Abstract
This study aims to verify whether using artificial neural networks (ANNs) to establish classification probabilities generates portfolios with higher excess returns than using ANNs in their traditional role of predicting portfolio returns. Our sample includes all companies listed on the Toronto Stock Exchange from 1994 to 2014 with a monthly average of 16,324 company-month observations. Results indicate that portfolios based on the classification probabilities yield mean returns ranging from 7.81 to 14.40% annually over a 16-year period and that portfolios based on both predicted returns and classification probabilities generate returns that are superior to the market index. In addition, there is evidence that ranking securities based on their probability of beating the market has some benefit.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
