LEE_İktisat Lisansüstü Programı - Doktora

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http://hdl.handle.net/11527/206

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A Dutch disease approach into the premature deindustrialization

(Graduate School, 2022-08-18) Çakır, Muhammet Sait ; Aydemir, Resul ; 412142006 ; Economics

We explore the main causes and consequences of the premature deindustrialization phenomena. We argue that local currency overvaluations mainly associated with a surge in capital inflows into the emerging market economies following the deregulation of their capital accounts severely hurt the output share of manufacturing industry. First, we empirically establish a causal link from capital flows to local overvaluations. According to the two-way error component model which controls for the full set of country and time fixed effects, a surge in capital flows by one standard deviation is associated with an overvaluation of 1.67 percent. To address the possible endogeneity between capital flows and real exchange rate, we run two-variate first-order panel vector autoregressive model since the feedback effects from overvaluation to net financial inflows might introduce a bias into the fixed effect estimation. When we isolate the effect of positive capital inflow shock of one standard deviation by the Cholesky decomposition, we find that it is statistically significantly associated with an immediate overvaluation in real terms with 95 percent confidence level. Then we construct our baseline regression model. Applying the second generation estimators allowing for cross-section dependency (Augmented Mean Group and Common Correlated Effects Mean Group), we run a panel data regression model based on a sample of 39 developing countries in Latin America, Sub-Saharan Africa, East Asia, North America, and Europe from 1960 to 2017. We find that an overvaluation of 50 percent which corresponds approximately to one and half standard deviations is associated with a contraction of manufacturing output share as high as 1,25 percent over the five year period. With the turn of new century, the developing countries also experienced a massive deindustrialization by shedding manufacturing value-added as large as 1.24% of national income. Moreover, the evidence suggests that the relationship between real exchange misalignments and the manufacturing share in output might be nonlinear so that the manufacturing competencies which have been eroded by local currency overvaluations in real terms cannot simply be brought back during the undervaluation periods. We also show that the baseline regression results are robust to different data sets, alternative real exchange rate/deindustrialization measurements, and dynamic model specifications which allow us to treat the real exchange rate as endogenous variable to address any potential concern regarding the simultaneity bias. As a further robustness check on our findings, we empirically examine the effects of supply chain disruptions, inequality shocks, and institutional innovations on the path of industrialization in developing countries by running a panel vector autoregressive model. We found that deterioration in income distribution unequivocally harms the developing countries' bid for industrialization while better institutions proxied by an improvement regulatory quality invariably foster it. On the other hand, the effects of supply chain disruptions on the pace of industrialization follow a nonlinear path, showing the great resilience of local industries in absorbing imported input bottlenecks through intermediate input import substitution. We also provide evidence that backward participation into GVCs and regulatory quality do not mutually Granger-cause each other, and suggest that the well-established link from better governance to GVCs may be missing in the developing country case. Based on these empirical findings, the need for a comprehensive industrial policy along with a firm use of capital controls and macroprudential measures given a robust institutional framework comes out as the main policy implication of our work, and they are duly discussed in light of recent developments in the literature.
Financial resilience of conventional versus participation banking: Evidence from macro stress testing approach and risk spillovers analysis

(Graduate School, 2022-06-10) Atan, Huzeyfe Zahit ; Aydemir, Resul ; 412152002 ; Economics

Islamic banking, which provides an alternative to the traditional banking system through the principals of interest-free finance, has risen in global markets over the last 20 years and is already present in many countries. Rising consumer demand for Islamic-law-based products and services has propelled Islamic banking to the forefront of the global economy during this period. Another factor that may appeal to investors is the approach Islamic banking manages financial risks. Several studies that showed, during the 2008 global recession, Islamic banks outperformed their conventional counterparts suggesting that the Islamic banking system may be a safe haven for global risk spillovers. In that regard, several studies have been performed to compare and contrast conventional and Islamic banking. In light to these advances in Islamic banking, this dissertation is made up of three chapters that compare Islamic and conventional banks on a worldwide scale as well as in the context of Turkey. In the first chapter, we examine how equity returns of conventional and Islamic banks are affected by shocks to major financial indices using the multivariate quantile autoregression technique. We analyze the resilience of the dual banking systems to financial risk spillovers at the global and regional levels based on data from 16 countries for the period between 2008-2018. The primary goal of this research is to see if there is a link between key financial indices and bank stock prices, as well as to compare conventional and Islamic banks in this regard. Recent research has found that bank stock price is linked to banks' overall performance. In this regard, bank stock performance is critical in terms of providing information about the bank's overall success. The findings of the first chapter show that there is no substantial difference in sensitivity to financial shocks between Islamic and conventional banks at the global and regional levels. As a result, this conclusion may suggest that Islamic markets have lost their safe haven status over interest-based financial systems since the 2008 financial crisis. Moreover, in contrast to earlier reports on Islamic banking competitiveness that argue that double layers of Shariah systems may generate heterogeneity across countries, our results reveal that the effects of individual shocks to Islamic banks are homogeneous in the Gulf, MENA, and Asian countries. This uniformity may be attributable, among other things, to recent advancements in Islamic financial principles and standards released by AAOIFI and IFSB. Second chapter extends the data timeline in first chapter to investigate stability and risk characteristics of dual banking system using 170 banks from 14 emerging market countries with multivariate quantile regression technique for 2008-2020 period. The primary purpose of this chapter is to examine how bank-specific factors influence risk spreads on conventional and Islamic banks' stock price returns. For this purpose, we choose leverage ratio, capital adequacy ratio and market value as bank specific determinants. Our findings reveal that bank equity prices tend to be more sensitive to shocks in major financial indices as bank's leverage ratio increases for both Islamic and conventional banks. This result is important especially for Islamic banks as recent reports claim that recently developed instruments in Islamic financial system which have led to increases in the debt and leverage ratio, endangers stability of the Islamic banks as they are relatively more affected to financial risk spillovers. For bank size, the impact of financial shocks over bank equity returns increases with bank size for Islamic banks. The primary cause might be that when Islamic banks increase in size, credit risk management gets more complex owing to specific risk management requirements for different PLS transactions which may cause moral hazard and adverse selection concerns. However, we observe the impact of the financial shocks doesn't vary according to different bank size levels for conventinal banks. Last, our findings imply that when the capital adequacy ratio reduces for conventional banks, they become more vulnerable to financial shocks. Third chapter compares determinants of capital adequacy requirements for participation and conventional banks in Turkey employing an innovative stress test approach. We use two models for our analysis: an additive semi-parametric quantile regression model (Koenker 2010,2011) and a semi-parametric quantile panel model (Cai et al (2018). Also, with probability analyses, the likelihood of capital adequacy ratio being lower or greater than a given value is calculated based on values of explanatory factors in the context of various scenarios. The public debt ratio for conventional banks and the exchange rate for Islamic banks are the key drivers in establishing the capital adequacy ratio, according to the findings in the third chapter. Although unemployment has a positive marginal impact on the capital adequacy ratio in both Islamic and conventinal banks, the results imply that this positive impact is unaffected by the fluctuations in the unemployment rate. The semi-parametric panel quantile technique is used only for conventional banks, as there are very few banks for Islamic banks in our sample, and the determinants of capital adequacy ratio are explored while taking bank size into account. Banks improve their capital adequacy ratios regarding increases in the public debt ratio and interest rate as their size and capital adequacy grow
Issues on labor market in Turkey

(Graduate School, 2024-12-27) Gider Zayim, Gülen Derya ; Yılmaz Kayaoğlu, Ayşegül ; 412172003 ; Economics

Education is widely recognized as a fundamental driver of economic development and an effective tool for shaping labor market outcomes and reducing inequalities. In recent years, Turkey has implemented significant educational reforms, such as the 2012 compulsory education reform, which extended mandatory schooling from eight to twelve years and lowered the school starting age. This dissertation examines the multifaceted impacts of this reform, investigating its implications for labor market participation, wage dynamics, and employment trajectories. The second and third chapters of the thesis examine the labor market outcomes of the 2012 reform from interconnected but distinct perspectives. Chapter 2 analyzes the impact of the reform's provision for earlier school enrollment, which implicitly offered mothers a form of public childcare, on maternal labor force participation. Chapter 3 focuses on the potential human capital effects of the reform, exploring how compulsory high school education affects a broad range of labor market variables, including individual wages, employment types, and participation in the formal sector. The fourth and final chapter examines the impact of education on wage inequality, investigating the heterogeneity in returns to education across different wage deciles. Chapter 2 provides the first causal evidence in Turkey on the role of public childcare services in enhancing maternal labor supply. Turkey's 2012 education reform reduced the compulsory school starting age from 72 to 66 months and allowed children aged 60–66 months to start school with parental consent. By lowering the starting age for primary education in public schools, the reform created an implicit form of public childcare that could influence maternal labor market participation. Using data from the Household Labor Force Surveys (HLFS) between 2004 and 2019, the analysis employs a difference-in-differences approach. The treatment group comprises mothers whose youngest child was aged 5–6 years (eligible to enroll due to the reform), while the control group includes mothers whose youngest child was 4 years old (ineligible). Key labor supply measures include labor force participation, employment, working hours, and part-time employment. The findings reveal that the implicit childcare option introduced by the reform is not strong enough to significantly increase maternal labor force participation. While minor increases were observed in some subgroups, these effects were not statistically significant. The limited impact is attributed to the prevalence of informal childcare options (e.g., family and community networks) in Turkey and cultural norms emphasizing parental care for young children, which may restrict the substitutability of public schools for other forms of childcare. These results highlight the need for complementary policies to enhance the effectiveness of educational reforms in reducing barriers to women's labor force participation. Such policies could include expanding access to affordable formal childcare services, addressing cultural norms, and providing targeted support for low-income and less-educated mothers. Chapter 3 examines the labor market impacts of Turkey's 2012 compulsory education reform, particularly its effects on human capital accumulation. By extending the duration of compulsory education to 12 years, the reform aimed to raise educational attainment and deliver long-term benefits for individuals and the economy. Using regression discontinuity design (RDD) with birth month and year as an assignment rule, this chapter analyzes the causal effects of the reform on variables such as high school graduation rates, university enrollment, employment status, formal sector participation, and hourly wages. The analysis draws on household survey data from the Turkish Statistical Institute, covering a wide range of variables. The findings indicate that the reform significantly increased high school graduation rates for women by 7–8%, particularly among cohorts born close to the eligibility cutoff, demonstrating its success in reducing early school dropout rates among girls. However, the reform had no comparable effect on male individuals, suggesting that women faced greater barriers to accessing education prior to the reform. Despite successfully narrowing the gender gap in education, the reform did not achieve similar improvements in women's labor market outcomes. Higher education levels did not translate into short-run gains in employment or wages for women. These results suggest structural barriers in the labor market that limit the absorption of highly educated women, such as limited job opportunities, cultural norms restricting female labor force participation, and mismatches between education curricula and labor market demands. The findings underscore the need for complementary policies to maximize the economic benefits of educational reforms, such as expanding childcare services and implementing targeted employment programs for women. Chapter 4 provides a comprehensive analysis of how education reshapes the wage structure in Turkey, with a particular focus on its effects on wage inequality. By examining the impacts of education on wages across different ability groups and over time, this chapter offers new insights into the dynamics of wage inequality. The analysis focuses on both between-group inequality (wage differences between individuals with varying education levels) and within-group inequality (wage disparities among individuals with the same education level). Using quantile regression (QR) and instrumental variable quantile regression (IVQR) methods with data from HLFS, the findings reveal that education exacerbates within-group wage inequality. Highly educated individuals disproportionately benefit from education, which is attributed to the complementarity between education and unobservable abilities. Individuals with higher ability levels derive greater returns from additional education, widening wage disparities among those with similar educational backgrounds. Furthermore, the effects of education on wage inequality have evolved over time. Since 2012, the degree of complementarity between education and unobservable abilities has increased, leading to higher returns for highly able individuals and relatively smaller benefits for lower-ability groups. The results highlight the need for policies that address structural barriers in the labor market and align education with labor market demands. Expanding access to childcare services, developing targeted employment programs for women, and aligning curricula with market needs are essential steps to ensure that increased educational attainment translates into reduced inequalities and improved economic outcomes.
Macro stress testing in Turkish banking sector and tail dependence in financial, energy and commodity markets

(Graduate School, 2024-02-07) Atik, Zehra ; Güloğlu, Bülent ; 412172007 ; Economics

This dissertation is a collection of three chapters on the resilience of Turkish banking system, nonlinear tail dependence from U.S. agricultural markets to Turkish agricultural markets, and nonlinear tail dependence from energy commodities to agricultural commodities. Financial institutions and financial markets exert significant effect on economies due to their critical role. When operating efficiently, they ensure the growth and stability of economies. Nevertheless, inefficiencies or vulnerabilities to shocks in these institutions and markets can result in systemic crises. For instance, deteriorations in the indicators of banks not only affect the banking system but also have repercussions across the entire economy through a feedback mechanism. As emphasized in the literature, higher nonperforming loans impede the real economy by slowing economic activity and credit growth. Financial markets, on the other hand, inherently contain the risk of financial contagion or spillover effects. While offering investment opportunities and facilitating price discovery, which contributing to wealth accumulation and economic advancement, they possess the potential to trigger joint collapses and substantial financial losses in the event of a shock. Thus, examining the vulnerabilities of financial institutions and the interdependence among various financial markets is exceedingly crucial, particularly considering the complexity of this interdependence. Accordingly, this dissertation evaluates the resilience of the Turkish banking system, the nonlinear dependence from US agricultural markets to Turkish agricultural markets, and the nonlinear dependence from energy commodities to agricultural commodities in separate chapters. The first chapter focuses on stress testing applied to conventional and participation banks within the Turkish banking sector. It employs the nonperforming loan ratio (NPL) as a pivotal stress variable. To conduct stress testing, the study uses an innovative additive nonparametric quantile regression technique. The objective is to assess how macroeconomic variables—exchange rates, interest rates, unemployment rates, and the public debt to GDP ratio—affect the NPL. The analysis covers monthly observations spanning from January 2005 to February 2020. Additionally, the chapter conducts scenario analyses based on various distributions and examines adverse scenarios concerning extreme tail values of macroeconomic variables. The study reveals the substantial influence of these macroeconomic factors on NPL, emphasizing the importance of stable exchange rates, controlled unemployment rates, and managed public debt. Furthermore, it highlights the necessity for tailored policies concerning different bank types, given observed disparities. The second chapter examines the relationship between U.S. and Turkish agricultural commodity markets, and also investigates the nonlinear tail dependence from Brent oil, USD/TRY currency rate, and overnight interest rate to Turkish agricultural commodities. It employs a novel nonlinear measure of tail dependence to explore the connection between these markets. The study analyzes the mean and tail dependence between the markets, considering both lower and upper continuum of quantiles. Additionally, it evaluates significant events such as the Turkish Mercantile Exchange (TMEX) launch and the Ukraine war. The dataset set contains daily returns from January 5, 2016, to May 31, 2022. To ensure robustness, a nonparametric test for Granger causality in distribution is employed. The findings reveal significant and persistent tail and mean dependence from U.S. agricultural futures to Turkish spot markets. This emphasizes the necessity for enhanced information exchange between spot and derivatives markets to strengthen market efficiency. The chapter also addresses the importance of monitoring interconnections and contagion risks among markets, especially during challenging market conditions. The third chapter investigates the impact of energy commodities—Brent oil, natural gas, and gasoline—on agricultural commodities. It employs a nonlinear measure of tail dependence to analyze the relationship across three distinct periods: pre-Covid-19, during Covid-19, and post-Covid-19. The dataset comprises daily records from June 1, 2017, to June 9, 2023, covering different market periods. Additionally, the chapter utilizes cross-quantilogram analysis and the nonparametric test for Granger causality in distribution for robustness checks. The research reveals significant and persistent lower and upper tail dependence between energy and agricultural commodities across each period. The findings demonstrate nuanced and dynamic tail dependence patterns, indicating asymmetric risk transmissions under varying market conditions. These results have critical implications for effective market strategies, efficient portfolio management in agricultural commodity markets, and the need for resilient regulations during extreme events.
Net neutrality in oligopolistic models of content provision and internet service provision markets

(Graduate School, 2022-09-13) Erkul, Turgut ; Ecer, Sencer ; 412152005 ; Economy

Importance of telecommunications in all societies and all industries is growing tremendously. From entertainment to even the most basic needs such as ordering potable water right at our doorsteps, we rely on the telecommunication networks to provide us the means. Behind the scenes there is a complex mesh of advanced technology with an evolving market interaction of Content Providers (CPs) and Internet Service Providers (ISPs) racing to profit from the end-users (EUs). National Telecommunications Regulatory Authorities (NTRA) in each country regulate the market to maximize the total welfare. Net Neutrality (NN) is the mechanism that is implemented and safeguarded by the NTRAs that protects against discrimination of data. As a principle, NN advocates that all data has been created equal and shall not be throttled, discarded, de-prioritized or charged differently than any other data. Furthermore, NN prevents ISPs asking for termination fees from CPs to give them access to the EUs. Content Providers (CPs) seem to be generally pro-NN and ISPs seem to be against NN, likely because of the relative inelasticity of end-user demand for ISPs compared to CPs, which is reflected in the joint demand structure as I model in this dissertation. Latest academic articles have focused on the successive monopoly or successive oligopoly models in vertically related markets to explain the dynamics of the CP, ISP and end-user interaction. In these models, upstream is the CP (e.g., Netflix, BluTV), downstream is the ISP (e.g., Comcast, TTNet). In early models, CPs and ISPs are assumed to be perfect complements. Therefore, the termination fee that the CP pays to the ISP becomes irrelevant, and hence does not impact the prices to the end-user or the total welfare. This result is not consistent with what we observe in the industry, like the case between South Korea Broadband and Netflix (Bae et al., 2021). Indeed, there is mounting pressure from ISPs to allow these payments, which means these fees are not irrelevant. My conclusion is that the perfect complementarity assumption is inappropriate to explain the industry. In my model, I introduce imperfect complementarity, which releases the constraint that the quantity of CP demanded, and the quantity ISP demanded to be equal, and I show that introducing a non-zero termination fee may indeed increase total welfare. Therefore, we recommend NTRAs to consider termination fee as a leverage to maximize the social welfare within each country. Furthermore, I show that the need for net neutrality depends on the level of complementarity and own price effects of the ISP and the CP relative to each other.
Network neutrality in the internet as a two sided market constituted by congestion sensitive end users and content providers

(Graduate School, 2023-03-01) Kaplanlıoğlu, Özgür ; Güloğlu, Bülent ; Ecer, Sencer ; 412152004 ; Economics

In this paper, I envision a two-sided market mediated by a monopolistic internet service provider, ISP. The ISP provides end-users internet access and carries content providers' (CPs') data packages on its network. I compare the case where network neutrality is strictly practiced with the case where the ISP can "throttle" the traffic of certain content providers. In the model, for simplicity, a single CP is exposed to throttling, while the other CPs, which are part of a continuum, are not. I then study the implications of the violation of network neutrality on total data consumption, congestion, and capacity investment. Under network neutrality, the decision variables of the ISP are end-user price and network bandwidth. I found that, in equilibrium, because of the monopolistic nature of the market is greater than the price under competitive equilibrium, and is lower than its socially optimum value. Thus, under neutrality, the ISP undersupplies both the capacity and the data. Under discrimination, the ISP is allowed to charge an access fee on unit bandwidth to one of the content providers (the discriminated CP). To reflect a scenario of great practical value, I choose the discriminated CP from one of the big OTTs such as YouTube, Instagram, Facebook, Netflix, etc. In this setting, to access the network, the discriminated CP needs to buy bandwidth from the ISP. However, the bandwidth bought by the discriminated CP is not for exclusive usage of the discriminated CP. It rather acts as an upper bandwidth limit for the discriminated CP. Under discrimination, the decision variables of the ISP are the end-user price, the network bandwidth, and the bandwidth price. I found that when allowed the ISP always prefers to deviate from network neutrality by charging a positive price for bandwidth. Also, the ISP sets just enough to keep the discriminated CP in the market. Comparing the equilibrium outcomes, I show that under discrimination, the ISP charges a lower price to end-users. However, the discrimination also leads to less network bandwidth installed. Both the lower end-user price and the lower network bandwidth contribute to the congestion. Thus, under discrimination, the congestion is higher than under neutrality. Considering its adverse effects on the network bandwidth and congestion, although the end-user price is lower under discrimination, I recommend that the network neutrality principle should not be abolished.
Origin and destination based demand of continuous pricing for airline revenue management

(Graduate School, 2023-08-31) Değirmenci, Mehmet Melih ; Aydemir, Resul ; 412162005 ; Economics

This thesis presents an approach for estimating sell-up rates, which indicate a passenger's likelihood to upgrade to a higher fare class, based on historical booking data categorized by fare classes. Previous models explored in the literature, including Direct Observation (DO) and Inverse Cumulative (IC), have demonstrated limitations when applied to real-world historical booking data, as their outcomes may not align with the desired business expectations. To address this limitation, we enhance these models by incorporating data pre-processing techniques and devising solution strategies that provided to the needs of industry practitioners when dealing with historical booking data. By incorporating fare class availability data and adjusting past bookings accordingly, our proposed model offers a robust estimation of sell-up rates. To validate the effectiveness of our approach, we conduct an analysis using data from a major European airline. The numerical results demonstrate a significant decrease in the Mean Absolute Percentage Error (MAPE) when employing our proposed method, indicating its superior accuracy in estimating sell-up rates. This research not only fills the gap in the existing literature but also provides practical implications for revenue management practitioners. By refining the sell-up rate estimation process and addressing the shortcomings of traditional models, our approach offers a valuable tool for airlines to optimize their revenue management strategies. The utilization of historical booking data, combined with our model's enhancements, ensures more reliable and actionable insights, empowering practitioners to make informed decisions. Furthermore, our study contributes to the field by introducing data pre-processing techniques tailored specifically for historical booking data analysis. These techniques facilitate the extraction of relevant information and enhance the accuracy of sell-up rate estimations. As such, our research provides a comprehensive framework that encompasses both theoretical advancements and practical applications, thus offering a holistic approach to addressing the challenges of sell-up rate estimation in revenue management. In summary, the first chapter introduces a new method for estimating sell-up rates by leveraging fare class-based historical booking data. Through the refinement of existing models, along with the incorporation of data pre-processing techniques and solution strategies, our approach yields more accurate sell-up rate estimations. The analysis of data from a major European airline demonstrates the effectiveness of our proposed method in reducing the Mean Absolute Percentage Error (MAPE). By enhancing sell-up rate estimation accuracy, our research contributes to the advancement of revenue management practices and provides valuable insights for industry practitioners. In the second chapter, we present an innovative model for forecasting airline flight load factors specifically designed to account for the unique circumstances brought about by the Covid-19 pandemic. By incorporating various variables, including bookings, capacity, booking trends, and seasonal effects, our model aims to provide accurate load factor predictions. To validate its effectiveness, we conducted an extensive analysis using the dataset of one of Europe's largest network airlines, spanning the entirety of 2021. The findings of our study reveal that machine learning algorithms offer substantial improvements in load factor predictions compared to the traditional pickup method. Notably, the Covid-19 pandemic period introduces distinctive patterns and challenges to airline load factor data, leading to decreased performance of the pickup method. However, by leveraging advanced machine learning models, we were able to effectively capture the complexities and variations in load factors during this turbulent period, resulting in significantly enhanced accuracy. Our proposed model demonstrates a remarkable reduction in the Mean Absolute Error (MAE) score for load factor forecasts. When compared to the pickup method, the MAE score decreased from 7.94 to an impressive 1.99. These results underscore the potential of advanced machine learning techniques in accurately predicting load factors, particularly in the face of unprecedented disruptions like the Covid-19 pandemic. The incorporation of diverse variables into our model enables a comprehensive assessment of the factors influencing load factor dynamics. By considering variables such as bookings, capacity, booking trends, and seasonal effects, our model captures the intricate interplay between these factors and load factor performance. This complete approach enhances the accuracy and reliability of load factor forecasts, providing airlines with valuable insights for informed decision-making. The outcomes of this research highlight the significance of leveraging advanced machine learning techniques for load factor forecasting during challenging periods like the Covid-19 pandemic. The ability to effectively capture and analyze complex data patterns empowers airlines to adapt their strategies and optimize resource allocation in response to changing demand dynamics. By embracing the potential of machine learning, airlines can gain a competitive edge and make data-driven decisions to navigate through turbulent times successfully. In conclusion, this chapter introduces a different model for forecasting airline flight load factors, specifically tailored to the unique circumstances presented by the Covid-19 pandemic. Through the utilization of machine learning algorithms and the incorporation of various variables, our model surpasses the traditional pickup method in terms of accuracy. The significant reduction in the Mean Absolute Error (MAE) score demonstrates the efficacy of our proposed model in capturing the complexities and variations in load factor data during the pandemic. By providing more accurate load factor forecasts, our research equips airlines with valuable insights to optimize their operations and navigate through challenging times effectively.
Sign predictability of intraday price returns to formulate appropriate trading strategies with optimum set of equities

(Graduate School, 2024-02-08) Kılıç, Abdurrahman ; Güloğlu, Bülent ; 412162001 ; Economics

The prediction of stock market returns has been a focused research area for computational finance, time-series econometrics, and computer science researchers. Typically, market participants rely on technical and fundamental analyses to make predictions, determining their buy or sell strategies based on these assessments. However, scholars in finance and economics have remained skeptical about the predictability of stock returns, particularly in efficient markets. Besides the stock market earnings, a literature on capital martket's direciton predcition has been emerged. Studies in the field of computer science also supported this research area with the help of machine lerning, deep lerning and artifical neural network techniques. Empirical studies in applied economics focuses on efficient market hypotesis whereas computer science studies mostly compares prediction performances of diffirent techniques. In this thesis, closing price direction of 26 stocks in 5 miutes intervals predictited. Only the higly liquid stocks existed in the BIST 30 index for the entire year of 2018 are examined. The data includes 251 trading days and 84 data nodes for each day. Transaction costs are considered to be 2.5 bps and 15 bps is determined as the treshold for a direction signal. If 5 minute closing price of an equity went up by 0.15 percent, it is tagged as "positive" and the opposite as "negative" and the inbetween in considered "steady" to achieve economically significant results. A significant data source—Borsa Istanbul's 'data analytics' information distributed through data vendors—is utilized. There are 39 analytics providing order book statistics regarding for last 1 minute, 5 minute and intraday periods. Initially, a specific method for extracting valuable insights from the complex raw data of Borsa Istanbul is described and implemented. Subsequently, standard scaling and dimension reduction methods, such as Principal Component Analysis, are employed to enhance efficiency. Seven different machine learning algorithms, Logistic Regression, K-Nearest Neighborhood, Support Vector Machines with radial and sigmoid kernels, Naïve Bayes, Decision Tree, Random Forest, are compared. All the methods trained with former 95 percent and tested using the last 5 percent of the complete dataset of the year 2018. The performance levels of each method for twenty-six highly liquid stocks are assessed in terms of Macro Averaged F-Measures. Furthermore, in this thesis, the effectiveness and significance of machine learning algorithms are compared using confidence intervals calculated from the confusion matrices for Macro Averaged F-Measures—an innovative approach within the economics and finance literature. Additionally, the use of three classes for stock price directions 'positive ,' 'negative" and 'steady' rather than just two is a rare aspect in academic studies, aligning more closely with data analytics practitioners' methodologies. Concerning the 5-minute lagged data, a statistically significant predictability is found in nine of the equities. K-Nearest Neighbors, Decision Tree and Random Forest yielded significant predictions. Naïve Bayes and SVM-sigmoid achieved better for a few equites while Logistic Regression and SVM-rbf not for any. However, for the 10 and 60-minute lagged data, predictability remains only in four and none of the equities, respectively. Essentially, markets assimilated Borsa Istanbul's data over time for those equities. Moreover, economic gains for the nine equities are analyzed with algorithms not allowing short selling and allowing short selling, based on these predictions. 'Positive", 'negative' and 'steady' signs treated as 'buy', 'sell' and 'keep the position' signals in the first scenerio while 'steady' allowed as the 'short-sell' signal in the second scenerio. Those scnerios are compared with passive buy and hold strategy and it has been revealed that determined trading strategies depending on the machine lerning algorithms performs higher earnings or less losses for KOZAA, KOZAL and KRDMD stocks when short selling is not allowed. If short selling is allowed TKFEN is also added to this list. Test period performances of the determined trading strategies and the stocks' own price performances are graphically illustrated in the thesis. Since four equities are observed to generate higher economic gains through machine learning-supported trading strategies compared to their own price performances, these findings for those wquities indicate, within the framework of the 'Efficient Market Hypothesis,' a lack of 'Semi-strong-form efficiency.' The methods and techniques used in this study such as creating tree dimensional confusion matrices, determining standard deviation and confidence intervals for MA F-Measures, and benchmarking them with a theorical MA F-Measure and developing trading strategies with three signals will support further research
The digital divide in Turkey

(Graduate School, 2023-04-05) Dalgıç Tetikol, Deniz Ece ; Güloğlu, Bülent ; 412162004 ; Economics

Information and communication technologies (ICT), which broadly include telecommunications, mobile telephony, the Internet, and various Internet-enabled devices, have permeated most aspects of life by offering new and more efficient ways for people to communicate, access information, and learn. With applications in education, banking, e-commerce, health, and government services, among other areas, ICT are a major force behind economic growth and productivity, connecting people to essential services and jobs while enabling businesses to operate. The digital divide describes the uneven distribution of ICT in society. At a high level, the digital divide is the gap between those who have and do not have access to the Internet. However, the digital divide is not a binary but multifaceted and includes many factors such as connectivity, access to equipment, affordability, quality of service, motivation to use the technology, digital skills, and so on. Therefore, the digital divide encompasses various Internet-related challenges, which results in different types (levels) of digital inequality. The digital divide literature categorized those challenges as follows: differences in access (first-level digital divide), usage and digital skills (second level digital divide), and outcomes of Internet use (third level digital divide). These digital gaps exist at the international level as well as within a country. Often these gaps fall along other social inequalities in a country – that is, the different levels of the digital divide usually reflect the gaps between individuals from different demographic backgrounds and at different socioeconomic levels with regard to their opportunity and ability to access and utilize ICT. This thesis empirically examines the different aspects of the digital divide in Turkey. It explores the demographic and socioeconomic determinants of the first level and second level of digital divide in Turkey and analyzes the data that substantiates digital inequalities between years 2008-2020. Digital transformation and technological advancements in ICT offer tremendous opportunities for countries, especially for emerging economies. However, the full potential of digital advancements cannot be achieved by focusing solely on supply-side policies such as investing in infrastructure deployment. Despite increased Internet penetration rates, Turkey has failed to create a digitally inclusive society and risk missing out on the benefits of digitization. One of the leading factors contributing to this problem is the lack of effective demand-side solutions mitigating the digital divide. The results of this thesis suggest that significant disparities persist between different social groups in Turkey in terms of Internet access and Internet usage patterns. The findings of this thesis point out the target groups of first priority to address the first level and second level digital divide in Turkey. We can conclude from the results that special initiatives and programs are required to increase widespread adoption of the Internet. Those initiatives and programs should be designed and implemented with a participatory approach, targeting the high priority groups: women, older citizens, citizens with lower household income, citizens with low educational background, homemakers, and retired people, as well as citizens of the Northeast Anatolia, Central East Anatolia, Southeast Anatolia and West Black Sea regions of Turkey in particular, while considering the factors that make Internet access and use difficult.
The impact of economic and financial shocks on macroeconomic fundamentals: Multi country new Keynesian approach

(Sosyal Bilimler Enstitüsü, 2020) Güngör, Mahmut Sami ; Güloğlu, Bülent ; 671998 ; İktisat

This dissertation examines the impact of various economic and financial shocks on macroeconomic fundamentals with the help of a multi country modeling approach for the post 1990 era. Its theoretical framework is entirely based on the new Keynesian synthesis. In the first empirical section, I estimate two distinct versions of the three equation new Keynesian model by using the Bayesian techniques to investigate the impact of structural shocks on macroeconomic fundamentals under aggressive monetary policy in Turkey for the period from 2000Q1 to 2019Q1. The practical role of this chapter is to introduce the basic new Keynesian model which forms the fundamental structure of modern macroeconomic models. In the second empirical section, a single country framework is extended to a multi country one to investigate transmission of shocks across economies. First, I define the major trading partners of Turkey to construct a new globe for the multi country new Keynesian analysis. Next, I estimate the global VAR model for the period from 1990Q1 to 2018Q4 in order to obtain steady state values for all variables used in the multi country analysis. Then, I estimate the multi country new Keynesian model for the member countries of the new globe by using the inequality constrained instrumental variables estimator for the period from 1990Q3 to 2018Q4. Finally, a number of plausible economic and financial scenarios are discussed by using both point and bootstrap estimates of impulse responses of related shocks in the dynamic analysis part.

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