Author:
Alwyn J. Hoffman
Affiliation:
Northwest University, South Africa
Keyword(s):
Multivariate models, Stock return prediction, Neural modelling.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
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Artificial Intelligence and Decision Support Systems
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Biomedical Engineering
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Biomedical Signal Processing
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Computational Intelligence
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Computer-Supported Education
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Domain Applications and Case Studies
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Enterprise Information Systems
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Fuzzy Systems
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Health Engineering and Technology Applications
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Human-Computer Interaction
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Industrial, Financial and Medical Applications
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Methodologies and Methods
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Neural Network Software and Applications
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Neural Networks
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Neurocomputing
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Neurotechnology, Electronics and Informatics
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Pattern Recognition
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Physiological Computing Systems
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Sensor Networks
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Signal Processing
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Soft Computing
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Theory and Methods
Abstract:
This paper describes the development of multivariate models used to identify stocks with above average return expectations. While most other research involving the development of stock return models involves time-series prediction of future returns, this paper focuses on the modelling of cross-sectional differences between stocks. The primary measure used in this paper to evaluate potential predictors of future stock returns is based on sorted category returns, an approach that was previously applied to NYSE listed stocks; in this paper the same approach is applied to stocks listed on the JSE. This measure is used to identify a number of fundamental and technical indicators that differentiates between high and low performing stock categories. Linear and non-linear multivariate models are subsequently developed, utilising these indicators to improve prediction performance. It is demonstrated that much of the useful stock return behaviour is present in the extremes of the population, t
hat significant differences exist between different size categories, and that different aspects of stock behaviour is exposed using appropriate measures for portfolio returns. Portfolio performance results achieved using individual indicators as well as multivariate models are reported and compared with previously published results, and planned future work to improve on the results is discussed.
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