Forecasting the market risk premium with artificial neural networks

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.