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Method for Determining the Risk Profile of Investors Based on the Relationship of Two Stock Investing Problems

https://doi.org/10.26794/2587-5671-2024-28-4-136-143

Abstract

   The subject of research in this paper is the investor’s risk profile as a characteristic of his behavior in the stock market.

   The purpose of the study is to assess the investor’s risk profile in the form of a risk ratio in a model with a linear convolution of expected return and variance.

   A financial consultant can use this information to create a portfolio of
financial instruments that corresponds with an investor’s acceptance of risk.

   This makes the study relevant because it addresses the problem of minimizing potential risks in the management of an investment portfolio, which is related to the investor’s attitude toward risk.

   The scientific novelty lies in the development of a mathematical approach to solving the problem of determining the risk profile based on the relationship between the solutions of two problems of choosing an investment portfolio, expressed as conditions on the parameters under which the solutions of these problems exist and coincide.

   Wherein, mathematical programming methods were used, as well as the Python programming language. As a result, the risk coefficient is expressed in terms of the model parameter with a constraint on profitability; a classification of the risk profile according to the acceptable value of the risk coefficient is proposed; the method is implemented as a set of programs and demonstrated on the example of the Russian stock market. The conclusion is made about the possibilities of trust managers using this approach when making decisions on choosing the best portfolio.

About the Authors

V. A. Gorelik
Federal Research Center “Informatics and Control” RAS; Pedagogical State University
Russian Federation

Victor A. Gorelik, Dr. Sci. (Phys. and Math.), Leading Researcher, Prof.

Department of Simulation Systems and Operations Research; Chair of the Theoretical Informatics and Discrete Mathematics

Moscow


Competing Interests:

The authors have no conflicts of interest to declare



T. V. Zolotova
Financial University
Russian Federation

Tatyana V. Zolotova, Dr. Sci. (Phys. and Math.), Assoc. Prof., Prof.

Chair of Modeling and System Analysis

Moscow


Competing Interests:

The authors have no conflicts of interest to declare



References

1. Bacon C. R. Practical portfolio performance measurement and attribution. New York, NY: John Wiley & Sons; 2023. 529 p.

2. Gorelik V. A., Zolotova T. V. Criteria for evaluation and optimality of risk in complex organizational systems. Moscow: Dorodnicyn Computing Centre of RAS; 2009. 162 p. (In Russ.).

3. Kalayci C. B., Ertenlice O., Akbay M. A. A comprehensive review of deterministic models and applications for mean-variance portfolio optimization. Expert Systems with Applications. 2019;125:345–368. DOI: 10.1016/j.eswa.2019.02.011

4. Dekanova K. V. Evaluation of opportunities to increase the potential for attracting funds from private investors through their rational interaction with an independent financial (investment) adviser. Sibirskaya finansovaya shkola = Siberian Financial School. 2022;(2):77–87. (In Russ.). DOI: 10.34020/1993-4386--2022-2-77-87

5. Kislitsyna L. V., Shnitova G. A., Makhonina O. V., Safonova V. A. Methodology for categorizing investors for the purposes of forming their investment portfolio. Zhurnal prikladnykh issledovanii = Journal of Applied Research. 2021;(6–7):667–672. (In Russ.). DOI: 10.47576/2712–7516_2021_6_7_667

6. Zhiluk D. A., Skorokhod A. Yu. Categorization of private investors: New risks and opportunities. Izvestiya Sankt-Peterburgskogo gosudarstvennogo ekonomicheskogo universiteta. 2020;(4):42–47. (In Russ.).

7. Kachalov A. A. Determining level of risk tolerance of individual investor. Finansovyi biznes = Financial Business. 2021;(11):56–58. (In Russ.).

8. Nguyen L., Gallery G., Newton C. The joint influence of financial risk perception and risk tolerance on individual investment decision-making. Accounting & Finance. 2019;59(S 1):747–771. DOI: 10.1111/acfi.12295

9. De Bortoli D., da Costa N., Jr., Goulart M., Campara J. Personality traits and investor profile analysis: A behavioral finance study. PloS One. 2019;14(3): e0214062. DOI: 10.1371/journal.pone.0214062

10. Goulart M., da Costa N. C.A., Jr., Andrade E. B., Santos A. A.P. Hedging against embarrassment. Journal of Economic Behavior & Organization. 2015;116:310–318. DOI: 10.1016/j.jebo.2015.04.014

11. Abraham F., Schmukler S. L., Tessada J. Robo-advisors: Investing through machines. Research and Policy Briefs. 2019;(21). URL: https://documents1.worldbank.org/curated/en/275041551196836758/pdf/Robo-Advisors-Investing-through-Machines.pdf

12. Capponi A., Ólafsson S., Zariphopoulou T. Personalized robo-advising: Enhancing investment through client interaction. Management Science. 2022;68(4):2485–2512. DOI: 10.1287/mnsc.2021.4014

13. Fisch C., Masiak C., Vismara S., Block J. Motives and profiles of ICO investors. Journal of Business Research. 2021:125:564–576. DOI: 10.1016/j.jbusres.2019.07.036

14. Mubaraq M. R., Anshori M., Trihatmoko H. The influence of financial knowledge and risk tolerance on investment decision making. Jurnal Ekonomi Bisnis dan Kewirausahaan. 2021;10(2):140–153. DOI: 10.26418/jebik.v10i2.47089

15. Ao M., Yingying L., Zheng X. Approaching mean-variance efficiency for large portfolios. The Review of Financial Studies. 2019;32(7):2890–2919. DOI: 10.1093/rfs/hhy105

16. García F., González-Bueno J.A, Oliver J. Mean-variance investment strategy applied in emerging financial markets: Evidence from the Colombian stock market. Intellectual Economics. 2015;9(1):22–29. DOI: 10.1016/j.intele.2015.09.003

17. Guo X., Chan R. H., Wong W.-K., Zhu L. Mean-variance, mean-VaR, and mean-CVaR models for portfolio selection with background risk. Risk Management. 2019;21(2):73–98. DOI: 10.1057/s41283–018–0043–2

18. Harman R., Prus M. Computing optimal experimental designs with respect to a compound Bayes risk criterion. Statistics & Probability Letters. 2018;137:135–141. DOI: 10.1016/j.spl.2018.01.017

19. Radner R. Decision and choice: Bounded rationality. In: Wright J. D., ed. International encyclopedia of the social & behavioral sciences. 2nd ed. Oxford: Elsevier Science; 2015:879–885.

20. Chen W., Zhang H., Mehlawat M. K., Jia L. Mean-variance portfolio optimization using machine learning-based stock price prediction. Applied Soft Computing. 2021;100:106943. DOI: 10.1016/j.asoc.2020.106943


Review

For citations:


Gorelik V.A., Zolotova T.V. Method for Determining the Risk Profile of Investors Based on the Relationship of Two Stock Investing Problems. Finance: Theory and Practice. 2024;28(4):136-143. https://doi.org/10.26794/2587-5671-2024-28-4-136-143

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ISSN 2587-5671 (Print)
ISSN 2587-7089 (Online)