Prediction Turkish Airlines BIST Stock Price Through Deep Artificial Neural Network Considering Transaction Volume and Seasonal Values

Muhammer İlkuçar

Makale detayları

Prediction Turkish Airlines BIST Stock Price Through Deep Artificial Neural Network Considering Transaction Volume and Seasonal Values

Yazarlar:

Muhammer İlkuçar

Yayıncı:

Gazi University

Ana Başlıklar:

Bilgisayar Yazılımı

Dergi:

Bilişim Teknolojileri Dergisi Vol.16 Issue 1, pp.43-53

Belge Tipi:

Makale

Yayın Tarihi:

1/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001283479

pISSN:

1307-9697

Anahtar kelimeler:

Özet:

 The collection of data in the information age has led to its analysis and use in different fields. Data can be used for different purposes, such as historical information, reporting, analysis, artificial intelligence, and machine learning. Artificial intelligence is used for different purposes in different disciplines such as engineering, health, industry, production, transportation, the stock market, education, and the social sciences. In this study, Turkish Airlines stock price prediction was made using machine learning. Different artificial neural network methods were used, such as an FNN, LSTM, and GRU. The data set consists of daily stock market index information for Turkish Airlines in BIST between the dates of January 4, 2010, and January 31, 2022. During the training of the system, it was assessed together with the transaction volume data to reduce the effect of possible speculative behavior. Since the income of airlines carrying passengers is seasonally affected, seasonal data are also considered. The system has been trained and tested with different short-long term memory-based artificial neural network models. The performance indicators of the models were used as R-Square MSE, RMSE, and MAE. According to the R-Square, performance score of the test, the success rate of system was 97 in FNN, and 99 in LSTM and GRU. It performed well despite extreme price fluctuations due to the pandemic and economic crisis. According to these results, machine learning can be used as a decision support system for sequential data set prediction. In this study, it can be concluded that FNN, LSTM, and its derivative machine learning methods can be successfully used in air transport sector index prediction.

 The collection of data in the information age has led to its analysis and use in different fields. Data can be used for different purposes, such as historical information, reporting, analysis, artificial intelligence, and machine learning. Artificial intelligence is used for different purposes in different disciplines such as engineering, health, industry, production, transportation, the stock market, education, and the social sciences. In this study, Turkish Airlines stock price prediction was made using machine learning. Different artificial neural network methods were used, such as an FNN, LSTM, and GRU. The data set consists of daily stock market index information for Turkish Airlines in BIST between the dates of January 4, 2010, and January 31, 2022. During the training of the system, it was assessed together with the transaction volume data to reduce the effect of possible speculative behavior. Since the income of airlines carrying passengers is seasonally affected, seasonal data are also considered. The system has been trained and tested with different short-long term memory-based artificial neural network models. The performance indicators of the models were used as R-Square MSE, RMSE, and MAE. According to the R-Square, performance score of the test, the success rate of system was 97 in FNN, and 99 in LSTM and GRU. It performed well despite extreme price fluctuations due to the pandemic and economic crisis. According to these results, machine learning can be used as a decision support system for sequential data set prediction. In this study, it can be concluded that FNN, LSTM, and its derivative machine learning methods can be successfully used in air transport sector index prediction.

 The collection of data in the information age has led to its analysis and use in different fields. Data can be used for different purposes, such as historical information, reporting, analysis, artificial intelligence, and machine learning. Artificial intelligence is used for different purposes in different disciplines such as engineering, health, industry, production, transportation, the stock market, education, and the social sciences. In this study, Turkish Airlines stock price prediction was made using machine learning. Different artificial neural network methods were used, such as an FNN, LSTM, and GRU. The data set consists of daily stock market index information for Turkish Airlines in BIST between the dates of January 4, 2010, and January 31, 2022. During the training of the system, it was assessed together with the transaction volume data to reduce the effect of possible speculative behavior. Since the income of airlines carrying passengers is seasonally affected, seasonal data are also considered. The system has been trained and tested with different short-long term memory-based artificial neural network models. The performance indicators of the models were used as R-Square MSE, RMSE, and MAE. According to the R-Square, performance score of the test, the success rate of system was 97 in FNN, and 99 in LSTM and GRU. It performed well despite extreme price fluctuations due to the pandemic and economic crisis. According to these results, machine learning can be used as a decision support system for sequential data set prediction. In this study, it can be concluded that FNN, LSTM, and its derivative machine learning methods can be successfully used in air transport sector index prediction.

 Bilişim çağının getirdiği veri birikimi, bunların analiz edilerek farklı alanlarda kullanılmasını da beraberinde getirmiştir Veriler, geçmişe dönük bilgi edinme, raporlama, analiz, yapay zekâ ve makine öğrenimi gibi farklı amaçlar için kullanılabilmektedir. Yapay zekâ mühendislik, sağlık, sanayi, üretim, ulaşım, borsa, eğitim, sosyal bilimler gibi farklı disiplinlerde farklı amaçlarla kullanılmaktadır. Bu çalışmada, makine öğrenmesi ile Türk Hava Yolları hisse senedi fiyat tahmini yapılmıştır. Makine öğrenmesi olarak FNN, LSTM ve GRU gibi farklı yapay sinir ağı derin öğrenme yöntemleri kullanılmıştır. Veri seti, Türk Hava Yollarının 4 Ocak 2010 ile 31 Ocak 2022 tarihleri arasında BİST teki günlük borsa endeks bilgilerinden oluşmaktadır. Sistemin eğitimi sırasında olası spekülatif davranışların etkisini azaltmak için işlem hacmi verileri ile birlikte değerlendirilmiştir. Yolcu taşıyan havayollarının gelirleri mevsimsel olarak etkileneceğinden mevsimsel veriler de dikkate alınmıştır. Sistem, farklı kısa-uzun süreli bellek tabanlı yapay sinir ağı modelleri ile eğitilmiş ve test edilmiştir. Modellerin performans göstergeleri olarak R-kare, MSE, RMSE ve MAE kullanılmıştır. Test R-kare performans değerlerine göre sistem FNN de 97, LSTM ve GRU da ise 99 başarı göstermiştir. Pandemi nedeniyle aşırı fiyat dalgalanmalarına ve ekonomik krize rağmen yüksek bir performans sergilediği söylenebilir. Bu sonuçlara göre, makine öğrenmesi, sıralı veri seti tahmini için bir karar destek sistemi olarak kullanılabilir. Çalışma ile FNN, LSTM ve türevleri makine öğrenme metotlarının hava yolu taşımacılık sektörü endeks tahmininde başarılı bir şekilde kullanılabileceği sonucuna varılabilir.

Makale detayları

Prediction Turkish Airlines BIST Stock Price Through Deep Artificial Neural Network Considering Transaction Volume and Seasonal Values

Yazarlar:

Muhammer İlkuçar

Yayıncı:

Gazi University

Ana Başlıklar:

Bilgisayar Yazılımı

Dergi:

Bilişim Teknolojileri Dergisi Vol.16 Issue 1, pp.43-53

Belge Tipi:

Makale

Yayın Tarihi:

1/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001283479

pISSN:

1307-9697

ANN

LSTM

GRU

BIST

THYAO

stock index predict

ANN

LSTM

GRU

BIST

THYAO

borsa endeks tahmini

Sayı

Makale detayları

Yazarlar: Muhammer İlkuçar

Yayıncı: Gazi University

Ana Başlıklar: Bilgisayar Yazılımı

Dergi: Bilişim Teknolojileri Dergisi Vol.16 Issue 1, pp.43-53

Belge Tipi: Makale

Yayın Tarihi: 1/2023

Belge Dili: İngilizce

Document ID (Org No.): 2001283479

pISSN: 1307-9697

Sayı

Yazarlar:

Muhammer İlkuçar

Yayıncı:

Gazi University

Ana Başlıklar:

Bilgisayar Yazılımı

Dergi:

Bilişim Teknolojileri Dergisi Vol.16 Issue 1, pp.43-53

Belge Tipi:

Makale

Yayın Tarihi:

1/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001283479

pISSN:

1307-9697