Predicting Financial Market Movements with Deep Learning Techniques

Abstract

The prediction of financial market movements is a challenging task due to the volatility and complexity of market data. Deep learning techniques, particularly recurrent neural networks (RNNs) and convolutional neural networks (CNNs), have shown great promise in predicting market trends by analyzing large datasets and capturing hidden patterns. This article explores the application of deep learning in financial market prediction, focusing on techniques such as RNNs, LSTMs, and CNNs for analyzing time-series data and financial indicators. Additionally, the article examines the challenges and opportunities of using deep learning in the financial industry, including issues related to data quality, model interpretability, and risk management.