Browsing by Author "Özen, Ercan"
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Item Digital era digital risks: the case study of Turkish crypto currencies market(INCE, 2022) Özen, Ercan; Vurur, N. SerapDigitalization, which has accelerated in the 21. century, has brought financial markets to meet new risks. Unlike traditional risks, there are new risks such as cyber security, privacy risk, data protection, new financial institution risks, technological infrastructure risks. Developments in communication technologies and decrease in trust in financial institutions after the 2008 crisis have led to crypto assets combined with blockchain technology that is not connected to governments. Cryptocurrencies are protected by highly powerful encryption technologies. But, these currencies platforms are pose some risks. There are two types of risks in digital platforms. The first risk is that these markets are not yet subject to adequate legal regulations, and the second is cyber attacks. The purpose of this study is to make the extent of the risks that may occur in these digital markets more obvious by Case Study. To explain digital risks, the Thodex cryptocurrency exchange scandal that broke out in Turkey in 2021 has been analyzed. Thodex was Turkey's first global crypto exchange. When the Thodex closed in April 2021, it had 391,000 users. Investors firstly began to complain that their trading orders did not occur on time and that the provisions of the sales did not transfer in their accounts. Thodex first announced that it had closed operations for 3 to 5 days for maintenance purposes against cyber attacks. In the following days, investors were unable to access their accounts, and it became clear that the owner of the firm fled abroad, taking with him corresponds to about $ 2 billion owned by the investors. This scandal reveals the need for legal regulation on cryptocurrency exchanges.Item Forecast of the energy consumption of Turkiye commerce sector: m-estimation model application(INCE, 2022) Çankaya, Mehmet Niyazi; Özen, ErcanNet electricity consumption use continue to be significant issues. There are various forms of energy use and production. This work uses robust and form of Mestimation by using the grid-search algorithm. Thus, since we use form of Huber Mestimation, the prediction performance can be increased; because, the data is tried to be modelled by using the different values of tuning parameter determined by the gridsearch which can be used to carry out the optimization proving the M-estimates of the parameters of regression model. The data sets which are year and the net electricity consumption are modelled by regression model in order to predict and forecast how much electricity consumption will be necessary for the commercial purpose firm used the electricity at the highest amount. The statistical inference for the regression model and its estimators of parameters in the model is also provided. Further, the illustrative results used for the grid-search and the analytical expression of regression model are given. Due to the fact that polynomial regression showing an increment in the polynomial trend can model the dependent variable well, the net electrical consumption in commerce at Turkiye increases and the bandwidths for the forecasting in the year 2021 and 2022 are given to conduct a planning in energy sector.Item The impact of artificial intelligence on strategic finance management success(INCE, 2023) Özen, Ercan; Atasever, MesutPurpose: The main aim of this article is to examine the impact and importance of artificial intelligence on strategic finance management and to provide a perspective on how businesses can use it in their financial decision-making processes. Today, businesses tend to adopt a more data-oriented and rational approach in their financial decisions in order to maintain their competitive advantage and ensure sustainable growth in rapidly changing market conditions. Artificial intelligence has had a great impact in the finance industry and has helped financial managers make more effective and accurate decisions in strategic finance management. Strategic finance management aims to achieve long-term goals by using the resources of companies effectively. The integration of artificial intelligence into financial decision-making processes improves thestrategic planning and risk management processes of financial managers by providing faster and more accurate data analytics. Research methods: This articlewill be a compilation of important sources and research in the literature, and the impact and applications of artificial intelligence in the field of strategic finance management will be emphasized. In addition, its effects on the performance and competitiveness of companies will be examined and the advantages and challenges of artificial intelligence in financial decision-making processes will be discussed. Results: The use of artificial intelligence in the field of financial management is increasing day by day. The contribution of this technology to the strategic financial management of enterprises is supported by the growth and success stories observed in many sectors. The ability of artificial intelligence to analyze and forecast financial data has allowed the acceleration of financial decision-making processes and the adoption of more data-driven strategies. For this reason, experts and researchers in the financial sector are interested in understanding the impact of artificial intelligence on strategic finance management success and determining its future potential.Item Risk rating from road traffic accident fatalities for the world insurance sector(INCE, ASEM, 2024) Çankaya, Mehmet Niyazi; Özen, ErcanTraffic insurance is a system that compensates the insured for various damages that may arise due to accidents. Insurance companies finance these damages with the premiums received from the insured. Suitable insurance premium tariff. It keeps the customer demand in optimum balance and at the same time affects the financial success of the insurance company. Therefore, there is a need for optimal pricing. In order for insurance companies to establish the optimal risk-pricing balance, they need to rate the risks. Rating the mortality risks due to traffic accidents also allows insurance companies to price appropriate premiums. For this reason, the aim of this study is to rate the global traffic accident related mortality risks by countries. This study examines the global distribution of road traffic mortality by analyzing data from 231 countries using the DBSCAN algorithm. By classifying the number of deaths per 100,000 population, we identify patterns of similarity and show that countries can be grouped into 27, 8 or even as few as 7 distinct classes, depending on the tuning parameters of the DBSCAN algorithm. These results suggest that, despite the large number of countries, road traffic fatalities show similar patterns that can be attributed primarily to human factors. The classification highlights the importance of focusing on vehicle and driver-related issues rather than infrastructure, which appears to be less of a differentiating factor between countries. This findings have important implications for policy makers and insurance companies aiming to reduce the number of road deaths through targeted interventions.