Ersin Kanat

This study aims to predict the bankruptcy risk of banks during financial crises by analyzing their financial structure using machine learning methods. Taking the 2001 Turkish economic crisis as a case study, the financial ratios of failed banks are compared with those that remained operational. Financial indicators such as profitability, liquidity, and capital adequacy are evaluated through traditional ratio analysis and advanced machine learning models, including C5.0 Decision Tree, CART, and XGBoost. The performance of these models is assessed using evaluation metrics such as the ROC curve and AUC value. The findings show that financial ratios like Net Working Capital/Total Assets, Net Interest Income after Non-performing Loans/Average Total Assets, and Interest Income/Interest Expense play a critical role in bankruptcy prediction. Among the models, CART and XGBoost performed with perfect classification accuracy (AUC = 1), while the C5.0 model also achieved a high level of success (AUC = 0.9318). The results provide practical insights for banks in managing financial sustainability and improving early warning systems. They emphasize the importance of maintaining a strong capital base and monitoring interest income and liquidity indicators, especially during periods of economic instability. This research contributes to financial risk management literature by integrating interpretable machine learning methods with financial ratio analysis.

Bu çalışma, Türkiye Cumhuriyeti Merkez Bankası'nın (TCMB) faiz oranları üzerindeki politika değişikliklerinin BIST Bankacılık Endeksi üzerindeki etkilerini incelemektedir. Faiz oranları, bankacılık sektörü için kritik öneme sahip olup, yatırımcıların davranışlarını ve dolayısıyla borsa endekslerini etkileyebilecek bir faktördür. 23 Eylül 2021 ile 22 Haziran 2023 tarihleri arasında TCMB faiz oranlarını sürekli olarak düşürmüş veya düşük tutmuş, bu tarihten sonra ise faiz oranlarını artırma kararı almıştır. Çalışma, bu iki farklı politika değişikliği tarihini esas alarak olay çalışması yöntemini kullanmış ve bu değişikliklerin BIST Bankacılık Endeksi’ne olan etkilerini ölçmeye odaklanmıştır. Olay çalışması yönteminin kullanıldığı bu çalışmada, 120 günlük tahmin penceresi tercih edilirken, anormal ve kümülatif anormal getiriler için (-5, +5) olay penceresi kullanılmıştır. Sonuçların anlamlılığı ise t-testi kullanılarak tespit edilmiştir. Elde edilen sonuçlar, faiz indirimi politikalarının banka endeksinde anlamlı bir etki yaratmadığını, ancak faiz artışlarının negatif anormal getirilerle sonuçlandığını göstermektedir. Çalışma, bu politika değişikliklerinin Borsa İstanbul Bankacılık Endeksine yansıyan etkilerini analiz ederek literatüre katkıda bulunmaktadır.

In the Covid-19 era, the income sources of sports clubs decreased, but the importance of fan tokens as new communication, management, and income source for sports clubs increased with Blockchain technology. For this reason, the relationships between fan token prices and club success, transaction volume, and attractiveness factors were examined and the important determinants of fan token prices were tried to be revealed. To determine the most suitable methods for panel data model, first of all, cross-section dependence, homogeneity, and unit root tests were carried out. Then the durbin test was performed and it was concluded that the variables were cointegrated. Finally, the coefficients were examined with the PMG/ARDL test and it was observed that the most important determinants of fan token prices were club success and transaction volume, respectively. While these factors affect fan token prices positively, the effect of the attractiveness factor is seen as less important and negative compared to other factors. In addition, in the error correction model established, the existence of a long-term equilibrium relationship between the variables was confirmed. It was concluded that the short-term deviations returned to the equilibrium after approximately 21 days.

Bankruptcy and Reorganization in the Digital Business Era examines the basic structure of the arrangement of bankruptcy within various countries and the evaluation of the financial results of the enterprises that have declared the arrangement of bankruptcy. Covering key topics such as economics, financial distress, risk management, and banking, this premier reference source is ideal for business owners, managers, industry professionals, researchers, scholars, academicians, practitioners, instructors, and students.

Purpose- This study aims to assess the effectiveness of technical analysis indicators used by investors in the cryptocurrency market for making informed decisions. Emphasizing the importance of accurate decision-making methods in financial markets, this research particularly focuses on the cryptocurrency market, which has gained significant attention among investors in recent years. Methodology- The study specifically examines technical analysis, a widely employed method in various financial markets, with a focus on its predictive capabilities concerning Bitcoin price forecasts. Leveraging advanced technologies, such as big data analysis and machine learning, the research utilizes daily trading data from January 1, 2017, to June 30, 2022, presenting technical indicators and their associated error margins. Findings- The study highlights the significance of using Weighted Moving Average (WMA) and Stochastic Oscillator (STO) indicators in combination, demonstrating that multiple indicators outperform individual ones. This research underscores the effectiveness of technical analysis methods in the cryptocurrency market, aiding the development of enhanced investment strategies. Conclusion- In conclusion, this study delves into the potency of technical analysis techniques employed by investors in cryptocurrency markets. The insights indicate that combining indicators and technical analysis methods holds promise for future investment strategies. It is essential to note that even the best method can lead to losses, as evidenced by the presence of error margins, and absolute profitability cannot be guaranteed through technical analysis methods. Keywords: Cryptocurrency, technical analysis, machine-learning, classification algorithms, investment. JEL Codes: C38, C55, G17

Many countries are trying to develop many financial education policies and strategies under the guidance of the top public administration under the heading of recommendations for increasing and disseminating financial literacy. In these initiatives, countries act independently of each other. In the globalizing digital world, especially in financial markets, positive or negative developments in one country affect other countries to a different extent. For this reason, it is imperative to produce policies that can act together with relevant stakeholders at the global level by acting with common financial literacy activities. In this chapter, the studies carried out to increase the financial literacy levels of economic units in Turkey and other countries will be explained comparatively. In addition, new policies and proposals will be developed by mentioning the joint activities that are currently partially carried out at the global level.

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. Sector-based 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 near-holiday 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.

Bu çalışmada ekonomik politika belirsizliğinin (EPU) kripto paralar üzerindeki etkisi panel veri yöntemleriyle araştırılmaktadır. Bu amaç doğrultusunda öncelikle küresel ekonomik politika belirsizliği endeksi ve en büyük dört kripto paranın aylık verileri elde edilmiştir. Çalışmada kullanılan kripto paralar; Bitcoin (BTC), Ethereum (ETH), BinanceCoin (BNB) ve Ripple (XRP)’dir. 2018:01-2020:12 dönemine ait verilerin kullanıldığı çalışmada, yatay kesit bağımlılığı ve homojenlik testleri gerçekleştirilmiştir. Daha sonra Kónya (2006) tarafından önerilen bootstrap panel nedensellik testi uygulanmıştır. Dört kripto paradan ilk sırada yer alan Bitcoin ile EPU arasında çift yönlü nedensellik ilişkisi bulunurken, son sırada bulunan XRP için herhangi bir nedensellik ilişkisine rastlanamamıştır. İkinci ve üçüncü sıradaki kripto paralarda ise EPU’dan bu paralara doğru tek yönlü nedensellik ilişkisi olduğu görülmüştür. Çalışmadan elde edilen bulgular, ekonomik politika belirsizliğinin kripto paraların değerleri üzerinde etkisi olabileceğini göstermektedir.

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.

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 stanbul 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.

The importance of psychological issues is increasing day by day in financial studies. When the subject is the human factor, a lot of unknowns arise. For this reason, the study of human perceptions and the attitudes of individuals constitutes an important research area in money related research. The purpose of this study, attitudes towards money of university employees in Turkey and the uncovering of belief. In this way, the research; it is believed to be helpful in using money, managing and achieving financial goals. In the study, a questionnaire was applied to the academic and administrative staff of universities in the Western Black Sea Region. Separate analyses were applied for both groups (Academic and Administrative staff) and the results were compared. Factor analysis was used to determine the validity of the scale used in Turkey. As a result of the analysis, it was revealed that the number of factors for both groups consisted of 4 factors as in the original study. In addition, the relations between the demographic characteristics and the factors were examined and the results regarding the status of academic and administrative staff were revealed. Various comments are given on the obtained results.

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 stanbul 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.

The importance of psychological issues is increasing day by day in financial studies. When the subject is the human factor, a lot of unknowns arise. For this reason, the study of human perceptions and the attitudes of individuals constitutes an important research area in money related research. The purpose of this study, attitudes towards money of university employees in Turkey and the uncovering of belief. In this way, the research; it is believed to be helpful in using money, managing and achieving financial goals. In the study, a questionnaire was applied to the academic and administrative staff of universities in the Western Black Sea Region. Separate analyses were applied for both groups (Academic and Administrative staff) and the results were compared. Factor analysis was used to determine the validity of the scale used in Turkey. As a result of the analysis, it was revealed that the number of factors for both groups consisted of 4 factors as in the original study. In addition, the relations between the demographic characteristics and the factors were examined and the results regarding the status of academic and administrative staff were revealed. Various comments are given on the obtained results.

Gunumuzde teknoloji ve iletisim araclarinin gelismesiyle sermaye hareketleri ve menkul kiymetlerle ilgili islemler kolay, etkili ve daha yogun sekilde gerceklesmektedir. Finansal piyasalarin kuresellesmesi, ulkeler acisindan menkul kiymet borsalarinin onemini arttirmistir. Bu calismada, ulkemiz icin de buyuk onem tasiyan menkul kiymet borsasinin gelisim surecini goz onune serebilmek amaciyla, Osmanli devleti donemindeki menkul kiymet islemleri arastirilmaktadir. Osmanli doneminde menkul kiymetlerle ilgili alim ve satim islemlerinin kesin olarak ne zaman basladigiyla ilgili net bir sey soylemek mumkun degildir. Fakat Galata ve cevresindeki tuccar, sarraf ve bankerler tarafindan bu tur ve benzeri islemlerin yapildigi bilinmektedir. Dunya’daki gelisimine bakildigi zaman, Avrupa’da borsa faaliyetleri Osmanli’ya gore daha erken baslamistir. Fakat Avrupa’daki borsalarin da gelisimi 19. yuzyilda gerceklesmistir. Bunun en buyuk sebeplerinden bir tanesi ise sanayilesme ve sirketlesmedir. Ayrica, bu donemde deniz asiri ticaretin ve yatirimlarinda arttigi gorulmektedir. Buna paralel olarak, tezgâh ustu de olsa Osmanlidaki borsa faaliyetleri de yine 19. yuzyilda artmaya baslamistir.

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, 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.

Döviz kurunun gelecekte ne olacağını tüm bireyler, şirketler ve devletler bilmek ister. Özellikle üretici, ithalatçı ve ihracatçı firmalar talep ve maliyet unsurlarını etkilediği için döviz kurlarını merak ederler ve tahminlerinin doğru çıkması halinde yüksek kazanç elde edebilirler. Döviz kurları, enflasyon, ekonomik büyüme, ihracat eğilimi, para arzı, ithalat eğilimi, hükümet kararları gibi pek çok değişkene bağlı olarak değişmektedir. Bu sebeple, döviz kurlarının ileriye dönük tahmini kesin olarak yapılamayabilir. Sadece yakın tahminlerde bulunmak mümkündür. Döviz kuru fiyatlarının tahmin edilebilmesi için genelde tek fiyat kanunundan yararlanılır. Tek fiyat kanununun uygulamalara yansımış hali satın alma gücü paritesi olarak görülür. Bunun yanında “big mac” endeksi veya yapay sinir ağları gibi birçok yöntem sıkça kullanılmaktadır. Bu yöntemlerin birçoğu tek fiyat kanunun uygulamada şekillenmiş halleridir. Döviz Kurlarındaki ani dalgalanmalar girişimcilere fazla getiri sağlayabileceği gibi çok büyük kayıplara da neden olabilir. Döviz kurlarındaki belirsizlik ve dalgalanmanın artması riski arttırmaktadır. Kurdaki volatilite büyüdükçe, girişimcilerin haklı olarak korkuları da büyümektedir. Bu nedenle fiyat tahmin yöntemlerinin yanı sıra volatiliteyi ölçmekte büyük yararlar sağlayabilir. Volatiliteyi ölçerken, EWMA ve GARCH modellerinin genellikle sağlıklı sonuçlar verdiği görülmektedir. Bu çalışmada, öncelikle döviz kurların girişimciler açısından önemi vurgulanmaya çalışılmıştır. Daha sonra, kur tahmin yöntemlerinin ve volatilite ölçme modellerinin başlıcaları incelenmiştir. Son olarak ise, GARCH modeli kullanılarak Türkiye’deki Euro ve Dolar kurunun belirli dönemlerdeki dalgalanma dereceleri irdelenmeye çalışılmıştır.