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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.
Michael Scholz is an Assistant Professor at the Institute of Economics at the University of Graz. He graduated in mathematical statistics at TU Dresden and obtained his PhD in econometrics from the University of Göttingen. In 2017, he worked at TU Dortmund as a visiting Professor in Statistics. He is a specialist in non- and semi-parametric methods and computational statistics. He was and is currently engaged in a number of interdisciplinary projects working together with (development) economists, actuaries, statisticians, econometricians, or geographers. He published in a variety of top-ranked scientific journals and was recently awarded the John W. Kendrick Prize of the International Association for Research in Income and Wealth.
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