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StockProF: a stock profiling framework using data mining approaches

Author

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  • Keng-Hoong Ng

    (Multimedia University)

  • Kok-Chin Khor

    (Multimedia University)

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.

Suggested Citation

  • Keng-Hoong Ng & Kok-Chin Khor, 2017. "StockProF: a stock profiling framework using data mining approaches," Information Systems and e-Business Management, Springer, vol. 15(1), pages 139-158, February.
    • Handle: RePEc:spr:infsem:v:15:y:2017:i:1:d:10.1007_s10257-016-0313-z
    DOI: 10.1007/s10257-016-0313-z
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    References listed on IDEAS

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    Cited by:

    1. Kuo-Chih Cheng & Mu-Jung Huang & Cheng-Kai Fu & Kuo-Hua Wang & Huo-Ming Wang & Lan-Hui Lin, 2021. "Establishing a Multiple-Criteria Decision-Making Model for Stock Investment Decisions Using Data Mining Techniques," Sustainability, MDPI, vol. 13(6), pages 1-14, March.

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