A Review of Deep Learning Methods for Financial Market Prediction

Financial market prediction plays a crucial role for investors and financial institutions aiming to optimize returns and minimize risks. Over the past decades, considerable research has focused on developing effective and accurate methods for forecasting financial market trends. Traditional statistical models often face limitations in capturing the nonlinear and complex dynamics of financial time series. In contrast, deep learning techniques provide advanced analytical and predictive frameworks capable of uncovering latent structures and intricate patterns within large-scale financial datasets. This study systematically reviews recent deep learning approaches applied to the prediction of financial time series, including recurrent neural networks (RNNs), long short-term memory networks (LSTMs), convolutional neural networks (CNNs), and hybrid models. We evaluate these methods based on their architecture, data representation, and predictive performance, highlighting their respective strengths and weaknesses. Our analysis demonstrates that deep learning algorithms exhibit superior capabilities in modeling nonlinear dependencies and temporal correlations in financial data, enabling accurate forecasting of stock prices, indices, and other market indicators. Furthermore, while individual network architectures perform effectively, combining recurrent and convolutional layers often enhances prediction accuracy and robustness. The findings underscore the potential of deep learning as a powerful tool for financial decision-making, offering valuable insights for both researchers and practitioners in the field of computational finance.

کلیدواژه ها:

Deep Learning ، Financial Markets ، time series prediction ، Stock Price ، Stock Exchange