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Abstract
The world economy, global markets and competition for international investments have undergone great change in recent years. Changes in the economy of a society affect many countries as a result of international commerce and agreements with other countries. This has given rise to many risks that governments have to deal with; hence, the importance of risk management has increased for all countries, especially in the finance sector. Global economics means that financial institutions need to make decisions about investment in other countries (eg opening a new bank branch or a participation company), so they need to measure the financial risks of candidate countries in fuzzy conditions. This study presents a novel method to meet that need. First, the most common financial risks are determined by means of a literature survey and consulting expert opinion. Secondly, the relative weight of each main and sub-risk is determined by expert opinion and hesitant fuzzy linguistic term sets (HFLTS). These weights are used to measure the financial risks of 30 countries to select the best four and the results are approved by the experts. Finally, the proposed method is implemented to aid the investment decision of a large financial institution. This paper proposes a new method to assess financial risks in fuzzy conditions.
The full article is available to subscribers to the journal.
Author's Biography
Ayfer Basar is a manager in an IT company serving one of the biggest banks in Turkey. She received her PhD from Istanbul Technical University (ITU) and an MSc degree in industrial engineering from Sabanci University. She is a part-time instructor in Ozyegin University. Her research interests include risk management, IT governance, applied optimisation and heuristic approaches. She has published articles in a number of indexed journals, such as Journal of the Operational Research Society and Optimization Letters.
Citation
Basar, Ayfer (2023, January 1). Assessing risks in international investments using hesitant fuzzy linguistic term sets. In the Journal of Risk Management in Financial Institutions, Volume 16, Issue 1. https://doi.org/10.69554/SFGG7761.Publications LLP