Multi-Agent Stock Prediction Systems: Machine Learning Models, Simulations, and Real-Time Trading Strategies

2025/02/21 by Daksh Dave, Dave, Daksh, Gauransh Sawhney +2
Decision Sciences · #FOS: Computer and information sciences #FOS: Economics and business #Machine Learning (cs.LG) #Statistical Finance (q-fin.ST) #Stock Market Forecasting Methods

paper · pdf · doi:10.48550/arxiv.2502.15853

openalex publication_date 2025/02/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This paper presents a comprehensive study on stock price prediction, leveragingadvanced machine learning (ML) and deep learning (DL) techniques to improve financial forecasting accuracy. The research evaluates the performance of various recurrent neural network (RNN) architectures, including Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRU), and attention-based models. These models are assessed for their ability to capture complex temporal dependencies inherent in stock market data. Our findings show that attention-based models outperform other architectures, achieving the highest accuracy by capturing both short and long-term dependencies. This study contributes valuable insights into AI-driven financial forecasting, offering practical guidance for developing more accurate and efficient trading systems.

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