Skip to main content
Article
Using Neural Networks and Technical Indicators for Generating Stock Trading Signals
Intelligent Engineering Systems Through Artificial Neural Networks
  • Vamsi Krishna Bogullu
  • Cihan H. Dagli, Missouri University of Science and Technology
  • David Lee Enke, Missouri University of Science and Technology
Abstract

Technical analysis is a common method used by financial managers and traders to predict buy and sell trading signals for individual stocks. Unfortunately, it is often the case that each trader, based on their own level of expertise, will have a different way of interpreting an indicator, or identifying the time series trend that is currently presented by the stock's price history. This study involves training feed-forward neural networks to generate buy and sell trading signals. The predictability and profitability results given by the trained neural networks (with both discrete and fuzzified technical indicators) are compared against rule-based models of the technical indicators, as well as a standard benchmark buy-and-hold strategy.

Meeting Name
Artificial Neural Networks in Engineering Conference, ANNIE 2002 (2002: Nov. 10-13, St. Louis, MO)
Department(s)
Engineering Management and Systems Engineering
Keywords and Phrases
  • Neural Networks,
  • Stock Trading,
  • Trading Signals,
  • Stocks
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2002 American Society of Mechanical Engineers (ASME), All rights reserved.
Publication Date
11-13-2002
Publication Date
13 Nov 2002
Citation Information
Vamsi Krishna Bogullu, Cihan H. Dagli and David Lee Enke. "Using Neural Networks and Technical Indicators for Generating Stock Trading Signals" Intelligent Engineering Systems Through Artificial Neural Networks (2002)
Available at: http://works.bepress.com/david-enke/27/