A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Neural Networks for Financial Forecasting
Neural Networks for Financial Forecasting
International Journal of Intelligent Systems in Accounting and Finance Management
International Journal of Intelligent Systems in Accounting and Finance Management
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Feature selection algorithm for mixed data with both nominal and continuous features
Pattern Recognition Letters
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on intelligent systems for financial engineering and computational finance
An Empirical Analysis of Data Requirements for Financial Forecasting with Neural Networks
Journal of Management Information Systems
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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.