FUNDAMENTAL ANALYSIS WITH ARTIFICIAL NEURAL NETWORK

Birol Yildiz and Ari Yezegel

The International Journal of Business and Finance Research, 2010, vol. 4, issue 1, 149-158

Abstract: This study performs fundamental analysis and cross-sectional prediction of stock return with neural network technology. Eighteen financial ratios are used as the input vector and one-year ahead stock returns are used as the output vector. The fundamental analysis trading strategy generated by artificial neural networks yields an average annual abnormal return of 22.32% after controlling for market risk, book-to-market, size and momentum effects. Our results highlight neural network’s ability to predict future returns in NYSE/AMEX/Nasdaq securities for the period 1990-2005. Artificial neural network technology stands out as a valuable tool for fundamental analysis and forecasting equity returns in the U.S. markets.

Keywords: Fundamental Analysis; Stock Market; Neural Network (search for similar items in EconPapers)
JEL-codes: C45 G11 M41 (search for similar items in EconPapers)
Date: 2010
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.theibfr2.com/RePEc/ibf/ijbfre/ijbfr-v4n1-2010/IJBFR-V4N1-2010-10.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ibf:ijbfre:v:4:y:2010:i:1:p:149-158

Access Statistics for this article

The International Journal of Business and Finance Research is currently edited by Terrance Jalbert

More articles in The International Journal of Business and Finance Research from The Institute for Business and Finance Research
Bibliographic data for series maintained by Mercedes Jalbert ( this e-mail address is bad, please contact ).