Investment portfolio balancing: application of a generic self‐organizing fuzzy neural network (GenSoFNN)

C. Quek, K. C. Yow, Philip Y. K. Cheng and C. C. Tan

Intelligent Systems in Accounting, Finance and Management, 2009, vol. 16, issue 1‐2, 147-164

Abstract: In contrast to short‐term stock trading, portfolio managers are interested in the medium‐ to long‐term peaks and troughs of the stock price cycles as signals to balance their stock portfolios – the predicted trough is the signal to buy the stock and the predicted peak is the signal to sell the stock. As statistical models are generally inadequate or incapable of providing such portfolio balancing signals, we propose using the generic self‐organizing fuzzy neural network (GenSoFNN)—a fuzzy neural system – as a tool for portfolio balancing. The network adopts the supervised learning approach to detect inflection points in the stock price cycles, and a modified locally weighted regression algorithm is employed to smooth the stock cycles. The GenSoFNN‐based portfolio balancing system was evaluated with experiments conducted using 23 stocks from the New York Stock Exchange and NASDAQ, and the results showed an average profit return of 65.66%. The contributions of the proposed GenSoFNN intelligent portfolio balancing system are twofold: it can be used as an efficient trading solution and it can provide decision support in trading via its generated rules. Copyright © 2009 John Wiley & Sons, Ltd.

Date: 2009
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