Deep learning for financial forecasting: A review of recent trends

Sofia Giantsidi and Claudia Tarantola

International Review of Economics & Finance, 2025, vol. 104, issue C

Abstract: Deep Learning (DL) has revolutionized financial forecasting, yet most reviews remain purely descriptive and lack actionable insight. This paper presents a comprehensive review of 187 Scopus-indexed studies (2020–2024) on DL applications for financial forecasting. We examine key DL architectures—Deep Multilayer Perceptrons, Recurrent Neural Network variants (Long Short-Term Memory (LSTM), Bidirectional LSTM, Gated Recurrent Units, and Reservoir Computing), Convolutional Neural Networks, Temporal Convolutional Networks, and Autoencoders. The studies are organized by forecasting task—stock, index, forex, commodity, bond, cryptocurrency, and volatility—and by model type (standalone vs. hybrid), preprocessing strategies, multi-modal feature integration (technical, fundamental, and sentiment signals), and novel methodological contributions.

Keywords: Machine learning; Deep learning; Time series prediction; Financial forecasting; Neural networks; Review (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:reveco:v:104:y:2025:i:c:s1059056025008822

DOI: 10.1016/j.iref.2025.104719

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