Title: Multi-Agent Stock Prediction Systems: Machine Learning Models, Simulations, and Real-Time Trading Strategies
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.
| Subjects: | Statistical Finance (q-fin.ST) ; Machine Learning (cs.LG) |
| Cite as: | arXiv:2502.15853 [q-fin.ST] |
| (or arXiv:2502.15853v1 [q-fin.ST] for this version) | |
| https://doi.org/10.48550/arXiv.2502.15853 |
A Multi-agent Q-learning Framework for Optimizing Stock Trading Systems
Abstract: Abstract. This paper presents a reinforcement learning framework for stock trading systems. Trading system parameters are optimized by Qlearning algorithm and neural networks are adopted for value approximation. In this framework, cooperative multiple agents are used to efficiently integrate global trend prediction and local trading strategy for obtaining better trading performance. Agents communicate with others sharing training episodes and learned policies, while keeping the overall scheme of conventional Q… Show more
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Cited by 23 publication s
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“…In some cases, cooperative agents represent the interest of a single company or individual, and merely fulfill different functions in the trading process, such as buying and selling [68] . In other cases, self-interested agents interact in parallel with the market [48, 98, 125] .…”
Section: Automated Trading mentioning
confidence: 99%
“…MARL approaches to automated trading typically involve temporal-difference [118] or Q-learning agents, using approximate representations of the Q-functions to handle the large state space [48, 68, 125] . In some cases, cooperative agents represent the interest of a single company or individual, and merely fulfill different functions in the trading process, such as buying and selling [68] .…”
Section: Automated Trading mentioning
confidence: 99%
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Multi-agent Reinforcement Learning: An Overview
Exaggerated anticipatory anxiety is common in social anxiety disorder (SAD). Neuroimaging studies have revealed altered neural activity in response to social stimuli in SAD, but fewer studies have examined neural activity during anticipation of feared social stimuli in SAD. The current study examined the time course and magnitude of activity in threat processing brain regions during speech anticipation in socially anxious individuals and healthy controls (HC). Method Participants (SAD n = 58; HC n = 16) underwent functional magnetic resonance imaging (fMRI) during which they completed a 90s control anticipation task and 90s speech anticipation task. Show abstract
Exaggerated anticipatory anxiety is common in social anxiety disorder (SAD). Neuroimaging studies have revealed altered neural activity in response to social stimuli in SAD, but fewer studies have examined neural activity during anticipation of feared social stimuli in SAD. The current study examined the time course and magnitude of activity in threat processing brain regions during speech anticipation in socially anxious individuals and healthy controls (HC). Method Participants (SAD n = 58; HC n = 16) underwent functional magnetic resonance imaging (fMRI) during which they completed a 90s control anticipation task and 90s speech anticipation task.
https://arxiv.org/abs/2502.15853https://scite.ai/reports/a-multi-agent-q-learning-framework-for-MRVO6l
