Nonlinearities in stock return prediction: A Blended Approach

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dc.contributor.authorHodnett, Kathleen
dc.contributor.authorHsieh, Heng-Hsing
dc.contributor.authorVan Rensberg, Paul
dc.date.accessioned2017-06-06T09:17:32Z
dc.date.available2017-06-06T09:17:32Z
dc.date.issued2013
dc.description.abstractOur prior research indicates that there are periods within which nonlinear stock selection models outperform their linear counterparts in the South African equity market. In order to explore the nonlinearities in stock return prediction, we propose a blended stock selection technique that has the potential of diversifying the risk of inaccurate forecasts of the linear and nonlinear models. The proposed technique has an objective of optimizing the Qian and Hua (2003) information ratio, which constitutes to the maximization of the forecasting accuracy per unit of forecasting volatility. The blended stock selection model is found to outperform the respective linear and nonlinear models in an out-of-sample fractile analysis on a risk-adjusted basis for South African stocks over the period from 2002 to 2007.en_US
dc.description.accreditationDepartment of HE and Training approved list
dc.identifier.citationHodnett, K. et al. (2013). Nonlinearities in stock return prediction: A Blended Approach. Journal of Applied Business Research, 29 (1): 7-22en_US
dc.identifier.issn0892-7626
dc.identifier.urihttp://hdl.handle.net/10566/2930
dc.language.isoenen_US
dc.publisherThe Clute Instituteen_US
dc.rightsPublisher retains copyright. Authors may archive the published version in their Institutional Repository.
dc.subjectStock selection modelsen_US
dc.subjectNonlinearitiesen_US
dc.subjectStock returnen_US
dc.subjectForecasting accuracy
dc.titleNonlinearities in stock return prediction: A Blended Approachen_US
dc.typeArticleen_US

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