Abstract

http://ssrn.com/abstract=1211962
 
 

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The Equity Premium Puzzle: An Artificial Neural Network Approach


Shee Q. Wong


Labovitz School of Business

Nik R. Hassan


University of Minnesota - Duluth

Ehsan H. Feroz


University of Washington, Tacoma-Milgard School of Business; Vernon Zimmerman Center, University of Illinois; US Government Accountability Office

August 8, 2008

Review of Accounting and Finance, Vol. 6, No. 2, pp. 150-161, 2007

Abstract:     
This paper presents evidence suggesting that artificial neural networks approach (ANNs) outperform traditional statistical methods and can forecast equity premiums reasonably well. The study replicates out-of-sample estimates of regression using ANN with economic fundamentals as inputs. The dividend yield variable was found to produce the best out-of-sample forecasts for equity premium. This result is useful for capital asset pricing model and in asset allocation decisions.

Number of Pages in PDF File: 23

Keywords: Equity premium, Forecasting, CAPM, Neural networks

JEL Classification: C45, G1, G2, G3

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Date posted: August 8, 2008 ; Last revised: August 28, 2014

Suggested Citation

Wong, Shee Q. and Hassan, Nik R. and Feroz, Ehsan H., The Equity Premium Puzzle: An Artificial Neural Network Approach (August 8, 2008). Review of Accounting and Finance, Vol. 6, No. 2, pp. 150-161, 2007 . Available at SSRN: http://ssrn.com/abstract=1211962

Contact Information

Shee Q. Wong
Labovitz School of Business ( email )
412 Library Drive
Duluth, MN 55812
United States
Nik R. Hassan
University of Minnesota - Duluth ( email )
1049 University Drive
Duluth, MN 55812
United States
Ehsan H. Feroz (Contact Author)
University of Washington, Tacoma-Milgard School of Business ( email )
1900 Commerce Street, Campus Box 358420
Tacoma, WA 98402-3100
United States
(253) 692 4728 (Phone)
253 692 4523 (Fax)
HOME PAGE: http://www.tacoma.washington.edu/business
Vernon Zimmerman Center, University of Illinois ( email )
515 East Gregory Drive# 2307
Champaign, IL 61820
United States
US Government Accountability Office ( email )
441 G Street NW
Washington, DC 20548-0001
United States
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