Kârlılık Tahmininde Yapay Sinir Ağları ve Panel Veri Analizinin Karşılaştırılması: Gayrimenkul Yatırım Ortaklıkları Örneği

Öz

Son yıllarda makine öğrenmesi teknikleri, hesaplamadaki esneklikleri, büyük ve çeşitli veri türleriyle çalışabilmeleri ve gerçek zamanlı değişiklikleri tahmin edebilme yetenekleri nedeniyle kârlılık tahmininde ön plana çıkmıştır. Ayrıca uygulamada kârlılığı tahmin etmek zordur ve uzmanlık gerektirir. Bu çalışmanın temel amacı, makine öğrenmesi tekniklerinden biri olan Yapay Sinir Ağları (YSA) algoritmalarını kullanarak en uygun kârlılık tahmin modelini belirlemektir. Ayrıca Borsa İstanbul’da (BİST) işlem gören Gayrimenkul Yatırım Ortaklıkları (GYO) firmalarının mali tablolarından oluşturulan 2010-2019 çeyrek dönemlerine ait veri setine YSA tahmin modeli uygulanmış ve elde edilen bulgular, panel veri analizi uygulanması sonucu elde edilen bulgularla karşılaştırılarak YSA tekniğinin tahmin başarısı yorumlanmıştır. Bu değerlerin yapılan panel veri analizi bulgularıyla karşılaştırılması neticesinde, YSA tahmin modellerinin, panel veri analiz modellerine göre daha başarılı tahmin yapabildiği sonucuna ulaşılmıştır.

Anahtar Kelimeler

• Karlılık Tahmini
• Yapay Sinir Ağı
• Panel Veri

The Comparison of Artificial Neural Networks and Panel Data Analysis on Profitability Prediction: The Case of Real Estate Investment Trusts

Öz

In recent years, machine learning techniques have come to the forefront for profitability forecasting due to their flexibility in computation, ability to work with large and diverse data types, and capability to predict real-time changes. In addition, predicting profitability in practice is challenging and requires expertise. The primary aim of this study is to determine the most suitable profitability prediction model using Artificial Neural Network (ANN) algorithms, one of the machine learning techniques. Furthermore, the ANN prediction model was applied to the data set for the 2010-2019 quarters created from the financial statements of Real Estate Investment Trusts (REITs) companies traded in Borsa Istanbul (BIST) and the prediction success of the ANN technique was interpreted by comparing the findings obtained with the findings obtained as a result of panel data analysis. The comparison of these values with the findings of the panel data analysis has led to the conclusion that ANN prediction models can make more successful forecasts than panel data analysis models.

Anahtar Kelimeler

• Profitability Prediction
• Artificial Neural Network
• Panel Data

Kaynakça

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