MACHINE LEARNING ALGORITHMS FOR PREDICTIVE ANALYTICS IN FINANCIAL MARKETS

2025-03-04T05:39:56+00:00

A research examines machine learning algorithm applications for financial data prediction in markets by using synthetic data to minimize reliance on traditional datasets. The study develops a hybrid framework that combines deep learning and reinforcement learning models to enhance forecasting precision. GANs and VAEs work together to generate synthetic financial data through advanced network architectures. The prepared data is improved through logarithmic returns and technical indicator techniques. Transformer-based models deliver better performance than LSTM networks because they decrease prediction errors by 12% compared to LSTM networks. At the same time Deep Q-Networks (DQNs) operating under the reinforcement learning framework generate 23% superior cumulative returns compared to typical trading methods. Research outcomes indicate that artificial intelligence-driven models can improve market predictions and company decision-making functions. The research develops a new synthetic data method that boosts financial prediction while delivering essential information for automated traders and financial establishments.

International Journal of Mathematics and Statistics

MACHINE LEARNING ALGORITHMS FOR PREDICTIVE ANALYTICS IN FINANCIAL MARKETS