{"title":"Sosyal Medya ve Diğer Yatırım Aracı Verilerine Dayalı Hisse Senedi Değeri Tahmini","authors":"Ömer Uyrun, İbrahim Sabuncu","doi":"10.26650/acin.934130","DOIUrl":"https://doi.org/10.26650/acin.934130","url":null,"abstract":"","PeriodicalId":309427,"journal":{"name":"Acta Infologica","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131226431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This work is licensed under Creative Commons Attribution-NonCommercial 4.0 International License ABSTRACT Recently, artificial neural networks have been successfully applied in many areas such as forecasting financial time series, predicting financial failure, and classification of ratings. However, it has hardly been applied in forecasting sukuk prices, which is considered the most common Islamic capital market instrument. Since sukuk is a new financial asset, there are not enough studies in this area. Therefore, this study aims to forecast the Turkish sovereign sukuk prices using with artificial neural network model and to reveal the determinants in the forecasting of sukuk prices. For this purpose, a multi-layer feed forward artificial neural network model is designed using dollar-based international sovereign sukuk price data issued by the Turkish Ministry of Treasury and Finance. The dollar index, volatility index, geopolitical risk index, Standard and Poor’s Middle East and North Africa sukuk index, and Eurobond prices constituted as input variables of the designed model and the sovereign sukuk prices formed the output. As a result, the sovereign sukuk prices were forecasted accurately at the success rate of 99.98%. The accurate forecasting of sukuk prices will play a critical role in reducing the risk perception of sukuk investors and increasing their profitability. The findings of the study are important in terms of proving that the artificial neural network model is an effective model for forecasting the sukuk prices and revealing that the dollar index, volatility index, geopolitical risk index, Standard and Poor’s MENA sukuk index, and Eurobond prices are determinants in forecasting sukuk prices.
{"title":"Forecasting of Turkish Sovereign Sukuk Prices Using Artificial Neural Network Model","authors":"Dilşad Tülgen Çetin, Sedat Metlek","doi":"10.26650/acin.907990","DOIUrl":"https://doi.org/10.26650/acin.907990","url":null,"abstract":"This work is licensed under Creative Commons Attribution-NonCommercial 4.0 International License ABSTRACT Recently, artificial neural networks have been successfully applied in many areas such as forecasting financial time series, predicting financial failure, and classification of ratings. However, it has hardly been applied in forecasting sukuk prices, which is considered the most common Islamic capital market instrument. Since sukuk is a new financial asset, there are not enough studies in this area. Therefore, this study aims to forecast the Turkish sovereign sukuk prices using with artificial neural network model and to reveal the determinants in the forecasting of sukuk prices. For this purpose, a multi-layer feed forward artificial neural network model is designed using dollar-based international sovereign sukuk price data issued by the Turkish Ministry of Treasury and Finance. The dollar index, volatility index, geopolitical risk index, Standard and Poor’s Middle East and North Africa sukuk index, and Eurobond prices constituted as input variables of the designed model and the sovereign sukuk prices formed the output. As a result, the sovereign sukuk prices were forecasted accurately at the success rate of 99.98%. The accurate forecasting of sukuk prices will play a critical role in reducing the risk perception of sukuk investors and increasing their profitability. The findings of the study are important in terms of proving that the artificial neural network model is an effective model for forecasting the sukuk prices and revealing that the dollar index, volatility index, geopolitical risk index, Standard and Poor’s MENA sukuk index, and Eurobond prices are determinants in forecasting sukuk prices.","PeriodicalId":309427,"journal":{"name":"Acta Infologica","volume":"8 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132503328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Siparişe Göre Üretim Yapan Firmalarda Sipariş Sıralaması ve Teslim Tarihi Problemi İçin Bir Karar Modeli","authors":"Alperen Calapoğlu, Melike ŞİŞECİ ÇEŞMELİ, Ihsan Pence, Özlem Çetinkaya Bozkurt","doi":"10.26650/acin.937835","DOIUrl":"https://doi.org/10.26650/acin.937835","url":null,"abstract":"","PeriodicalId":309427,"journal":{"name":"Acta Infologica","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129414573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
analiz ABSTRACT It is very important to understand people’s thoughts regarding social events occurring in the world and to make some inferences by analyzing these thoughts. With these analysis and inferences, various projects can be initiated and decision-making processes can be formed. One of the procedures used for these purposes is the sentiment analysis which is performed by classifying text with various computer algorithms. The methods used to perform sentiment analysis are generally categorized as dictionary-based methods and machine learning approaches. In this paper, a sentiment analysis study has been carried out by considering a number of frequently spoken terms on the Twitter social media platform regarding the coronavirus (Covid-19) pandemic, which has affected the world and is still ongoing. For this, some Turkish titles related to the subject were collected and sentiment analysis was conducted by classifying these titles as positive and negative thoughts. For this analysis, a system using a Long Short-Term Memory (LSTM) structure, which is one of the deep learning methods, was proposed. The proposed system was applied on the obtained data sets and a maximum 97% accuracy was achieved.
{"title":"LSTM Derin Öğrenme Yaklaşımı ile Covid-19 Pandemi Sürecinde Twitter Verilerinden Duygu Analizi","authors":"M. Yilmaz, Zeynep Orman","doi":"10.26650/acin.947747","DOIUrl":"https://doi.org/10.26650/acin.947747","url":null,"abstract":"analiz ABSTRACT It is very important to understand people’s thoughts regarding social events occurring in the world and to make some inferences by analyzing these thoughts. With these analysis and inferences, various projects can be initiated and decision-making processes can be formed. One of the procedures used for these purposes is the sentiment analysis which is performed by classifying text with various computer algorithms. The methods used to perform sentiment analysis are generally categorized as dictionary-based methods and machine learning approaches. In this paper, a sentiment analysis study has been carried out by considering a number of frequently spoken terms on the Twitter social media platform regarding the coronavirus (Covid-19) pandemic, which has affected the world and is still ongoing. For this, some Turkish titles related to the subject were collected and sentiment analysis was conducted by classifying these titles as positive and negative thoughts. For this analysis, a system using a Long Short-Term Memory (LSTM) structure, which is one of the deep learning methods, was proposed. The proposed system was applied on the obtained data sets and a maximum 97% accuracy was achieved.","PeriodicalId":309427,"journal":{"name":"Acta Infologica","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115324671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study examines the social anxiety of university students in online live lessons in terms of their digital literacy levels, gender, previous distance education experiences, and the way they interact with the teacher in live lessons. The study was conducted with a causal-comparative and correlational research design. Data was obtained from 167 university students with an online questionnaire. The instruments used in the study were the general information form, the student-teacher interaction subscale of the social anxiety scale in e-learning environments, and the digital literacy scale. The data analysis was carried out with correlation analysis and an independent sample t-test. Results of the research showed that the social anxiety of students in synchronous learning environments has a negative relationship with their digital skills. The social anxiety of female students was found to be higher than male students. Also, students who did not actively listen to the lesson and interact with the teacher through live chat were more anxious. The social anxiety did not differ according to previous distance education experience and the use of microphones in lessons.
{"title":"Examining the Social Anxiety of University Students in Synchronous Online Learning Environments","authors":"E. Bahçekapılı","doi":"10.26650/acin.934636","DOIUrl":"https://doi.org/10.26650/acin.934636","url":null,"abstract":"This study examines the social anxiety of university students in online live lessons in terms of their digital literacy levels, gender, previous distance education experiences, and the way they interact with the teacher in live lessons. The study was conducted with a causal-comparative and correlational research design. Data was obtained from 167 university students with an online questionnaire. The instruments used in the study were the general information form, the student-teacher interaction subscale of the social anxiety scale in e-learning environments, and the digital literacy scale. The data analysis was carried out with correlation analysis and an independent sample t-test. Results of the research showed that the social anxiety of students in synchronous learning environments has a negative relationship with their digital skills. The social anxiety of female students was found to be higher than male students. Also, students who did not actively listen to the lesson and interact with the teacher through live chat were more anxious. The social anxiety did not differ according to previous distance education experience and the use of microphones in lessons.","PeriodicalId":309427,"journal":{"name":"Acta Infologica","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116212704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Coğrafi Ağırlıklı Regresyon Modelinde Kernel Fonksiyonlarının Karşılaştırılması: Bir Uygulama Olarak İntihar Verileri","authors":"Tuba Koç, Pelin Akın","doi":"10.26650/acin.914952","DOIUrl":"https://doi.org/10.26650/acin.914952","url":null,"abstract":"","PeriodicalId":309427,"journal":{"name":"Acta Infologica","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129862158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Medikal Görüntü İşlemede Derin Öğrenme Uygulamaları","authors":"A. Eker, N. Duru","doi":"10.26650/acin.927561","DOIUrl":"https://doi.org/10.26650/acin.927561","url":null,"abstract":"","PeriodicalId":309427,"journal":{"name":"Acta Infologica","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129431732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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