Bayesian neural networks for stock price forecasting before and during COVID-19 pandemic.

ABSTRACT: Recently, there has been much attention in the use of machine learning methods, particularly deep learning for stock price prediction. A major limitation of conventional deep learning is uncertainty quantification in predictions which affect investor confidence. Bayesian neural networks feature Bayesian inference for providing inference (training) of model parameters that provides a rigorous methodology for uncertainty quantification in predictions. Markov Chain Monte Carlo (MCMC) sampling methods have been prominent in implementing inference of Bayesian neural networks; however certain limitations existed due to a large number of parameters and the need for better computational resources. Recently, there has been much progress in the area of Bayesian neural networks given the use of Lange

SUBMITTER: Chandra R 

PROVIDER: S-EPMC8248663 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature