BIST30 HİSSE SENETLERİ ÜZERİNE BİR UYGULAMA: ETS, MLP, BATS VE TBATS YÖNTEMLERİ İLE ÖNGÖRÜDE BULUNMA

Yıl 2024, , 123 - 144, 31.10.2024

Cebeli İnan

https://doi.org/10.61964/dade.1533987

Öz

Ekonomide geleceği tahmin etmek ve öngörüde bulunmak, ekonomistlerin ve politika yapıcıların en önemli amaçlarından biridir. Bunun için yapılan ekonometrik modellemelerde, istatistiksel ve matematiksel yöntemleri kullanarak ekonomik ilişkileri tanımlamaya ve gelecekteki ekonomik değişkenleri tahmin etmeye çalışılır. Bundan dolayı bu modeller genellikle geçmiş verilerin analizine dayanır yani geçmiş fiyat hareketlerini ve hacim verilerini inceleyerek gelecekteki trendleri ve fiyat hareketlerini tahmin etmeyi amaçlar. Günümüzde teknolojinin hızlı bir şekilde ilerlemesi, büyük verileri işleyebilecek paket programlarının gelişmesi ve yapay zekanın yardımı ile öngörüde bulunmak daha kolaylaşmış ve bunun sonucunda yapılan tahminlerin hata oranları azalmıştır. Bu çalışmada, BIST30 hisse senetlerinden bazı bankaların kapanış değerlerinin ETS, MLP, BATS ve TBATS modelleri kullanılarak zaman serisi analizleri yapılmış ve 24 aylık öngörüleri hesaplanmıştır. Bu modellerin tahmin doğruluklarını karşılaştırmak için çeşitli performans ölçüt kriterleri uygulanmıştır. Bu performans ölçüt kriterlerine göre en düşük hata değerini veren model diğer modellere göre daha başarılı olduğu sonucuna varılmıştır.

Anahtar Kelimeler

Ets Mlp, Bats, Tbats, Zaman Serileri.

AN APPLICATION ON BIST30 STOCKS: FORECASTING WITH ETS, MLP, BATS AND TBATS METHODS

Öz

Forecasting future economic conditions is a primary objective for economists and policymakers. Econometric modeling uses statistical and mathematical methods to define economic relationships and forecast future economic variables. These models generally rely on historical data analysis, aiming to predict future trends and price movements by examining past price changes and volume data. Today, with the rapid advancement of technology, the development of software capable of processing big data, and the assistance of artificial intelligence, forecasting has become more efficient, resulting in reduced error rates in predictions. This study performs time series analyses on the closing values of selected banks from the BIST30 index using ETS, MLP, BATS, and TBATS models, calculating 24-month forecasts. Various performance criteria were applied to assess the predictive accuracy of these models. Based on these criteria, the model with the lowest error value was determined to be more successful than the others.

Anahtar Kelimeler

Ets, Mlp, Bats, Tbats, Tıme Series.

Kaynakça

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