Prediction of Stock Returns: A New Way to Look at It

Abstract

While the traditional R 2 value is useful to evaluate the quality of a fit, it does not work when it comes to evaluating the predictive power of estimated financial models in finite samples. In this paper we introduce a validated R 2 V value useful for prediction. Based on data from the Danish stock market, using this measure we find that the dividend-price ratio has predictive power. The best horizon for prediction seems to be four years. On a one year horizon, we find that while inflation and interest rate do not add to the predictive power of the dividend-price ratio then last years excess stock return does.

Forecasting benchmarks of long-term stock returns via machine learning

“…We take the long-term view using yearly data and predict at a one-year horizon as we are interested in actuarial models of long-term savings and potential econometric improvements of such models (see, for example, Guillén et al 2013a, b;Owadally et al 2013;Bikker et al 2012;Guillén et al 2014, or Gerrard et al 2014. For this, the methodology we adopt for validating our sparse long-term yearly data originates from the actuarial literature (see Nielsen and Sperlich 2003).…”

“…"-" are not applicable cases of matched covariate with benchmark is well-known that importing more structure in the estimation process can help reduce or circumvent such problems. For example, Nielsen and Sperlich (2003) investigate an additive functional structure in the context of predictability of excess stock returns (as proposed in the statistical literature by Stone 1985). Their results indicate a more complex structure than additivity, as the fully nonparametric models always do better in terms of validated R 2 than the additive counterparts.…”

“…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 method lets the data speak in themselves via training and learning, while being intuitively informative so that we can identify the covariates driving the system.…”

Nonparametric prediction of stock returns based on yearly data: The long-term view

“…Our reason for doing so is that we are really interested in actuarial models of long-term savings and potential econometric improvements to such models (see, for example, Guillen et al (2013a); Guillen et al (2013b); Owadally et al (2013); Bikker et al (2012); Guillen et al (2014); or Gerrard et al (2014)). It is, therefore, perhaps not a surprise that our favored methodology for validating our sparse long-term yearly data originates from the actuarial literature (see Nielsen and Sperlich (2003)).…”

“…The cross-validated measure of performance used here is a generalized version of the validated R 2 of Nielsen and Sperlich (2003). This measure of prediction allows for direct comparisons between proposed models.…”