Proceedings of the 2nd International Academic Conference on Blockchain, Information Technology and Smart Finance (ICBIS 2023)

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Stock Price Forecast: Comparison of LSTM, HMM, and Transformer

Authors
Qianzhun Wang1, *, Yingqing Yuan2
1Intelligent Manufacturing Engineering, School of Mechatronics Engineering and Automation, Shanghai University, Shanghai, China
2Finance, School of Economics, Ocean University of China, Qingdao, China
*Corresponding author. Email: nemowang@shu.edu.cn
Corresponding Author
Qianzhun Wang
Available Online 10 August 2023.
DOI
10.2991/978-94-6463-198-2_15How to use a DOI?
Keywords
LSTM; HMM; Transformer; Stock Price Prediction; Time-series Forecasting
Abstract

With the development of deep learning, different kinds of neural network models are applied to the analysis and prediction of time series data. In the field of finance, deep learning models are widely used to forecast the stock market, which is an integration of technical data that can directly provide advice to investors. We chose three neural network models that have been very popular in the last decade: Long Short-Term Memory (LSTM), Hidden Markov model (HMM), and Transformer. We use the data of the new energy vehicles sector in the A-share market to establish and evaluate the model and compare the predictive performance of the three models. The result shows that Transformer performed the best-predicting capability of stocks of the new energy sector in the A-share market. The model’s performance was quantified using the Mean Absolute Percentage Error (MAPE) and Matthews Correlation Coefficient (MCC).

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2nd International Academic Conference on Blockchain, Information Technology and Smart Finance (ICBIS 2023)
Series
Atlantis Highlights in Computer Sciences
Publication Date
10 August 2023
ISBN
10.2991/978-94-6463-198-2_15
ISSN
2589-4900
DOI
10.2991/978-94-6463-198-2_15How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Qianzhun Wang
AU  - Yingqing Yuan
PY  - 2023
DA  - 2023/08/10
TI  - Stock Price Forecast: Comparison of LSTM, HMM, and Transformer
BT  - Proceedings of the 2nd International Academic Conference on Blockchain, Information Technology and Smart Finance (ICBIS 2023)
PB  - Atlantis Press
SP  - 126
EP  - 136
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-198-2_15
DO  - 10.2991/978-94-6463-198-2_15
ID  - Wang2023
ER  -