360 research outputs found
Backcasting Bitcoin volatility: ARCH and GARCH approaches
No full textPurpose- The primary purpose of this study is to model Bitcoin price volatility and forecast its future price returns using advanced econometric models such as ARCH and GARCH. The study aims to enhance risk management strategies and support informed investment decisions by addressing the time-varying nature of Bitcoin’s volatility. The research explores the persistence of volatility shocks and the clustering of price movements to provide insights into market dynamics. Methodology- This research examines daily Bitcoin closing prices over the period from January 2020 to October 2024. The data was preprocessed to ensure reliability, including applying logarithmic transformations to standardize the data and eliminate trends. Stationarity tests, such as the Augmented Dickey-Fuller (ADF), Phillips-Perron (PP), and KPSS tests, were conducted to confirm the series' stationarity. The ARCH-LM test was utilized to detect volatility clustering which is essential for validating the use of ARCH and GARCH models. Following this, ARIMA models were employed to define mean equations and GARCH models were used to estimate conditional variance and capture volatility dynamics. The dataset was split into training and validation subsets with data from July to October 2024 reserved for validation. Findings- The findings demonstrate that Bitcoin’s price movements exhibit significant volatility clustering and persistence of shocks which are key characteristics effectively captured by ARCH and GARCH models. These models provide valuable insights into the volatility patterns of Bitcoin, supporting their application in cryptocurrency analysis. Despite their robustness, the models face limitations in precise return forecasting during highly volatile periods, suggesting the need for further refinement or integration with advanced approaches. Conclusion- The research concludes that ARCH and GARCH models are effective tools for understanding and forecasting Bitcoin’s volatility. The study underscores the importance of acknowledging volatility persistence and clustering effects when analyzing cryptocurrency price behavior. However, it also highlights areas for improvement in econometric modelling by including the exploration of hybrid models and the integration of macroeconomic factors to enhance forecasting accuracy.Publisher's Versio
National income distribution: a countrywise analysis
No full textPurpose- This study aimsto analyze the changes in income distribution for selected developing countries over a time period in between 2015 and 2022, 8 years of observations. It hypothesizes that Covid19 pandemic period of 2020 and 2021 significantly impacted income distribution in all developing countries investigated. Methodology- Income distribution data for this study are extracted from the World Inequality Database addressing household income adjusted for after-tax income. Each household’s income is equally divided among the adult population aged 20 or older. The data are categorized into 10% income groups resulting in ten distinct income levels for the analysis. The study examines income distribution of five developing comprising Turkiye, Czechia, Greece, Hungary, and Romania. Findings- The top 10% of the population in the developing countries take 33% of national income on average. The average per capita income was 42,610 in 2022 after a dip of with a similar Covid19 dip. However, social policies generally failed resulting in income shifting from lower and middle-income groups to the top 30%. Conclusion- All countries implemented various social programs to support those most affected by Covid19. The social policies and measures implemented by governments to mitigate the effects of Covid19 appear to have been more successful in some of the developing countries comparing to the other developing countries. Although the developing countries could manage to increase their overall national income, they failed to restore their pre-pandemic income distribution. Significant income transfer occurred from the bottom 20% and middle 50% to the top 30% in these countries.Publisher's Versio
Gender difference in risk and confidence perception: implementation with logit model
No full textPurpose - This study aims to understand individuals’ behaviors and perceptions regarding investment preferences. Specifically, it examines the differences in investors’ perceptions of “concerns about losing money due to investment decisions” and “confidence in their knowledge of portfolio creation and management” based on the gender variable. Methodology - The study utilizes survey data from 69 participants (29 female and 40 male). It employslogit models to analyze two dependent variables: (1) the stress level due to concerns about investment decisions and (2) confidence in portfolio management and financial knowledge. Gender is the key independent variable, with marginal effects calculated for unambiguous interpretation. Findings - The results indicate that gender has a statistically significant impact on both stress and confidence levels. Women are 21.2% more likely than malesto experience stress due to concerns about investment decisions. Conversely, females are 18.5% less likely to feel confident about their financial knowledge and portfolio management abilities than males. These findings reflect the gender-based differences in risk perception and confidence. Conclusion -The study highlights the critical role of gender in shaping investment behaviors. Women tend to exhibit higher risk aversion and lower financial confidence than men. To address this disparity, targeted financial education programs and awareness initiatives are recommended to enhance women’s financial literacy and confidence. Bridging this gap can contribute to improved financial participation and decision-making among women.Publisher's Versio
Determinants of Bitcoin price movements
No full textPurpose- Investors want to include Bitcoin in their portfolios due to its high returns. However, high returns also come with high risks. For this reason, the volatility prediction of Bitcoin prices is the focus of attention of investors. Because Bitcoin's volatility is used as an important input in portfolio selection and risk management. This means that the models to be used in predicting Bitcoin volatility increases the importance of performance. In this research; A comparative examination of the models applied for Bitcoin shows an effective performance in volatility prediction. It is very important for evaluation. The aim of this study is to model Bitcoin price returns and to examine future return predictions and return directions using historical Bitcoin prices. Methodology- Many models have been used in studies on financial instruments and price predictions. Models such as linear and nonlinear regression, Random Walk Model, GARCH and ARIMA fall into this category. Nonlinear econometric models such as ARCH and GARCH are used for financial time series with variable volatility. These models assume that the variance is not constant. In this study, first Bitcoin price returns for the period between January 2020 and December 2023 will be modeled with the GARCH model, and then the ARCH-GARCH models will be used for future prediction of returns for the period between January 2024 and June 2024. Finally, the actual values will be compared with the forecasted values. In other words, the primary aim of this study is to use the daily Bitcoin closing price between May 2020 and December 2023 to estimate the returns for the periods of 2024 and compare it with the actual returns. Findings- The analysis reveals that GARCH Model results showed that in the mean and variance equations, it is seen that all variables are except intercept of the mean equation significant according to the error level of 0.05. Namely, the reaction and persistence parameters are significant accourding to 0.05 in the variance equation. Both the coefficient of the reaction parameter and the coefficient of the persistent parameter are higher than zero (positive). Also, the coefficient of the reaction parameter plus the coefficient of the persistent parameter approximately equals 0.72. That is, it is lower than 1 and higher than zero (positive). The level of persistence is not too high. So, we do not think about non-stationary variance in the model. Reaction parameter’s coefficient is 0.13. And persistence parameter’s coefficient is 0.58. As we can see, persistent parameter is much higher than reaction parameter. That is, when there is a new shock that creates the persistent parameter, that shock will be in effect for a long time, it will not disappear immediately. That is, a significant part of the shock that occurs in one period flows into the next period. After determining the appropriate mean and variance models, a forecast is made using Automatic ARIMA forecasting for BITCOIN return forecasting. This forecast is made for the first five months of 2024, without adding the actual values of the first five months of 2024 to the data. The program ranks the most appropriate model. The program chose GARCH(3,3) as the most appropriate model in "bitcoin return prediction". Conclusion- The results of the test applied in the study can be summarized that the unit root test results showed that it was necessary to work with return series. GARCH(1,1) model results show when there is a new shock that creates the persistent parameter, that shock will be in effect for a long time, it will not disappear immediately. That is, a significant part of the shock that occurs in one period flows into the next period. According to GARCH automatic forecasting results, the best GARCH model that models Bitcoin return is the GARCH(3,3) model. According to these model results, although the slopes of the actual and forecasted return series move in the same direction, the model remains weak for forecasting. In future studies, it may be recommended to estimate Bitcoin returns with non-linear models.Publisher's Versio
Export potential of Turkish SMEs
No full textPurpose- Digital channels are gaining more and more share from trade and commerce, especially after Covid 19 pandemic. People have adopted to online buying and marketplaces became important retailing tools for manufacturers. E-commerce is rising not only in closed commercial areas but also across different countries, even continents with developments in cross-border e-commerce. Governments, global digital platforms, consumer habits are creating and supporting the demand of buying online from anywhere and numbers are showing that this creates an opportunity for Turkish businesses to become exporters. This study aims to highlight the potential for small and medium sized businesses in Turkey to become exporters. Methodology- The study examines historical export growth data of Turkey in detail using secondary data. The historical data is used to make a projection for future and highlight the potential of growth for Turkish SMEs. Current marketplace platforms’ business models are also examined and carefully analyzed to present an understanding of the potential business models. Findings- The numbers are showing that Turkish exports are growing in Europe and USA. Capex heavy industries have the highest share among the exports but e-commerce is also growing. Some industries like textile, jewellry and small appliences has a higher growth potential withing cross border e-commerce. Conclusion- Adoption to online retail is getting higher and higher. More people are buying from online marketplaces and the origin of the transaction is losing its importance with one-day deliveries. It is important to open shops not only physical but also on different platforms. It is easier for business owners to sell across the world and become exporters. By having international customers, businesses distribute regional risks and also become financially stronger. It is important for Turkish SMEs to understand their risks and seek international growth opportunities, such as doing exports. Turkey’s unique geographical location is a very important asset but Turkish businesses should keep in mind that all international producers are now seeking opportunities to create through online platforms.Publisher's Versio
Dilek Güngör: Ich bin Özlem
No full textThis paper analyzes the novel "Ich bin Özlem" of Dilek Güngör, author of post-migration German literature. This article analyzes: structure and narrative style, the complex personality of the protagonist and the first steps towards the perception of one's own identit
METAVERSE AND CHATGPT: INNOVATIVE LEARNING EXPERIENCES IN EDUCATION AND INTERACTIVE STRATEGIES IN MARKETING
No full textPurpose- This study investigates the potential of ChatGPT and the metaverse as transformative digital technologies, with a focus on their applications and benefits within education and marketing sectors. It aims to understand how ChatGPT, as an AI language model, can enhance interactivity in virtual environments, particularly within the metaverse. Methodology- The research involves an in-depth literature review on digital technologies driving the metaverse, specifically examining ChatGPT's integration and its impact on user engagement in virtual spaces. The study also reviews current applications and explores potential roles for ChatGPT in marketing, branding, and educational contexts within the metaverse. Findings- Results indicate that ChatGPT can significantly enhance user interaction in the metaverse by enabling more personalized, responsive virtual assistants. In education and marketing, this integration facilitates immersive experiences, providing tailored support, information, and engagement opportunities in a virtual format. Conclusion- The combined application of ChatGPT and the metaverse holds significant promise, presenting opportunities for enhanced digital interaction and personalized experiences across industries. However, limitations such as technological constraints and privacy concerns require ongoing attention to maximize these benefits effectively
A COMPARATIVE EXPERIMENTAL STUDY ON ARTIFICIAL INTELLIGENCE- AND HUMAN-DRIVEN SOCIAL MEDIA MARKETING CAMPAIGNS
No full textPurpose- The rapid advancement in digital marketing, driven by technologies such as artificial intelligence (AI), forms the backdrop for this research. This study aims to investigate the performance differences between AI-driven and human-managed digital marketing campaigns by means of a true field experiment. Selected Key Performance Indicators (KPIs) are evaluated on the Meta platform to make a statement regarding the performance. Methodology- The study has an experimantal research method. Two concurrent marketing campaigns for the Paul Kenzie brand were conducted over a two-week period: one fully created by ChatGPT-4 and the other by a human expert. Key KPIs measured include Click-Through Rate (CTR), number of conversions, conversion rate, and Return on Advertising Spend (ROAS). Findings- The results indicate that AI-driven campaigns outperform human-managed campaigns in terms of CTR, conversion rate, and ROAS, suggesting higher efficiency and effectiveness in reaching and engaging the target audience. Conclusion- The findings highlight the potential of integrating AI technologies with human creativity to optimize digital marketing strategies
Kruskal's minimum spanning tree approach to brain fiber tractography computation
No full textGöksel Duru, Dilek (Arel Author) --- Conference: 22nd Signal Processing and Communications Applications Conference, SIU 2014
The interaction between corporate governance and financial performance: An implementation for the UK banks
No full textBu çalışma, 2011'den 2020'ye kadar Birleşik Krallık'ta faaliyet gösteren FTSE350'de işlem gören on bir bankanın iç kurumsal yönetişim faktörleri ile performansı arasındaki ilişkiyi incelemektedir. Esas olarak bir bankanın performansı arasındaki ilişkileri incelemektedir (Tobin'in Q, Varlık Getirisi (ROA), Özkaynak Getirisi (ROE(), kurumsal yönetişim faktörleri (yönetim kurulunun büyüklüğü, bağımsızlık, toplantılar ve cinsiyet çeşitliliği, mülkiyet yoğunluğu, kurumsal yatırımcılar, denetim komitesinin bağımsızlığı, toplantılar) ve uzmanlık) ve kontrol değişkenleri (finansal kaldıraç, banka büyüklüğü, yaş ve sermaye yeterliliği). Araştırmanın yürütülmesi için farklı sorular hazırlanmış ve bunlara cevap bulmak için veriler toplanmış, çoklu hipotezler test edilmiş ve kurumsal yönetişim arasındaki ilişkiyi incelemek için çok değişkenli sabit etkili regresyon (Hausman Testi) kullanılarak analiz edilmiştir. FTSE350'de listelenen bankaların mekanizmaları ve finansal performansı, ardından varsa istatistiksel bir sorunu tespit etmek için korelasyonlara uygulanan sağlamlık testleri. Çalışmanın bulguları, bazı yönetişim mekanizmaları ile bankaların performansı arasında bir ilişki olduğuna dair kanıtlar sunmaktadır. Yönetim kurullarına ilişkin bazı faktörlerin (yönetim kurulu toplantıları ve cinsiyet çeşitliliği) olumsuz ve anlamsız bir etkisinin olduğu tespit edilmiştir. Buna karşılık, diğerleri (yönetim kurulu bağımsızlığı ve büyüklüğü) kurumsal yönetişimi ve banka performansını olumsuz ama önemli ölçüde etkiledi. Çok fazla yöneticinin banka performansı için etkisiz olduğu anlamına gelir. Ayrıca, kurumsal yönetişim dinamiklerinin sahiplik yapısı (sahiplik yoğunluğu ve kurumsal yatırımcılar) dışındaki tüm banka performans ölçütleri ile negatif yönde ilişkili olduğu tespit edilmiştir. Denetim komitelerinin mali nitelikleri, uzmanlıkları ve bağımsızlığı, seçilen bankaların performans göstergelerini önemli ölçüde ve olumlu yönde etkilemiştir. Ayrıca, finansal kaldıraç ve yeterlilik durumları dışında kontrol değişkenleri negatif bir korelasyon göstermiştir. . Anahtar Sözcükler: Kurumsal yönetişim, Banka performansı, Kurul yapısı, Sahiplik yapısı ve Denetim komitesi yapısı. Tarih:4/08/2023This study examines the relationship between factors of internal corporate governance and the performance of eleven FTSE-listed banks operating in the UK from 2011 to 2020. It mainly examines the relations among a bank's performance (measured by Tobin's Q, Return on Assets (ROA), Return on Equity (ROE(), factors of corporate governance (board's size, independence, meetings, and gender diversity, ownership concentration, institutional investors, audit committee's independence, meetings, and expertise) and control variables (financial leverage, bank size, age, and capital adequacy). In order to conduct the research, different questions were prepared, and to answer them, data were collected, multiple hypotheses were tested and analyzed using multivariate fixed-effect regression (Hausman Test) to examine the relationship between corporate governance mechanisms and the financial performance of FTSE350-listed banks, followed by robustness tests performed to the correlations to detect a statistical issue, if any. The study's findings provide evidence of a relationship between some governance mechanisms and banks' performance. It was found that some factors pertaining to boards of directors (board meetings and gender diversity) had a negative and insignificant effect. In contrast, others (board independence and size) negatively but significantly affected corporate governance and bank performance. It implies that too many directors are ineffective for bank performance. Moreover, it was found that the corporate governance dynamics negatively correlate with all bank performance measures except for ownership structure (ownership concentration and institutional investors). The audit committees' financial qualifications, expertise, and independence significantly and positively affected the selected banks' performance indicators. Furthermore, the control variables indicated a negative correlation, except for the cases of financial leverage and adequacy. Keywords: Corporate governance, Bank performance, Board structure, Ownership structure, and Audit committee's structure. Data: 4/08/2023
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