Advances in Economics, Management and Political Sciences

- The Open Access Proceedings Series for Conferences


Proceedings of the 7th International Conference on Economic Management and Green Development

Series Vol. 36 , 10 November 2023


Open Access | Article

Stock Price Prediction: Moving Average and Markov Chain

Hongyi Qian * 1
1 Tsinghua University

* Author to whom correspondence should be addressed.

Advances in Economics, Management and Political Sciences, Vol. 36, 66-71
Published 10 November 2023. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Hongyi Qian. Stock Price Prediction: Moving Average and Markov Chain. AEMPS (2023) Vol. 36: 66-71. DOI: 10.54254/2754-1169/36/20231786.

Abstract

Under the assumption that the operation of corporations is stable and the economic environment is steady, there are mathematical rules in the changing of stock prices, making prediction of stock price possible. This paper selects stock price data of Xiaomi Inc, one of the leading technology companies in China, from January 2, 2019 to December 31, 2021 from Yahoo Finance. Three moving average methods, SMA, EMA and MACD, are implemented to analyze the data. Among the three moving average methods, the effect of MACD is better than that of SMA and EMA. Afterwards, Markov chain is employed to calculate from another angle The stable probability distribution of stock price is obtained by using the stability and ergodicity of Markov chain. The feasibility and accuracy of two angles of forecasting methods are verified through being compared.

Keywords

stock price prediction, simple moving average (SMA), exponential moving average(EMA), moving average convergence divergence (MACD), Markov chain

References

1. Yuanlie Lin. (2002). Applied Stochastic Processes. Tsinghua University Press.

2. Wang Y F. On-demand forecasting of stock prices using a real-time predictor[J]. IEEE Transactions on Knowledge and Data Engineering, 2003, 15(4): 1033-1037.

3. Choji D N, Eduno S N, Kassem G T. Markov chain model application on share price movement in stock market[J]. Computer Engineering and Intelligent Systems, 2013, 4(10): 84-95.

4. Long Yan, Cong Lin, Jiahui Zhu. Application of Markov chain in financial investment [J]. Journal of Ningbo Institute of Technology,2017,29(04):1-8.

5. Chowdhury R, Mahdy M R C, Alam T N, et al. Predicting the stock price of frontier markets using machine learning and modified Black–Scholes Option pricing model[J]. Physica A: Statistical Mechanics and its Applications, 2020, 555: 124444.

6. Leung C K S, MacKinnon R K, Wang Y. A machine learning approach for stock price prediction[C]//Proceedings of the 18th International Database Engineering & Applications Symposium. 2014: 274-277.

7. Yongjie Yang. Prediction Method of Stock price Change Trend Based on Optimized MACD Model [J]. Journal of Guangxi Academy of Sciences,2017,33(1):65-70

8. Cihua Liu. Stochastic Process [M]. Wuhan: Huazhong University of Science and Technology Press, 2001:20-35.

Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
Proceedings of the 7th International Conference on Economic Management and Green Development
ISBN (Print)
978-1-83558-093-6
ISBN (Online)
978-1-83558-094-3
Published Date
10 November 2023
Series
Advances in Economics, Management and Political Sciences
ISSN (Print)
2754-1169
ISSN (Online)
2754-1177
DOI
10.54254/2754-1169/36/20231786
Copyright
© 2023 The Author(s)
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated