Browsing by Author "Bulkan, S."

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Algorithmic pairs trading with expert inputs, a fuzzy statistical arbitrage framework
(IOS Press, 2020) Bayram, M.; Akat, Muzaffer; Bulkan, S.; International Finance; AKAT, Muzaffer
Pairs trading is a widespread market-neutral trading strategy aiming to utilize the relationship between pairs of financial instruments in efficient markets, where predictability of separate asset movements is theoretically not possible. The implication of trading pairs, following statistical analysis, is to buy the underpriced asset while short selling the overpriced. The predicted price relationship is determined through analysis of historical spread data between the members of the corresponding pair. The investor expects the price difference, in an efficient market, should converge and stocks return to their ‘fair value’, where the positions are closed and profit is realized. The main focus of this study is the contribution of the fuzzy engine to the existing pairs trading strategy. Widespread classical ‘crisp’ technique is chosen, utilized and compared with the developed ‘fuzzy’ model throughout the paper. In order to further improve this contribution, the expert opinions extracted from the Bloomberg database are also integrated into the fuzzy decision-making process. In most studies, transaction costs are simply ignored. As a final robustness check, the transaction costs are also considered. The improvement reached by the developed fuzzy technique is observed to be even more remarkable in this case.
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A cost-sensitive decision tree approach for fraud detection
(Elsevier, 2013-11-01) Sahin, Y.; Bulkan, S.; Duman, Ekrem; Industrial Engineering; DUMAN, Ekrem
With the developments in the information technology, fraud is spreading all over the world, resulting in huge financial losses. Though fraud prevention mechanisms such as CHIP&PIN are developed for credit card systems, these mechanisms do not prevent the most common fraud types such as fraudulent credit card usages over virtual POS (Point Of Sale) terminals or mail orders so called online credit card fraud. As a result, fraud detection becomes the essential tool and probably the best way to stop such fraud types. In this study, a new cost-sensitive decision tree approach which minimizes the sum of misclassification costs while selecting the splitting attribute at each non-terminal node is developed and the performance of this approach is compared with the well-known traditional classification models on a real world credit card data set. In this approach, misclassification costs are taken as varying. The results show that this cost-sensitive decision tree algorithm outperforms the existing well-known methods on the given problem set with respect to the well-known performance metrics such as accuracy and true positive rate, but also a newly defined cost-sensitive metric specific to credit card fraud detection domain. Accordingly, financial losses due to fraudulent transactions can be decreased more by the implementation of this approach in fraud detection systems.