Bayesian neural networks for stock price forecasting before and during COVID-19 pandemic.
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 Baye...
Main Authors: | Rohitash Chandra, Yixuan He |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2021-01-01
|
Series: | PLoS ONE |
Online Access: | https://doi.org/10.1371/journal.pone.0253217 |
Similar Items
-
Modelling and Forecasting Crude Oil Prices during COVID-19 Pandemic
by: Ernie Hendrawaty, et al.
Published: (2021-02-01) -
Stock price volatility during the COVID-19 pandemic: The GARCH model
by: Endri Endri, et al.
Published: (2021-10-01) -
Investigating Performance of Bayesian and Levenberg-Marquardt Neural Network in Comparison Classical Models in
Stock Price Forecasting
by: Hossein Fakhari, et al.
Published: (2017-07-01) -
Bayesian Forecasting of Stock Prices Via the Ohlson Model
by: Lu, Qunfang Flora
Published: (2005) -
Bayesian Graph Convolutional Neural Networks via Tempered MCMC
by: Rohitash Chandra, et al.
Published: (2021-01-01)