IDEAS home Printed from https://ideas.repec.org/a/spr/infsem/v15y2017i1d10.1007_s10257-016-0313-z.html
   My bibliography  Save this article

StockProF: a stock profiling framework using data mining approaches

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10257-016-0313-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10257-016-0313-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Roger Farmer, 2012. "The Stock Market Crash of 2008 Caused the Great Recession," 2012 Meeting Papers 145, Society for Economic Dynamics.
    2. Robert Tibshirani & Guenther Walther & Trevor Hastie, 2001. "Estimating the number of clusters in a data set via the gap statistic," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 411-423.
    3. Farmer, Roger E.A., 2012. "The stock market crash of 2008 caused the Great Recession: Theory and evidence," Journal of Economic Dynamics and Control, Elsevier, vol. 36(5), pages 693-707.
    4. Chen, Andrew H. & Siems, Thomas F., 2004. "The effects of terrorism on global capital markets," European Journal of Political Economy, Elsevier, vol. 20(2), pages 349-366, June.
    5. Wing-Keung Wong & Meher Manzur & Boon-Kiat Chew, 2003. "How rewarding is technical analysis? Evidence from Singapore stock market," Applied Financial Economics, Taylor & Francis Journals, vol. 13(7), pages 543-551.
    6. Kasa, Kenneth, 1992. "Common stochastic trends in international stock markets," Journal of Monetary Economics, Elsevier, vol. 29(1), pages 95-124, February.
    7. Benmelech, Efraim & Dvir, Eyal, 2013. "Does Short-Term Debt Increase Vulnerability to Crisis? Evidence from the East Asian Financial Crisis," Journal of International Economics, Elsevier, vol. 89(2), pages 485-494.
    8. Dechow, Patricia M. & Hutton, Amy P. & Meulbroek, Lisa & Sloan, Richard G., 2001. "Short-sellers, fundamental analysis, and stock returns," Journal of Financial Economics, Elsevier, vol. 61(1), pages 77-106, July.
    9. Basu, S, 1977. "Investment Performance of Common Stocks in Relation to Their Price-Earnings Ratios: A Test of the Efficient Market Hypothesis," Journal of Finance, American Finance Association, vol. 32(3), pages 663-682, June.
    10. Alan Siu & Y. C. Richard Wong, 2004. "Economic Impact of SARS: The Case of Hong Kong," Asian Economic Papers, MIT Press, vol. 3(1), pages 62-83.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hollander, Hylton & Liu, Guangling, 2016. "Credit spread variability in the U.S. business cycle: The Great Moderation versus the Great Recession," Journal of Banking & Finance, Elsevier, vol. 67(C), pages 37-52.
    2. Greg Kaplan & Guido Menzio, 2016. "Shopping Externalities and Self-Fulfilling Unemployment Fluctuations," Journal of Political Economy, University of Chicago Press, vol. 124(3), pages 771-825.
    3. Julien Pascal, 2020. "Search, matching and heterogeneity," Sciences Po publications info:hdl:2441/qqo2oivo980, Sciences Po.
    4. Pan, Wei-Fong, 2018. "Does the stock market really cause unemployment? A cross-country analysis," The North American Journal of Economics and Finance, Elsevier, vol. 44(C), pages 34-43.
    5. Dées, Stephane & Zimic, Srečko, 2019. "Animal spirits, fundamental factors and business cycle fluctuations," Journal of Macroeconomics, Elsevier, vol. 61(C), pages 1-1.
    6. Guglielmo Maria Caporale & Juncal Cuñado & Luis A. Gil-Alana, 2013. "Modelling long-run trends and cycles in financial time series data," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(3), pages 405-421, May.
    7. Kydland, Finn E. & Zarazaga, Carlos E.J.M., 2016. "Fiscal sentiment and the weak recovery from the Great Recession: A quantitative exploration," Journal of Monetary Economics, Elsevier, vol. 79(C), pages 109-125.
    8. Athreya, Kartik B., 2014. "Big Ideas in Macroeconomics: A Nontechnical View," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262019736, December.
    9. Döpke, Jörg & Fritsche, Ulrich & Pierdzioch, Christian, 2017. "Predicting recessions with boosted regression trees," International Journal of Forecasting, Elsevier, vol. 33(4), pages 745-759.
    10. Pablo Guerróon‐Quintana & Molin Zhong, 2023. "Macroeconomic forecasting in times of crises," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(3), pages 295-320, April.
    11. Choi, Sangyup & Loungani, Prakash, 2015. "Uncertainty and unemployment: The effects of aggregate and sectoral channels," Journal of Macroeconomics, Elsevier, vol. 46(C), pages 344-358.
    12. Greg Kaplan & Guido Menzio, 2016. "Shopping Externalities and Self-Fulfilling Unemployment Fluctuations," Journal of Political Economy, University of Chicago Press, vol. 124(3), pages 771-825.
    13. James B. Bullard, 2012. "Death of a theory," Review, Federal Reserve Bank of St. Louis, vol. 94(Mar), pages 83-102.
    14. Ülkü, Numan & Kuruppuarachchi, Duminda & Kuzmicheva, Olga, 2017. "Stock market's response to real output shocks in Eastern European frontier markets: A VARwAL model," Emerging Markets Review, Elsevier, vol. 33(C), pages 140-154.
    15. Farmer, Roger E.A. & Platonov, Konstantin, 2019. "Animal spirits in a monetary model," European Economic Review, Elsevier, vol. 115(C), pages 60-77.
    16. Andrew Y. Chen & Tom Zimmermann, 2022. "Open Source Cross-Sectional Asset Pricing," Critical Finance Review, now publishers, vol. 11(2), pages 207-264, May.
    17. Makridis, Christos A., 2022. "The social transmission of economic sentiment on consumption," European Economic Review, Elsevier, vol. 148(C).
    18. Goel, Sanjay & Cagle, Seth & Shawky, Hany, 2017. "How vulnerable are international financial markets to terrorism? An empirical study based on terrorist incidents worldwide," Journal of Financial Stability, Elsevier, vol. 33(C), pages 120-132.
    19. Sin-Yu Ho, 2018. "Determinants of economic growth in Hong Kong: The role of stock market development," Cogent Economics & Finance, Taylor & Francis Journals, vol. 6(1), pages 1510718-151, January.
    20. repec:fip:fedlps:y:2012:i:jan13 is not listed on IDEAS
    21. Numan Ülkü & Kexing Wu, 2023. "Stock Market's Response to Real Output Shocks in China: A VARwAL Estimation," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 31(5), pages 1-25, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:infsem:v:15:y:2017:i:1:d:10.1007_s10257-016-0313-z. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.