Search Results - subject:"Bayesian regularization"

Design of neuro-stochastic Bayesian networks for nonlinear chaotic differential systems in financial mathematics

Syed, Farwah Ali; Fang, Kwo-Ting; Kiani, Adiqa Kausar - In: Computational economics 65 (2025) 1, pp. 241-270

Persistent link: https://www.econbiz.de/10015195764

Indian stock market prediction using artificial neural networks on tick data

Selvamuthu, Dharmaraja; Kumar, Vineet; Mishra, Abhishek - In: Financial Innovation 5 (2019) 1, pp. 1-12

.2%, 97.0% and 98.9% for LM, SCG and Bayesian Regularization respectively which is significantly poor in comparison with that … networks based on three different learning algorithms, i.e., Levenberg-Marquardt, Scaled Conjugate Gradient and Bayesian … Regularization for stock market prediction based on tick data as well as 15-min data of an Indian company and their results compared …

Persistent link: https://www.econbiz.de/10012602812

Indian stock market prediction using artificial neural networks on tick data

Selvamuthu, Dharmaraja; Kumar, Vineet; Mishra, Abhishek - In: Financial innovation : FIN 5 (2019) 16, pp. 1-12

Persistent link: https://www.econbiz.de/10012266638

Bayesian regularized artificial neural networks for the estimation of the probability of default

Sariev, Eduard; Germano, Guido - In: Quantitative finance 20 (2020) 2, pp. 311-328

Persistent link: https://www.econbiz.de/10012194868

Modelling regional patterns of inefficiency : a Bayesian approach to geoadditive panel stochastic frontier analysis with an application to cereal production in England and Wales

Klein, Nadja; Herwartz, Helmut; Kneib, Thomas - In: Journal of econometrics 214 (2020) 2, pp. 513-539

Persistent link: https://www.econbiz.de/10012439076

Nonlinear Forecasting Using a Large Number of Predictors

Alessandro, Giovannelli - In: Rivista italiana degli economisti (2012) 1, pp. 143-150

This paper aims to introduce a nonlinear model to forecast macroeconomic time series using a large number of predictors. The technique used to summarize the predictors in a small number of variables is Principal Component Analysis (PC), while the method used to capture nonlinearity is artificial...

Persistent link: https://www.econbiz.de/10010968839

Nonlinear Forecasting Using a Large Number of Predictors

GIOVANNELLI, Alessandro - In: Rivista Italiana degli Economisti 17 (2012) 1, pp. 143-150

Persistent link: https://www.econbiz.de/10009652377

Degree of Mispricing with the Black-Scholes Model and Nonparametric Cures

Gencay, Ramazan; Salih, Aslihan - In: Annals of Economics and Finance 4 (2003) 1, pp. 73-101

The Black-Scholes pricing errors are larger in the deeper out-of-the-money options relative to the near out-of-the-money options, and mispricing worsens with increased volatility. Our results indicate that the Black-Scholes model is not the proper pricing tool in high volatility situations...

Persistent link: https://www.econbiz.de/10009144549

Artificial Neural Network Modeling for Groundwater Level Forecasting in a River Island of Eastern India

Mohanty, Sheelabhadra; Jha, Madan; Kumar, Ashwani; … - In: Water Resources Management 24 (2010) 9, pp. 1845-1865

rate backpropagation (GDX) algorithm, Levenberg–Marquardt (LM) algorithm and Bayesian regularization (BR) algorithm were …

Persistent link: https://www.econbiz.de/10010998089

Using The Artificial Neural Network (ANN) to Assess Bank Credit Risk: A Case Study of Indonesia

Hall, Maximilian J. B.; Muljawan, Dadang; Suprayogi; … - School of Business and Economics, Loughborough University - 2008

. The default rates are forecasted using the Artificial Neural Network (ANN) methodology, which incorporates the Bayesian … Regularization technique. From the sensitivity analysis, it is shown that stock prices could be used as a leading indicator of future …

Persistent link: https://www.econbiz.de/10005423023