The Identification of Blue Chip Stocks in Underdeveloped Stock Markets of South-Eastern Europe
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Abstract
The main goal of this paper is to explain the discriminatory variables between the blue chip and second-grade stocks in the underdeveloped stock markets of the South Eastern European (SEE) region. Since there is relatively less empirical research on the stock selection in underdeveloped markets, with even less studies on the markets in the transition economies of the SEE region, this paper is designed to shed some light on the identification of blue chip stocks from this region. Results presented in this paper provide confirmatory evidence that the blue chip stocks from the selected underdeveloped stock markets of the SEE region can be identified by examining their dividend yields, price to cash flow and EPS. Therefore, both institutional and individual investors need to focus on these variables when selecting stocks from these markets in order to reduce the risk associated with investing in equities.
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References
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Treynor, Jack and Black, Fisher. 1973. “How to Use Security Analysis to Improve Portfolio Selection.” The Journal of Business. 46(1): 66-86.
Vu, Jo. 2013. “Predictability of High-value Stocks on Australian and Shanghai Stock Exchange. “International Review of Business Research Papers.9(4): 22-32.
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Yoon, Youngohc, Swales, George., and Margavio, Thomas. 1993. “A Comparison of Discriminant Analysis versus Artificial Neural Networks.”Journal of the Operational Research Society.44(1): 51-60.
Yu, Lean, Wang, Shouyang, and Lai, Kin. 2008. “Neural network-based mean–variance–skewness model for portfolio selection.” Computers & Operations Research. 35(1): 34-46.
Alam, Asad, Caser, Paloma Anós, Khan, Faruk, and Udomsaph, Charles. 2008. Unleashing Prosperity Productivity Growth in Eastern Europe and the former Soviet Union. Washington D.C.: The International Bank for Reconstruction and Development/The World Bank.
Allen, Franklin, and Karjalainen, Risto. 1999. “Using genetic algorithms to find technical trading rules.“Journal of Financial Economics. 51: 245-271.
Altman, Edward. 1968.“Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy.” The Journal of Finance.XXIII(4): 589-609.
Aono, Kohei, and Iwaisako, Tokuo. 2010. “On the Predictability of Japanese Stock Returns Using Dividend Yield.” Asia-Pacific Finance Markets.17(2): 141-149.
Brock, William, Lakonishok, Josef, and LeBaron, Blake. 1992. “Simple Technical Trading Rules and the Stohastic Properties of Stock Returns.” The Journal of Finance. 47(5): 1731-1764.
Brown, Michael, and Wicker, Lori. 2000. “Discriminant Analysis.” In Handbook of Applied Multivariate Statistics and Mathematical Modeling, ed. Tinsley, Howard, and Brown, Steven. 209-235. London: Academic Press.
Burns, R. P. and Burns, R. 2008. Business Research Methods and Statistics Using SPSS.New York: SAGE Publications, Ltd.
Dann, Larry, Mayers, David, and Raab, Robert. 1977. “Trading rules, large blocks and the speed of price adjustment.” Journal of Financial Economics. 4(1): 3–22.
Fernández, Albertoand Gómez, Sergio. 2007. “Portfolio selection using neural networks.” Computers & Operations Research. 34(4): 1177-1191.
Fusion Media Ltd. 2014. Investing.com. http://www.investing.com/ (accessed August 1, 2014)
Gencay, Ramazan, and Stengos, Thanasis. 1997. “Technical trading rules and the size of the risk premium in security returns.”Studies in Nonlinear Dynamics and Econometrics. 2: 23–34.
Graham, Benjamin, and Dodd, David. 2009. Security Analysis: Principles and Technique (6th Ed.). New York: The McGraw-Hill Companies, Inc.
Hair, Josef, Anderson, Rolph, Tatham, Ronald, and Black, William. 1998. Multivariate Data Analysis (5th Ed.). New Jersey: Prentice – Hall, Inc.
Ionescu, Stefan, Murgoci, Cristiana, Gheorghe, Camelia, and Ionescu, Emilia. 2008. “Profitability on the Financial Markets; A Discriminant Analysis Approach.” International Journal of Applied Mathematics and Informatics. 3(2): 76-87.
Kaastra, Iebeling, and Boyd, Milton. 1996. “Designing a neural network for forecasting financial and economic time series.” Neurocomputing. 10: 215-236.
Khan, Muhammad Bilal, Gul, Sajid, Rehman, Shafiq, Ur Rehman, Razzaq, Nasir, and Kamran, Ali. 2012. “Financial Ratios and Stock Return Predictability (Evidence from Pakistan).” Research Journal of Finance and Accounting.3(10): 1-6.
Kheradyar, S., Ibrahim, I. and Mat Nor, F. 2011. “Stock Return Predictability with Financial Ratios.”International of Trade Economics and Finance.2(5): 391-396.
Lee, Wen-Shiung, Tzeng, Gwo-Hshiung, Guan, Jyh-Liang, Chien, Kuo-Ting, and Chien Huang, Juan-Ming. 2009. “Combined MCDM techniques for exploring stock selection based on Gordon model.” Expert Systems with Applications. 36(3): 6421-6430.
Lin, Chi-Ming, Huang, Jih-Jeng, Gen, Mitsuo, and Tzeng, Gwo-Hshiung. 2006. “Recurrent neural network for dynamic portfolio selection.” Applied Mathematics and Computation. 175: 1139–1146.
Malhotra, Karesh. 2004. Marketing Research: An Applied Orientation (4th Ed.). New Jersey: Pearson Education, Ltd.
Markowitz, Harry. 1952. “Portfolio Selection.” The Journal of Finance. 7(1): 77-91.
Merton, Robert. 1969. “Lifetime Portfolio Selection under Uncertainty: The Continuous Time Case.” Review of Economics and Statistics. 51(3): 247–257.
Mills, Terence. 1997. “Technical Analysis and the London Stock Exchange: Testing Trading Rules Using the FT30.” International Journal of Finance & Economics. 2(4): 319-331.
MSCI Inc. 2014. MSCI Market Classification Framework. http://www.msci.com/resources/products/indexes/global_equity_indexes/gimi/stdindex/MSCI_Market_Classification_Framework.pdf (accessed June 15, 2014)
Oz, Bulent, Ayricay, Yucel,.andKalkan, Gokturk. 2011. “Predicting Stock Returns with Financial Ratios: A Discriminant Analysis Application on the ISE 30 Index Stocks.”Anadolu University Journal of Social Sciences. 11(3): 51-64.
Quah, Tong-Seng, and Srinivasan, Bobby. 1999. “Improving returns on stock investment through neural network selection.” Expert Systems with Applications. 17: 295–301.
Research Centre International Economics. 2010. Policy Brief No. 4. http://www.fiw.ac.at/fileadmin/Documents/Publikationen/Policy_Briefs/04.FIW_PolicyBrief.Which_Growth_Model_for_Central_and_Eastern_Europe_after_the_Crisis_final.pdf (accessed February 16, 2015)
Samuelson, Paul. 1969. “Lifetime Portfolio Selection by Dynamic Stochastic Programming.” Review of Economics and Statistics. 51(3): 239–246.
Şenol, Emir, Dinçer, Hasan, and Timor, Mehpare. 2012. “Stock Selection Model Based on Fundamental and Technical Analysis Variables by Using Artificial Neural Networks and Support Vector Machines.” International Review of Economics & Finance. 02(03): 106-122.
Siqueira, Elisa, Otuki, Thiago, and da Costa, Newton. 2012. “Stock Return and Fundamental Variables: A Discriminant Analysis Approach.” Applied Mathematical Sciences. 6(115): 5719-5733.
Sorensen, Eric, Miller, Keith, and Ooi, Chee. 2000. “The Decision Tree Approach to Stock Selection.” The Journal of Portfolio Management. 27(1): 42-52.
Treynor, Jack and Black, Fisher. 1973. “How to Use Security Analysis to Improve Portfolio Selection.” The Journal of Business. 46(1): 66-86.
Vu, Jo. 2013. “Predictability of High-value Stocks on Australian and Shanghai Stock Exchange. “International Review of Business Research Papers.9(4): 22-32.
Wiener Börse AG. 2014. Index values. http://en.indices.cc/indices/details/sxe/composition/ (accessed July 10, 2014)
Yoon, Youngohc, Swales, George., and Margavio, Thomas. 1993. “A Comparison of Discriminant Analysis versus Artificial Neural Networks.”Journal of the Operational Research Society.44(1): 51-60.
Yu, Lean, Wang, Shouyang, and Lai, Kin. 2008. “Neural network-based mean–variance–skewness model for portfolio selection.” Computers & Operations Research. 35(1): 34-46.