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

Michael Scholz, Jens Perch Nielsen and Stefan Sperlich ()

Insurance: Mathematics and Economics, 2015, vol. 65, issue C, 143-155

Abstract: One of the most studied questions in economics and finance is whether empirical models can be used to predict equity returns or premiums. In this paper, we take the actuarial long-term view and base our prediction on yearly data from 1872 through 2014. While many authors favor the historical mean or other parametric methods, this article focuses on nonlinear relationships between a set of covariates. A bootstrap test on the true functional form of the conditional expected returns confirms that yearly returns on the S&P500 are predictable. The inclusion of prior knowledge in our nonlinear model shows notable improvement in the prediction of excess stock returns compared to a fully nonparametric model. Statistically, a bias and dimension reduction method is proposed to import more structure in the estimation process as an adequate way to circumvent the curse of dimensionality.

Keywords: Prediction of stock returns; Cross-validation; Prior knowledge; Bias reduction; Dimension reduction (search for similar items in EconPapers)
JEL-codes: C14 C53 C58 G17 G22 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (18)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:insuma:v:65:y:2015:i:c:p:143-155

DOI: 10.1016/j.insmatheco.2015.09.011

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