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Robust Regression and its Application in Financial Data Analysis

Authors: Mansoor Momeni, Mahmoud Dehghan Nayeri, Ali Faal Ghayoumi, Hoda Ghorbani

Abstract:

This research is aimed to describe the application of robust regression and its advantages over the least square regression method in analyzing financial data. To do this, relationship between earning per share, book value of equity per share and share price as price model and earning per share, annual change of earning per share and return of stock as return model is discussed using both robust and least square regressions, and finally the outcomes are compared. Comparing the results from the robust regression and the least square regression shows that the former can provide the possibility of a better and more realistic analysis owing to eliminating or reducing the contribution of outliers and influential data. Therefore, robust regression is recommended for getting more precise results in financial data analysis.

Keywords: Financial data analysis, Influential data, Outliers, Robust regression.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1084332

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[1] Azar, A. and Momeni,M., Business statistics, 3th Edition, Samt Publications,Tehran, 2008.
[2] Anderson, D. R. and Sweeney, D. J, Statistics for Business and Economics, 7th edition, Williams, T.A., South western college, 1998.
[3] Bamett, V. and Lewis. T, Outliers in Statistical data, Third ed., Wiley, Chichester, 1993.
[4] Chatterjee, S. and Hadi A.S., Regression analysis by example, 4th edition, Wiley, New Jersey, 2006.
[5] Chatterjee, S. and Hadi A.S., "Influential Observations, High Leverage Points, and Outliers in Linear Regression", Statical Science, Vol. 1, No. 3, 1986, pp.379-416.
[6] Chatterjee, S. and M├ñchler, M., "Robust regression: A weighted least squares approach", Communications in Statistics ÔÇö Theory and Methods, No.26, 1997, pp.1381-1394.
[7] Chen, C., "Robust Regression and Outlier Detection with the ROBUSTREG Procedure", presented at SUGI, No.27, 2002, pp.265.
[8] Cook, R. D., "Detection of influential observations in linear regression", Technometrics, No. 19,1977, pp.15-18.
[9] Easton, P., & Harris. T., "Earnings as an Explanatory Variable for Returns", Journal of Accounting Research, 1991, No.29, pp.19-36.
[10] Field, C. and Zhou, J., "Confidence intervals based on robust regression", Journal of Statistical Planning and Inference, No.115, 2003, pp.425 - 439.
[11] Gujarati, D.N., Basic Econometrics, Third edition, Mc Graw-Hill international edition, 1995.
[12] Hoaglin,D.C. and Welsch,R.E., "The hat matrix in regressin and ANOVA", The American Statistician, No.32, 1978, pp.17-22.
[13] Hodges J.L., Proc. Fifth Berkeley Symp. Math. Stat. Probab.,No.1, 1967, pp.163-168. http: // www. 2sas . com / proceedings /sugi27/pdf.
[14] Liang,Y.Z. and Kvalheim O.M., "Robust methods for multivariate analysis - a tutorial review", Chemometrics and Intelligent Laboratory Systems, No.32, 1996, pp.1-10.
[15] Martin, R. D. "Robust Statistics with the S-Plus Robust Library and Financial Applications", Vol. 1 and 2, Insightful Corp Presentation, New York, NY, 2002, No.17-18.
[16] Neter, J. and Kunter, M. H., Nachtsheim, C. J. and Wasserman. W., Applied Linear Regression Models, Third edition, Mc Graw-Hill, USA, 1996.
[17] Ohlson, J., "Earnings, Book Values, and Dividends in Equity Valuation", Contemporary Accounting Research, (Spring), 1995, pp.661-687.
[18] Preminger, A. and Franck, R., "Forecasting exchange rates: A robust regression approach", International Journal of Forecasting, No.23, 2007, pp.71- 84.
[19] Rousseeuw P. J. and Zomeren B. C., "A comparison of some quick algorithms for robust regression", Computational Statistics & Data Analysis, Vol.14, 1992, pp.107-116.
[20] Rousseeuw, P.J. and Leroy, A.M., Robust Regression and Outlier Detection, Wiley, New York, 1987.
[21] Rousseeuw, P.J. and Van Driessen, K., "Computing LTS Regression for Large Data Sets", Technical Report, University of Antwerp, 1998.
[22] Rousseeuw, P.J., "Least Median of Squares Regression", Journal of the American Statistical Association, No.79, 1984, pp. 871-880.
[23] Yaffee, R. A., "Robust Regression Analysis: Some Popular Statistical Package Options", Statistics, Social Science, and Mapping Group,Academic Computing Ser, 2002. www.nyu.edu/its/socsci/docs/RobustReg2.pdf.