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Forecasting the Market Risk Premium with Artificial Neural Networks

Listed author(s):
  • Leoni Eleni Oikonomikou

    (Georg-August University Göttingen)

This paper aims to forecast the Market Risk premium (MRP) in the US stock market by applying machine learning techniques, namely the Multilayer Perceptron Network (MLP), the Elman Network (EN) and the Higher Order Neural Network (HONN). Furthermore, Univariate ARMA and Exponential Smoothing models are also tested. The Market Risk Premium is defined as the historical differential between the return of the benchmark stock index over a short-term interest rate. Data are taken in daily frequency from January 2007 through December 2014. All these models outperform a Naive benchmark model. The Elman network outperforms all the other models during the insample period, whereas the MLP network provides superior results in the out-of-sample period. The contribution of this paper to the existing literature is twofold. First, it is the first study that attempts to forecast the Market Risk Premium in a daily basis using Artificial Neural Networks (ANNs). Second, it is not based on a theoretical model but is mainly data driven. The chosen calculation approach fits quite well with the characteristics of ANNs. The forecasting model is tested with data from the US stock market. The proposed model-based forecasting method aims to capture patterns in the data that improve the forecasting accuracy of the Market Risk Premium in the tested market and indicates potential key metrics for investment and trading purposes for short time horizons.

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File URL: http://www2.vwl.wiso.uni-goettingen.de/courant-papers/CRC-PEG_DP_202.pdf
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Paper provided by Courant Research Centre PEG in its series Courant Research Centre: Poverty, Equity and Growth - Discussion Papers with number 202.

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Date of creation: 14 Apr 2016
Handle: RePEc:got:gotcrc:202
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  1. Martin Lettau & Stijn Van Nieuwerburgh, 2008. "Reconciling the Return Predictability Evidence," Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1607-1652, July.
  2. Ivo Welch & Amit Goyal, 2008. "A Comprehensive Look at The Empirical Performance of Equity Premium Prediction," Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1455-1508, July.
  3. Qi, Min, 1999. "Nonlinear Predictability of Stock Returns Using Financial and Economic Variables," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(4), pages 419-429, October.
  4. Llubos Pástor, 2001. "The Equity Premium and Structural Breaks," Journal of Finance, American Finance Association, vol. 56(4), pages 1207-1239, August.
  5. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
  6. Goetzmann, William N. & Ibbotson, Roger G., 2006. "The Equity Risk Premium: Essays and Explorations," OUP Catalogue, Oxford University Press, number 9780195148145.
  7. Olson, Dennis & Mossman, Charles, 2003. "Neural network forecasts of Canadian stock returns using accounting ratios," International Journal of Forecasting, Elsevier, vol. 19(3), pages 453-465.
  8. Perez-Rodriguez, Jorge V. & Torra, Salvador & Andrada-Felix, Julian, 2005. "STAR and ANN models: forecasting performance on the Spanish "Ibex-35" stock index," Journal of Empirical Finance, Elsevier, vol. 12(3), pages 490-509, June.
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