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    Introducing the overall risk scoring as an early warning system
    (Elsevier, 2024) Pehlivanlı, Davut; Alp, Elçin Aykaç; Katanalp, Bilge
    Business performance is a critical field of study, which should be assessed in more than just credit risk in the banking context. However, much previous research takes business performance in the context of credit risk in banking. In this study, risks will be analyzed to measure business performance in an integrated manner within the framework of non-financial sector dynamics. To this end, brainstorming sessions, risk workshops, surveys, and face-to-face interviews were held with representatives of small medium enterprises in 11 different sectors. Through field studies, risks have been identified and assessed in terms of impact and probability, Key Risk Indicators have been determined, and risks have been scored based on financial and non-financial metrics to estimate the Overall Risk Scoring. Moreover, the Overall Risk Scoring model has been tested using Logit Regression and Artificial Neural Networks (ANN), the Naïve Bayes Algorithm, and the C4.5 decision tree model. All methods have statistically verified that the predictive power of the structure of the Overall Risk Scoring is high. Our findings reveal that when business performance is analyzed with non-financial and financial metrics, dynamic and static data, market expectations, and banking needs, the predictive power of the calculated Overall Risk Scoring increases. The created Overall Risk Scoring Model can be used as an early warning system for many objectives by business executives, suppliers, consumers, investors, financial institutions, public bodies, credit rating agencies, and entities like the Credit Guarantee Fund.
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    News sentiment and cryptocurrency volatility
    (Springer Science and Business Media Deutschland GmbH, 2019) Çankaya, Serkan; Alp, Elçin Aykaç; Fındıkçı, Mefule
    The cryptocurrency market has shown remarkable growth in the last decade, resulting in heightened interest in research on several aspects of cryptocurrencies. The drastic price fluctuations have attracted attention from investors, but they have also raised concerns from national regulatory institutions. Several studies are conducted to understand the factors and the dynamics of its value formation. It is becoming more important to be able to value cryptocurrencies as an investor and as part of the process to legitimize them as a financial asset. This study aims to contribute to this field of research by examining the relationship between cryptocurrency’s volatile returns and the effects of different types of news on selected cryptocurrencies. This paper categorizes the news about cryptocurrencies and determines the effect of news from each category on the return structure of each cryptocurrency. By using 1054 news sources, 22 categories are created, and a clustering analysis is used to set these categories into six groups. These groups are modelized in proper ARCH family models, which are created for different cryptocurrencies to analyze the effect on volatility. The results show that different cryptocurrencies react differently to various news categories. News about regulations from national authorities exhibit a significant effect on all selected cryptocurrencies. © Springer International Publishing AG, part of Springer Nature 2018.