research-article

Stock price prediction based on PCA-LSTM model

Authors:

Xinyuan Zheng,

Naiping XiongAuthors Info & Claims

ICoMS '22: Proceedings of the 2022 5th International Conference on Mathematics and Statistics

Pages 79 - 83

https://doi.org/10.1145/3545839.3545852

Published: 10 September 2022 Publication History

Get Access

Abstract

In order to improve the prediction accuracy, this study proposes an new PCA-LSTM neural network stock price prediction model that combines principal component analysis(PCA) and long-term and short-term memory neural network (LSTM). We download time series indicators and technical indicators of PingAn insurance (X601318) form Tushare interface and Wind database. PCA method was used to reduce the technical indicators dimension, LSTM model was used to predict the next day stock closing price. The results show that PCA-LSTM model can greatly reduce data redundancy and obtain better prediction accuracy compared with the simple LSTM model.

Additional Keywords and Phrases: stock price prediction, PCA, LSTM

References

[1]

Funt, M. J. Stock market indicators. Pennsylvania dental journal[J]. 2010, 77(3), 37-38.

Google Scholar

[2]

Han, Taedong. Stock Price Prediction Using LSTM: Focusing on the Combination of Technical indicators, Macroeconomic Indicators, and Market Sentiment[J]. The Society of Convergence Knowledge Transactions.2021, 9 (4) :189-198.

Google Scholar

[3]

Shouvik Banik, Nonita Sharma, Monika Mangla, LSTM based decision support system for swing trading in stock market[J]. Knowledge-Based Systems, 2022, 239:107994.

Digital Library

Google Scholar

[4]

Yinghao Ren, Fangqing Liao, Yongjing Gong. Impact of News on the Trend of Stock Price Change: an Analysis based on the Deep Bidirectiona LSTM Model[J]. Procedia Computer Science, 2020, 174:128-140.

Crossref

Google Scholar

[5]

Moghar A, Hamiche M. Stock Market Prediction Using LSTM Recurrent Neural Network[J]. Procedia Computer Science, 2020, 170:1168-1173.

Crossref

Google Scholar

[6]

Yadav A, Jha C K, Sharan A. Optimizing LSTM for time series prediction in Indian stock market[J]. Procedia Computer Science, 2020, 167:2091-2100.

Digital Library

Google Scholar

[7]

Tushare. https://tushare.pro/.

Google Scholar

[8]

Wind Economic Database. https://www.wind.com.cn/en/edb.html.

Google Scholar

[9]

Nema Salem, Sahar Hussein. Data dimensional reduction and principal components analysis[J]. Procedia Computer Science, 2019,163:292-299.

Digital Library

Google Scholar

[10]

Hess, A. S., & Hess, J. R. Principal component analysis. Transfusion, 2018, 58(7), 1580-1582.

Crossref

Google Scholar

[11]

Revelle W (2022). psych: Procedures for Psychological, Psychometric, and Personality Research. Northwestern University, Evanston, Illinois. R package version 2.2.3, https://CRAN.R-project.org/package=psych.

Google Scholar

[12]

Chenjie Sang, Massimo Di Pierro. Improving trading technical analysis with TensorFlow Long Short-Term Memory (LSTM) Neural Network[J]. The Journal of Finance and Data Science, 2019, 5(1):1-11.

Crossref

Google Scholar

Cited By

View all

Bragone FMorozovska KRosén TLaneryd TSöderberg DMarkidis S(2025)Automatic learning analysis of flow-induced birefringence in cellulose nanofibrilsJournal of Computational Science10.1016/j.jocs.2025.10253685(102536)Online publication date: Feb-2025
https://doi.org/10.1016/j.jocs.2025.102536
Bragone FMorozovska KRosén TSöderberg DMarkidis S(2024)Time Series Predictions Based on PCA and LSTM Networks: A Framework for Predicting Brownian Rotary Diffusion of Cellulose NanofibrilsComputational Science – ICCS 202410.1007/978-3-031-63749-0_15(209-223)Online publication date: 28-Jun-2024
https://doi.org/10.1007/978-3-031-63749-0_15
Singh HMalhotra M(2023)Artificial Intelligence Based Hybrid Models for Prediction of Stock Prices2023 2nd International Conference for Innovation in Technology (INOCON)10.1109/INOCON57975.2023.10101297(1-6)Online publication date: 3-Mar-2023
https://doi.org/10.1109/INOCON57975.2023.10101297
Show More Cited By

Index Terms

Stock price prediction based on PCA-LSTM model
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

Recommendations

Research on the impact of PCA-LSTM on stock price forecast
ICDLT '23: Proceedings of the 2023 7th International Conference on Deep Learning Technologies

Stock price prediction has always been a difficult problem for investors. In the past, investors used traditional analysis methods such as candlestick charts and cross lines to predict stock trends. However, with the progress of technology and the ...
Stock Price Prediction Based on LSTM Neural Network: Take Dongfeng Motor's Stock Price as an Example
EITCE '22: Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering

In recent years, stocks have gradually entered our field of vision. Due to the unstable fluctuation of stocks, there are often large fluctuations due to national and social policies, which also makes it difficult for investors to achieve investment ...
Stock price prediction based on LSTM and LightGBM hybrid model
Abstract
Finding an accurate, stable and effective model to predict the rise and fall of stocks has become a task increasingly favored by scholars. This paper proposes a long short-term memory (LSTM) network based on Pearson's correlation coefficient and a ...

Comments

Information & Contributors

Information

Published In

ICoMS '22: Proceedings of the 2022 5th International Conference on Mathematics and Statistics

June 2022

137 pages

ISBN:9781450396233

DOI:10.1145/3545839

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 ACM 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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 September 2022

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Research-article
Research
Refereed limited

Conference

ICoMS 2022

ICoMS 2022: 2022 5th International Conference on Mathematics and Statistics

June 17 - 19, 2022

Paris, France

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
139
Total Downloads

Downloads (Last 12 months)41
Downloads (Last 6 weeks)7

Reflects downloads up to 05 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Bragone FMorozovska KRosén TLaneryd TSöderberg DMarkidis S(2025)Automatic learning analysis of flow-induced birefringence in cellulose nanofibrilsJournal of Computational Science10.1016/j.jocs.2025.10253685(102536)Online publication date: Feb-2025
https://doi.org/10.1016/j.jocs.2025.102536
Bragone FMorozovska KRosén TSöderberg DMarkidis S(2024)Time Series Predictions Based on PCA and LSTM Networks: A Framework for Predicting Brownian Rotary Diffusion of Cellulose NanofibrilsComputational Science – ICCS 202410.1007/978-3-031-63749-0_15(209-223)Online publication date: 28-Jun-2024
https://doi.org/10.1007/978-3-031-63749-0_15
Singh HMalhotra M(2023)Artificial Intelligence Based Hybrid Models for Prediction of Stock Prices2023 2nd International Conference for Innovation in Technology (INOCON)10.1109/INOCON57975.2023.10101297(1-6)Online publication date: 3-Mar-2023
https://doi.org/10.1109/INOCON57975.2023.10101297
Singh HMalhotra M(2023)A Time Series Analysis-Based Stock Price Prediction Framework Using Artificial IntelligenceArtificial Intelligence of Things10.1007/978-3-031-48781-1_22(280-289)Online publication date: 3-Dec-2023
https://doi.org/10.1007/978-3-031-48781-1_22

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Abstract

References

Cited By

Index Terms

Recommendations

Research on the impact of PCA-LSTM on stock price forecast

Stock Price Prediction Based on LSTM Neural Network: Take Dongfeng Motor's Stock Price as an Example

Stock price prediction based on LSTM and LightGBM hybrid model

Comments

Information

Published In

Publisher

Publication History

Permissions

Check for updates

Qualifiers

Conference

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

HTML Format

Share

Share this Publication link

Share on social media

Affiliations