Estimating Bist 100 with the interactıon of financial instruments of developing countries using deep learning and machine learning methods

Serap Akbulut; Kemal Adem

Makale detayları

Estimating Bist 100 with the interactıon of financial instruments of developing countries using deep learning and machine learning methods

Yazarlar:

Serap Akbulut | Kemal Adem

Yayıncı:

Nigde Omer Halisdemir University

Ana Başlıklar:

Bilgisayar Yazılımı

Dergi:

Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi Vol.12 Issue 1, pp.52-63

Belge Tipi:

Makale

Yayın Tarihi:

1/2023

Belge Dili:

Türkçe

Document ID (Org No.):

2001282497

pISSN:

2564-6605

Anahtar kelimeler:

Özet:

 The effect of foreign exchange markets, commodity markets and stock market indices of developing countries on Bist100 is very important. The economies of the countries are strongly dependent on the economies of both their own and other countries, and therefore the markets are affected. Forecasting methods are used to follow the market economically and to make correct decisions. The dataset consists of closing data between January 2017 and October 2021. In order to ensure objectivity in experimental studies, a k5 and 10-fold cross-validation model was applied. In comparison of models; Mean Absolute Error MAE, Relative Absolute Error RAE, Root Mean Square Error RMSE and long short-term memory LSTM were used. As a result of experimental studies, it has been seen that the LSTM model gives better results than machine learning models. When the test results for the LSTM model are examined, the MAE value is 10.27, the RMSE value is 14.15, and the RAE value is 6.06.

 The effect of foreign exchange markets, commodity markets and stock market indices of developing countries on Bist100 is very important. The economies of the countries are strongly dependent on the economies of both their own and other countries, and therefore the markets are affected. Forecasting methods are used to follow the market economically and to make correct decisions. The dataset consists of closing data between January 2017 and October 2021. In order to ensure objectivity in experimental studies, a k5 and 10-fold cross-validation model was applied. In comparison of models; Mean Absolute Error MAE, Relative Absolute Error RAE, Root Mean Square Error RMSE and long short-term memory LSTM were used. As a result of experimental studies, it has been seen that the LSTM model gives better results than machine learning models. When the test results for the LSTM model are examined, the MAE value is 10.27, the RMSE value is 14.15, and the RAE value is 6.06.

 The effect of foreign exchange markets, commodity markets and stock market indices of developing countries on Bist100 is very important. The economies of the countries are strongly dependent on the economies of both their own and other countries, and therefore the markets are affected. Forecasting methods are used to follow the market economically and to make correct decisions. The dataset consists of closing data between January 2017 and October 2021. In order to ensure objectivity in experimental studies, a k5 and 10-fold cross-validation model was applied. In comparison of models; Mean Absolute Error MAE, Relative Absolute Error RAE, Root Mean Square Error RMSE and long short-term memory LSTM were used. As a result of experimental studies, it has been seen that the LSTM model gives better results than machine learning models. When the test results for the LSTM model are examined, the MAE value is 10.27, the RMSE value is 14.15, and the RAE value is 6.06.

 Döviz piyasaları, emtia piyasaları ve gelişmekte olan ülkelerin borsa endekslerinin Bist100 üzerindeki etkisi oldukça önemlidir. Ülke ekonomileri hem kendi hem de diğer ülkelerin ekonomilerine güçlü bir şekilde bağlıdır ve bu sebeple piyasalar etkilenmektedirler. Ekonomik açıdan piyasayı takip etmek kararları doğru verebilmek için tahmin yöntemleri kullanılmaktadır. Veri kümesi Ocak 2017  Ekim 2021 tarihleri arasındaki kapanış verilerinden oluşmaktadır. Deneysel çalışmalarda objektifliğin sağlanması amacıyla k5 ve 10 katlı çapraz geçerlilik modeli uygulanmıştır. Modellerin karşılaştırılmasında; Ortalama Mutlak Hata MAE, Bağıl Mutlak Hata RAE, Ortalama Karesel Hata Karekökü RMSE ve uzun kısa süreli bellek LSTM kullanılmıştır. Deneysel çalışmalar sonucunda, LSTM modelinin makine öğrenmesi modellerine göre daha iyi sonuç verdiği görülmüştür. LSTM modeli için test sonuçları incelendiğinde MAE değeri 10.27, RMSE değeri 14.15 ve RAE değeri ise 6.06dir.

Makale detayları

Estimating Bist 100 with the interactıon of financial instruments of developing countries using deep learning and machine learning methods

Yazarlar:

Serap Akbulut | Kemal Adem

Yayıncı:

Nigde Omer Halisdemir University

Ana Başlıklar:

Bilgisayar Yazılımı

Dergi:

Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi Vol.12 Issue 1, pp.52-63

Belge Tipi:

Makale

Yayın Tarihi:

1/2023

Belge Dili:

Türkçe

Document ID (Org No.):

2001282497

pISSN:

2564-6605

Artificial intelligence

Deep learning

Data mining

Financial estimate

Yapay Zeka

Derin Öğrenme

Veri Madenciliği

Finansal Tahmin.

Sayı

Makale detayları

Yazarlar: Serap Akbulut | Kemal Adem

Yayıncı: Nigde Omer Halisdemir University

Ana Başlıklar: Bilgisayar Yazılımı

Dergi: Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi Vol.12 Issue 1, pp.52-63

Belge Tipi: Makale

Yayın Tarihi: 1/2023

Belge Dili: Türkçe

Document ID (Org No.): 2001282497

pISSN: 2564-6605

Sayı

Yazarlar:

Serap Akbulut | Kemal Adem

Yayıncı:

Nigde Omer Halisdemir University

Ana Başlıklar:

Bilgisayar Yazılımı

Dergi:

Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi Vol.12 Issue 1, pp.52-63

Belge Tipi:

Makale

Yayın Tarihi:

1/2023

Belge Dili:

Türkçe

Document ID (Org No.):

2001282497

pISSN:

2564-6605