Nonparametric predictive inference for stock returns

Baker, R.M.; Coolen-Maturi, T.; Coolen, F.P.A.

doi:10.1080/02664763.2016.1204429

Nonparametric predictive inference for stock returns

Baker, R.M.; Coolen-Maturi, T.; Coolen, F.P.A.

Authors

R.M. Baker

Dr Tahani Coolen-Maturi tahani.maturi@durham.ac.uk
Associate Professor

Professor Frank Coolen frank.coolen@durham.ac.uk
Professor

Abstract

In finance, inferences about future asset returns are typically quantified with the use of parametric distributions and single-valued probabilities. It is attractive to use less restrictive inferential methods, including nonparametric methods which do not require distributional assumptions about variables, and imprecise probability methods which generalize the classical concept of probability to set-valued quantities. Main attractions include the flexibility of the inferences to adapt to the available data and that the level of imprecision in inferences can reflect the amount of data on which these are based. This paper introduces nonparametric predictive inference (NPI) for stock returns. NPI is a statistical approach based on few assumptions, with inferences strongly based on data and with uncertainty quantified via lower and upper probabilities. NPI is presented for inference about future stock returns, as a measure for risk and uncertainty, and for pairwise comparison of two stocks based on their future aggregate returns. The proposed NPI methods are illustrated using historical stock market data.

Citation

Baker, R., Coolen-Maturi, T., & Coolen, F. (2017). Nonparametric predictive inference for stock returns. Journal of Applied Statistics, 44(8), 1333-1349. https://doi.org/10.1080/02664763.2016.1204429

Journal Article Type	Article
Acceptance Date	Jun 17, 2016
Online Publication Date	Jul 3, 2016
Publication Date	Jun 11, 2017
Deposit Date	Jun 17, 2016
Publicly Available Date	Jul 3, 2017
Journal	Journal of Applied Statistics
Print ISSN	0266-4763
Electronic ISSN	1360-0532
Publisher	Taylor and Francis Group
Peer Reviewed	Peer Reviewed
Volume	44
Issue	8
Pages	1333-1349
DOI	https://doi.org/10.1080/02664763.2016.1204429

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Copyright Statement
This is an Accepted Manuscript of an article published by Taylor & Francis Group in Journal of Applied Statistics on 03/07/2016, available online at: http://www.tandfonline.com/10.1080/02664763.2016.1204429.