This paper presents a simulative model of a financial market, based on a fully operating order book with limit and market orders. The heterogeneity of traders is characterized not only with regards to their trading rules, but also by introducing a behavioral individual risk aversion and a...

This paper presents a simulative model of a financial market, based on a fully operating order book with limit and market orders. The heterogeneity of traders is characterized not only with regards to their trading rules, but also by introducing a behavioral individual risk aversion and a...

Several approaches for subset recovery and improved forecasting accuracy have been proposed and studied. One way is to apply a regularization strategy and solve the model selection task as a continuous optimization problem. One of the most popular approaches in this research field is given by...

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...