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

Abstract

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.