A Hybrid CNN-LSTM Approach for Effective Stock Price Prediction in Optimizing Investment Strategies

DOI

10.2991/978-94-6463-652-9_65How to use a DOI?

Keywords

Stock Price Prediction; Machine Learning; CNN; LSTM

Abstract

Stock price prediction is important for crafting optimal investment strategies in the financial sector. Traditional models like Autoregressive Integrated Moving Average (ARIMA), often used for their predictive simplicity, struggle with the dynamic nature of stock markets due to their linear constraints. This study explores a hybrid approach combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, utilizing their strengths to enhance prediction accuracy in various market conditions. For this analysis, twelve stocks representing diverse market performances over the past year were selected. These stocks were trained on nine years of historical data—incorporating daily open, low, high prices, and percentage changes as key features. The training involved 800 cycles per stock, each running 500 epochs, with varying hyperparameter combinations to optimize the model. The evaluation focused on the minimum mean absolute error recorded as the test loss and the mean absolute percentage error to assess precision. Results revealed that the CNN-LSTM model generally predicts stock prices effectively, with a minimum mean absolute percentage error of 0.00791. However, challenges arose with certain stocks, particularly those subject to abrupt price surges and external influences, which were less predictable despite hyperparameter adjustments. This analysis not only highlights the model’s abilities but also underscores the influence of external factors on prediction accuracy. The study of hyperparameters further demonstrated that while most stock prices are predictable, some remain challenging due to these unpredictable elements.

Copyright

© 2025 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.

Download article (PDF)

TY  - CONF
AU  - Haotian Liu
PY  - 2025
DA  - 2025/02/24
TI  - A Hybrid CNN-LSTM Approach for Effective Stock Price Prediction in Optimizing Investment Strategies
BT  - Proceedings of the International Workshop on Navigating the Digital Business Frontier for Sustainable Financial Innovation (ICDEBA 2024)
PB  - Atlantis Press
SP  - 628
EP  - 640
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-652-9_65
DO  - 10.2991/978-94-6463-652-9_65
ID  - Liu2025
ER  -