Search Results - subject:"Bayesian regularization"

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

.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/10012266638

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

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

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

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