Doctoral Dissertations

Abstract

"This dissertation proposes the development of a hybrid intelligent system applied to technical analysis based equivolume charting for stock trading. A Neuro-Fuzzy based Genetic Algorithms (NF-GA) system of the Volume Adjusted Moving Average (VAMA) membership functions is introduced to evaluate the effectiveness of using a hybrid intelligent system that integrates neural networks, fuzzy logic, and genetic algorithms techniques for increasing the efficiency of technical analysis based equivolume charting for trading stocks"--Introduction, page 1.

Advisor(s)

Enke, David Lee, 1965-

Committee Member(s)

Davis, Michael
Nystrom, Halvard E.
Grasman, Scott E. (Scott Erwin)
Dagli, Cihan H., 1949-

Department(s)

Engineering Management and Systems Engineering

Degree Name

Ph. D. in Engineering Management

Publisher

University of Missouri--Rolla

Publication Date

Summer 2007

Journal article titles appearing in thesis/dissertation

  • Intelligent hybrid stock trading system for technical analysis based equivolume charting
  • Intelligent technical analysis based equivolume charting for stock trading using neural networks
  • Neruo-fuzzy volume adjusted moving averages for intelligent trading decisions

Pagination

xi, 156 pages

Note about bibliography

Includes bibliographical references.

Geographic Coverage

United States

Rights

© 2007 Thira Chavarnakul, All rights reserved.

Document Type

Dissertation - Open Access

File Type

text

Language

English

Library of Congress Subject Headings

Investment analysis
Stock price forecasting -- Mathematical models
Stocks -- Charts, diagrams, etc. -- United States

Thesis Number

T 9207

Print OCLC #

182627336

Electronic OCLC #

176898851

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