ADVANCED NEURAL NETWORKS AND DEEP LEARNING TECHNIQUES IN FINANCIAL MARKET PREDICTION

Ene Cezar Catalin
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Ene Cezar Catalin: UNIVERSITY OF CRAIOVA EUGENIU CARADA DOCTORAL SCHOOL OF ECONOMIC SCIENCES

Annals - Economy Series, 2025, vol. 2, 184-195

Abstract: This study investigates the role of artificial neural networks (ANN) and deep learning (DL) in the financial sector, focusing on their theoretical applications. ANN and DL have significantly reshaped financial analysis due to their capacity to identify complex patterns and improve market predictions. Mimicking the computational structure of the human brain, ANN processes interconnected data points enabling efficient analysis and forecasting. Deep learning, which is a specialized branch of machine learning, utilizes multiple layers of neural networks, offering enhanced capabilities to model relationships and process large datasets. This has led to advancements in predicting stock prices, market trends, and managing financial risks. The discussion covers key architectures, including feedforward neural networks, convolutional neural networks (CNN), and recurrent neural networks (RNN) with specific emphasis on advanced forms like Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRU). These models effectively capture dependencies in time-series data, making them valuable for market forecasting, sentiment analysis, and detecting anomalies. The use of deep learning approaches in finance extends beyond traditional prediction tasks, providing new insights for automated trading systems and risk assessment tools. Artificial neural networks and deep learning enhance modeling capabilities by improving accuracy and adaptability, thereby aiding decision-making within dynamic financial contexts. This analysis highlights their contributions to refining predictive performance and advancing the comprehension of intricate financial systems.

Keywords: Artificial neural networks (ANN); deep learning (DL); financial prediction; market forecasting; time series analysis (search for similar items in EconPapers)
Date: 2025
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