<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.3 20210610//EN" "JATS-journalpublishing1-3.dtd">
<article article-type="research-article" dtd-version="1.3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:lang="ru"><front><journal-meta><journal-id journal-id-type="publisher-id">finance</journal-id><journal-title-group><journal-title xml:lang="ru">Финансы: теория и практика/Finance: Theory and Practice</journal-title><trans-title-group xml:lang="en"><trans-title>Finance: Theory and Practice</trans-title></trans-title-group></journal-title-group><issn pub-type="ppub">2587-5671</issn><issn pub-type="epub">2587-7089</issn><publisher><publisher-name>Financial University under The Government of Russian Federation</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.26794/2587-5671-2024-28-4-136-143</article-id><article-id custom-type="elpub" pub-id-type="custom">finance-3070</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="ru"><subject>ФИНАНСОВЫЕ РИСКИ</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>FINANCIAL RISKS</subject></subj-group></article-categories><title-group><article-title>Метод определения риск-профиля инвестора на основе взаимосвязи двух задач фондового инвестирования</article-title><trans-title-group xml:lang="en"><trans-title>Method for Determining the Risk Profile of Investors Based on the Relationship of Two Stock Investing Problems</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0003-2435-0796</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Горелик</surname><given-names>В. A.</given-names></name><name name-style="western" xml:lang="en"><surname>Gorelik</surname><given-names>V. A.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Виктор Александрович Горелик, доктор физико-математических наук, ведущий научный сотрудник, профессор</p><p>отдел имитационных систем и исследования операций; кафедра теоретической информатики и дискретной математики</p><p>Москва</p></bio><bio xml:lang="en"><p>Victor A. Gorelik, Dr. Sci. (Phys. and Math.), Leading Researcher, Prof.</p><p>Department of Simulation Systems and Operations Research; Chair of the Theoretical Informatics and Discrete Mathematics</p><p>Moscow</p></bio><email xlink:type="simple">gorelik@ccas.ru</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0001-5185-0687</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Золотова</surname><given-names>T. В.</given-names></name><name name-style="western" xml:lang="en"><surname>Zolotova</surname><given-names>T. V.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Tатьяна Валерьяновна Золотова, доктор физико-математических наук, доцент, профессор</p><p>кафедра моделирования и системного анализа</p><p>Москва</p></bio><bio xml:lang="en"><p>Tatyana V. Zolotova, Dr. Sci. (Phys. and Math.), Assoc. Prof., Prof.</p><p>Chair of Modeling and System Analysis</p><p>Moscow</p></bio><email xlink:type="simple">tzolotova@fa.ru</email><xref ref-type="aff" rid="aff-2"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru"><institution>Федеральный исследовательский центр «Информатика и управление» РАН; Московский педагогический государственный университет</institution><country>Россия</country></aff><aff xml:lang="en"><institution>Federal Research Center “Informatics and Control” RAS; Pedagogical State University</institution><country>Russian Federation</country></aff></aff-alternatives><aff-alternatives id="aff-2"><aff xml:lang="ru"><institution>Финансовый университет</institution><country>Россия</country></aff><aff xml:lang="en"><institution>Financial University</institution><country>Russian Federation</country></aff></aff-alternatives><pub-date pub-type="collection"><year>2024</year></pub-date><pub-date pub-type="epub"><day>29</day><month>08</month><year>2024</year></pub-date><volume>28</volume><issue>4</issue><fpage>136</fpage><lpage>143</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Горелик В.A., Золотова T.В., 2024</copyright-statement><copyright-year>2024</copyright-year><copyright-holder xml:lang="ru">Горелик В.A., Золотова T.В.</copyright-holder><copyright-holder xml:lang="en">Gorelik V.A., Zolotova T.V.</copyright-holder><license xml:lang="ru" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>Данная работа распространяется под лицензией Creative Commons Attribution 4.0.</license-p></license><license xml:lang="en" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>This work is licensed under a Creative Commons Attribution 4.0 License.</license-p></license></permissions><self-uri xlink:href="https://financetp.fa.ru/jour/article/view/3070">https://financetp.fa.ru/jour/article/view/3070</self-uri><abstract><p>   Предметом исследования в данной работе является профиль риска инвестора как характеристики его поведения на фондовом рынке.</p><p>   Цель исследования — дать оценку риск-профиля инвестора в виде коэффициента риска в модели с линейной сверткой ожидаемой доходности и дисперсии.</p><p>   Актуальность такого исследования, с одной стороны, обусловлена проблемой снижения возможных рисков при управлении инвестиционным портфелем, которая, в свою очередь, связана с отношением самого инвестора к риску, а с другой стороны — дает возможность финансовому консультанту создать портфель финансовых инструментов в соответствии с уровнем согласия инвестора на риск.</p><p>   Научная новизна заключается в разработке математического подхода к решению проблемы определения риск-профиля на основе взаимосвязи решений двух задач выбора портфеля инвестиций, выраженной в виде условий на параметры, при которых решения этих задач существуют и совпадают.</p><p>   При этом использовались методы математического программирования, а также язык программирования Python. В результате коэффициент риска выражен через параметр модели с ограничением на доходность, предложена классификация риск-профиля по допустимому значению коэффициента риска; метод реализован в виде комплекса программ и продемонстрирован на примере российского фондового рынка. Сделан вывод о возможностях применения данного подхода доверительным управляющим при принятии решений о выборе наилучшего портфеля.</p></abstract><trans-abstract xml:lang="en"><p>   The subject of research in this paper is the investor’s risk profile as a characteristic of his behavior in the stock market.</p><p>   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.</p><p>   A financial consultant can use this information to create a portfolio offinancial instruments that corresponds with an investor’s acceptance of risk.</p><p>   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.</p><p>   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.</p><p>   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.</p></trans-abstract><kwd-group xml:lang="ru"><kwd>коэффициент риска</kwd><kwd>риск-профиль</kwd><kwd>ожидаемая доходность</kwd><kwd>свертка критериев</kwd><kwd>инвестиционный портфель</kwd></kwd-group><kwd-group xml:lang="en"><kwd>risk coefficient</kwd><kwd>risk profile</kwd><kwd>expected return</kwd><kwd>criteria convolution</kwd><kwd>investment portfolio</kwd></kwd-group></article-meta></front><back><ref-list><title>References</title><ref id="cit1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">Bacon C. R. Practical portfolio performance measurement and attribution. New York, NY: John Wiley &amp; Sons; 2023. 529 p.</mixed-citation><mixed-citation xml:lang="en">Bacon C. R. Practical portfolio performance measurement and attribution. New York, NY: John Wiley &amp; Sons; 2023. 529 p.</mixed-citation></citation-alternatives></ref><ref id="cit2"><label>2</label><citation-alternatives><mixed-citation xml:lang="ru">Горелик В. А., Золотова Т. В. Критерии оценки оптимальности риска в сложных организационных системах. М.: ВЦ РАН; 2009. 162 с.</mixed-citation><mixed-citation xml:lang="en">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.).</mixed-citation></citation-alternatives></ref><ref id="cit3"><label>3</label><citation-alternatives><mixed-citation xml:lang="ru">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</mixed-citation><mixed-citation xml:lang="en">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</mixed-citation></citation-alternatives></ref><ref id="cit4"><label>4</label><citation-alternatives><mixed-citation xml:lang="ru">Деканова К. В. Оценка возможностей повышения потенциала привлечения средств частных инвесторов за счет рационального их взаимодействия с независимым финансовым (инвестиционным) советником. Сибирская финансовая школа. 2022;(2):77–87. DOI: 10.34020/1993-4386--2022-2-77-87</mixed-citation><mixed-citation xml:lang="en">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</mixed-citation></citation-alternatives></ref><ref id="cit5"><label>5</label><citation-alternatives><mixed-citation xml:lang="ru">Кислицына Л. В., Шнитова Г. А., Махонина О. В., Сафонова В. А. Методика категоризации инвесторов для целей формирования их инвестиционного портфеля. Журнал прикладных исследований. 2021;(6–7):667–672. DOI: 10.47576/2712–7516_2021_6_7_667</mixed-citation><mixed-citation xml:lang="en">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</mixed-citation></citation-alternatives></ref><ref id="cit6"><label>6</label><citation-alternatives><mixed-citation xml:lang="ru">Жилюк Д. А., Скороход А. Ю. Категоризация частных инвесторов: новые риски и возможности. Известия Санкт-Петербургского государственного экономического университета. 2020;(4):42–47.</mixed-citation><mixed-citation xml:lang="en">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.).</mixed-citation></citation-alternatives></ref><ref id="cit7"><label>7</label><citation-alternatives><mixed-citation xml:lang="ru">Качалов А. А. Особенности процесса определения риск-профиля частного инвестора. Финансовый бизнес. 2021;(11):56–58.</mixed-citation><mixed-citation xml:lang="en">Kachalov A. A. Determining level of risk tolerance of individual investor. Finansovyi biznes = Financial Business. 2021;(11):56–58. (In Russ.).</mixed-citation></citation-alternatives></ref><ref id="cit8"><label>8</label><citation-alternatives><mixed-citation xml:lang="ru">Nguyen L., Gallery G., Newton C. The joint influence of financial risk perception and risk tolerance on individual investment decision-making. Accounting &amp; Finance. 2019;59(S 1):747–771. DOI: 10.1111/acfi.12295</mixed-citation><mixed-citation xml:lang="en">Nguyen L., Gallery G., Newton C. The joint influence of financial risk perception and risk tolerance on individual investment decision-making. Accounting &amp; Finance. 2019;59(S 1):747–771. DOI: 10.1111/acfi.12295</mixed-citation></citation-alternatives></ref><ref id="cit9"><label>9</label><citation-alternatives><mixed-citation xml:lang="ru">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</mixed-citation><mixed-citation xml:lang="en">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</mixed-citation></citation-alternatives></ref><ref id="cit10"><label>10</label><citation-alternatives><mixed-citation xml:lang="ru">Goulart M., da Costa N. C.A., Jr., Andrade E. B., Santos A. A.P. Hedging against embarrassment. Journal of Economic Behavior &amp; Organization. 2015;116:310–318. DOI: 10.1016/j.jebo.2015.04.014</mixed-citation><mixed-citation xml:lang="en">Goulart M., da Costa N. C.A., Jr., Andrade E. B., Santos A. A.P. Hedging against embarrassment. Journal of Economic Behavior &amp; Organization. 2015;116:310–318. DOI: 10.1016/j.jebo.2015.04.014</mixed-citation></citation-alternatives></ref><ref id="cit11"><label>11</label><citation-alternatives><mixed-citation xml:lang="ru">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</mixed-citation><mixed-citation xml:lang="en">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</mixed-citation></citation-alternatives></ref><ref id="cit12"><label>12</label><citation-alternatives><mixed-citation xml:lang="ru">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</mixed-citation><mixed-citation xml:lang="en">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</mixed-citation></citation-alternatives></ref><ref id="cit13"><label>13</label><citation-alternatives><mixed-citation xml:lang="ru">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</mixed-citation><mixed-citation xml:lang="en">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</mixed-citation></citation-alternatives></ref><ref id="cit14"><label>14</label><citation-alternatives><mixed-citation xml:lang="ru">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</mixed-citation><mixed-citation xml:lang="en">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</mixed-citation></citation-alternatives></ref><ref id="cit15"><label>15</label><citation-alternatives><mixed-citation xml:lang="ru">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</mixed-citation><mixed-citation xml:lang="en">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</mixed-citation></citation-alternatives></ref><ref id="cit16"><label>16</label><citation-alternatives><mixed-citation xml:lang="ru">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</mixed-citation><mixed-citation xml:lang="en">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</mixed-citation></citation-alternatives></ref><ref id="cit17"><label>17</label><citation-alternatives><mixed-citation xml:lang="ru">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</mixed-citation><mixed-citation xml:lang="en">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</mixed-citation></citation-alternatives></ref><ref id="cit18"><label>18</label><citation-alternatives><mixed-citation xml:lang="ru">Harman R., Prus M. Computing optimal experimental designs with respect to a compound Bayes risk criterion. Statistics &amp; Probability Letters. 2018;137:135–141. DOI: 10.1016/j.spl.2018.01.017</mixed-citation><mixed-citation xml:lang="en">Harman R., Prus M. Computing optimal experimental designs with respect to a compound Bayes risk criterion. Statistics &amp; Probability Letters. 2018;137:135–141. DOI: 10.1016/j.spl.2018.01.017</mixed-citation></citation-alternatives></ref><ref id="cit19"><label>19</label><citation-alternatives><mixed-citation xml:lang="ru">Radner R. Decision and choice: Bounded rationality. In: Wright J. D., ed. International encyclopedia of the social &amp; behavioral sciences. 2nd ed. Oxford: Elsevier Science; 2015:879–885.</mixed-citation><mixed-citation xml:lang="en">Radner R. Decision and choice: Bounded rationality. In: Wright J. D., ed. International encyclopedia of the social &amp; behavioral sciences. 2nd ed. Oxford: Elsevier Science; 2015:879–885.</mixed-citation></citation-alternatives></ref><ref id="cit20"><label>20</label><citation-alternatives><mixed-citation xml:lang="ru">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</mixed-citation><mixed-citation xml:lang="en">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</mixed-citation></citation-alternatives></ref></ref-list><fn-group><fn fn-type="conflict"><p>The authors declare that there are no conflicts of interest present.</p></fn></fn-group></back></article>
