Portfolio Construction Based on Bigdata Analysis in Terms of Minimum Variance Model

Ziyu Guo

doi:10.54691/bcpbm.v26i.1998

Authors

Ziyu Guo

DOI:

https://doi.org/10.54691/bcpbm.v26i.1998

Keywords:

Portfolio Design; Bigdata Analysis; Time-series Analysis; Minimum Variance Model.

Abstract

Contemporarily, A-share market keeps being in a trough, hence investors (especially the retail investors) seek ways to expand profit and reduce loss when speculating in stock market, but sometimes fail because of the lack of knowledge in investment. To remedy this problem, this paper introduces a complete process from the concepts of bigdata analysis to figure out investment decisions, including evaluating single stocks and choosing stocks to construct portfolios. The objectives of portfolio construction used in this paper are minimum variance and maximum Sharpe Ratio. The whole article presents an example of making investment decisions in A-share market. In the particular situation, two best portfolios which minimizes variance and maximizes Sharpe Ratio respectively are constructed among the training of abundant data capacity. It is also observed that with different goal of investment the best choice for investors could be greatly different. Investment strategy given in this paper may be helpful for investors, which offers a guideline for constructing portfolio under some objectives.

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