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Borsa İstanbul'da bilgiye dayalı alım ve satımlar
This thesis investigates how information asymmetry affects asset prices in Borsa İstanbul. In the first chapter, we introduce the R package InfoTrad that estimates the probability of informed trading. Next, we examine the relationship between information asymmetry and stock returns in Borsa İstanbul. Firm-level cross-sectional regressions indicate an economically insignificant relationship between PIN and future returns. Moreover, univariate and multivariate portfolio analyses show that portfolios of stocks with high levels of informed trading do not realize significant return premiums. Consequently, our results, suggest that information asymmetry is a firm-specific risk and it can be eliminated with portfolio diversification. Finally, we compare the informational (dis)advantage of foreign investors trading in Borsa İstanbul. We first show that an average foreign trade creates buy pressure whereas an average local trade generates a sell pressure. The permanent impact foreign investors over and above local investors is significant only for 24 stocks which correspond to 7% of our sample. Importantly, we show that foreign price impact occurs primarily in a period of political instability which started with the Gezi Park protests in June 2013. In a panel setting, we also show that adverse selection cost due to foreign trading significantly increases even when we control for firm-specific factors along with global and local macroeconomic conditions. Domestic investors with undiversified portfolios may be more risk-averse during periods of increased turmoil. This may enable foreign investors to have a better position to take advantage of potential price misalignments, especially for stocks of commercial banks.
Türk ordusu askere alma sisteminin değerlendirilmesi ve askere alma sisteminin optimizasyonu için bir model önerisi
This study evaluates the Turkish Army recruitment system by measuring thetotal deviations of personnel inventories from the target value of inventory level.The target inventory level is the average of projected available human source inthe next 19 years. In our study we offer a model that minimizes the deviationsfrom the targeted inventory level of soldiers. The study also computes the zerovalue of total number of deviations for different service time durations in themilitary with applying different ages for the people of same birth year at the timeof recruitment. The results of the study indicate that even without the flexibilityof applying different ages, the model always achieves better results than thecurrent recruitment system.Keywords: Recruitment, Goal programmingi
Deterministik ve gürbüz kesikli zaman/maliyet ödünleşim problemleri için modeller ve algoritmalar
Projects are subject to various sources of uncertainties that often negatively impact activity durations and costs. Therefore, it is of crucial importance to develop effective approaches to generate robust project schedules that are less vulnerable to disruptions caused by uncontrollable factors. This dissertation concentrates on robust scheduling in project environments; specifically, we address the discrete time/cost trade-off problem (DTCTP).Firstly, Benders Decomposition based exact algorithms to solve the deadline and the budget versions of the deterministic DTCTP of realistic sizes are proposed. We have included several features to accelerate the convergence and solve large instances to optimality. Secondly, we incorporate uncertainty in activity costs. We formulate robust DTCTP using three alternative models. We develop exact and heuristic algorithms to solve the robust models in which uncertainty is modeled via interval costs. The main contribution is the incorporation of uncertainty into a practically relevant project scheduling problem and developing problem specific solution approaches. To the best of our knowledge, this research is the first application of robust optimization to DTCTP.Finally, we introduce some surrogate measures that aim at providing an accurate estimate of the schedule robustness. The pertinence of proposed measures is assessed through computational experiments. Using the insight revealed by the computational study, we propose a two-stage robust scheduling algorithm. Furthermore, we provide evidence that the proposed approach can be extended to solve a scheduling problem with tardiness penalties and earliness rewards.
Gelişmekte olan menkul kıymet borsalarında finansal liberalizasyon, yabancı hisse senedi yatırımı ve volatilite
In this thesis, the effects of financial liberalization and foreign equity investment on the return volatility of stocks in emerging stock exchanges are investigated. At the aggregate level analyses, it is shown that the degree of financial liberalization has an increasing impact on the aggregated total volatility of stocks. The analysis of the components of the aggregated total volatility indicates that that the degree of financial liberalization impacts the aggregated total volatility through aggregated idiosyncratic and local volatility. In the second part of the aggregate level analyses, the effect of foreign equity investment on the return volatility of stocks is investigated by using foreign equity flow data which is available for İstanbul Stock Exchange. It is found that foreign equity inflow and outflow have asymmetric effects on average stock-return volatility. While an inflow has a decreasing impact on aggregated stock return volatility, an outflow has an increasing impact. At the firm level analysis, the time-series variation in return volatility of stocks that are cross-listed on US exchanges is examined. Unlike previous studies in cross-listing literature, return volatility is analyzed using conditional heteroscedasticity models. It?s found that firms? exposure to risks such as local and global market betas remain unchanged after cross-listing. Moreover, no change in the dynamics of the volatility of cross-listed stocks is detected. Furthermore, it?s shown that the mean level of conditional variance is not affected by the decision to cross-list. Thus, it is concluded that share holders of cross-listed stocks are not subject to adverse volatility effects.
Finansal refahın tahminlenmesinde kestirimci unsur olarak büyük beşli kişilk özelliklerinin kullanılması: Büyük veri yaklaşımı
Research has posited credit card transactions as highly probable to be grounded on the personality of the card holder. In this research, we investigate whether the big five personality traits of customers derived from credit card transactions predict their financial wellbeing. Our approach uses real data from a private Turkish bank, which contain both the demographic and financial records of 10,172 consumers located in Istanbul with 911,280 transactions. We filter purchasing categories related to the big five personality traits from Matz, Gladstone, and Stillwell's study (2016). First, we link spending categories to the big five personality traits by considering Matz et al.'s study (2016). Then we calculate the big five factor scores of customers by monthly aggregating the individual big five scores of their transactions. Next, we investigate the relationship between the monthly big five personality scores and payment behavior of their credit card statements. In our main model, we estimated customers' on-time payment behavior of the full amount due 8.8 % better than a random prediction (with 54.4 % AUROC value) by using their monthly big five personality scores and yearly and six-month based trends as independent variables.
Müzik duygusu tanıma: Çok-modlu makine öğrenmesi yaklaşımı
Music emotion recognition (MER) is an emerging domain of the Music Information Retrieval (MIR) scientific community, and besides, music searches through emotions are one of the major preferences utilized by web users. As the world goes to digital, the musical contents in online databases, such as Last.fm have expanded exponentially, which require substantial manual efforts for managing them and also keeping them updated. Therefore, the demand for advanced and flexible search mechanisms, which can be personalized according to the emotional state of users, has received increasing attention in recent years. This thesis concentrates on addressing music emotion recognition problem by presenting several classification models, which were fed by textual features, as well as audio attributes extracted from the music. In this study, we build both supervised and semi-supervised classification designs under four research experiments, that addresses the emotional role of audio features, such as tempo, acousticness, and energy, and also the impact of textual features extracted by two different approaches, which are TF-IDF and Word2Vec. Furthermore, we proposed a multi-modal approach by using a combined feature-set consisting of the features from the audio content, as well as from context-aware data. For this purpose, we generated a ground truth dataset containing over 1500 labeled song lyrics and also unlabeled big data, which stands for more than 2.5 million Turkish documents, for achieving to generate an accurate automatic emotion classification system. The analytical models were conducted by adopting several algorithms on the cross-validated data by using Python. As a conclusion of the experiments, the best-attained performance was 44.2% when employing only audio features, whereas, with the usage of textual features, better performances were observed with 46.3% and 51.3% accuracy scores considering supervised and semi-supervised learning paradigms, respectively. As of last, even though we created a comprehensive feature set with the combination of audio and textual features, this approach did not display any significant improvement for classification performance.
Zengin araç rotalama: Bir lojistik firması için veri odaklı sezgisel uygulama
Changing online shopping behaviors have resulted in the emergence of different product and services that aim high customer satisfaction. In this thesis, we develop an alternative approach to solve problem of a logistics company, which operates solely for e-commerce transactions, using an Adaptive Large Neighborhood Search (ALNS) heuristic. To understand the nature of the distribution system and for the development of the solution procedure, we create, preprocess and analyze a dataset constructed from company's database that is used for daily operations. The proposed solution provides a prioritization mechanism for the deliveries based on certain specifications related to deliveries. To evaluate the performance of the proposed ALNS, we perform computational experiments using scenarios with real-life instances extracted from the dataset. Our results show that, the proposed ALNS can produce solutions with high quality regarding customer satisfaction.
Ürün ağlarında betimleyici ve ön görücü analitiklerin etkileşimi: Sam's club vakası
Due to the fact that there are massive amounts of available data all around the world, big data analytics has become an extremely important phenomenon in many disciplines. As the data grows, the need for businesses to achieve more reliable and accurate data-driven management decisions and to create value with big data applications grows as well. That is the reason why big data analytics become a primary tech priority today. In this thesis, initially we use a two-stage clustering algorithms in the customer segmentation setting. After the clustering stage, the customer lifetime value (CLV) of clusters are calculated based on the purchasing behaviors of the customers in order to reveal managerial insights and develop marketing strategies for each segment. At the second stage, we used HITS algorithm in product network analysis to achieve valuable insights from generated patterns, with the aim of discovering cross-selling effects, identifying recurring purchasing patterns, and trigger products within the networks. This is important for practitioners in real-life application in terms of emphasizing the relatively important transactions by ranking them with corresponding item sets. From practical point of view, we foresee that our proposed methodology is adaptable and applicable to other similar businesses throughout the world, providing a road map for the potential applications.