DOI: 10.32725/978-80-7694-102-1.40
Inproforum 2024 (vol. 18):277-284 | DOI: 10.32725/978-80-7694-102-1.40
Comparative Case Study on Portfolio Optimization: Modified Continuous Rank Length vs. Stochastic Dominance
- University of South Bohemia/Faculty of Economics, Department of Applied Mathematics and Informatics, Studentská 13, 370 05 Èeské Budìjovice 5, capkot00@jcu.cz.
This paper conducts a case study comparing the effectiveness of a modified Continuous Rank Length (CRL) metric with the Stochastic Dominance approach in portfolio optimization. The modified CRL method, designed to assess portfolio performance across the entire time interval, provides a holistic evaluation of rank stability, while Stochastic Dominance offers a robust framework for comparing return distributions. The analysis of simulated portfolios demonstrates that SD consistently identifies portfolios with the highest returns and lowest risks, particularly in early periods of the dataset. Conversely, CRL excels at selecting portfolios with enhanced long-term stability but is less efficient due to computational demands and broader stock inclusion. These findings provide actionable insights into the trade-offs between maximizing returns, ensuring stability, and managing computational resources in portfolio selection.
Keywords: Portfolio Performance, GET package, ERL, CRL, Extreme Rank Length, Continuous Rank Length, Financial Management, Financial Analysis, Stochastic Dominance
Published: January 11, 2025 Show citation
References
- Kozmík, V. (2019). Robust approaches in portfolio optimization with stochastic dominance [Master's thesis, Charles University]. Charles University Digital Repository.
- Hadar, J., & Russell, W. R. (1969). Rules for ordering uncertain prospects. The American Economic Review, 59(1), 25-34.
- Hahn, U. (2015). "A Note on Simultaneous Monte Carlo Tests." Technical report, Centre for Stochastic Geometry and Advanced Bioimaging, Aarhus University.
- Hanoch, G., & Levy, H. (1969). The efficiency analysis of choices involving risk. The Review of Economic Studies, 36(3), 335-346.
Go to original source... - Rothschild, M., & Stiglitz, J. E. (1970). Increasing risk: I. A definition. Journal of Economic Theory, 2(3), 225-243.
Go to original source... - Levy, H. (1992). Stochastic dominance and expected utility: Survey and analysis. Management Science, 38(4), 555-593.
Go to original source... - Fishburn, P. C. (1974). Third-order stochastic dominance and risk. Management Science, 21(11), 1341-1346.
- Mrkvièka, T., Myllymäki, M., Kuronen, M., & Narisetty, N. N. (2022). "New Methods for Multiple Testing in Permutati-on Inference for the General Linear Model." Statistics in Medicine, 41(2), 276-297. doi:10.1002/sim.9236.
Go to original source... - Myllymäki, M., & Mrkvièka, T. (2020). "GET: Global Envelopes in R." arXiv:1911.06583 [stat.ME].
Go to original source... - Ryan JA, Ulrich JM (2024). _quantmod: Quantitative Financial Modelling Framework_. R package version 0.4.26.
- Stewart, S., Piros, C. D., & Heisler, J. (2019). Portfolio management: Theory and practice. John Wiley & Sons Inc.
- Whitmore, G. A., & Findlay, M. C. (1978). Stochastic dominance: An approach to decision making under risk. D.C. Heath and Company.


