DocumentCode :
1407073
Title :
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
Link To Document :
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