Traditional time series forecasting models are difficult to capture the nonlinear patterns. Support vector regression (SVR) has been successfully used to solve nonlinear regression and times series problems. However, parameters determination for a SVR model is competent to the forecasting...

Traditional time series forecasting models are difficult to capture the nonlinear patterns. Support vector regression (SVR) has been successfully used to solve nonlinear regression and times series problems. However, parameters determination for a SVR model is competent to the forecasting...

In this thesis, a new methodology using convex risk measures is developed to incorporate parameter risk into prices of financial derivatives, provided that a distribution on the parameter space is given. A technique to induce a parameter distribution in case of calibration to market prices is...

Purpose - In this research, the main purpose is to use a suitable structure to predict the trading signals of the stock market with high accuracy. For this purpose, two models for the analysis of technical adaptation were used in this study. Design/methodology/approach - It can be seen that...