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    Makine öğrenmesi yöntemleri ile hisse senedi fiyat tahmini: kâğıt firması örneği
    (2024) Bardak, Selahattin; Ersen, Nadir; Polat, Kinyas; Akyüz, Kadri Cemil
    Bir finansal formül kullanarak hisse senedi fiyatlarını tahmin etmek zordur. Hisse senetleri fiyatları, siyasi gelişmeler, küresel ekonomi, beklenmedik olaylar, piyasa anormallikleri ve ilgili şirketlerin özellikleri gibi çok sayıda faktörden etkilenir. Hisse senedi fiyatlarına ilişkin daha doğru tahminler yapmak için bilgisayar biliminin gelişmesiyle birlikte birçok bilgisayar bilimi yöntemi kullanılmaktadır. Bu çalışmada, Kartonsan şirketinin hisse senedi fiyatını tahmin etmek için doğrusal regresyon (LR) algoritmaları, rastgele orman (RF), gradyan güçlendirme makinesi (GBM) ve yapay sinir ağları (YSA) gibi makine öğrenmesi teknikleri kullanılmıştır. Daha sonra kullanılan algoritmaların sonuçları karşılaştırılmıştır. Hisse senedi fiyatı tahmini için ilk olarak BIST (Borsa İstanbul)’te işlem gören Kartonsan firmasının 2011-2022 yılları arasındaki üçer aylık finansal çizelgeler kullanılarak firmaya ait finansal oran hesaplanmıştır ve bu oranlar girdi olarak kullanılmıştır. Çıktı olarak kullanılan firmanın hisse senedi fiyatlarının ise üçer aylık ortalamaları alınmıştır. GBM ve RF algoritmaları başarılı tahmin sonuçlarına sahip olmasına rağmen GBM algoritması en başarılı sonucu vermiştir. RF algoritmasının ise LR ve YSA’ya göre daha iyi performans gösterdiği bulunmuştur. YSA’nın hisse senedi fiyat tahmininde en kötü performansa sahip teknik olduğu belirlenmiştir.
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    Öğe
    Prediction of Values of Borsa Istanbul Forest, Paper, and Printing Index Using Machine Learning Methods
    (North Carolina State Univ Dept Wood & Paper Sci, 2024) Akyuz, Ilker; Polat, Kinyas; Bardak, Selahattin; Ersen, Nadir
    It is difficult to predict index values or stock prices with a single financial formula. They are affected by many factors, such as political conditions, global economy, unexpected events, market anomalies, and the characteristics of the relevant companies, and many computer science techniques are being used to make more accurate predictions about them. This study aimed to predict the values of the XKAGT index by using the monthly closing values of the Borsa Istanbul (BIST) Forestry, Paper and Printing (XKAGT) index between 2002 and 2023, and the machine learning techniques artificial neural networks (ANN), random forest (RF), k-nearest neighbor (KNN), and gradient boosting machine (GBM). Furthermore, the performances of four machine learning techniques were compared. Factors affecting stock prices are generally classified as macroeconomic and microeconomic factors. As a result of examining the studies on determining the macroeconomic factors affecting the stock markets, 10 macroeconomic factors were determined as input. The macroeconomic variables used were crude oil price, exchange rate of USD/TRY, dollar index, BIST100 index, gold price, money supply (M2), S&P 500 index, US 10-year bond interest, export-import coverage rate in the forest products sector, and deposits interest rate. It was determined that all machine learning techniques used in the study performed successfully in predicting the index value, but the k-nearest neighbor algorithm showed the best performance with R-2=0.996, RMSE=71.36, and a MAE of 40.8. Therefore, in line with the current variables, investors can make analyzes using any of the ANN, RF, KNN, and GBM techniques to predict the future index value, which will lead them to accurate results.
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    The Photocatalytic Performance of Ag /TiO2/Nylon 6/PMMA System
    (Springer Heidelberg, 2024) Polat, Kinyas; Bursali, Elif Ant; Yurdakoc, Muruvvet; Yurdakoc, Kadir
    Herein, Ag/TiO2/Nylon 6/PMMA catalyst system was developed, and its photocatalytic properties were fully analyzed. XRD, SEM, EDS, and PL analyses were carried out to get information on crystal structure, micromorphology, elemental composition, and band gap energy. Photocatalytic degradation was followed by UV-vis spectrophotometry. The band gap of the catalyst was found to be between 2.51 and 3.1 eV. Up to 80% degradation was achieved under UV light irradiation. No bulk adsorption was observed, which means that the reaction was completely carried by the photocatalytic process. pH 3 value was determined to be suitable for a high rate. Reaction kinetic was determined to be matched with the first-order rate law. The newly developed polymer blend matrix system here may have broad application prospects in the remediation of water resources.
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    ZnO/PMMA Nanofibers for the Photocatalytic Water Remediation
    (Springer/Plenum Publishers, 2025) Polat, Kinyas; Bursali, Elif Ant; Yurdakoc, Muruvvet
    In this study, a novel ZnO/PMMA nanofiber catalyst was fabricated using electrospinning, resulting in a barbed wire-like structure that enhances photocatalytic performance. The research aimed to investigate the material's effectiveness in degrading organic pollutants under UV light, providing a sustainable solution for water purification. Comprehensive characterization techniques, including XRD, XPS, SEM, EDS, and FTIR, were employed to analyze the crystal structure, micromorphology, and elemental composition of the catalyst. Photocatalytic degradation experiments showed that up to 91% degradation was achieved after 60 min of UV light irradiation at pH 11, with no significant bulk adsorption observed, confirming the dominance of the photocatalytic mechanism. The optimized pH of 11 was found to be ideal for achieving high degradation rates. This novel ZnO/PMMA nanofiber structure demonstrates significant potential for environmental applications, particularly in water purification, offering an efficient and sustainable approach to pollutant removal.