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2021 | 43 | 157-178

Article title

Ranking of optimal stock portfolios determined on the basis of expected utility maximization criterion

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Content

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Languages of publication

EN

Abstracts

EN
Aim/purpose – The aim of the paper is to rank the optimal portfolios of shares of companies listed on the Warsaw Stock Exchange, taking into account the investor’s propensity to risk. Design/methodology/approach – Investment portfolios consisting of varied number of companies selected from WIG 20 index were built. Next, the weights of equity holdings of these companies in the entire portfolio were determined, maximizing portfolio’s expected (square) utility function, and then the obtained structures were compared between investors with various levels of risk propensity. Using Hellwig’s taxonomic development measure, a ranking of optimum stock portfolios depending on the investor’s risk propensity was prepared. The research analyzed quotations from 248 trading sessions. Findings – The findings indicated that whilst there are differences in the weight structures of equity holdings in the entire portfolio between the investor characterized by aversion to risk at the level of γ = 10 and the investor characterized by aversion to risk at the level of γ = 100, the rankings of the constructed optimum portfolios demonstrate strong similarity. The study validated, in conformity with the literature, that with the increase in the number of equity holdings in the portfolio, the portfolio risk initially decreases and then becomes stable at a certain level. Research implications/limitations – The study used data from the past as for which there is no guarantee that they will be adequate for the future. There is sensitivity to the selection of the period from which the historic data come. When changing the period of the analyzed historic data by a small time unit it may prove that the portfolio composition will become totally different. Originality/value/contribution – The paper compares the composition of optimum stock portfolios depending on the investor’s propensity to risk. Their ranking was created using the taxonomic method for this purpose. Taking advantage of this method also additional variables can be taken into account, which describe and differentiate the portfolio and they can be assigned relevant significance depending on the investor’s preferences.

Year

Volume

43

Pages

157-178

Physical description

Contributors

author
  • University of Economics in Katowice

References

  • Almgren, R., Thum, C., Hauptmann, E., & Li, H. (2005). Direct estimation of equity market impact. Risk, 18, 5-26. Retrieved from https://citeseerx.ist.psu.edu/viewdoc/ download?doi=10.1.1.146.1241&rep=rep1&type=pdf
  • Aouni, B., Colapinto, C., & La Torre, D. (2014, April). Financial portfolio management through the goal programming model: Current state-of-the-art. European Journal of Operational Research, 234(2), 536-545. https://doi.org/10.1016/j.ejor.2013.09.040
  • Aouni, B., Doumpos, M., Pérez-Gladish, B., & Steuer, R. E. (2018). On the increasing importance of multiple criteria decision aid methods for portfolio selection. Journal of the Operational Research Society, 69(10), 1525-1542. https://doi.org/10.1080/ 01605682.2018.1475118
  • Azmi, R., & Tamiz, M. (2010). A review of goal programming for portfolio selection. In D. Jones, M. Tamiz, & J. Ries (Eds.), New developments in multiple objective and goal programming (Lecture Notes in Economics and Mathematical Systems, Vol. 638; pp. 15-33). Berlin: Springer. https://doi.org/10.1007/978-3-642-10354-4_2
  • Balcerowicz-Szkutnik, M., & Sojka E. (2011). Metody jakościowe i ilościowe w rozwią-zywaniu problemów społecznych [Qualitative and quantitative methods in solving social problems]. Katowice: Wydawnictwo Uniwersytetu Ekonomicznego.
  • Bodnar, T., Okhrin, Y., Vitlinskyy, V., & Zabolotskyy, T. (2018). Determination and estimation of risk aversion coefficients. Computational Management Science, 15, 297-317. https://doi.org/10.1007/s10287-018-0317-x
  • Bodnar, T., & Schmid, W. (2008). Estimation of optimal portfolio compositions for gaussian returns. Statistics & Risk Modeling, 26(3), 179-201. https://doi.org/10.1524/ stnd.2008.0918
  • Bodnar, T., & Schmid, W. (2009). Econometrical analysis of the sample efficient fron-tier. European Journal of Finance, 15(3) 317-335. https://doi.org/10.1080/13518 470802423478
  • Bodnar, T., & Schmid, W. (2011). On the exact distribution of the estimated expected utility portfolio weights: Theory and applications. Statistics & Risk Modeling, 28(4), 319-342. https://doi.org/10.1524/strm.2011.1080
  • Boyd, S., Mueller, M. T., O’Donoghue, B., & Wang, Y. (2013) Performance bounds and suboptimal policies for multi-period investment. Foundations and Trends in Optimi-zation, 1(1), 1-69. Retrieved from https://citeseerx.ist.psu.edu/viewdoc/download? doi=10.1.1.398.7263&rep=rep1&type=pdf
  • Brandt, M. W., & Santa-Clara, P. (2006). Dynamic portfolio selection by augmenting the asset space. Journal of Finance, 61(5), 2187-2217. https://doi.org/10.1111/j.1540-6261.2006.01055.x
  • Chopra, V. K., & Ziemba, W. T. (2011). The effect of errors in means, variances, and covariances on optimal portfolio choice. In C. L. MacLean, E. O. Thorp, & W. T. Ziemba (Eds.), The Kelly capital growth investment criterion: Theory and practice (World Scientific Handbook in Financial Economic Series, pp. 249-257). Hackensack, NJ: World Scientific Publishing. https://doi.org/10.1142/7598
  • Clarke, R., De Silva, H., & Thorley, S. (2002). Portfolio constraints and the fundamental law of active management. Financial Analysts Journal, 58, 48-66, Retrieved from https://faculty.fuqua.duke.edu/~charvey/Teaching/BA491_2005/Transfer_coefficient.pdf
  • Cremers, J.-H., Kritzman, M., & Page, S. (2005). Optimal hedge fund allocations: Do higher moments matter? Journal of Portfolio Management, 31(3), 70-81. https://doi.org/10.3905/jpm.2005.500356
  • Doering, J., Juan, A. A., Kizys, R., Fito, A., & Calvet, L. (2016). Solving realistic port-folio optimization problems via metaheuristics: A survey and an example. In R. León, M. Muñoz-Torres, J. Moneva (Eds.), Modeling and simulation in engi-neering, economics and management (Lecture Notes in Business Information Pro-cessing, Vol. 254, pp. 22-30). Cham: Springer. https://doi.org/10.1007/978-3-319-40506-3_3
  • Duan, Y. C. (2007). A multi-objective approach to portfolio optimization. Rose-Hulman Undergraduate Mathematics Journal, 8(1), 1-18. Retrieved from https://scholar. rose-hulman.edu/rhumj/vol8/iss1/12
  • Farkhati, F., Hoyyi, A., & Wilandari, Y. (2014). Analisis pembentukan portofolio opti-mal saham dengan pendekatan optimisasi multiobjektif untuk pengukuran value at risk [Analysis of the optimal creation of a stock portfolio with a multi-target opti-mization approach to measure value at risk]. Jurnal Gussian, 3(3), 371-380. Retrieved from https://media.neliti.com/media/publications/95899-ID-none.pdf
  • Hasbrouck, J. (1991). Measuring the information content of stock trades. Journal of Finance, 46(1), 179-207. https://doi.org/10.2307/2328693
  • Hellwig, Z. (1968). Zastosowanie metody taksonomicznej do typologicznego podziału krajów ze względu na poziom ich rozwoju oraz zasoby i strukturę wykwalif-ikowanych kadr [Application of the taxonomic method to the typological division of countries according to the level of their development and the resources and structure of qualified personnel]. Przegląd Statystyczny, 4, 307-326.
  • Kim, W. C., Kim, J. H., & Fabozzi, F. J. (2014, August). Deciphering robust portfolios. Journal of Banking and Finance, 45, 1-8. https://doi.org/10.1016/j.jbankfin. 2014.04.021
  • Kolm, P. N., Tutuncu, R., Fabozzi, F. J. (2014, April). 60 years of portfolio optimization: Practical challenges and current trend. European Journal of Operational Research, 234, 356-371. https://doi.org/10.1016/j.ejor.2013.10.060
  • Kourtis, A., Dotsis, G., & Markellos, R. N. (2012). Parameter uncertainty in portfolio selection: Shrinking the inverse covariance matrix. Journal of Banking and Fi-nance, 36(9), 2522-2531. https://doi.org/10.1016/j.jbankfin.2012.05.005
  • Kroll, Y., Levy, H., & Markowitz, H. M. (1984). Mean-variance versus direct utility maximization. Journal of Finance, 39(1), 47-61. https://doi.org/10.2307/2327667
  • Lillo, F., Farmer, J. D., & Mantegna, R. N. (2003). Master curve for price-impact func-tion. Nature, 421, 129-130. https://doi.org/10.1038/421129a
  • Mansini, R., Ogryczak, W., & Speranza, M. G. (2014). Twenty years of linear program-ming-based portfolio optimization. European Journal of Operational Research, 234(2), 518-535. https://doi.org/10.1016/j.ejor.2013.08.035
  • Markowitz, H. (1952). Portfolio selection. Journal of Finance, 7(1), 77-91. https:// doi.org/10.1111/j.1540-6261.1952.tb01525.x
  • Masmoudi, M., & Abdelaziz, F. B. (2018). Portfolio selection problem: A review of deterministic and stochastic multiple objective programming models. Annals of Operations Research, 267, 335-352. https://doi.org/10.1007/s10479-017-2466-7
  • Merton, R. C. (1980, December). On estimating the expected return on the market: An exploratory investigation. Journal of Financial Economics, 8(4), 323-361. https:// doi.org/10.1016/0304-405X(80)90007-0
  • Metaxiotis, K., & Liagkouras, K. (2012, October). Multi-objective evolutionary algorithms for portfolio management: A comprehensive literature review. Expert Systems with Ap-plications, 39(14), 11685-11698. https://doi.org/10.1016/j.eswa.2012.04.053
  • Okhrin, Y., & Schmid, W. (2006). Distributional properties of portfolio weights. Journal of Econometrics, 134(1), 235-256, https://doi.org/10.1016/j.jeconom.2005.06.022
  • Pera, K., Buła, R., & Mitrenga D. (2014). Modele inwestycyjne [Investment models]. Warszawa: C.H. Beck.
  • Ponsich, A., Jaimes, A. L., & Coello Coello, C. A. (2013). A survey on multi-objective evolutionary algorithms for the solution of the portfolio optimization problem and other finance and economics applications. IEEE Transactions on Evolutionary Computation, 17(3), 321-344. https://doi.org/10.1109/TEVC.2012.2196800
  • Ross, S. A. (1981). Some stronger measures of risk aversion in the small and the large with applications. Econometrica, 49(3), 621-638. https://doi.org/10.2307/1911515
  • Scherer, B., & Xu, X. (2007). The impact of constraints on value-added. Journal of Port-folio Management, 33(4), 45-54. https://doi.org/10.3905/jpm.2007.690605
  • Septiano, R., Syafriand, S., & Rosha, M. (2019). Pembentukan Portofolio Optimal Menggunakan Metode Optimasi Multiobjektif pada Saham di Bursa Efek Indonesia [Optimal portfolio building with the application of the Indonesian Stock Exchange Multi-Target Equity Optimization method]. UNP Journal of Mathematics, 2, 10-15. Retrieved from http://ejournal.unp.ac.id/students/index.php/mat/article/view/ 6298/3206
  • Tobin, J. (1958, February). Liquidity preference as behaviour towards risk. The Review of Economic Studies, 25(2), 65-86. https://doi.org/10.2307/2296205
  • Tütüncü, R. H., & Koenig, M. (2004). Robust asset allocation. Annals of Operations Research, 132, 157-187. https://doi.org/10.1023/B:ANOR.0000045281.41041.ed
  • Warsaw Stock Exchange. (n.d.). Retrieved from https://www.gpw.pl/spolki
  • Zhang, Y., Li, X., & Guo, S. (2018). Portfolio selection problems with Markowitz’s mean-variance framework: A review of literature. Fuzzy Optimization and Decision Making, 17, 125-158. https://doi.org/10.1007/s10700-017-9266-z

Document Type

Publication order reference

Identifiers

ISSN
1732-1948

YADDA identifier

bwmeta1.element.cejsh-bf5efbe2-a0bc-4f40-bd0f-d3034c0e9b13
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