Air transportation is the significant mode of transmission, enabling the worldwide spread of infectious diseases through the mobility of infected persons. Therefore, governments applied the most comprehensive restrictions and preventions in civil aviation during COVID-19. The industry is one of the most economically impacted due to travel, and flight restrictions. This paper aims to investigate the long- and short-term nexuses between government responses to COVID-19 and the aviation stock prices traded in Borsa Istanbul. The OxCGRT stringency Turkey index is used to measure the Turkish government responses and policies to COVID-19. In the study, the daily data of Turkish Airlines, Pegasus Airlines, Do&Co Catering, TAV Airport Holding, Celebi Ground Handling stock prices, and the OxCGRT stringency Turkey index for the 24.01.2020-11.11.2021 period were used, and Granger causality and Engle-Granger cointegration tests were applied to reveal the nexuses. In conclusion, there is a cointegration nexus and one-way causality from the index to all Turkish aviation stock prices, except the Celebi Ground Handling stock prices. The contribution of this study is that it is probably the first one in Turkey to reveal the nexus between the government's policy and responses to COVID-19 and aviation stock prices.

Recently, artificial neural networks have been successfully applied in many areas such as forecasting financial time series, predicting financial failure, and classification of ratings. However, it has hardly been applied in forecasting sukuk prices, which is considered the most common Islamic capital market instrument. Since sukuk is a new financial asset, there are not enough studies in this area. Therefore, this study aims to forecast the Turkish sovereign sukuk prices using with artificial neural network model and to reveal the determinants in the forecasting of sukuk prices. For this purpose, a multi-layer feed forward artificial neural network model is designed using dollar-based international sovereign sukuk price data issued by the Turkish Ministry of Treasury and Finance. The dollar index, volatility index, geopolitical risk index, Standard and Poor's Middle East and North Africa sukuk index, and Eurobond prices constituted as input variables of the designed model and the sovereign sukuk prices formed the output. As a result, the sovereign sukuk prices were forecasted accurately at the success rate of 99.98%. The accurate forecasting of sukuk prices will play a critical role in reducing the risk perception of sukuk investors and increasing their profitability. The findings of the study are important in terms of proving that the artificial neural network model is an effective model for forecasting the sukuk prices and revealing that the dollar index, volatility index, geopolitical risk index, Standard and Poor's MENA sukuk index, and Eurobond prices are determinants in forecasting sukuk prices.

The effects of global climate change and increasing environmental awareness have led to an increase in the significance of climate projects and, accordingly, climate finance and green bonds. Despite the increasing significance, the fact that the price forecasting studies on green bonds are extremely scarce has been the main motivation of this study. The aim of this paper is to forecast the corporate green bond prices with the Artificial Neural Network model and to determine the predictor by addressing the conceptual framework of green bonds. For this purpose, the Multi-Layer Feedback Artificial Neural Network (MLF-ANN) model, in which S&P 500 index prices are determined as input and S&P green bond index prices as output, is designed. To determine whether the conventional bond prices are the predictor of the corporate green bonds, the S&P 500 index was used as the sole input of the forecasting model. The findings show that corporate green bond prices are forecasted with 1.13% Mean Absolute Percentage Error (MAPE) and 98.93% Regression Determination Coefficient (R2). The results of the research provide data to maximize profits and/or minimize risk for green bond investors and market makers, while providing insight into the effectiveness of green bonds in financing climate projects for policy makers. This paper is the first study in the literature in terms of proving the effectiveness of the MLF-ANN model in forecasting corporate green bonds and revealing that conventional bonds are predictor of green bonds. Thus, it is expected that the study will shed light on future studies.

The crisis-resistant structure of the Islamic finance sector and the rich fund resources of the Gulf countries attract non-Muslim countries as well as countries with a large Muslim population. The aim of this study is to examine the development of the Islamic finance sector in non-Muslim countries, to define the challenges encountered in these countries in terms of Islamic finance and to offer constructive recommendations to overcome these challenges. The scope of the study is limited to the UK, USA and Canada, which are among the top non-Muslim countries in the Islamic finance country index, and Luxembourg, which has broken grounds in Islamic finance in Europe. Incompatibility of legal regulations in non-Muslim countries with Islamic principles, lack of qualified Shariah advisors, insufficient standardization of Islamic financial products, incompatibilities in financial reporting and accounting policies are among the difficulties encountered. With the efforts of governments and international Islamic financial institutions to overcome these obstacles over time, Islamic financial markets in non-Muslim countries are expected to develop significantly.

2022 yılı ilk çeyreğinde, dünyada konut fiyatlarının en fazla arttığı ülke Türkiye olurken, Türkiye’de en fazla artış gösteren bölge ise Antalya-Isparta-Burdur olmuştur. Bu nedenle, çalışmada Antalya-Isparta-Burdur bölgesi konut fiyatlarının yapay zekâ kullanılarak tahmin edilmesi amaçlanmıştır. Yapay sinir ağı modelinde, Ocak 2010-Temmuz 2022 dönemi için tüketici fiyat endeksi, konut kredisi faiz oranları, dolar kuru, Türkiye konut fiyat endeksi ve BIST 100 endeksi girdi parametreler olarak belirlenmiştir. Sonuç olarak bölgenin konut fiyatları, ‰ 5,6 Ortalama Mutlak Yüzde Hata (MAPE) ve %99,97 R2 oranında yüksek doğrulukta tahmin edilmiştir. Ayrıca çalışmada, 2022 yılı Haziran, Temmuz ve Ağustos ayları için geleceğe yönelik tahmin yapılmıştır. Çalışmanın, bölgesel olarak konut fiyatlarını yapay zekâ ile geleceğe yönelik olarak tahmin etmesi ve tahmin edici parametre olarak makroekonomik değişkenlerin yanı sıra hisse senedi endeksini kullanması bakımından literatüre katkı sağlayacağı umulmaktadır.