• A
  • A
  • A
  • ABC
  • ABC
  • ABC
  • А
  • А
  • А
  • А
  • А
Regular version of the site
Contacts

119049 Moscow, Russia
11 Pokrovskiy boulevard, room S629

Phone:

+7 (495) 772-95-90*27447, *27947, *27190
+7 (495) 916-88-08 (Master’s Programme Corporate Finance)

- Email: df@hse.ru

finance@hse.ru 

Administration
Head of the School Irina Ivashkovskaya

Head of Corporate Finance Research Center, Dr., tenured professor

Райн Анна Сергеевна
Administrator Райн Анна Сергеевна

+7495-772-95-90 (add. 27447)

Tatyana Gennadevna Lipatova
Administrator Tatyana Gennadevna Lipatova

+7495-772-95-90 (add. 27947)

Article
Investment in ESG Projects and Corporate Performance of Multinational Companies

Cherkasova V. A., Nenuzhenko I.

Journal of Economic Integration. 2022. Vol. 37. No. 1. P. 54-92.

Article
Bankruptcy factors at different stages of the lifecycle for Russian companies

Zelenkov Y., Fedorova E.

Electronic Journal of Applied Statistical Analysis. 2022. Vol. 15. No. 1. P. 187-210.

Working paper
Do Non-Interest Income Activities Matter For Banking Sector Efficiency? A Net Interest Margin Perspective

Kolade S. A., Semenova M.

Financial Economics. FE. Высшая школа экономики, 2022. No. WP BRP 87/FE/2022.

Book chapter
Validation of the effectiveness of the bank retail portfolio risk management procedure

Pomazanov M. V.

In bk.: The 8th International Conference on Information Technology and Quantitative Management (ITQM 2020 & 2021): Developing Global Digital Economy after COVID-19. Vol. 199: The 8th International Conference on Information Technology and Quantitative Management (ITQM 2020 & 2021): Developing Global Digital Economy after COVID-19. Manchester: Elsevier, 2022. P. 798-805.

Article
CEO Power and Risk-taking: Intermediate Role of Personality Traits

Korablev D., Poduhovich D.

Journal of Corporate Finance Research. 2022. Vol. 16. No. 1. P. 136-145.

Article
Economic Growth Models and FDI in the CIS Countries During the Period of Digitalization

Olkhovik V., Lyutova O. I., Juchnevicius E.

Научно-исследовательский финансовый институт. Финансовый журнал. 2022. Vol. 14. No. 2. P. 73-90.

Article
Special issue with the 2019 Future Directions in Accounting and Finance Education Conference, Moscow, Russia

Churyk N. T., Anna Vysotskaya, Kolk B. v.

Journal of Accounting Education. 2022. Vol. 58.

Book
Тенденции развития интернета: от цифровых возможностей к цифровой реальности

Абдрахманова Г. И., Васильковский С. А., Вишневский К. О. и др.

М.: Национальный исследовательский университет "Высшая школа экономики", 2022.

Article
Разработка рейтинга проектных рисков для телекоммуникационной компании

Гришунин С. В., Сулоева С. Б., Пищалкина И. И.

Организатор производства. 2022. Т. 30. № 1. С. 60-72.

Article
Разработка механизма гибкого управления рисками в сфере телекоммуникаций

Гришунин С. В., Сулоева С. Б., Пищалкина И. И.

Экономический анализ: теория и практика. 2022. Т. 21. № 3. С. 478-496.

Article
Development of the horizon index to evaluate long-termism of Russian non-financial companies

S. Grishunin, E. Naumova, N. Lukshina et al.

Russian Management Journal. 2021. Vol. 19. No. 4. P. 475-493.

Book chapter
Analysing the Determinants of Insolvency and Developing the Rating System for Russian Insurance Companies

Grishunin S., Bukreeva Alesya, Alyona A.

In bk.: The 8th International Conference on Information Technology and Quantitative Management (ITQM 2020 & 2021): Developing Global Digital Economy after COVID-19. Vol. 199: The 8th International Conference on Information Technology and Quantitative Management (ITQM 2020 & 2021): Developing Global Digital Economy after COVID-19. Manchester: Elsevier, 2022. P. 190-197.

Book
International Conference “Future Directions in Accounting and Finance Education”, 27-28 May 2019, Moscow, Russia

Edited by: А. Б. Высотская, B. v. Kolk.

Vol. 58. Elsevier, 2022.

Article
Prudential policies and systemic risk: The role of interconnections

Karamysheva M., Seregina E.

Journal of International Money and Finance. 2022. Vol. 127.

Article
How do fiscal adjustments work? An empirical investigation
In press

Karamysheva M.

Journal of Economic Dynamics and Control. 2022. Vol. 137.

Article
Do we reject restrictions identifying fiscal shocks? identification based on non-Gaussian innovations

Karamysheva M., Skrobotov A.

Journal of Economic Dynamics and Control. 2022. Vol. 138.

Article
ЛАТИНОАМЕРИКАНСКАЯ ТЕОЛОГИЯ ОСВОБОЖДЕНИЯ: ЭКОНОМИЧЕСКИЕ ПРЕДПОСЫЛКИ, СОСТОЯНИЕ, ОПЫТ ПРАВОСЛАВНОЙ РЕФЛЕКСИИ

Тихомиров Д. В.

Известия Санкт-Петербургского государственного экономического университета. 2022. № 4. С. 144-155.

Book chapter
Students’ Survey: Propensity to Innovate

Evdokimova M., Stepanova A. N.

In bk.: 38th EBES Conference - Program and Abstract Book. Istanbul: EBES, 2022. P. 39.

Article
Prove them wrong: Do professional athletes perform better when facing their former clubs?

Assanskiy A., Shaposhnikov D., Tylkin I. et al.

Journal of Behavioral and Experimental Economics. 2022. Vol. 98.

Article
Black-Litterman model with copula-based views in mean-CVaR portfolio optimization framework with weight constraints

Teplova T., Mikova E., Munir Q. et al.

Economic Change and Restructuring. 2023. Vol. 56. No. 1. P. 515-535.

Article
Институциональные инвесторы, инвестиционный горизонт и корпоративное управление

Повх К. С., Кокорева М. С., Степанова А. Н.

Экономический журнал Высшей школы экономики. 2022. Т. 26. № 1. С. 9-36.

Article
Credit scoring methods: latest trends and points to consider

Anton Markov, Zinaida Seleznyova, Victor Lapshin.

Journal of Finance and Data Science. 2022. Vol. 8. P. 180-201.

HSE Faculty of Computer Science Launches ‘AI in Mathematical Finance’ Laboratory

HSE Faculty of Computer Science Launches ‘AI in Mathematical Finance’ Laboratory

© iStock

At the end of September, the HSE University Faculty of Computer Science launched the laboratory ‘Artificial Intelligence in Mathematical Finance’, which brought together more than 70 participants. Representatives of the laboratory told students and staff about the key tasks and goals of the new research department.

Peter Lukianchenko, Head of the laboratory, as well as lab research fellows Denis Bogutsky (also research fellow at the AI and Digital Science Institute) and Vladimir Naumenko (also head of trading and banking book models validation at SBER) delivered reports at the event.

The speakers discussed promising areas of AI application in financial mathematics, the use of reinforcement learning to solve a market maker's problem, and deep generative models to assess the potential losses of a trading portfolio.

Peter Lukianchenko

‘The active development of deep data analysis technologies makes it possible to reconsider the solution of problems in the field of mathematical finance. The establishment of a laboratory at the Faculty of Computer Science allows us to focus the potential of AI technologies to solve urgent problems from the point of view of both the academic community and the faculty’s  industry partners.

Changes in the structure of client groups and in the trading modes of the stock market mean that previously developed models and methods are no longer applicable. As part of the laboratory's activities, we plan to conduct research aimed at developing new methods for solving problems in the field of mathematical finance that will meet modern challenges and the requirements of companies in the field of financial markets.

We are planning to start research in three key areas: multi-agent technologies for simulating market trading, the use of RL for optimal hedging and market maker tasks, and generative models.

We are glad that the launch of the laboratory has aroused great interest among students. We invite anyone interested in our research field to join us and write theses and term papers as part of the project. The best students will be able to continue their scientific activity as research assistants at the lab and work with real data.’

Alexey Masyutin, Head of the HSE AI Research Centre, Director of the AI and Digital Science Institute, Head of the Joint Department with Sberbank ‘Financial Technologies and Data Analysis’ and Academic Supervisor of the ‘Financial Technologies and Data Analysis’ programme, spoke on the areas of focus for the new laboratory.

Alexey Masyutin

‘Detailed data on financial markets gives students the opportunity to develop skills in working with big data. Our laboratory, with the support of the MICEX, accumulated a log order on the stock and futures markets of the Moscow Exchange. In just one year, the amount of data available for research reaches the impressive size of 188 GB.

Our colleagues focus on two areas of research: the use of RL in problems in financial markets and the use of generative models for a more accurate and rapid assessment of classical risk parameters of portfolio securities—value-at-risk and expected shortfall.’

The lab offers three topics as part of the campaign to select the themes of projects, term papers and theses:

1. Multi agent modelling of financial markets

2. Agent based modelling of behavioural problems

3. Change point detection under coloured noises

Please contact Peter Lukyanchenko at plukyanchenko@hse.ru to choose a project topic.

A video of the lab opening event is available here. Materials from the meeting can be found here.