Advances in Economics, Management and Political Sciences

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Proceedings of the 2023 International Conference on Management Research and Economic Development

Series Vol. 20 , 13 September 2023


Open Access | Article

Predicting Stock Prices Using Markov Chain: The Stock Price Forecast based on Shanghai Securities

Langyu Gu * 1 , Kerui Zeng 2
1 Xi'an Yuandong No. 1 Middle School
2 Chongqing No.1 International Studies School

* Author to whom correspondence should be addressed.

Advances in Economics, Management and Political Sciences, Vol. 20, 1-7
Published 13 September 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 Langyu Gu, Kerui Zeng. Predicting Stock Prices Using Markov Chain: The Stock Price Forecast based on Shanghai Securities. AEMPS (2023) Vol. 20: 1-7. DOI: 10.54254/2754-1169/20/20230163.

Abstract

This study investigates and predicts the stock price of Shanghai Securities. Our analysis lemma the C-K equation,n step transition to predict the stock price of Shanghai Securities. In this paper, we have put our model into different stocks in reality to test its feasibility. Finally, we envisaged the probable scope for this approach and listed some shortages of using Markov chain in predicting stock price. A great discovery in this page is that utilizing the stock's Markov property; we concluded that Shanghai Securities is martensitic. Also, we have proved the economic benefit of this numerical model.

Keywords

stoke prediction, numerical models, markov chain, finance, probability transfer

References

1. Hsu, Y. T. , Liu, M. C. , Yeh, J. , & Hung, H. F. . (2009). Forecasting the turning time of stock market based on markov–fourier grey model. Expert Systems with Applications An International Journal, 36(4), 8597-8603.

2. Liu, S. , Zhang, X. , Wang, Y. , & Feng, G.. (2020). Recurrent convolutional neural kernel model for stock price movement prediction. PLOS ONE, 15..

3. Kaur, S. , & Mangat, V. . (2012). Improved accuracy of pso and de using normalization: an application to stock price prediction. International Journal of Advanced Computer Science & Applications, 3(9), 115-120..

4. Zhang, J. , Wang, Y. , Zhao, Y. , & Fang, H. (2021). Multi-scale flood prediction based on gm (1,2)-fuzzy weighted markov and wavelet analysis. Journal of Water and Climate Change(5)..

5. Besag, J. (1994). Discussion: markov chains for exploring posterior distributions. The Annals of Statistics(4).

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 2023 International Conference on Management Research and Economic Development
ISBN (Print)
978-1-915371-83-6
ISBN (Online)
978-1-915371-84-3
Published Date
13 September 2023
Series
Advances in Economics, Management and Political Sciences
ISSN (Print)
2754-1169
ISSN (Online)
2754-1177
DOI
10.54254/2754-1169/20/20230163
Copyright
13 September 2023
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