Research on Stock Price Forecasting Model Based on Deep Learning

2021 ◽  
Author(s):  
Guoxian Wang ◽  
Yingbing Fan
Keyword(s):  
Deep Learning ◽  
Stock Price ◽  
Forecasting Model ◽  
Price Forecasting ◽  
Stock Price Forecasting ◽  
Model Based

A hybrid model based on rough sets theory and genetic algorithms for stock price forecasting

2010 ◽  
Vol 180 (9) ◽  
pp. 1610-1629 ◽  
Author(s):  
Ching-Hsue Cheng ◽  
Tai-Liang Chen ◽  
Liang-Ying Wei
Keyword(s):  
Genetic Algorithms ◽  
Hybrid Model ◽  
Rough Sets ◽  
Stock Price ◽  
Price Forecasting ◽  
Stock Price Forecasting ◽  
Rough Sets Theory ◽  
Model Based ◽  
Sets Theory

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

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0253217
Author(s):  
Rohitash Chandra ◽  
Yixuan He
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Uncertainty Quantification ◽  
Stock Markets ◽  
Stock Price ◽  
Model Parameters ◽  
Price Forecasting ◽  
Stock Price Forecasting ◽  
Bayesian Neural Networks ◽  
Stock Price Prediction

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 Langevin gradients with parallel tempering MCMC that can be implemented in a parallel computing environment. The COVID-19 pandemic had a drastic impact in the world economy and stock markets given different levels of lockdowns due to rise and fall of daily infections. It is important to investigate the performance of related forecasting models during the COVID-19 pandemic given the volatility in stock markets. In this paper, we use novel Bayesian neural networks for multi-step-ahead stock price forecasting before and during COVID-19. We also investigate if the pre-COVID-19 datasets are useful of modelling stock price forecasting during COVID-19. Our results indicate due to high volatility in the stock-price during COVID-19, it is more challenging to provide forecasting. However, we found that Bayesian neural networks could provide reasonable predictions with uncertainty quantification despite high market volatility during the first peak of the COVID-19 pandemic.

A Novel Hybrid Deep Learning Model For Stock Price Forecasting

2021 ◽  
Author(s):  
Dhaifallah Alghamdi ◽  
Faris Alotaibi ◽  
Jayant Rajgopal
Keyword(s):  
Deep Learning ◽  
Stock Price ◽  
Learning Model ◽  
Price Forecasting ◽  
Stock Price Forecasting ◽  
Deep Learning Model
Sign in / Sign up

Export Citation Format

Share Document