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An empirical study of AI-based multiple domain models for selecting attributes that drive company wealth

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Abstract

This paper explores what attributes drive company wealth creation in the Miscellaneous Industrials sector of the Australian Stock Market. We examine traditional and artificial intelligent (AI) feature selection techniques, to select attributes that drive company wealth and observe if a multiple domain model outperforms single domain models with regards to predicting company wealth. Using a large number of calculated attributes, our empirical findings suggest that a multiple domain model was most effective. We find that Market Capitalisation, Debt Asset Ratio, ROI and Beta Value attributes are the main contributors to following the directional change in the shareholder value. This was using a J4.8 and GRNN multiple domain model.

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Authors and Affiliations

  1. School of Business Systems, Monash University, Clayton Campus, Victoria, Australia

    Mark B. Barnes & Vincent C. S. Lee

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  1. Mark B. Barnes
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  2. Vincent C. S. Lee
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Correspondence to Mark B. Barnes.

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Barnes, M.B., Lee, V.C.S. An empirical study of AI-based multiple domain models for selecting attributes that drive company wealth. Soft Comput 11, 1193–1198 (2007). https://doi.org/10.1007/s00500-007-0160-4

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  • DOI: https://doi.org/10.1007/s00500-007-0160-4

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