An Improvement to StockProF: Profiling Clustered Stocks with Class Association Rule Mining


Khor, Kok Chin and Ng, Keng Hoong (2016) An Improvement to StockProF: Profiling Clustered Stocks with Class Association Rule Mining. In: Computational Intelligence in Information Systems. Advances in Intelligent Systems and Computing (AISC), 532 . Springer, pp. 143-151. ISBN 2194-5357

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Using StockProF developed in our previous work, we are able to identify outliers from a pool of stocks and form clusters with the remaining stocks based on their financial performance. The financial performance is measured using financial ratios obtained directly or derived from financial reports. The resulted clusters are then profiled manually using mean and 5-number summary calculated from the financial ratios. However, this is time consuming and a disadvantage to novice investors who are lacking of skills in interpreting financial ratios. In this study, we utilized class association rule mining to overcome the problems. Class association rule mining was used to form rules by finding financial ratios that were strongly associated with a particular cluster. The resulted rules were more intuitive to investors as compared with our previous work. Thus, the profiling process became easier. The evaluation results also showed that profiling stocks using class association rules helps investors in making better investment decisions.

Item Type: Book Section
Uncontrolled Keywords: Ratio analysis, Profiling stocks, Class association rule mining, StockProF
Subjects: H Social Sciences > HF Commerce > HF5001-6182 Business > HF5601-5689 Accounting. Bookkeeping
Divisions: Faculty of Computing and Informatics (FCI)
Depositing User: Ms Rosnani Abd Wahab
Date Deposited: 27 Oct 2020 20:59
Last Modified: 27 Oct 2020 20:59


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