NDLI: MNFS-FPM: A novel memetic neuro-fuzzy system based financial portfolio management

Content Provider	IEEE Xplore Digital Library
Author	Lumanpauw, E. Pasquier, M. Chai Quek
Copyright Year	2007
Description	Author affiliation: Nanyang Technol. Univ., Nanyang (Lumanpauw, E.; Pasquier, M.; Chai Quek)
Abstract	Portfolio management consists of deciding what assets to include in a portfolio given the investor's objectives and changing market and economic conditions. The always difficult selection process includes identifying which assets to purchase, how much, and when. This paper presents a novel memetic neuro-fuzzy system for financial portfolio management (MNFS-FPM) which emulates the thinking process of a rational investor and generates the optimal portfolio from a collection of assets based on a chosen investment style. The system consists mainly of two modules: the generic self-organizing fuzzy neural network realizing Yager inference (GenSoFNN-Yager), to predict the expected return of each asset, and a memetic algorithm using simplex local searches (MA-NM/SMD) to determine the optimal investment weight allocation for all assets in the portfolio. Experimental results on Dow Jones industrial average (DJIA) stocks show that the proposed system yields better performance compared to that of existing financial models: statistical mean-variance analysis and capital asset pricing model (CAPM).
Starting Page	2554
Ending Page	2561
File Size	352967
Page Count	8
File Format	PDF
ISBN	9781424413393
DOI	10.1109/CEC.2007.4424792
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-09-25
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy neural networks Portfolios Financial management Security Asset management Investments Time series analysis Instruments Support vector machines Economic forecasting
Content Type	Text
Resource Type	Article

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