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

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The S&P 500 Index Prediction Based on N-BEATS

Authors
Yichen Liu1, Chengcheng Zhong2, *, Qiaoyu Ma3, Yanan Jiang4, Chunlei Zhang5
1Cyber Science and Engineering, Qufu Normal University, Jining, 273165, China
2School of Science, China University of Geosciences, Beijing, 100083, China
3College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China
4School of Mathematical Sciences, Beijing Normal University, Beijing, 100875, China
5Beijing Zhongdirunde Petroleum Technology Co. Ltd., Beijing, 100083, China
*Corresponding author. Email: 2119200033@cugb.edu.cn
Corresponding Author
Chengcheng Zhong
Available Online 10 August 2023.
DOI
10.2991/978-94-6463-198-2_96How to use a DOI?
Keywords
Stock market prediction; S&P 500; N-BEATS; Time-series
Abstract

The stock market prediction has been a hot topic in the field of economics and finance. As a consequence of the complex and volatile nature of the stock market, it is challenging to accurately forecast the stock S&P 500 index. Currently, with the purpose of predicting stock market, intelligent algorithms via computer have been proved superior in recent studies. We have introduced the N-BEATS algorithm to precisely estimate the stock S&P 500 index which are tailored towards the drawbacks that most algorithms cannot incorporate with historical information for time-series data. The features extracted by the N-BEATS algorithm are more consistent with the temporal features through the forward and backward coefficients. On the basis of the comparison of four evaluation metrics obtained from the S&P 500 index corresponding to 500 base stocks in this study, the N-BEATS algorithm outperforms other estimators. It can be demonstrated that the N-BEATS is a more suitable and promising method for stock market prediction, which has widespread application value.

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
978-94-6463-198-2
ISSN
2589-4900
DOI
10.2991/978-94-6463-198-2_96How 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  - Yichen Liu
AU  - Chengcheng Zhong
AU  - Qiaoyu Ma
AU  - Yanan Jiang
AU  - Chunlei Zhang
PY  - 2023
DA  - 2023/08/10
TI  - The S&P 500 Index Prediction Based on N-BEATS
BT  - Proceedings of the 2nd International Academic Conference on Blockchain, Information Technology and Smart Finance (ICBIS 2023)
PB  - Atlantis Press
SP  - 923
EP  - 929
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-198-2_96
DO  - 10.2991/978-94-6463-198-2_96
ID  - Liu2023
ER  -