Machine learning and deep learning for financial data analysis

Abstract

Supervised learning is commonly used in digital imagery, computational linguistics, and digital sound classification. Deep learning’s astounding achievement as an online analytical approach has piqued the curiosity of the scientific establishment. With the rise of Fintech in current history, the application of machine learning (ML) in financial products and activities has become commonplace. However, there is a shortage of a systematic assessment of future research directions in finance and economics in the actual knowledge. This work evaluates the ability of the convolutional neural network in important financial and accounting areas to give a comprehensive study due to the advent, input variables, and parameter estimation. Finally, we address three factors that may impact the results of monetary neural network architectures. This study offers scholars and operators insight and perspective on the state-of-the-art deep teaching methods in accounting and investment banking.