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