Discussion about the dependence of stock-market price and the deviation of individual stock

Xinyi Ge; Ao Shen; Kailin Zhu

doi:10.1117/12.2628059

22 April 2022 Discussion about the dependence of stock-market price and the deviation of individual stock

Xinyi Ge, Ao Shen, Kailin Zhu

Proceedings Volume 12163, International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021); 121631C (2022) https://doi.org/10.1117/12.2628059
Event: International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021), 2021, Nanjing, China

Abstract

This research applies simple statistical models to study the stock market's dependence in the United States for getting an overview of how the stock price of all companies in this country behaves. This study checks out the SPY data and implement the regression model and random walk model analysis to approach future prices into selected periods of the history data. The predicted result from the two models is compared to the actual more current; that is, future data for the past, and find that the regression model can make closer predictions to the future stock-market price. The result displays that the future stock-market price is relative to the behaviours of the price before. The stock market price is dependent. Based on the stock price behaviours of four typical sports companies from 2015 to August 2021, the deviation of individual stock price trends from SPY is being discussed. This presents an exploratory proposal to make individual stock price change steadily, following the normal trend if the company maintains a good income, which requires constant high-quality products and good marketing strategies, etc.

Citation Download Citation

Xinyi Ge, Ao Shen, and Kailin Zhu "Discussion about the dependence of stock-market price and the deviation of individual stock", Proc. SPIE 12163, International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021), 121631C (22 April 2022); https://doi.org/10.1117/12.2628059

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KEYWORDS

Data modeling

Statistical analysis

Statistical modeling

Computational modeling

Data analysis

Numerical analysis

Numerical modeling

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