Research on Efficient Stock Prediction Method Based on LSTM Network

DOI

10.2991/978-94-6463-572-0_8How to use a DOI?

Keywords

Stock Prediction; Long Short-Term Memory Network; Neural networks

Abstract

Stock prediction is an important application in the field of data mining, which can help investors mitigate risks, estimate returns, and also anticipate company development. By providing historical stock prices of a certain company, machine learning/deep learning methods can be employed to estimate the future stock performance of the company. Due to the ability of deep networks to extract discriminative features and possess good time modeling capabilities, using deep networks for stock price estimation is an effective stock prediction method. In this paper, we utilize Long Short-Term Memory (LSTM) networks to predict the stock prices of the S&P500. By providing the historical stock data for the previous 9 days, the closing price on the 10th day is taken as the prediction indicator. Mean Squared Error (MSE) is used as the evaluation metric for this method. Experimental results demonstrate that our approach can effectively estimate future stock prices and achieve the MSE indicator for the prediction accuracy of the stock price prediction model.

Copyright

© 2024 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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TY  - CONF
AU  - Dong Li
AU  - Iokieng Liao
PY  - 2024
DA  - 2024/11/19
TI  - Research on Efficient Stock Prediction Method Based on LSTM Network
BT  - Proceedings of the 3rd International Conference on Financial Innovation, FinTech and Information Technology (FFIT 2024)
PB  - Atlantis Press
SP  - 65
EP  - 75
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-572-0_8
DO  - 10.2991/978-94-6463-572-0_8
ID  - Li2024
ER  -