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

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)1141-1147
Number of pages7
JournalSoft Computing
Volume11
Issue number12
DOIs
Publication statusPublished - 2007 Oct 1

Fingerprint

Model Selection
Forecasting
Internal
Refining
Time series
Efficient Frontier
Portfolio Optimization
Inference Rules
Time Series Models
Simulation Experiment
Forecast
Experiments
Alternatives
Experimental Results
Model

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Geometry and Topology

Cite this

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Intelligent forecasting models-selection system for the portfolio internal structure change. / Wang, Hsing-Wen.

In: Soft Computing, Vol. 11, No. 12, 01.10.2007, p. 1141-1147.

Research output: Contribution to journalArticle

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