English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Sparse index tracking is one of the prominent passive portfolio managementstrategies that construct a sparse portfolio to track a financial index. Asparse portfolio is desirable over a full portfolio in terms of transactioncost reduction and avoiding illiquid assets. To enforce the sparsity of theportfolio, conventional studies have proposed formulations based on$\ell_p$-norm regularizations as a continuous surrogate of the $\ell_0$-normregularization. Although such formulations can be used to construct sparseportfolios, they are not easy to use in actual investments because parametertuning to specify the exact upper bound on the number of assets in theportfolio is delicate and time-consuming. In this paper, we propose a newproblem formulation of sparse index tracking using an $\ell_0$-norm constraintthat enables easy control of the upper bound on the number of assets in theportfolio. In addition, our formulation allows the choice between portfoliosparsity and turnover sparsity constraints, which also reduces transactioncosts by limiting the number of assets that are updated at each rebalancing.Furthermore, we develop an efficient algorithm for solving this problem basedon a primal-dual splitting method. Finally, we illustrate the effectiveness ofthe proposed method through experiments on the S\&P500 and NASDAQ100 indexdatasets.

Sparse Index Tracking: Simultaneous Asset Selection and Capital Allocation via $\ell_0$-Constrained Portfolio