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PREDICTING THE IMPACT OF ANTICIPATORY ACTION ON U.S. STOCK MARKET—AN EVENT STUDY USING ANFIS (A NEURAL FUZZY MODEL)
Author(s) -
Cheng P.,
Quek C.,
Mah M.L.
Publication year - 2007
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/j.1467-8640.2007.00304.x
Subject(s) - adaptive neuro fuzzy inference system , stock market , artificial neural network , stock (firearms) , econometrics , earnings , outcome (game theory) , computer science , economics , artificial intelligence , fuzzy logic , financial economics , finance , fuzzy control system , engineering , microeconomics , mechanical engineering , paleontology , horse , biology
In this study, the adaptive neural fuzzy inference system (ANFIS), a hybrid fuzzy neural network, is adopted to predict the actions of the investors (when and whether they buy or sell) in a stock market in anticipation of an event–changes in interest rate, announcement of its earnings by a major corporation in the industry, or the outcome of a political election for example. Generally, the model is relatively more successful in predicting when the investors take actions than what actions they take and the extent of their activities. The findings do demonstrate the learning and predicting potential of the ANFIS model in financial applications, but at the same time, suggest that some of the market behaviors are too complex to be predictable.