Intelligent forecasting models-selection system for the portfolio internal structure change

In this study, an intelligent forecasting models-selection system for refining portfolio structural estimation is proposed selecting different forecasts time series models, as well as the contents’ trend with refining the risk-return matrices of components. Based on the four inference rules in intelligent selection mechanism, the support system seeks to find the appropriate model solutions satisfying the tracking for the behavior of indices prices in portfolio optimization. The feasibility of the system is verified with a practical simulation experiment. The experimental results show that, for all examined investment assets, the presented system is an efficient way of solving the portfolio internal structure change problem. In addition, we also find that the presented system can also be used as an alternative method for evaluating various forecasting models. By means of global major market as the empirical evidences of portfolio contents, it will show that the proposed system can serve as improving efficient frontier of a portfolio.