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Cluster Computing

Erschienen in:

29.01.2018

Black–Litterman asset allocation model based on principal component analysis (PCA) under uncertainty

verfasst von: Ding Lei

Erschienen in: Cluster Computing | Sonderheft 2/2019

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Abstract

In order to improve the prediction accuracy of asset allocation model, the Black–Litterman (BL) asset allocation model based on principal component analysis (PCA) under uncertainty is proposed in the thesis. Firstly, the main idea and calculative process of BL model are introduced, and the BL model formula under uncertainty is inferred, then the BL model principle and main steps under uncertainty are provided; secondly, the BL model is subject to the comprehensive evaluation through the introduction of PCA, and the model coefficients of BL model PCA is subject to the assignment through analytic hierarchy process so as to improve the prediction accuracy of the asset allocation model; finally, the effectiveness of the algorithm mentioned is verified based on the positive analysis on data of Shanghai and Shenzhen 300 indexes and Shanghai and Shenzhen industry indexes.

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Titel: Black–Litterman asset allocation model based on principal component analysis (PCA) under uncertainty
verfasst von: Ding Lei
Publikationsdatum: 29.01.2018
Verlag: Springer US
Erschienen in: Cluster Computing / Ausgabe Sonderheft 2/2019
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI: https://doi.org/10.1007/s10586-018-1864-1

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