Many investors include cryptocurrencies as potential investment tools in their portfolios. Previous studies have mostly analyzed Bitcoin regarding its hedge and safe haven features. Although the cryptocurrency market has expanded far beyond Bitcoin, few studies have examined the interaction among all other cryptocurrencies and conventional financial assets. For this purpose, as the dependent variable, we included the cryptocurrency index to represent the cryptocurrency market, whereas international stocks, bonds, United States (US) dollars, gold, and commodities as independent variables in the analysis. The interactions among the variables were analyzed using the Granger causality tests. The analysis results revealed a two-way causality relationship between the cryptocurrency market and the bond markets, indicating that the cryptocurrency index can be used to predict bond prices and vice versa.

Since investor interest is not a directly measurable concept, search engine and social media data can be used to measure active investor interest. Google search volume data has the potential to help customers, investors, and policymakers make better decisions. When looking for information to make investment decisions, investors consider Google trends as they provide news about changes in prices. Studies examining investor interest in the literature have often been carried out with the applications of linear models. This suggests that possible structural changes are not taken into account in the time series. When we look at the literature, it has been shown that the prediction performance of nonlinear models is better than linear models. In addition, it is seen that the studies conducted to investigate the relationship between the return of the stock markets and the trading volume and the investor interest are frequently included in the international literature. In contrast, the studies are limited in the national literature. In this direction of the study, the relationship between the trade volume and the investor's discovery of information on the stock market by Google is examined through linear and nonlinear econometric techniques in the investor reputation hypothesis. According to the investor reputation hypothesis, investors only invest in stocks they are aware of without adequate research and knowledge. In this context, the study results were realized in a way that supports the investor recognition hypothesis within the scope of 2020. In the context of 2021, it is seen that it does not support the investor reputation hypothesis. In future studies to be carried out in this area, it seems possible to determine the degree of effect of nonlinear regression estimations and the relationship between variables.

Bilgisayar ağlarının planlanması ve kaliteli ağ servisi sağlamak için internet veri trafiği modellerinin tahminlenmesine ihtiyaç duyulmaktadır. Bu çalışmada amaç, tıklama verilerinden meydana gelen sayfa merkezli verilere ait dağılımların modellenmesi ile benzetim ve planlama modellerine kaynak oluşturmaktır. Bu amaç doğrultusunda hedef olarak belirlenen web siteleri, aksesuar ve hazır giyimin pazarlandığı e-ticaret ile bilgi edinme amacı taşıyan haber siteleridir. Bu web sitelerinin göstergelerinden olan hemen çıkma oranı, sitede kalma süresi ve sayfa görüntüleme sayısına ait dağılımlar incelenmiştir. Beş ay aralıkla elde edilen verilerin karşılaştırmalı analiz edilmesiyle, hemen çıkma oranı ve sayfa görüntüleme sayısı göstergelerinin, yapılacak benzetim modellerinde Dagum ve Johnson SB dağılımları ile incelenebilir olduğu çıkarımı yapılabilmektedir. İnternet kullanıcılarının davranış şeklinin değişmesi nedeniyle, araştırmacıların diğer web sitesi göstergeleri için sürekli güncel verilere dayalı dağılımları kullanarak değerlendirmeler yapmaları uygun olacaktır. Elde edilen dağılım bilgileriyle, özellikle özel sektörde kullanılan satış simülasyonlarında, web siteleri arasındaki performans değerlendirmesi gibi çeşitli alanlarda kullanılabileceği düşünülmektedir.

Suicidal behaviour, like other human behaviours, is a result of socioeconomic and psychological conditions. The research, it is aimed to examine the relationship between socioeconomic factors such as economic income levels, social status and quality of life, and suicidal behaviour in comparison with classical and Bayesian negative binomial regression models. The findings showed that suicidal behaviour increased due to the decrease in economic status and inequality of income distribution among young people. At the same time, it has been determined that employment and divorce rates reduce suicidal behaviour. As a result, it was determined that social and economic factors affected suicidal behaviour in the 15-24 age group, and solutions were suggested.

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