StockProF: a stock profiling framework using data mining approaches

Keng-Hoong Ng () and Kok-Chin Khor ()
Additional contact information
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: 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)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s10257-016-0313-z Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:infsem:v:15:y:2017:i:1:d:10.1007_s10257-016-0313-z

Ordering information: This journal article can be ordered from
http://www.springer. ... ystems/journal/10257

DOI: 10.1007/s10257-016-0313-z

Access Statistics for this article

Information Systems and e-Business Management is currently edited by Jörg Becker and Michael J. Shaw

More articles in Information Systems and e-Business Management from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().