An artificial neural network-GARCH model for international stock return volatility

1997 ◽  
Vol 4 (1) ◽  
pp. 17-46 ◽  
Author(s):  
R.Glen Donaldson ◽  
Mark Kamstra
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stock Return ◽  
Garch Model ◽  
Return Volatility ◽  
Stock Return Volatility ◽  
Artificial Neural

Proposed Framework for Predicting Stock Return Volatility Using Neural Network: An Applied Study on the Egyptian Stock Exchange

2018 ◽  
Vol 8 (4) ◽  
pp. 424
Author(s):  
Osama EL-Ansary ◽  
Nazeer Elshahat ◽  
Maha Saad Metawea
Keyword(s):  
Neural Network ◽  
Stock Returns ◽  
Stock Return ◽  
Stock Exchange ◽  
Return Volatility ◽  
Stock Return Volatility ◽  
The Neural Network ◽  
High Volatility ◽  
Internal And External Factors ◽  
Egyptian Stock Exchange

Purpose: the primary purpose of the study is to determine the effect of both internal and external factors on stock returns volatility using different statistical methods, applied on Egyptian stock exchange.Methodology: the researchers have compared the accuracy of (GLS Model, GARCH Model, and Neural Network) in predicting the stock return volatility to choose the most accurate one. Data was collected from the Egyptian Stock Exchange (EGYX 30) for the period (2014 to 2017) on a monthly basis.Findings: The results of the study revealed that the Neural Network Model has proven to outperform the traditional models in the prediction of stock return volatility.Originality: the study contributes to literature as it used Artificial Neural Network in two functions (Prediction of stock return volatility) and (Classification of the volatility to –high volatility and Low volatility). Also few studies concerned with stock return volatility in developing countries, especially Egypt.

How Does News Affect Stock Return Volatility in a Frontier Market?

2020 ◽  
Vol 45 (4) ◽  
pp. 433-443
Author(s):  
Kalu O. Emenike ◽  
Omweno N. Enock
Keyword(s):  
Stock Market ◽  
Stock Return ◽  
Empirical Studies ◽  
Garch Model ◽  
Bad News ◽  
Good News ◽  
Return Volatility ◽  
Stock Market Returns ◽  
Stock Return Volatility ◽  
Market Return

Many empirical studies have analysed the effect of good news and bad news on equity market return volatility using both developed and emerging markets data, with scant literature for frontier stock markets. This study evaluates how news affects stock market return volatility in a frontier market using Uganda data. It specifically analyses the reaction of stock return volatility to news filtering into a frontier market using the exponential generalized autoregressive conditional heteroscedasticity (GARCH) model on daily data ranging from 1 September 2011 to 31 December 2017. Estimates of the shape parameter from generalized error distribution indicate the existence of leptokurtic return distribution. Results from the exponential GARCH model show that the effect of bad news and good news on the frontier market return volatility differs, thus suggesting existence of leverage effect in the period studied. Overall results from the study suggest that positive news impacts stock market returns volatility more than negative news of the same magnitude. An important implication of our results is that investors, analysts, brokers and dealers should be conscious of the nature of news filtering into the stock market as such information might improve their expected volatility forecast.

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural
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