Abstract
Recent advances in pension product development seem to favour alternatives to the risk free asset often used in the financial theory as a performance standard for measuring the value generated by an investment or a reference point for determining the value of a financial instrument. To this end, in this paper, we apply the simplest machine learning technique, namely, a fully nonparametric smoother with the covariates and the smoothing parameter chosen by cross-validation to forecast stock returns in excess of different benchmarks, including the short-term interest rate, long-term interest rate, earnings-by-price ratio, and the inflation. We find that, net-of-inflation, the combined earnings-by-price and long-short rate spread form our best-performing two-dimensional set of predictors for future annual stock returns. This is a crucial conclusion for actuarial applications that aim to provide real-income forecasts for pensioners.
Highlights
One of the key messages of Merton (2014) is that pension forecasts must be in real terms
In this paper, we apply the simplest machine learning technique, namely, a fully nonparametric smoother with the covariates and the smoothing parameter chosen by cross-validation to forecast stock returns in excess of different benchmarks, including the short-term interest rate, long-term interest rate, earnings-by-price ratio, and the inflation
We apply to forecasting stock returns in excess of different benchmarks, including the inflation, long interest rate and earnings-by-price ratio to supplement the short interest rate which is by far the most commonly used in finance
Summary
One of the key messages of Merton (2014) is that pension forecasts must be in real terms. The purpose of the current research is to make the first few investigations on suitable benchmark selection from an econometric perspective We achieve this by machine learning based on the cross-validated time series approach of Nielsen and Sperlich (2003) , Scholz et al (2015) and Scholz et al (2016) to optimize the fully nonparametric statistical estimation and forecasting of the risky asset returns in excess of four different benchmarks: the risk free rate, the long-term interest rate, the earnings-by-price ratio, and the inflation. Our investigations show that the latter approach uncovers the predictability of earnings which, when combined with the long-short spread, in real terms result in optimal forecasts with a predictive power of at least 18% This is important for long-term saving strategies, where one is interested in real value, corroborating the change of the classical risk free asset benchmark to inflation, as suggested in the abovementioned researches.
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