The Equity Premium Puzzle: An Artificial Neural Network Approach
23 Pages Posted: 8 Aug 2008 Last revised: 4 Jan 2019
Date Written: August 8, 2008
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.
Keywords: Equity premium, Forecasting, CAPM, Neural networks
JEL Classification: C45, G1, G2, G3
Suggested Citation: Suggested Citation
Do you have negative results from your research you’d like to share?
Recommended Papers
-
Consumption, Aggregate Wealth and Expected Stock Returns
By Martin Lettau and Sydney C. Ludvigson
-
Risks for the Long Run: A Potential Resolution of Asset Pricing Puzzles
By Ravi Bansal and Amir Yaron
-
Dividend Yields and Expected Stock Returns: Alternative Procedures for Interference and Measurement
-
Resurrecting the (C)Capm: A Cross-Sectional Test When Risk Premia are Time-Varying
By Martin Lettau and Sydney C. Ludvigson
-
Stock Return Predictability: Is it There?
By Geert Bekaert and Andrew Ang
-
Stock Return Predictability: Is it There?
By Geert Bekaert and Andrew Ang
-
Resurrecting the (C)Capm: A Cross-Sectional Test When Risk Premia Wre Time-Varying
By Martin Lettau and Sydney C. Ludvigson