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Exploring local–global stock price interconnections & patterns via augmented deep neural links for stock predictions

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Abstract

In an era marked by the increasing influence of global market dynamics on local stocks through international trade, accurate stock price prediction remains a formidable challenge. Conventional stock prediction models often falter in capturing the intricate interplay of factors, including market sentiment, commodity price fluctuations, global market trends, and the symbiotic relationships between stocks and their parent companies. This research introduces a pioneering Interconnection Exploration Layer (IEL) that orchestrates the fusion of multiple event sources, enabling a precise estimation of a stock's direction and its delta-intensity. At its core, a deep learning model is meticulously crafted to enhance stock value prediction by harnessing the power of a Pattern Recognition Layer (PRL), adept at deciphering diverse temporal parameters extracted from global stock indices, and a Commodity Estimation Layer (CEL), finely attuned to tracking the temporal dynamics of gold and silver commodities. The innovation culminates in the creation of an exceptionally efficient stock representation vector, pivotal for training a bespoke 2D Convolutional Neural Network (CNN). Empirical results substantiate the model's efficacy, showcasing a remarkable 5% improvement in prediction accuracy compared to prevailing state-of-the-art stock prediction methodologies. Furthermore, the model's inherent scalability affords versatility, making it adaptable across diverse stock types.

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  1. Department of CSE Cyber Security, Shri Ramdeobaba College of Engineering and Management, Nagpur, Maharashtra, 440013, India

    Charanjeet Dadiyala & Rashmi Welekar

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  1. Charanjeet Dadiyala
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Correspondence to Charanjeet Dadiyala.

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Dadiyala, C., Welekar, R. Exploring local–global stock price interconnections & patterns via augmented deep neural links for stock predictions. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-19099-7

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