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Yayın Optimal investment levels to eliminate inventory inaccuracy in a two-level supply chain(Istanbul Technical Univ, 2007) Uçkun, Canan; Karaesmen, Ahmet Fikri; Savaş, SelçukInventory inaccuracy is a major problem in supply chains. RFID technology is anticipated to alleviate this problem at the expense of the required hardware and software investment. For a supply chain consisting of single supplier and multiple warehouses, we investigate the optimal levels of investment in order to decrease inventory inaccuracy. The analysis yields in-sights on the relative benefits of RFID implementation depending on factors such as demand and inaccuracy variability, financial parameters and supply chain structure.Yayın ISIKUN at the FinCausal 2020: Linguistically informed machine-learning approach for causality identification in financial documents(Association for Computational Linguistics (ACL), 2020) Özenir, Hüseyin Gökberk; Karadeniz, İlknurThis paper presents our participation to the FinCausal-2020 Shared Task whose ultimate aim is to extract cause-effect relations from a given financial text. Our participation includes two systems for the two sub-tasks of the FinCausal-2020 Shared Task. The first sub-task (Task-1) consists of the binary classification of the given sentences as causal meaningful (1) or causal meaningless (0). Our approach for the Task-1 includes applying linear support vector machines after transforming the input sentences into vector representations using term frequency-inverse document frequency scheme with 3-grams. The second sub-task (Task-2) consists of the identification of the cause-effect relations in the sentences, which are detected as causal meaningful. Our approach for the Task-2 is a CRF-based model which uses linguistically informed features. For the Task-1, the obtained results show that there is a small difference between the proposed approach based on linear support vector machines (F-score 94%), which requires less time compared to the BERT-based baseline (F-score 95%). For the Task-2, although a minor modifications such as the learning algorithm type and the feature representations are made in the conditional random fields based baseline (F-score 52%), we have obtained better results (F-score 60%). The source codes for the both tasks are available online (https://github.com/ozenirgokberk/FinCausal2020.git/).Yayın Comparison of evolutionary techniques for Value-at-Risk calculation(Springer-Verlag Berlin, 2007) Uludağ, Gönül; Etaner Uyar, Ayşe Şima; Senel, Kerem; Dağ, HasanThe Value-at-Risk (VaR) approach has been used for measuring and controlling the market risks in financial institutions. Studies show that the t-distribution is more suited to representing the financial asset returns in VaR calculations than the commonly used normal distribution. The frequency of extremely positive or extremely negative financial asset returns is higher than that is suggested by normal distribution. Such a leptokurtic distribution can better be approximated by a t-distribution. The aim of this study is to asses the performance of a real coded Genetic Algorithm (CA) with Evolutionary Strategies (ES) approach for Maximum Likelihood (ML) parameter estimation. Using Monte Carlo (MC) simulations, we compare the test results of VaR simulations using the t-distribution, whose optimal parameters are generated by the Evolutionary Algorithms (EAs), to that of the normal distribution. It turns out that the VaR figures calculated with the assumption of normal distribution significantly understate the VaR figures computed from the actual historical distribution at high confidence levels. On the other hand, for the same confidence levels, the VaR figures calculated with the assumption of t-distribution are very close to the results found using the actual historical distribution. Finally, in order to speed up the MC simulation technique, which is not commonly preferred in financial applications due to its time consuming algorithm, we implement a parallel version of it.
