research-article

Inference of the Us and Chinese Stock Markets Using Statistical and Computational Methods

Author:

Junjin RanAuthors Info & Claims

IC4E '20: Proceedings of the 2020 11th International Conference on E-Education, E-Business, E-Management, and E-Learning

Pages 300 - 309

https://doi.org/10.1145/3377571.3377626

Published: 03 May 2020 Publication History

Abstract

In the contemporary era, people have strong incentives to explore the underlying principles of stock markets and China and the US are the 2 largest economies across the world. So, it is the stock markets in these two countries that we need to explore and study in this paper. In order to test whether the trends of the US and Chinese stock market are predictable and identify the difference between these two markets, we employed various models to study the S&P 500 and CSI 300 indexes' trends. Specifically, in this paper, we included the Markov chain, hidden Markov model (HMM), logistical regression with lasso, autoregressive integrated moving average (ARIMA) and support vector machine (SVM) to achieve our target.Therefore, we obtained several interesting key findings in our paper. We found that the Chinese stock market is more likely to be affected by technical indicators instead of historical information, as logistical regression with lasso selected most of the technical indicators and the estimated order of the Markov chain is zero when modelling the CSI 300 trends, which is different from the US stock market. Also, the AUC value of SVM outperformed other models used in the US stock market, at 0.731, while ARIMA model resulted in high AUC values in both markets, at 0.606 and 0.622 for the US and Chinese stock markets respectively. So, we confirmed that the Chinese stock market is less efficient than the US stock market. What's more, to predict the future trends in the US stock market, SVM could be the best choice, while ARIMA model works effectively for both markets.

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Cited By

Koçak İTarkun SÇınar M(2024)Kirli ve Temiz Tanker Endeksi Volatiliteleri ile BRIC Borsaları Arasındaki Nedensellik İlişkisiAnkara Hacı Bayram Veli Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi10.26745/ahbvuibfd.147058926:3(915-936)Online publication date: 25-Dec-2024
https://doi.org/10.26745/ahbvuibfd.1470589

Index Terms

Inference of the Us and Chinese Stock Markets Using Statistical and Computational Methods
1. Applied computing
  1. Electronic commerce
    1. Digital cash

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IC4E '20: Proceedings of the 2020 11th International Conference on E-Education, E-Business, E-Management, and E-Learning

January 2020

441 pages

ISBN:9781450372947

DOI:10.1145/3377571

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Ritsumeikan University: Ritsumeikan University

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Published: 03 May 2020

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IC4E 2020: 2020 the 11th International Conference on E-Education, E-Business, E-Management, and E-Learning

January 10 - 12, 2020

Osaka, Japan

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Cited By

Koçak İTarkun SÇınar M(2024)Kirli ve Temiz Tanker Endeksi Volatiliteleri ile BRIC Borsaları Arasındaki Nedensellik İlişkisiAnkara Hacı Bayram Veli Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi10.26745/ahbvuibfd.147058926:3(915-936)Online publication date: 25-Dec-2024
https://doi.org/10.26745/ahbvuibfd.1470589

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