Forecasting of Turkish Sovereign Sukuk Prices  Using Artificial Neural Network Model

ÇETİN>, Dilşad Tülgen; Metlek, Sedat

doi:10.26650/acin.907990

Cited by 7 publications

(3 citation statements)

References 16 publications

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“…ANN, RNN and other artificial intelligence techniques are described in the literature and used for many applications across a wide range of fields (Çetin and Metlek, 2021; Çetiner, 2021; Çetiner and Çetiner, 2022; Metlek, 2022). In the study, deep learning architectures in artificial intelligence algorithms were used, and the results were evaluated.…”

Section: Methodsmentioning

confidence: 99%

A new proposal for the prediction of an aircraft engine fuel consumption: a novel CNN-BiLSTM deep neural network model

Metlek

2023

AEAT

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Purpose The purpose of this study is to develop and test a new deep learning model to predict aircraft fuel consumption. For this purpose, real data obtained from different landings and take-offs were used. As a result, a new hybrid convolutional neural network (CNN)-bi-directional long short term memory (BiLSTM) model was developed as intended. Design/methodology/approach The data used are divided into training and testing according to the k-fold 5 value. In this study, 13 different parameters were used together as input parameters. Fuel consumption was used as the output parameter. Thus, the effect of many input parameters on fuel flow was modeled simultaneously using the deep learning method in this study. In addition, the developed hybrid model was compared with the existing deep learning models long short term memory (LSTM) and BiLSTM. Findings In this study, when tested with LSTM, one of the existing deep learning models, values of 0.9162, 6.476, and 5.76 were obtained for R2, root mean square error (RMSE), and mean absolute percentage error (MAPE), respectively. For the BiLSTM model when tested, values of 0.9471, 5.847 and 4.62 were obtained for R2, RMSE and MAPE, respectively. In the proposed hybrid model when tested, values of 0.9743, 2.539 and 1.62 were obtained for R2, RMSE and MAPE, respectively. The results obtained according to the LSTM and BiLSTM models are much closer to the actual fuel consumption values. The error of the models used was verified against the actual fuel flow reports, and an average absolute percent error value of less than 2% was obtained. Originality/value In this study, a new hybrid CNN-BiLSTM model is proposed. The proposed model is trained and tested with real flight data for fuel consumption estimation. As a result of the test, it is seen that it gives much better results than the LSTM and BiLSTM methods found in the literature. For this reason, it can be used in many different engine types and applications in different fields, especially the turboprop engine used in the study. Because it can be applied to different engines than the engine type used in the study, it can be easily integrated into many simulation models.

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Section: Methodsmentioning

confidence: 99%

A new proposal for the prediction of an aircraft engine fuel consumption: a novel CNN-BiLSTM deep neural network model

Metlek

2023

AEAT

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“…With the emergence of AlexNet architecture, which is one of the deep neural networks, CNNbased models are rapidly applied for various tasks in the field of computer vision, especially image recognition, classification and segmentation problems [6]- [8]. Deep learning models can show superior performance in solving problems where big data is increasing day by day.…”

Section: Introductionmentioning

confidence: 99%

Measuring the Effect of Data Augmentation in a CNN-Based Deep Neural Network Model

ÇETİNER

Metlek

2023

ICSIS

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Traditional classification methods have difficulty in meeting the changing needs according tothe ever-increasing data piles. With the development of processors with high performance as memory andprocessing capabilities, deep learning-based methods have been widely used. A large amount of data isneeded to train a deep learning-based model, which is a computational science field. CIFAR-10, whichcontains images of 10 different objects in the world, is a benchmark dataset used effectively in imageidentification and classification. The proposed deep learning-based models should be tested in a computerenvironment in order to be used in real life. The proposed model performs the testing process with imagesthat it has never encountered during the training phase. In this article, a deep learning model is proposedthat performs classification on the CIFAR-10 dataset, which contains images of objects in the world. Aneffective classification method has been developed by removing the overfitting effect, if any, on theproposed model. Proposed model, classification process was carried out both with and without dataaugmentation. The data set used was expanded with random crop, scale transformation, vertical andhorizontal flipping data augmentation techniques. In the experimental studies, there was a big differencebetween the performance of the process using the data augmentation technique and the process without anyaugmentation. Using different augmentation techniques together or individually did not improve modelperformance. Proposed model achieved success rates of 91.93%, 93.63% and 90.49%, respectively,including train accuracy, precision, recall. According to the results obtained, it can be said that the studyhas achieved results that can compete with the literature.

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“…19 Year 2008, which is for state budget deficit and government project financing. Other research on Sovereign Sukuk evaluation mainly assesses the acceptance of investors (Fauziah et al , 2021a; Muchtar, 2021), Sharia law compliance (Adeagbo, 2021; Ahmed et al , 2019; Noordin et al , 2018), the performance of Sukuk as an investment instrument in the bond market (Bella et al , 2021; Solihat et al , 2020; Sukmana, 2020; Tülgen Çetin and Metlek, 2021) and the extended proposal for Sukuk fund utilisation (Al-Suwailem, 2021; Anindito Mujizat, 2021; Fauziah et al , 2021b; Handayani and Surachman, 2017; Hariyanto, 2020; Mawardi et al , 2021; Pribadi, 2020; Rusydiana and Irfany, 2021). None of the extant research discusses the effectiveness of Sukuk in financing government projects or evaluates the mechanism of Sukuk fund disbursement.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Sovereign Sukuk as a government financing instrument with stakeholder theory: a moving forward policy from Indonesia’s experience

Surachman

Hermawan²,

Handayani

et al. 2022

JIABR

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Purpose This study aims to examine the performance of government projects financed by the issuance of Indonesia Sovereign Sukuk (SBSN Project) from stakeholder theory perspective and propose policy recommendations to improve its effectiveness as a government financing instrument. Design/methodology/approach The authors applied a qualitative interpretive approach in this study by conducting content analysis using stakeholder theory. Big data from official webinars about Indonesia Sovereign Sukuk issuance on the internet were used and coded by qualitative data analysis software. Findings The results reveal the stakeholders’ concerns regarding the project implementation. The cluster analysis confirms that technical ministries are the instrumental stakeholders who have the authority and tools to achieve SBSN Project success. The authors propose inclusive policy recommendations for each stakeholder, such as establishing an SBSN Project Master Plan, presuming disincentive to use project extension facility and setting up a comprehensive approach to assist working units in technical ministries in project preparation, development and service delivery. From the perspective of sustainability, the Government of Indonesia should focus more on education to the local community as end-users, value creation to integrate SBSN Projects with other financing schemes and regulation to ensure the effectiveness of a reward–punishment mechanism. Practical implications The findings of this study may be useful to the Government of Indonesia, especially the Ministry of Finance, in determining and establishing moving forward policies that are relevant and contribute significantly to the sustainability of the Sovereign Sukuk programme. Originality/value This study is one of a few studies exploring Islamic public financing instruments, with its originality lying in the examination of the SBSN Project performance from an academic approach, specifically stakeholder theory. This study uses big data available from the public domain to formulate proposed actions to achieve a sustainable SBSN Project programme.

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Forecasting of Turkish Sovereign Sukuk Prices Using Artificial Neural Network Model

Cited by 7 publications

References 16 publications

A new proposal for the prediction of an aircraft engine fuel consumption: a novel CNN-BiLSTM deep neural network model

A new proposal for the prediction of an aircraft engine fuel consumption: a novel CNN-BiLSTM deep neural network model

Measuring the Effect of Data Augmentation in a CNN-Based Deep Neural Network Model

Evaluation of Sovereign Sukuk as a government financing instrument with stakeholder theory: a moving forward policy from Indonesia’s experience

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