Cart (Loading....) | Create Account
Close category search window

Learning Pattern Recognition Techniques Applied to Stock Market Forecasting

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)

Most of investment analysis involves decision making by weighing evidence. Such decision processes can be formalized with the aid of pattern recognition (PR) techniques. Specifically, we have applied generalized perceptron-type PR techniques to both general market forecasting and investment selection. And after the investment decision system has been implemented and put into operation, its performance is then gradually improved through learning from previous decision making experiences. Iterative probabilistic learning algorithms (based on stochastic approximation techniques) have been used. Decision models for both investment selection and market forecasting have been realized and tested in actual investment analysis. The experimental results indicate that with the aid of PR techniques we may obtain above average investment performance.

Published in:

Systems, Man and Cybernetics, IEEE Transactions on  (Volume:SMC-5 ,  Issue: 6 )

Date of Publication:

Nov. 1975

Need Help?

IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.