INCORPORATING THE FUNDAMENTAL ANALYSIS INTO THE ROBUST MEAN – VARIANCE ANALYSIS: AN APPLICATION ON THE TURKISH BANKING STOCKS

Öz

Robust optimization is an important tool to deal with the uncertainty of parameters. However, due to the worst-case orientation, the existing robust mean – variance (MV) models ignore the plausible portfolio choices, backed by additional criteria or subjective judgements. Thus, we propose a way to incorporate the fundamental analysis into the robust MV analysis under the assumption that the risk-free asset and short positioning are allowed. After laying down the theoretical points, we give an explanatory example by using the real data set of six banking stocks trading on the Borsa Istanbul (BIST).

Anahtar Kelimeler

• Portfolio selection
• fundamental analysis
• principal components analysis
• robust optimization
• mean - variance model

Kaynakça

1. Berkowitz, J. (2000). A coherent framework for stress-testing. Journal of Risk, 2(2), 1–11.
2. Breuer, T. (2006). Providing against the worst: risk capital for worst case scenarios. Managerial Finance, 32(9), 716–730.
3. Buckley, J. J., Feuring, T. and Hayashi, Y. (2001). Fuzzy hierarchical analysis revisited. European Journal of Operational Research, 129(1), 48-64.
4. De Miguel, V., Garlappi, L., Nogales, F. J. and Uppal, J. (2009). A generalized approach to portfolio optimization: improving performance by constraining portfolio norms. Management Science, 55(5), 798 - 812.
5. Girko, V. L. (1998). An introduction to statistical analysis of random arrays. Vsp
6. Goktas, F. and Duran, A. (2020). New robust portfolio selection models based on the principal components Analysis. Journal of Multiple Valued Logic & Soft Computing, 34(1-2), 43-58.
7. Goldfarb, D., and Iyengar, G. (2003). Robust portfolio selection problems. Mathematics of Operations Research, 28(1), 1-38.
8. Huang, D., Zhu, S., Fabozzi, F. J., and Fukushima, M. (2010). Portfolio selection under distributional uncertainty: a relative robust CVaR approach. European Journal of Operational Research, 203(1), 185-194.

9. Johnson, R. A. and Wichern, D. (2007). Applied multivariate statistical analysis (sixth edition). Prentice hall.
10. Jolliffe, I. T. (2002). Principal component analysis (second edition). Springer.
11. Levy, H. (2016). Stochastic dominance: Investment decision making under uncertainty (third edition). Springer.
12. Malakooti, B. (2013). Operations and production systems with multiple objectives. John Wiley & Sons.
13. Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77-91.
14. Saaty, T. L. (2003). Decision making with the AHP: why is the principal eigenvector necessary. European Journal of Operational Research, 145(1), 85-91.
15. Saaty, T. L., Rogers, P. C. and Pell, R. (1980). Portfolio selection through hierarchies. The Journal of Portfolio Management, 6(3), 16-21.
16. Saaty, T. L. and Vargas, L. G. (2012). Models, methods, concepts & applications of the analytic hierarchy process. Springer.
17. Saaty, T. L. and Tran, L. T. (2007). On the invalidity of fuzzifying numerical judgments in the Analytic Hierarchy Process. Mathematical and Computer Modelling, 46, 962-975.
18. Tiryaki, F. and Ahlatcioglu, B. (2009). Fuzzy portfolio selection using fuzzy analytic hierarchy process. Information Sciences, 179(1), 53-69.