StockProF: a stock profiling framework using data mining approaches
Keng-Hoong Ng () and
Kok-Chin Khor ()
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Keng-Hoong Ng: Multimedia University
Kok-Chin Khor: Multimedia University
Information Systems and e-Business Management, 2017, vol. 15, issue 1, No 7, 139-158
Abstract 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.
Keywords: StockProF; Data mining; Stock market; Local Outlier Factor; EM clustering (search for similar items in EconPapers)
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