THE ANALYSIS OF FINANCIAL BETA BEHAVIOUR VIA PANEL QUANTILE REGRESSION APPROACH

Abstract

In finance theory, Market model has been a major issue for decades. Especially, it is used by most of the researchers to estimate financial beta coefficient. It is obvious that there are some weaknesses to use Ordinary Least Squares (OLS) for estimation of the market model. The coefficients estimated by OLS explain only for mid-point of distribution and the OLS estimator does not consider extreme values. Therefore, Quantile Regression technique provides considering outliers and a detailed report while estimating the market model. The aim of the study is investigating the differences of financial beta coefficients on different quantiles via panel quantile regression technique. For this purpose, daily stock returns which traded in Borsa Istanbul and New York Stock Exchange are used for 2011-2015 period. Findings show that financial beta coefficients change for different points of stock returns for both markets.  It is clear that investors which regard differences of the financial beta coefficient on different quantiles prevent the possible strategic mistakes and losses. Besides, findings contain some important evidences about investor behaviors.

Keywords

• Financial beta,market model,panel quantile regression

References

1. Allen, D. E. & Singh, A. K. (2009). Minimizing Loss at Times of Financial Crisis: Quantile Regression as a Tool for Portfolio Investment Decisions. School of Accounting, Finance and Economics & FEMARC Working Paper Series, No: 0912.
2. Allen, D. E., Gerrans, P., Singh, A. K. & Powell, P. (2009). Quantile regression: its application in investment analysis. The Finsia Journal of Applied Finance, Issue: 4.
3. Barnes, M. L. & Huges, A. W. (2002). A Quantile Regression Analysis of the Cross Section of Stock Market Returns. Federal Reserve Bank of Boston in its series Working Papers with number 02-2.
4. Breusch, T.S. & Pagan, A.R. (1980). The Lagrange Multiplier Test and Its Applications to Model Specification in Econometrics. Review of Economic Studies, 47, 239–253.
5. Chang, M. C., Hung, J-C. & Nieh, C-C. (2011). Reexamination of capital asset pricing model (CAPM): An application of quantile regression. African Journal of Business Management, Vol. 5(33), pp. 12684-12690.
6. Cohen, K.J., Pogue, J.A. (1967). An Empirical Evaluation of Alternative Portfolio Selection Models, Journal of Business, vol.40, no. 2 (April):166-193.
7. Elton, E.J. & Gruber M.J. (1973). Estimating the Dependence Structure of Share Prices, Journal of Finance, vol. 28, no. 5 (December):1203-32.
8. Fama, E. F. & French, K. R., (1996). Multi-Factor Explanations of Asset Pricing Anomalies. Journal of Finance, 51: 55-84.

9. Irmak, S. & Çetin K. (2009). Hisse Senetlerinin Korelasyon Uzaklıklarına Dayalı Olarak Kümelenmesi. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, C.14, S.1 s.395-406.
10. Kahneman, D., & Tvertsky, A., (1979). Prospect Theory: An Analysis of Decision Under Risk. Econometrica, 47: 2, 263-291.
11. Koenker R., Bassett, G. Jr. (1978). Regression Quantiles. Econometrica, 46(1), pp.33-50.
12. Koenker, R. (2004). Quantile regression for longitudinal data. Journal of Multivariate Analysis 91, pp.74–89.
13. Küden, M. (2014). Davranışsal Finans Açısından Bireysel Yatırım Tercihlerinin Değerlendirilmesi. Yayımlanmış Yüksek Lisans Tezi, İzmir.
14. Li, T., Sun, L. & Zou, L. (2009). State ownership and corporate performance: A quantile regression analysis of Chinese listed companies. China Economic Review, 20: 703–716.
15. Lintner, J. (1965). The Valuation of Risk Assets and the Selection of Risky Investments in Stock Portfolios and Capital Budgets, Review of Economics and Statistics, vol. 47, no. 1 (February):13-37.
16. Ma, L. & Polhman, L. (2008). Return Forecasts and Optimal Portfolio Construction: A Quantile Regression Approach. The European Journal of Finance, 14:5, 409-425.
17. Markowitz, H. (1952). Portfolio Selection, Journal of Finance, 7(1), pp.77-91.
18. Mossin, J. (1966). Equilibrium in a Capital Asset Market, Econometrica, ol. 35, no. 4 (October):768-783.
19. Pesaran, M. H. (2004). General Diagnostic Tests for Cross Section Dependence in Panels. University of Cambridge, Faculty of Economics, Cambridge Working Papers in Economics No. 0435.
20. Pesaran, M. H. (2007). A Simple Panel Unit Root Test in the Presence of Cross Section Dependence. Journal of Applied Econometrics, 22(2), pp.265-312.
21. Roll, R. & Ross, S. A., (1980). An Empirical Investigation of the Arbitrage Pricing Theory. Journal of Finance, 35: 1073-1103.
22. Rosenberg, B. (1974). Extra-Market Components of Covariance in Security Returns, Journal of Financial and Quantitative Analysis, Vol. 9, No. 2 (March):263-273.
23. Ross, S. A. (1976). The Arbitrage Theory of Capital Asset Pricing. Journal of Economic Theory, 13, pp. 341-360.
24. Schoemaker, P., J., H., (1982). The Expected Utility Model: Its Variants, Purposes, Evidences and Limitations. Journal of Economic Literature, 20: 2, 529-563.
25. Sharpe, W. F. (1963). A Simplified Model for Portfolio Analysis. Management Science, Vol. 9, No. 2 (January):277-293.
26. Sharpe, W. F. (1964). Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk. The Journal of Finance, Vol. 19, No. 3, pp. 425-442.
27. Sharpe, W. F., Alexander, G. J. & Bailey, J. V. (1999). Invesments. Prentice-Hall, USA.
28. Taylor, M. & Sarno, L. (1998). The Behaviour Of Real Exchange Rates During The Post-Bretton Woods Period. Journal of International Economics, 46, 281–312.
29. Tobin, J. (1958). Liquidity Preference as Behavior towards Risk, Review of Economic Studies, vol. 25, no. 1 (February):65-86.
30. Tolga, A. & Şahin, I. (2009). Belirsizlik Altında Karar Alma: Geleneksel ve Modern Yaklaşımlar. Türkiye Ekonomi Kurumu Tartışma Metni, No: 2009/7.
31. Uyar (2015). Finansal Raporlama Standartları’nın Piyasa Değerini Açıklama Gücü Üzerine Etkisi. Pamukkale Üniversitesi, Sosyal Bilimler Enstitüsü, Yayımlanmamış Doktora Tezi, Denizli.
32. Von Neuman, J. & Morgenstein, O. (1944). Theory of Games and Economic Behavior. Princeton Princeton University Press, USA.