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ИД «Финансы и кредит»






Finance and Credit

An end-to-end technology management model in cross-border M&A transactions

Vol. 29, Iss. 8, AUGUST 2023

Received: 24 April 2023

Received in revised form: 15 May 2023

Accepted: 29 May 2023

Available online: 30 August 2023

Subject Heading: INVESTING

JEL Classification: G34

Pages: 1709–1729


Valerii V. IVANOV Russian Foreign Trade Academy of Ministry of Economic Development of the Russian Federation (RFTA), Moscow, Russian Federation

ORCID id: not available

Maksim V. DENISOV Russian Foreign Trade Academy of Ministry of Economic Development of the Russian Federation (RFTA), Moscow, Russian Federation

ORCID id: not available

Subject. This article examines an adapted management model based on the use of end-to-end technologies in key business processes for finding target companies and deciding on the feasibility of implementing mergers and acquisitions.
Objectives. The article aims to present an author-developed model for managing end-to-end technologies in cross-border mergers and acquisitions.
Methods. For the study, we used empirical and logical constructions, analysis and synthesis, generalization, formalization, systems approach, and the graphic and tabular methods of visualization.
Results. The article identifies trends in the use of artificial intelligence in the main elements of the developed management model along with traditional ways of managing mergers and acquisitions. The proposed system management integrator helps use machine learning algorithms and business process controlling to increase the accuracy and efficiency of decisions and maximize the synergy of the buyer and the target company after the implementation of mergers and acquisitions, which is verified using mathematical algorithms and developed indicators for the use of artificial intelligence and big data business process management.
Conclusions. The management model of cross-border mergers and acquisitions of companies determines the use of end-to-end technologies to improve the time and quality of management decision-making.

Keywords: mergers and acquisitions, end-to-end technologies, artificial intelligence, cross-border markets, business processes


  1. Kiporenko S.S., Yurchuk N.P. Artificial Intelligence in Business: Threats, Benefits, Trends. Colloquium-journal, 2021, no. 17, pp. 83–91. URL: Link
  2. Mishra S., Tripathi A.R. AI Business Model: An Integrative Business Approach. Journal of Innovation and Entrepreneurship, 2021, vol. 10. URL: Link
  3. Chopra R., Sharma G.D. Application of Artificial Intelligence in Stock Market Forecasting: A Critique, Review, and Research Agenda. Journal of Risk and Financial Management, 2021, vol. 14. URL: Link
  4. Ustinova O.E. [Artificial intelligence in company management]. Kreativnaya ekonomika = Journal of Creative Economy, 2020, vol. 14, no. 5, pp. 885–904. URL: Link (In Russ.)
  5. Chernenko V.A., Yur'ev S.V. [Efficiency of M&A transactions in developed and emerging markets on the example of the Russian offline retail market]. Izvestiâ Sankt-Peterburgskogo gosudarstvennogo èkonomičeskogo universiteta, 2019, no. 6, pp. 12–20. URL: Link (In Russ.)
  6. Kolosova D.M., Kuz'min K.A. [Mergers and acquisitions: role in socio-economic development]. Ekonomika i biznes: teoriya i praktika = Journal of Economy and Business, 2021, no. 12-1, pp. 194–197. URL: Link (In Russ.)
  7. Nesterenko N.Yu. [Efficacy of mergers and acquisitions: complex evaluation (Russia, St. Petersburg)]. Problemy sovremennoi ekonomiki = Problems of Modern Economics, 2016, no. 1, pp. 71–75. URL: Link (In Russ.)
  8. Kostyukova A.S. [Analysis of factors affecting mergers and acquisitions in Russia]. Ekonomika i biznes: teoriya i praktika = Journal of Economy and Business, 2020, no. 4-1, pp. 165–170. URL: Link (In Russ.)
  9. Reddy K.S. Determinants of Cross-border Mergers and Acquisitions: A Comprehensive Review and Future Direction. MPRA Paper, 2015, vol. 63969, 61 p. URL: Link
  10. Rossi S., Volpin P.F. Cross-Country Determinants of Mergers and Acquisitions. Journal of Financial Economics, 2004, vol. 74, iss. 2, pp. 277–304. URL: Link
  11. Kapranova L.D., Zazulya E.O. [Specifics of development of merger and acquisition market in Russia]. Finansovaya analitika: problemy i resheniya = Financial Analytics: Science and Experience, 2014, vol. 7, iss. 24, pp. 23–28. URL: Link (In Russ.)
  12. Pashtova L.G., Maimulov M.S. [M&A Market Efficiency in Russia: Problems and Prospects]. Finansy: teoriya i praktika = Finance: Theory and Practice, 2020, vol. 24, no. 1, pp. 76–86. (In Russ.) URL: Link
  13. Volkova T.V., Rakhlina L.V. [Investment activity in Russia amid the COVID-19 pandemic]. Vestnik ekonomicheskoi bezopasnosti = Vestnik of Economic Security, 2022, no. 1, pp. 250–256. URL: Link (In Russ.)
  14. Salmanov O., Babina N., Bashirova S., Samoshkina M. Multiples for Valuation Estimates of Companies in the Technology Sector of Emerging Markets. Asian Social Science, 2015, vol. 11, no. 8, pp. 253–263. URL: Link
  15. Kruglova O., Zubkov S. Methodological Principles of Estimating Efficiency of Merger and Acquisition Processes of Enterprises. Financial and Credit Activity – Problems of Theory and Practice, 2017, vol. 2, pp. 167–174.
  16. Chao M., Zhen J.-Q. Comparative Research on Performance of Cross-Border Mergers and Acquisitions Implemented by State-Owned and Private Enterprises Based on Event Study Model and Fama-French Three Factor Model. Journal of Interdisciplinary Mathematics, 2017, vol. 20, iss. 6-7, pp. 1483–1487. URL: Link
  17. Sakhanevich D.Yu. [Research of approaches and methods of applying artificial intelligence and machine learning in socio-economic processes]. Vestnik Omskogo universiteta. Seriya: Ekonomika = Herald of Omsk University. Series: Economics, 2020, vol. 18, no. 2, pp. 65–79. URL: Link (In Russ.)
  18. Partin I.M., Vasin A.D. [The influence of corporate life cycle on M&A activity of the company in developing capital markets]. Korporativnye finansy = Journal of Corporate Finance Research, 2014, vol. 8, no. 3, pp. 23–27. (In Russ.) URL: Link
  19. Kazakov A.V., Kolyshkin A.V. [The development of bankruptcy prediction models in modern Russian economy]. Vestnik Sankt-Peterburgskogo universiteta. Ekonomika = St. Petersburg University Journal of Economic Studies, 2018, vol. 34, no. 2, pp. 241–266. (In Russ.) URL: Link
  20. Hong X., Lin X., Fang L. et al. Application of Machine Learning Models for Predictions on Cross-Border Merger and Acquisition Decisions with ESG Characteristics from an Ecosystem and Sustainable Development Perspective. Sustainability, 2022, vol. 14, iss. 5, pp. 28–38. URL: Link
  21. Silkina G.Yu., Alekseeva N.S., Shevchenko S.Yu. [End-to-end production and management technologies: effects of industry application and potential synergy]. π-Economy, 2022, vol. 15, no. 5, pp. 43–57. (In Russ.) URL: Link
  22. Meador A., Church P.H., Rayburn L. Development of Prediction Models for Horizontal and Vertical Mergers. Journal of Finance and Strategic Decisions, 1996, vol. 9, no. 1, pp. 11–23. URL: Link
  23. Rasmussen C.E. Gaussian Processes in Machine. In: Bousquet O., von Luxburg U., Rätsch G. (eds) Advanced Lectures on Machine Learning. ML 2003. Lecture Notes in Computer Science, vol. 3176. Springer, Berlin, Heidelberg, 2004. URL: Link
  24. Zhou L., Lai K.K. AdaBoost Models for Corporate Bankruptcy Prediction with Missing Data. Computational Economics, 2017, vol. 50, pp. 69–94. URL: Link
  25. Botalova V.V. [Theoretical basis for mergers and acquisitions in Russia and abroad]. Rossiiskoe predprinimatel'stvo = Russian Journal of Entrepreneurship, 2013, no. 10, pp. 76–87. URL: Link (In Russ.)

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