   影响因子和分区

1469-7688

1469-7696

When a portfolio consists of a large number of assets, it generally incorporates too many small and illiquid positions and needs a large amount of rebalancing, which can involve large transaction costs. For financial index tracking, it is desirable to avoid such atomized, unstable portfolios, which are difficult to realize and manage. A natural way of achieving this goal is to build a tracking portfolio that is sparse with only a small number of assets in practice. The cardinality constraint approach, by directly restricting the number of assets held in the tracking portfolio, is a natural idea. However, it requires the pre-specification of the maximum number of assets selected, which is rarely practicable. Moreover, the cardinality constrained optimization problem is shown to be NP-hard. Solving such a problem will be computationally expensive, especially in high-dimensional settings. Motivated by this, this paper employs a regularization approach based on the adaptive elastic-net (Aenet) model for high-dimensional index tracking. The proposed method represents a family of convex regularization methods, which nests the traditional Lasso, adaptive Lasso (Alasso), and elastic-net (Enet) as special cases. To make the formulation more practical and general, we also take the full investment condition and turnover restrictions (or transaction costs) into account. An efficient algorithm based on coordinate descent with closed-form updates is derived to tackle the resulting optimization problem. Empirical results show that the proposed method is computationally efficient and has competitive out-of-sample performance, especially in high-dimensional settings.

Index tracking Sparsity Cardinality Lasso

SCI ; SSCI

英语

其他

National Natural Science Foundation of China[11771199]

Business & Economics ; Mathematics ; Mathematical Methods In Social Sciences

Business, Finance ; Economics ; Mathematics, Interdisciplinary Applications ; Social Sciences, Mathematical Methods

WOS:000527966600001

ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD

ECONOMICS BUSINESS

Web of Science

1.Univ Macau, Fac Business, Macau, Peoples R China
2.Southern Univ Sci & Technol, Dept Stat & Data Sci, Shenzhen, Peoples R China

Shu, Lianjie,Shi, Fangquan,Tian, Guoliang. High-dimensional index tracking based on the adaptive elastic net[J]. QUANTITATIVE FINANCE,2020,20(9):1513-1530.

Shu, Lianjie,Shi, Fangquan,&Tian, Guoliang.(2020).High-dimensional index tracking based on the adaptive elastic net.QUANTITATIVE FINANCE,20(9),1513-1530.

Shu, Lianjie,et al."High-dimensional index tracking based on the adaptive elastic net".QUANTITATIVE FINANCE 20.9(2020):1513-1530.