Проекты с научно-учебной лабораторией корпоративных финансов

We aim to determine and classify the variety of Environmental, social and governance (ESG) transformation practices when companies decide to monitor and manage ESG risks in their investing policies and initiate new approaches to financing policies. Identification of most effective and less effective practices can provide a clue to better understand ESG contribution to corporate performance. The project combines quantitative and qualitatives (applied) studies to:

• identify the algorithmes of ESG transformation of a company, show how they differ due to industry specifics • collect and classify existing and emerging practices of business transformation, develop consolidated matrices that help to find similarities and differences across industry's cases

• identify ESG materiality issues and how they differ between industries and within financial institutions

• collect and compare practices in investing decisions to cope with ESG risks, project's portfolios to understand the drivers behind the most and least effective ones

• collect best practices for ESG backed financial management at different industries and how it contributes to stakeholder value creation

• identify financial policies, green instruments and their determinants

• collect best practices for the applications of "alternative assets" when banks provide ESG financing including private public partnerships for large projects and their impact over company's performance

• study the effectiveness of green financing, how these types of instruments impact the cost of debt and whether they impact the cost of equity

• identify the trends and changes in applying green and social financing at times of turbulence peaks

• analyse the trends and practices in applications of fintech to meet sustainability goals and how these new instruments can impact the companies in ESG transformations

• investigate the role of disclosure of non-financial information (ESG reports, integrated reporting) for sustainable financing

The outputs of the project: team based drafts of articles; book chapters; collection of cases-best practices; course papers and diploma (team based allowed)

Stage 1:Construct and apply metrics to identify behavioural biases of CEOs/CFOs and members of the Boards Construct and apply index for personal traits (human capital) of CEOs/CFOs and Boards Stage 2: Add new data to the data base on corporate directors - their personal traits and behavioural traits with textual analysis of different sources- to understand their roles in mitigating CEOs/CFOs decision biases. Stage 3: develop research models to capture the impact of personal and behavioural traits of CEOs/CFOs on corporate decisions and innovations including digital transformation of a company

Stage 4. Identify the board's mitigating roles to offset negative effects over different corporate decisions and performance.

Final products: team based lab drafts of research papers to apply to global academic events for presentations, article's drafts. Draft of the book to Springer Nature series "Advanced studies in emerging market's finance". Case studies on selected companies prepared for publishing. Term and graduation papers of students.

Проект направлен на совершенствование существующих и разработку новых методологий оценки эффективности и финансовой устойчивости страховых компаний, а также управление стоимостью, рисками, платежеспособностью страховых компаний с учетом требований Solvency II, повестки ESG. Среди задач:

• сравнительный анализ подходов к оценке стоимости страховых компаний,
• анализ существующих и разработка новых методических подходов к оценке риска ликвидности страховщиков,
• оценка страхового риска,
• риска расторжений договоров.

Анализ осуществляется на основе данных из вторичных открытых источников (баз данных) и первичных данных полученных в ходе проводимых исследований.

В проекте могут потребоваться навыки:

1. поиска информации в интернете,
2. первичного сбора данных (автоматизировано и вручную),
3. использование методов машинного обучения
4. программирования
5. использования статистических пакетов для обработки информации