StockProF: a stock profiling framework using data mining approaches


Ng, Keng Hoong and Khor, Kok Chin (2015) StockProF: a stock profiling framework using data mining approaches. Information Systems and e-Business Management, 15 (1). ISSN 1617-9846

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Analysing stock financial data and producing an insight into it are not easy tasks for many stock investors, particularly individual investors. Therefore, building a good stock portfolio from a pool of stocks often requires Herculean efforts. This paper proposes a stock profiling framework, StockProF, for building stock portfolios rapidly. StockProF utilizes data mining approaches, namely, (1) Local Outlier Factor (LOF) and (2) Expectation Maximization (EM). LOF first detects outliers (stocks) that are superior or poor in financial performance. After removing the outliers, EM clusters the remaining stocks. The investors can then profile the resulted clusters using mean and 5-number summary. This study utilized the financial data of the plantation stocks listed on Bursa Malaysia. The authors used 1-year stock price movements to evaluate the performance of the outliers as well as the clusters. The results showed that StockProF is effective as the profiling corresponded to the average capital gain or loss of the plantation stocks.

Item Type: Article
Uncontrolled Keywords: StockProF, Data mining, Stock market, Local Outlier, Factor EM clustering
Subjects: Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions: Faculty of Computing and Informatics (FCI)
Depositing User: Ms Rosnani Abd Wahab
Date Deposited: 20 Nov 2017 13:01
Last Modified: 20 Nov 2017 13:01


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