Deterministic regression model and visual basic code for optimal.

A new, non-statistical method is presented for analysis of the past history and current evolution of economic and financial processes. The method is based on the sliding model approach using linear differential or difference equations applied to discrete information in the form of known chronological data (time series) about the process. An algorithm is proposed that allows one to project the current evolution of the process onto some period of its future development. Computer code in visual basic is developed that has been validated in application to American stock index S&P 500, with predicted values within 5% of real data over long periods of the recent past history. The algorithm and the code can be applied to practical problems in finance and economy in time of its normal evolution without catastrophic events.Sliding deterministic regression models; Optimal forecasting in finance;

Deterministic regression model and visual basic code for optimal

A new, non-statistical method is presented for analysis of the past history and current evolution of economic and financial processes. The method is based on the sliding model approach using linear differential or difference equations applied to discrete information in the form of known chronological data (time series) about the process. An algorithm is proposed that allows one to project the current evolution of the process onto some period of its future development. Computer code in visual basic is developed that has been validated in application to American stock index S&P 500, with predicted values within 5% of real data over long periods of the recent past history. The algorithm and the code can be applied to practical problems in finance and economy in time of its normal evolution without catastrophic events.Alejandro Balbás also thanks the partial support provided by Welzia Management SGIIC SA, RD_Sistemas SA, Comunidad Autónoma de Madrid (Spain), Grant s-0505/tic/000230, and MEyC (Spain), Grant SEJ2006-15401-C04-03Publicad

Min-Max Formulation of the Balance Number in Multiobjetive Global Optimization

The notion of the balance number introduced in [3,page 139] through a certain set contraction procedure for nonscalarized multiobjective global optimization is represented via a min-max operation on the data of the problem. This representation yields a different computational procedure for the calculation of the balance number and allows us to generalize the approach for problems with countably many performance criteria

Min-Max Formulation of the Balance Number in Multiobjetive Global Optimization

The notion of the balance number introduced in [3,page 139] through a certain set contraction procedure for nonscalarized multiobjective global optimization is represented via a min-max operation on the data of the problem. This representation yields a different computational procedure for the calculation of the balance number and allows us to generalize the approach for problems with countably many performance criteria