Bu çalışmada, isminden son yıllarda sıkça bahsettiren ve kripto paralardan biri olan Bitcoin fiyatı ile Türkiye ve G7 ülkelerine ait borsa endeksleri arasındaki nedensellik ilişkisi incelenmektedir. Bitcoin fiyatlarındaki dalgalanmanın 2013 yılından itibaren başlaması nedeni ile çalışmada 01.01.2013-26.01.2018 arasındaki günlük veriler kullanılmıştır. Çalışmada öncelikle birim kök testleri ve eşbütünleşme analizi gerçekleştirilmiştir. Değişkenler arasındaki ilişkinin uzun dönemde dengede olup olmadığını analiz edebilmek için vektör hata düzeltme modeli (VECM) kullanılmış, kısa dönemli ilişkiler ise Granger Nedensellik/WALD testi yardımıyla incelenmiştir. Yapılan analizler sonucunda, Bitcoin ile diğer ülke borsaları arasında herhangi bir uzun dönemli denge ilişkisinden söz edilemeyeceği bulunurken, kısa dönemde İngiltere borsasının (FTSE) Bitcoin'in nedeni olduğu sonucuna ulaşılmıştır. Ayrıca, Bitcoin'in de S&P 500 ve Kanada Borsasının (STSX) nedeni olduğu görülmüştür. Sonuç olarak, Bitcoin fiyatının dalgalanması hakkında kısa vadede bu üç borsa endeksinin de fikir verebileceği ortaya çıkmaktadır. Yatırımcılar hem araştırmaya konu olan bu borsalar arasında hem de Bitcoin'e yatırım yaparak risklerini çeşitlendirme yoluna gidebilir.

Bu çalışmada, teknik analiz, temel analiz ve piyasa anomalileri bir arada kullanılarak, alım ve satım sinyali veren bir sistem kurulmuştur. Daha sonra, ilgili yöntem çeşitli kriterlere göre seçilmiş olan BİST 30 firmaları üzerinde denenmiştir. Son olarak, bulanık mantık yaklaşımına göre kurulmuş yeni bir sistem yardımıyla, bahsi geçen pay senetleri üzerinde tekrar analiz işlemi gerçekleştirilmiştir. Çalışmada, 2005 yılının başından 2016 yılının son gününe kadar olan veriler kullanılmıştır ve bulanık mantık yaklaşımına göre yapılan analizlerin, klasik mantığa göre daha iyi sonuçlar verdiği gözlenmiştir. Ayrıca; analiz yöntemlerinin ve anomalilerin bir arada kullanılmasının, ayrı ayrı kullanılmalarından daha yararlı olabileceği sonucuna ulaşılmıştır.

An event occurring anywhere in the world can affect many regions with the development of globalization and communication networks. This case is also true for diseases. When the history of the world is examined, it is seen that various global outbreaks have occurred that have affected the world. However, today the spread of diseases and information about these diseases is happening faster than in the past. For this reason, the economic, sociological and psychological effects of the epidemics are felt more. In this study, the effects of global outbreaks on stock returns are investigated. The aim of the study is to show the effects of significant diseases, which occurred globally after 2000, on the stock returns of insurance companies located on the Turkish and G7 country exchanges. Event study method is used in the research. Selected events consist of global outbreak announcements and notices made by World Health Organization (WHO). It is understood from the results of the study that some country markets are more susceptible to most epidemics than others. In general, the effects of other global outbreaks outside the COVID-19 have lasted much shorter on the countries' stock exchanges. Markets appear to normalize more rapidly during other epidemics.

In this study, calendar anomalies, a topic that many researchers focus on, are examined. It is being investigated whether it is possible to obtain an abnormal return with the perception of anomaly in a market. For this reason, the Event Study method is used and the holiday effect anomaly is emphasized. Within the scope of the study, national and religious holidays in Turkey were discussed. In the study; daily data were used for the years 2017, 2018 and 2019. Thus, it has tried to determine whether there is a holiday anomaly could provide abnormal returns in Turkey. Sectorbased research results do not reveal any important situation in other sectors except the service sector. In the service sector, it has been observed that abnormal returns are predominantly positive before national holidays and negative abnormal returns before religious holidays. Briefly, it is necessary to pay attention especially to the service sector around the holiday periods. In the nearholiday periods, there is an opportunity in order to obtain an extraordinary return from the service sector, as well as the sector risks more during these periods.

Artificial intelligence applications are widely used in the field of finance as in all fields. In the field of finance, artificial neural networks are the most commonly used artificial intelligence methods for the analysis of time series. The fact that artificial intelligence methods make similar inferences to the human brain or that they are systems based on learning is the most important of their contributions to analysis. Of course, the development process of these methods is closely related to the development of technology should not be forgotten. In other words, despite all the advantages, the demand for the use of methods is now due to the fact that transactions can be done more quickly and simply. Past studies of artificial neural networks show that many of them make successful predictions. However, it is also known that the results are different. This is because there are elements that can affect the performance of the system, such as network architectures, number of layers, and educational algorithms. In this study, the optimum artificial neural network that should be established in Istanbul Stock Exchange is emphasized. How a network structure will work with better performance in Borsa İstanbul is examined. For this purpose, many analyses were carried out with network architectures, hidden layer numbers and educational algorithms. In the analysis, fundamental and technical data were used as input variables in order to predict the BIST100 index. The main variables are US Dollars, interest rates on deposits and money supply (M2). The technical variables are; MACD, RSI, Momentum and Stochastic. As a result of the study, it was observed that the use of feed-forward networks and Bayesian Regulation training algorithm would be appropriate in the analyzes made with BIST100 index. In addition, it was observed that increasing the number of hidden layers increases the performance, but it is observed that performance increase slows down if more than 5 layers are selected. Therefore, it is considered that the ideal number of layers should be 5 against the speed of the system and the problems of memorization of the network.