{"id":18957,"date":"2024-04-04T10:09:33","date_gmt":"2024-04-04T07:09:33","guid":{"rendered":"https:\/\/modecon.mnau.edu.ua\/?p=18957"},"modified":"2024-04-04T10:09:33","modified_gmt":"2024-04-04T07:09:33","slug":"analysis-of-the-impact-of-2","status":"publish","type":"post","link":"https:\/\/modecon.mnau.edu.ua\/en\/analysis-of-the-impact-of-2\/","title":{"rendered":"Tyshchenko S., Parkhomenko O. Analysis of the Impact of Digital Threats on Financial Markets Using Methods of Probability Theory and Python"},"content":{"rendered":"

[vc_row][vc_column][vc_column_text]<\/p>\n\n\n\n
JEL Classification<\/strong>: \u04211, \u04215, \u04218.
\n<\/span><\/td>\n		DOI<\/b>: https:\/\/doi.org\/10.31521\/modecon.V43(2024)-16<\/a><\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

[\/vc_column_text][vc_column_text]Tyshchenko<\/strong> Svitlana, <\/strong>PhD (Pedagogy), Associate Professor of the Department of Economic Cybernetics, Computer Sciences and Information Technologies, Mykolayiv National Agrarian University, Mykolayiv, Ukraine<\/p>\n

ORCID ID:<\/strong> 0000-0001-7881-8740<\/a>
\ne-mail: <\/strong>tyschenko@mnau.edu.ua<\/a><\/p>\n

Parkhomenko Oleksandr, <\/strong>PhD (Physics and Mathematics), Associate Professor of the Department of Economic Cybernetics, Computer Sciences and Information Technologies, Mykolayiv National Agrarian University, Mykolayiv, Ukraine<\/p>\n

ORCID ID:<\/strong> 0000-0002-7940-7414<\/a>
\ne-mail: <\/strong>alex777par@gmail.com<\/a><\/p>\n

<\/h2>\n

Analysis of the Impact of Digital Threats on Financial Markets Using Methods of Probability Theory and Python<\/strong><\/h2>\n

\u00a0\u00a0<\/strong><\/p>\n

Abstract. Introduction<\/strong>. With the development of digital technologies and the growth of cyber threats, financial markets are becoming increasingly vulnerable to various cyber attacks, data theft, securities fraud, market manipulation and the spread of misinformation. These threats can have serious implications for the stability and efficiency of financial markets, reducing investor confidence and causing significant financial losses.<\/p>\n

Purpose. <\/strong>This article presents an approach to analysing and mitigating the impact of digital threats on financial markets by integrating probability theory methods and modern Python programming techniques.<\/p>\n

Results. <\/strong>First, we identify and classify the main types of digital threats: cyber attacks on critical infrastructure, data theft and confidential information leaks, securities fraud and market manipulation, and the spread of disinformation and fake news. Each type of threat is analysed in terms of its characteristics, sources, and potential consequences for the financial system.<\/p>\n

The article then discusses the use of probabilistic models to quantify the risks associated with digital threats. In particular, it demonstrates the use of Bayesian networks to calculate the probability of a successful cyberattack based on risk factors such as the level of cybersecurity, the presence of vulnerabilities in systems, and the history of previous attacks. Monte Carlo simulation modelling is also used to generate a large number of possible scenarios and assess their consequences, including changes in asset prices, market volatility and liquidity.<\/p>\n

To forecast future financial performance and assess the impact of digital incidents, the ARIMA time series model is built. This model takes into account the influence of past values, volatility and the effects of digital threats, allowing to predict changes in asset prices and volatility in the markets.<\/p>\n

All of the methods and algorithms described above are implemented using the Python programming language and its powerful libraries, such as NumPy, Pandas, scikit-learn, pgmpy, Arch, and Statsmodels. This provides flexibility, scalability, and the ability to integrate with a variety of data processing and analysis tools.<\/p>\n

The article provides specific examples of the application of the methods discussed, including detailed Python code. It demonstrates the practical use of Bayesian networks, the Monte Carlo method, and the ARIMA model to analyse synthetic datasets representing various digital threat scenarios.<\/p>\n

Conclusions. <\/strong>The results of the study demonstrate the effectiveness of the proposed approach and its ability to provide accurate risk assessment, forecasting the consequences of digital incidents and early detection of potential threats. This makes this approach a useful tool for financial institutions, regulators, and market participants in mitigating the impact of digital threats and strengthening the protection of the financial system.<\/p>\n

Overall, the article demonstrates the potential of integrating probability theory, machine learning, and modern programming technologies to address current issues in the financial sector related to growing cyber risks. The presented methods and tools can serve as a basis for further research and development of more advanced solutions for managing the risks of digital threats in financial markets.<\/p>\n

Keywords:<\/strong> digital threats, cyber attacks, probability theory, Python.<\/p>\n

References:<\/strong><\/p>\n

    \n
  1. Garita, M. (2020). Applied Quantitative Finance: Using Python for Financial Analysis. Palgrave Pivot.<\/li>\n
  2. Naik, K. (2019). Hands-On Python for Finance: A Practical Guide to Implementing Financial Analysis Strategies Using Python. Packt Publishing.<\/li>\n
  3. Jansen, S. (2020). Machine Learning for Algorithmic Trading: Predictive models to extract signals from market and alternative data for systematic trading strategies with Python, Packt Publishing.<\/li>\n
  4. Shin, J., Son, H., Khalil, R., & Heo, G. (2015). Development of a cyber security risk model using Bayesian networks. Reliability Engineering & System Safety<\/em>, 134, 208\u2013217. https:\/\/doi.org\/10.1016\/j.ress.2014.10.006<\/li>\n
  5. Yang, J., Zhao, Y., Han, C., Liu, Y., & Yang, M. (2022). Big data, big challenges: risk management of financial market in the digital economy. Journal of Enterprise Information Management<\/em>, 35, 4\/5, 1288-1304. https:\/\/doi.org\/10.1108\/JEIM-01-2021-0057.<\/li>\n
  6. Wang, J., Neil, M., & Fenton, N. (2020). Bayesian network approach for cybersecurity risk assessment implementing and extending the FAIR model. Computers & Security<\/em>, 89,101659. https:\/\/doi.org\/10.1016\/j.cose.2019.101659<\/li>\n
  7. Ahsan, M., Nygard, K. E., Gomes, R., Chowdhury, M., Rifat, N., & Connolly J. F. (2022). Cybersecurity Threats and Their Mitigation Approaches Using Machine Learning\u2013A Review. Journal of Cybersecurity and Privacy<\/em>, 2 (3), 527\u2013555. https:\/\/doi.org\/10.3390\/jcp2030027<\/li>\n
  8. Kasianova N., Bilychenko M., Severynenko A. (2023). Modeling the digital security of the enterprise. Modern Economics<\/em>, 39(2023), 54-61. https:\/\/doi.org\/10.31521\/modecon.V39(2023)-08.<\/li>\n
  9. Kuchmiiova T. (2023). Impact of Digital Technologies on Modern Society: Transformational Aspects. Modern Economics<\/em>, 41(2023), 67-72. https:\/\/doi.org\/10.31521\/modecon.V41(2023)-10.<\/li>\n
  10. Samoilenko, Y., Britchenko, I., Levchenko, I., Lo\u0161onczi, P., Bilichenko, O., & Bodnar, O. (2022). Economic security of the enterprise within the conditions of digital transformation. Economic Affairs<\/em>, 67(4). https:\/\/doi.org\/10.46852\/0424-2513.4.2022.28.<\/li>\n
  11. Sirenko, N., Baryshevska, I., & Melnyk, O. (2022). The genesis of financial market institutionalisation in Ukraine: An international perspective. Scientific Horizons<\/em>, 24(10), 97\u2013108. https:\/\/doi.org\/10.48077\/scihor.24(10).2021.97-108.<\/li>\n
  12. Spivakovskyy, S., Kochubei, O., Shebanina, O., Sokhatska, O., Yaroshenko, I., & Nych, T. (2021). The impact of digital transformation on the economic security of Ukraine. Studies of Applied Economics<\/em>, 39(5). https:\/\/doi.org\/10.25115\/eea.v39i5.5040.<\/li>\n
  13. Poltorak, A., Volosyuk, Y., Tyshchenko, S., Khrystenko, O., & Rybachuk, V. (2023). Development of directions for improving the monitoring of the state economic security under conditions of global instability. Eastern-European Journal of Enterprise Technologies<\/em>, 2(13 (122)), 17\u201327. https:\/\/doi.org\/10.15587\/1729-4061.2023.275834<\/li>\n<\/ol>\n

    [\/vc_column_text][\/vc_column][\/vc_row][vc_row][vc_column][vc_column_text]Received:<\/strong> 12 February 2024<\/p>\n

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    Tyshchenko S., Parkhomenko O. (2024). Analysis of the Impact of Digital Threats on Financial Markets Using Methods of Probability Theory and Python . Modern Economics, 43(2024), 118-124. DOI: https:\/\/doi.org\/10.31521\/modecon.V43(2024)-16.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
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    [vc_row][vc_column][vc_column_text] JEL Classification: \u04211, \u04215, \u04218. DOI: https:\/\/doi.org\/10.31521\/modecon.V43(2024)-16 [\/vc_column_text][vc_column_text]Tyshchenko Svitlana, PhD (Pedagogy), Associate Professor of the Department of Economic Cybernetics, Computer Sciences and Information Technologies, Mykolayiv National Agrarian University, Mykolayiv, Ukraine ORCID ID: 0000-0001-7881-8740 e-mail: tyschenko@mnau.edu.ua Parkhomenko Oleksandr, PhD (Physics and Mathematics), Associate Professor of the Department of Economic Cybernetics, Computer Sciences and Information Technologies,
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