Proceedings of the 2023 2nd International Conference on Social Sciences and Humanities and Arts (SSHA 2023)

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Stock Price Prediction by Using RNN Method

Authors
Ao Zhang1, *, Jiaheng Cai2, Bo Zhu3, Wentao Yu4, Yuhan Huang5, Lin Zhou6
1Department of Physical Science, University of California, Irvine, Irvine, 92617, USA
2Department of Mathematics, University of California, Los Angeles, Los Angeles, 90024, USA
3Department of Computer Science, University of Wisconsin-Madison, Madison, 52703, USA
4Department of Mathematics, University of California, Santa Barbara, Santa Barbara, 93117, USA
5Wuhan Britain-China School, Wuhan, 430000, China
6Department of Mathematics, University of California, San Diego, La Jolla, 92093, USA
*Corresponding author. Email: aoz6@uci.edu
Corresponding Author
Ao Zhang
Available Online 11 July 2023.
DOI
10.2991/978-2-38476-062-6_120How to use a DOI?
Keywords
RNN; Prediction; Price
Abstract

In this essay, we mainly focused on how to predict the price of stocks. Our group studied Apple, Google, Microsoft, and Amazon stock prices. To solve the problem, we started with Recurrent Neural Network (RNN) to predict the stock’s price. Then, we used Long Short-Term Memory (LSTM) to grasp the pictures for those companies. The result showed us that stock price is a robust linear relationship. Luckily, through the training, the accuracy of our prediction is within an acceptable range.

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 2023 2nd International Conference on Social Sciences and Humanities and Arts (SSHA 2023)
Series
Advances in Social Science, Education and Humanities Research
Publication Date
11 July 2023
ISBN
978-2-38476-062-6
ISSN
2352-5398
DOI
10.2991/978-2-38476-062-6_120How 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  - Ao Zhang
AU  - Jiaheng Cai
AU  - Bo Zhu
AU  - Wentao Yu
AU  - Yuhan Huang
AU  - Lin Zhou
PY  - 2023
DA  - 2023/07/11
TI  - Stock Price Prediction by Using RNN Method
BT  - Proceedings of the 2023 2nd International Conference on Social Sciences and Humanities and Arts (SSHA 2023)
PB  - Atlantis Press
SP  - 923
EP  - 929
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-062-6_120
DO  - 10.2991/978-2-38476-062-6_120
ID  - Zhang2023
ER  -