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Modeling financial market dynamics with the use of fuzzy

https://doi.org/10.26425/1816-4277-2024-7-170-180

Abstract

In   the modern world, financial markets play an   important role in   the economy and people’s lives. They provide access to   financial resources and are also a   source of   profit for many companies. However, instability in  the financial markets can lead to   serious consequences such as   financial crises and loss of   investor confidence. In   this regard, modelling the financial market dynamics becomes increasingly relevant. This work considered the use of   fuzzy mathematics for this purpose. Fuzzy mathematics is a   branch of   mathematics that studies methods and algorithms for dealing with fuzzy data and fuzzy objects. It   allows to   consider uncertainty and incompleteness of  information, which is  especially important in  the financial markets where data is   often incomplete and inaccurate. The purpose of   this research is   to establish the relationship between financial asset prices while using behavioural factors (investor sentiment), fundamental (market returns), and microstructural ones (company size, ratio of   book and market values of   the company). The application of   fuzzy mathematics in   financial modelling will improve the accuracy and reliability of   forecasts as   well as   increase the stability of   the model to   various sources of  uncertainty.

About the Authors

K. A. Polekhina
Bauman Moscow State Technical University
Russian Federation

Ksenia A. Polekhina, Graduate Student

Moscow



G. Eu. Polekhina
Bauman Moscow State Technical University; Сivil Protection Academy of the Russian Ministry for Civil Defense, Emergencies, and Elimination of Consequences of Natural Disasters
Russian Federation

Galina Eu. Polekhina, Cand. Sci. (Ped.), Assoc. Prof. at the Computational Mathematics and Computational Physics Department; Assoc. Prof. at the Higher Mathematics Department

Moscow



References

1. Kostikova A.V., Kuznetsov S.Yu., Tereliansky P.V. Application of the fuzzy sets theory in the problem of products competitiveness evaluation. E-management. 2023;2(6):37–48. (In Russian). https://doi.org/10.26425/2658-3445-2023-6-2-37-48

2. Krasnykh S.S. Adaptive potential of Russian economic growth in the context of decline in foreign direct investment. E-Management. 2024;1(7):36–47. (In Russian). https://doi.org/10.26425/2658-3445-2024-7-1-36-47

3. Shvachko A.A. Vectors for improving development strategies of Russian enterprises in the context of prerequisites for transformation of the Russian economy. E-Management. 2024;1(7):48–60. (In Russian). https://doi.org/10.26425/2658-3445-2024-7-1-48-60

4. Fama E., French K. The cross-section of expected stock return. Journal of Finance. 1992;2(47):427–465. https://doi.org/10.2307/2329112

5. Fedorova E.А, Sivak A.R. Comparison of the CAPM and Fama-French models on the Russian stock market. Finance and Credit. 2012;42(522):42–48. (In Russian).

6. Acharya V.V., Pedersen L.H. Asset pricing with liquidity risk. Journal of Financial Economics. 2005;77:375–410. https://doi.org/10.1016/j.jfineco.2004.06.007

7. Kuskova E.A., Kan Yu.S. Modelling the dynamics of the RTSI index. Modelling and data analysis. 2019;2:39–47. (In Russian).

8. Polyanina P.V., Rodionov D.G., Konnikov E.A. Modeling financial market conditions in an intelligent economy based on a fuzzy set approach. π-Economy. 2023;16(5):78–90. (In Russian). https://doi.org/10.18721/JE.16506

9. Fisher K.L., Statman M. Investor sentiment and stock returns. Financial Analyst Journal. 2000;2(56):16–23. http://dx.doi.org/10.2469/faj.v56.n2.2340

10. Neal R., Wheatley S. Do measures of investor sentiment predict stock returns? Journal of Financial and Quantitative Analysis. 1998;33(4):523–547. https://doi.org/10.2307/2331130

11. Brown G., Cliff M . Investor sentiment and the near-term stock market. Journal of Empirical Finance. 2004;11.

12. Price K., Storn R. Differential evolution – a simple evolution strategy for fast optimization. Journal of Global Optimization. 1997;11:341–359. http://dx.doi.org/10.1023/A:1008202821328

13. Fama E., French K. Common risk factors in the returns on stocks and bonds. Journal of Financial Economics. 1993;33(1):3–56. https://doi.org/10.1016/0304-405X(93)90023-5;

14. Hachicha N., Bouri A. Behavioral beta and asset valuation models. International Research Journal of Finance and Economics. 2008;16:175–192. https://doi.org/10.22051/jfm.2018.16581.1445

15. Fama E., French K., Davis J. Characteristics, covariances, and average returns: 1929 to 1997. The Journal of Finance. 2000;55(1).

16. Karr C. Applying genetics to fuzzy logic. AI Expert. 1991;3(36):38–43.

17. Hachicha N., Jarboui B., Siarry P. A fuzzy logic control using a differential evolution algorithm aimed at modelling the financial market dynamics. Information Sciences. 2011;1(181):79–91. http://dx.doi.org/10.1016/j.ins.2010.09.010


Review

For citations:


Polekhina K.A., Polekhina G.E. Modeling financial market dynamics with the use of fuzzy. Vestnik Universiteta. 2024;(7):170-180. (In Russ.) https://doi.org/10.26425/1816-4277-2024-7-170-180

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ISSN 1816-4277 (Print)
ISSN 2686-8415 (Online)