Neural Networks in Algorithmic Trading for Financial Markets

Abstract

This study focuses on how neural networks may enhance financial market algorithmic trading and decision-making. The primary objectives are to evaluate Feedforward, Convolutional, Recurrent, and Deep Reinforcement Learning neural networks in trading applications. The study uses a systematic secondary data evaluation of model literature and performance indicators. CNNs and RNNs excel in pattern recognition and time-series data prediction, improving trading signals and strategy optimization. More data, overfitting, and model interpretability help these models. The research recommends data pretreatment, regularization, and explainable AI to address these issues. Policy consequences include data quality, transparency, and computer resource allocation. To increase the financial neural network application, these obstacles must be overcome and favorable rules created. Trading tactics improve and adapt.