Analysis of stock index with a generalized BN-S model: an approach based on machine learning and fuzzy parameters
Abstract
In this paper we implement a combination of data-science and fuzzy theory to improve the classical Barndorff-Nielsen and Shephard model, and implement this to analyze the S&P 500 index. We pre-process the index data based on fuzzy theory. After that, S&P 500 stock index data for the past ten years are analyzed, and a deterministic parameter is extracted using various machine and deep learning methods. The results show that the new model, where fuzzy parameters are incorporated, can incorporate the long-term dependence in the classical Barndorff-Nielsen and Shephard model. The modification is based on only a few changes compared to the classical model. At the same time, the resulting analysis effectively captures the stochastic dynamics of the stock index time series.
Keywords
Cite
@article{arxiv.2101.08984,
title = {Analysis of stock index with a generalized BN-S model: an approach based on machine learning and fuzzy parameters},
author = {Xianfei Hui and Baiqing Sun and Hui Jiang and Indranil SenGupta},
journal= {arXiv preprint arXiv:2101.08984},
year = {2022}
}
Comments
13 figures, 12 Tables