Welcome to Francis Academic Press

Academic Journal of Business & Management, 2022, 4(4); doi: 10.25236/AJBM.2022.040408.

Research on Stock Price Prediction Based on Markov-LSTM Neural Network -Take the New Energy Industry as an Example


Ling Gu1, Chuyu Feng2

Corresponding Author:
​Ling Gu

1School of Economics and Management, Southwest Jiaotong University, Chengdu, Sichuan, 610000, China

2School of International Business Administration, South China Normal University, Guangzhou, Guangdong, 510000, China


With the increasingly close combination of the financial field and the real industry, as a new economic growth point, it is of great significance to study its price fluctuation prediction in the financial stock market. Based on this, we propose a neural network prediction method via Markov chain and LSTM neural network. Then we select three stocks as the research object, K-mean clustering algorithm is used to determine Markov chain’s relative states, K was obtained using the cross validation method. The empirical results show that the new method is better than LSTM neural network in the evaluation indexes of average relative error, a posteriori error, mean square error and small robability error of stock price prediction. This shows that under certain conditions, Markov chain-LSTM neural network can make more accurate prediction of stock price, and is more suitable for systems with high volatility and strong randomness. Finally, we discuss some promotion and improvement methods of Markov chain-LSTM neural network, and put forward relevant suggestions for the high-quality development of new energy industry from the perspective of finance.


New energy industry; Stock price forecast; LSTM neural network; Markov chain

Cite This Paper

Ling Gu, Chuyu Feng. Research on Stock Price Prediction Based on Markov-LSTM Neural Network -Take the New Energy Industry as an Example. Academic Journal of Business & Management (2022) Vol. 4, Issue 4: 42-47. https://doi.org/10.25236/AJBM.2022.040408.


[1] Chen, Z., Prediction of Shanghai Composite Index Based on Markov chain model [J] Financial economics, 2008 (14): pp.58-59

[2] Ouyang, J. and Lu, L., Application of comprehensively improved BP neural network algorithm in stock price prediction [J] Computer and digital engineering, 2011, 39 (02): pp.57-59.

[3] Wang, J. and Wang, Z., Research on stock price prediction based on Markov chain of neural network and genetic algorithm [J] Journal of Ningbo Institute of engineering, 2012, 24 (03): pp.29-37

[4] Liu, Z., Application of grey prediction in stock prediction [J] China market, 2013 (46): pp.143-144.

[5] Pei, T. and Pei, Y., Prediction of highway traffic volume based on Markov chain BP neural network model [J] Journal of Chongqing Jiaotong University (NATURAL SCIENCE EDITION), 2021, 40 (02): pp.35-41.

[6] Hu, Z. and Wang, Z. and Tang, B., Research on house price prediction of Liuzhou City Based on Markov chain [J] Journal of Guangxi University of science and technology, 2018, 29 (04): pp.79-83

[7] Cao, B. and Gao, M., Research on short-term traffic flow prediction based on LSTM [J] Modern computer (Professional Edition), 2018 (25): pp.3-7.

[8] Li, H., Using Markov prediction method to predict stock price [J] Statistics and decision making, 2002 (05): pp.25-26.

[9] Lai, M., Research on enterprise financial risk prediction method based on BP neural network algorithm [J] Microcomputer applications, 2021,37 (12): pp.155-157.

[10] Liu, C. and Chen, L. and Zhang, Y. and Zhang, Z. and Xu, H., Load demand power prediction of electric transmission armored vehicles based on Improved Grey Markov chain [J] Journal of Ordnance Engineering, 2021, 42 (10): pp.2130-2144.