Neuro-Fuzzy Volume Adjusted Moving Averages for Intelligent Trading Decisions
Abstract
Previous research has shown that the profitability of stock trading using neural networks to assist the trading heuristic of a Volume Adjusted Moving Averages (VAMA). However, the interpretation of the VAMA trading heuristic depends on the experience and the sentiment of the individual investor. This results since the VAMA trading heuristic does not have as precise a threshold for making a decision of when to buy, sell, or hold a particular stock. This research studies the effectiveness of applying fuzzy logic as an extension to the results of the combined neural networks and VAMA model to extract an intelligent trading decision. Fuzzy logic is used for representing the linguistic uncertainty of the VAMA trading heuristics that are generated from the neural networks model. the results show that stock trading using this neuro-fuzzy modeling approach can help to improve the profitability of the combined neural network and VAMA model.
Recommended Citation
T. Chavarnakul and D. L. Enke, "Neuro-Fuzzy Volume Adjusted Moving Averages for Intelligent Trading Decisions," 27th Annual National Conference of the American Society for Engineering Management 2006 - Managing Change: Managing People and Technology in a Rapidly Changing World, ASEM 2006, pp. 279 - 288, American Society for Engineering Management, Dec 2006.
Department(s)
Engineering Management and Systems Engineering
Keywords and Phrases
Financial engineering; Fuzzy logic; Neuro-fuzzy; Stock trading; Technical analysis
International Standard Book Number (ISBN)
978-160423714-6
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 American Society for Engineering Management , All rights reserved.
Publication Date
01 Dec 2006
