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This article provides an overview of deep learning applications in financial trend prediction.

December 19, 2025Open Access

Advances in the Application of Deep Learning in Financial Market Trend Prediction

Key Points

This article provides an overview of deep learning applications in financial trend prediction.
Review of various deep learning architectures including RNNs and CNNs
Analysis of deep learning in predicting costs of financial instruments
Discussion of deep learning challenges and future directions
Deep learning algorithms uncover complex patterns in financial data
Different architectures offer distinct advantages for trend prediction
Challenges identified include data acquisition and model interpretability

Abstract

It is hard to guess what will happen in the financial market because financial data is always changing and is often noisy. Deep learning (DL) algorithms have become valuable tools for predicting financial trends because they can uncover complicated patterns and relationships that aren't straight lines. This review article provides a comprehensive overview of the latest advancements in employing deep learning to predict trends in the financial market. We examine various deep learning architectures, including recurrent neural networks (RNNs), convolutional neural networks (CNNs), and hybrid models. We also examine at how they can help us determine out how much stocks, currencies, and other financial instruments will cost. The report also talks about the issues and future of deep learning in finance, such as how hard it is to get data, how easy it is to understand models, and how strong they are. We look at the pros and cons of employing different DL approaches.

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