Title: Novel deep learning model with fusion of multiple pipelines for stock market prediction

Authors: Andrew Quintanilla; Abhishek Verma

Addresses: Department of Computer Science, California State University, Fullerton, California 92834, USA ' Department of Computer Science, New Jersey City University, Jersey City, NJ 07305, USA

Abstract: Deep learning has become a powerful tool in modelling complex relationships in data. Convolutional neural networks constitute the backbone of modern machine intelligence applications, while long short-term memory (LSTM) layers have been widely applied towards problems involving sequential data, such as text classification and temporal data. By combining the power of multiple pipelines of CNN in extracting features from data and LSTM in analysing sequential data, we have produced a novel model with improved performance in stock market prediction by 20% upon single pipeline model and by five times upon support vector regressor model. We also present multiple variations of our model to show how we have increased accuracy while minimising the effects of overfitting. Specifically, we show how changes in the parameters of our model affect its scores for training and testing, and compare the performance of a multiple pipelines model using three different kernel sizes versus a single pipeline model.

Keywords: stock prediction; S&P500; CNN; long short-term memory; LSTM; deep learning.

DOI: 10.1504/IJAIP.2025.147662

International Journal of Advanced Intelligence Paradigms, 2025 Vol.30 No.3, pp.247 - 259

Received: 29 Nov 2018
Accepted: 22 Jan 2019
Published online: 25 Jul 2025 *

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