Construct sparse portfolio with mutual fund's favourite stocks in China A share market

Auteurs : Ke Zhang

arXiv: 2305.01642v1 - DOI (q-fin.PM)
Portfolio construction, Sparsity, Index tracking, Mutual fund, China A share market
Licence : CC BY 4.0

Résumé : Unlike developed market, some emerging markets are dominated by retail and unprofessional trading. China A share market is a good and fitting example in last 20 years. Meanwhile, lots of research show professional investor in China A share market continuously generate excess return compare with total market index. Specifically, this excess return mostly come from stock selectivity ability instead of market timing. However for some reason such as fund capacity limit, fund manager change or market regional switch, it is very hard to find a fund could continuously beat market. Therefore, in order to get excess return from mutual fund industry, we use quantitative way to build the sparse portfolio that take advantage of favorite stocks by mutual fund in China A market. Firstly we do the analysis about favourite stocks by mutual fund and compare the different method to construct our portfolio. Then we build a sparse stock portfolio with constraint on both individual stock and industry exposure using portfolio optimizer to closely track the partial equity funds index 930950.CSI with median 0.985 correlation. This problem is much more difficult than tracking full information index or traditional ETF as higher turnover of mutual fund, just first 10 holding of mutual fund available and fund report updated quarterly with 15 days delay. Finally we build another low risk and balanced sparse portfolio that consistently outperform benchmark 930950.CSI.

Soumis à arXiv le 24 Avr. 2023

Explorez l'arbre d'article

Cliquez sur les nœuds de l'arborescence pour être redirigé vers un article donné et accéder à leurs résumés et assistant virtuel

Accédez également à nos Résumés, ou posez des questions sur cet article à notre Assistant IA.

Recherchez des articles similaires (en version bêta)

En cliquant sur le bouton ci-dessus, notre algorithme analysera tous les articles de notre base de données pour trouver le plus proche en fonction du contenu des articles complets et pas seulement des métadonnées. Veuillez noter que cela ne fonctionne que pour les articles pour lesquels nous avons généré des résumés et que vous pouvez le réexécuter de temps en temps pour obtenir un résultat plus précis pendant que notre base de données s'agrandit.