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Comparative Study of Outlier Detection Algorithms via Fundamental Analysis Variables:An Application on Firms Listed in Borsa Istanbul


  • Senol Emir

    (Asst. Prof., Faculty of Economics, Istanbul University, 34126 Beyazit, Istanbul, Turkey)

  • Hasan Dincer

    (Assoc.Prof. of Finance, Istanbul Medipol University, School of Business and Management, Beykoz, 34810, Istanbul, Turkey)

  • Umit Hacioglu

    (Assoc.Prof. of Finance, Istanbul Medipol University, School of Business and Management, Beykoz, 34810, Istanbul, Turkey)

  • Serhat Yuksel

    (Asst.Prof. of Economics & Finance, Konya Food & Agriculture University, Faculty of Social Sciences and Humanities, Konya, Turkey)


In a data set, an outlier refers to a data point that is considerably different from the others. Detecting outliers provides useful application-specific insights and leads to choosing right prediction models. Outlier detection (also known as anomaly detection or novelty detection) has been studied in statistics and machine learning for a long time. It is an essential preprocessing step of data mining process. In this study, outlier detection step in the data mining process is applied for identifying the top 20 outlier firms. Three outlier detection algorithms are utilized using fundamental analysis variables of firms listed in Borsa Istanbul for the 2011-2014 period. The results of each algorithm are presented and compared. Findings show that 15 different firms are identified by three different outlier detection methods. KCHOL and SAHOL have the greatest number of appearances with 12 observations among these firms. By investigating the results, it is concluded that each of three algorithms makes different outlier firm lists due to differences in their approaches for outlier detection. Key Words:Outlier Detection, Fundamental Analysis, Stock Exchange, k-Nearest Neighbor (k-NN) Global Outlier Score, Histogram Based Outlier Score (HBOS), Robust Principal Component Analysis (rPCA) Outlier Score

Suggested Citation

  • Senol Emir & Hasan Dincer & Umit Hacioglu & Serhat Yuksel, 2015. "Comparative Study of Outlier Detection Algorithms via Fundamental Analysis Variables:An Application on Firms Listed in Borsa Istanbul," International Journal of Research in Business and Social Science (2147-4478), Center for the Strategic Studies in Business and Finance, vol. 4(4), pages 45-60, October.
  • Handle: RePEc:rbs:ijbrss:v:4:y:2015:i:4:p:45-60

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    References listed on IDEAS

    1. Fama, Eugene F., 1998. "Market efficiency, long-term returns, and behavioral finance," Journal of Financial Economics, Elsevier, vol. 49(3), pages 283-306, September.
    2. Shawn D. Howton & Steven B. Perfect, 1998. "Currency and Interest-Rate Derivatives Use in US Firms," Financial Management, Financial Management Association, vol. 27(4), Winter.
    3. Ivo Welch, 2011. "Two Common Problems in Capital Structure Research: The Financial‐Debt‐To‐Asset Ratio and Issuing Activity Versus Leverage Changes," International Review of Finance, International Review of Finance Ltd., vol. 11(1), pages 1-17, March.
    4. Nadeem Iqbal & Sajid Rahman Khattak & Muhammad Arif Khattak, 2013. "Does Fundamental Analysis Predict Stock Returns? Evidence from Non-Financial Companies Listed on KSE," Knowledge Horizons - Economics, Faculty of Finance, Banking and Accountancy Bucharest,"Dimitrie Cantemir" Christian University Bucharest, vol. 5(4), pages 182-190, December.
    5. Edmister, Robert O., 1972. "An Empirical Test of Financial Ratio Analysis for Small Business Failure Prediction," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 7(02), pages 1477-1493, March.
    6. Peek, Joe & Rosengren, Eric, 1995. "The Capital Crunch: Neither a Borrower nor a Lender Be," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 27(3), pages 625-638, August.
    7. Johnson, Craig G, 1970. "Ratio Analysis and the Predicition of Firm Failure," Journal of Finance, American Finance Association, vol. 25(5), pages 1166-1168, December.
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