A1883
Title: Forecasting benchmarks of long-term stock returns via machine learning
Authors: Parastoo Mousavi - Cass business school (United Kingdom) [presenting]
Jens Perch Nielsen - City, University of London (United Kingdom)
Ioannis Kyriakou - Cass Business School (United Kingdom)
Michael Scholz - University of Klagenfurt (Austria)
Abstract: Recent advances in pension product development seem to favour alternatives to the risk-free asset often used in 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 show that net of inflation, the combined earnings-by-price ratio and long-short rate spread form one of our best-performing two-dimensional sets of predictors to forecast both one and five-year horizon stock returns. This is a crucial conclusion for actuarial applications aiming to provide pensioners with real-income forecasts.
