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Title: Forecasting benchmarks of long-term stock returns via machine learning Authors:  Michael Scholz - University of Graz (Austria) [presenting]
Jens Perch Nielsen - Cass Business School (United Kingdom)
Ioannis Kyriakou - Cass Business School (United Kingdom)
Parastoo Mousavi - Cass Business School (United Kingdom)
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, 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.