Financial and Trading Forecasting with Advanced Artificial Intelligence Techniques

Artificial Intelligence (AI) is now widely used in financial markets. This has changed how financial transactions are managed and made financial services more efficient, secure, and tailored to individual needs. Technical evaluation, basic questions, and investor views form the base for AI trading systems. This paper looks at the latest research on trading algorithms, showing which combinations worked well in improving trading results and which helped improve outcomes by using the mentioned data and steps together. Compilation of significant market news, historical stock prices, and fundamental financial indicators obtained from reputable sources is used as a dataset. Several AI architectures are applied, among them, Temporal Convolutional Networks (TCN), Long Short-Term Memory (LSTM) networks, Transformer-based models (Temporal Fusion Transformer, Informer), Graph Neural Networks to capture cross-asset relations, and probabilistic models to estimate uncertainty. To solve this trading problem, decision-aware layers are trained, and reinforcement learning agents are developed in direct optimization of trading performance, besides transaction costs. The study found that developing a method to determine the optimal duration for training programs is essential. Future research will involve creating an automated financial trading system that integrates both fundamental and technical analysis.

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