Applied Mathematics and Nonlinear Sciences
Journal license

Journal

Applied Mathematics and Nonlinear Sciences


Volume
& Issue

Volume 8, Issue 2


Published
on

October 30, 2023


Pages


DOI

Article

A study of asset portfolio risk control based on stochastic optimization

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Authors

Yucui Bai Affiliation:
Department of Accounting, Shijiazhuang Information Engineering Vocational College, Shijiazhuang, Hebei, 050000, China.
, Ran Chen Affiliation:
Department of Accounting, Shijiazhuang Information Engineering Vocational College, Shijiazhuang, Hebei, 050000, China.
, Lin Liu Affiliation:
Department of Accounting, Shijiazhuang Information Engineering Vocational College, Shijiazhuang, Hebei, 050000, China.
and Yi Luo Affiliation:
Department of Accounting, Shijiazhuang Information Engineering Vocational College, Shijiazhuang, Hebei, 050000, China.


Abstract

This paper analyzes the main methods of stochastic optimization algorithms to construct a stochastic optimization model. The focus is on the calculation method for risk minimization, combined with the SGD algorithm to guarantee the speed of sublinear convergence. The mean variance of the risk evaluation model is determined by constructing the objective function and constraints, and the investor’s risk is minimized based on calculating the minimum variance of the model. The asset portfolio risk evaluation model can accurately describe the risk of different industries, as demonstrated by the results. According to the correlation coefficient reality, the correlation between industry indices is relatively strong, where the correlation coefficient between raw materials and the optional consumption industry is 0.865, and the correlation coefficient between the optional consumption industry and the financial industry is 0.697.


Keywords

Stochastic optimization algorithm, Minimum variance, Mean-variance, Risk evaluation, SGD algorithm, 91B44


Citation

Bai, Y., Chen, R., Liu, L., & Luo, Y. (2023). A study of asset portfolio risk control based on stochastic optimization. Applied Mathematics and Nonlinear Sciences, 8(2). https://doi.org/10.2478/amns.2023.2.00884

Published by: Engineering Journals

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