Stock price prediction using LSTM neural networks: Techniques and applications

Zian Wang

doi:10.54254/2755-2721/86/20241605

Research Article

Open access

Published on 14 August 2024

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Wang,Z. (2024). Stock price prediction using LSTM neural networks: Techniques and applications. Applied and Computational Engineering,86,275-281.

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Stock price prediction using LSTM neural networks: Techniques and applications

Zian Wang *^,1,

¹ University of Leeds

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/86/20241605

Abstract

The prediction of stock prices has garnered significant attention due to the potential financial gains and complex questions involved. This paper elaborates a comparison between the Long Short-Term Memory (LSTM) model, optimised using the early-stopping method, and the conventional mathematical method Autoregressive Integrated Moving Average Model(ARIMA), which is conducted using the S&P500 from 2022, May 01 to 2024, May 01. The results indicate that the LSTM surpasses ARIMA. To be more specific, LSTM achieves a 92% reduction in error rates compared to ARIMA. In addition, when the optimised LSTM is implemented in 6 different stocks, the results indicate a negative correlation between the volatility and accuracy of the stocks. This study demonstrates the advantages of optimised LSTM for predicting stock prices, and the importance of market volatility as a crucial aspect that significantly impacts the accuracy of stock price forecast.

Keywords

LSTM, ARIMA, Stock price prediction, Time series forecasting

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References

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Cite this article

Wang,Z. (2024). Stock price prediction using LSTM neural networks: Techniques and applications. Applied and Computational Engineering,86,275-281.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 6th International Conference on Computing and Data Science

Conference website: https://www.confcds.org/

ISBN：978-1-83558-583-2(Print) / 978-1-83558-584-9(Online)

Conference date: 12 September 2024

Editor：Alan Wang, Roman Bauer

Series: Applied and Computational Engineering

Volume number: Vol.86

ISSN：2755-2721(Print) / 2755-273X(Online)

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