StockProF: a stock profiling framework using data mining approaches

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• 作者：Keng-Hoong Ng ; Kok-Chin Khor
• 关键词：StockProF ; Data mining ; Stock market ; Local Outlier Factor ; EM clustering
• 刊名：Information Systems and e-Business Management
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：15
• 期：1
• 页码：139-158
• 全文大小：
• 刊物类别：Business and Economics
• 刊物主题：IT in Business; Information Systems Applications (incl.Internet); Management;
• 出版者：Springer Berlin Heidelberg
• ISSN：1617-9854
• 卷排序：15

文摘

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.