Feature Selection Techniques, Company Wealth Assessment and Intra-sectoral Firm Behaviours
Mark B. Barnes1
and Vincent C. S. Lee1 
| (1) |
School of Business Systems, Monash University, Clayton Campus, Victoria, Australia |
Abstract
This paper explores the attributes that drive company wealth creation in the Miscellaneous Industrials sector of the Australian
Stock Market. It looks at how the company’s wealth creation changes in comparison to the changes in the Miscellaneous Industrial
Index. 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 a single domain model 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 found that WACC, Funds from Operation / EBITDA and EPS assist in guiding the direction of change in shareholder
wealth. Whereas ROA, Capital Turnover and Gross Debt / Cashflow are key attributes in understanding the behaviour of the relative
shareholder growth. We observed that ROIC, Ordinary Share Price, EVA, EPS and Trading Revenue / Total Assets are the important
attributes that drive relative shareholder wealth in this industry.
Keywords Feature Selection - Artificial Intelligence - Company wealth and Intra sectoral firm behaviours
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