Financial prediction and trading strategies using neurofuzzy approaches

Author

Pantazopoulos, Konstantinos N. ; Tsoukalas, Lefteri H. ; Bourbakis, Nikolaos G. ; Brün, J. ; Houstis, Elias N.

Author_Institution

Dept. of Comput. Sci., Purdue Univ., West Lafayette, IN, USA

Volume

28

Issue

4

fYear

1998

fDate

8/1/1998 12:00:00 AM

Firstpage

520

Lastpage

531

Abstract

Neurofuzzy approaches for predicting financial time series are investigated and shown to perform well in the context of various trading strategies involving stocks and options. The horizon of prediction is typically a few days and trading strategies are examined using historical data. Two methodologies are presented wherein neural predictors are used to anticipate the general behavior of financial indexes (moving up, down, or staying constant) in the context of stocks and options trading. The methodologies are tested with actual financial data and show considerable promise as a decision making and planning tool

Keywords

commodity trading; financial data processing; fuzzy neural nets; time series; decision making and planning tool; financial indexes; financial prediction and trading strategies; financial time series; neural predictors; neurofuzzy approaches; options; stocks; Chemical technology; Decision making; Economic forecasting; Fuzzy neural networks; Helium; Investments; Neural networks; Power generation economics; System testing; Uninterruptible power systems;

fLanguage

English

Journal_Title

Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on

Publisher

ieee

ISSN

1083-4419

Type

jour

DOI

10.1109/3477.704291

Filename

704291