Predicting stock returns and assessing prediction performance

Baker, RD ORCID: and Belgorodski, A 2007, 'Predicting stock returns and assessing prediction performance' , IMA Journal of Management Mathematics, 18 (4) , pp. 413-433.

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We use regression methods to predict the expected monthly return on stocks and the covariance matrix of returns, the predictor variables being a company's ‘fundamentals’, such as dividend yield and the history of previous returns. Predictions are evaluated out of sample for shares traded on the London Stock Exchange from 1976 to 2005. We explore and evaluate many modelling and inferential approaches, including the use of weighted regression, discounted regression, shrinkage of regression coefficients and the transformation to normality of predictor variables. We also investigate alternative covariance matrix models, such as a two-index model and a shrinkage model. Using suitable statistics to enable the out-of-sample performance of competing methodologies to be compared is crucial, and we develop some new statistics and a graphical aid for this purpose. What is original in this paper is an evaluation of many modelling and inferential procedures for which conflicting claims have been made in the literature and the development of new measures of portfolio performance.

Item Type: Article
Themes: Subjects / Themes > H Social Sciences > HB Economic Theory
Subjects / Themes > H Social Sciences > HA Statistics
Subjects outside of the University Themes
Schools: Schools > Salford Business School > Salford Business School Research Centre
Journal or Publication Title: IMA Journal of Management Mathematics
Publisher: Oxford University Press
Refereed: Yes
ISSN: 1471-678X
Depositing User: H Kenna
Date Deposited: 22 Aug 2007 10:50
Last Modified: 27 Aug 2021 19:48

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