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

Ayşegül Peker and Duygu Tunali

Journal of Research in Economics, Politics & Finance, 2025, vol. 10, issue 1, 160-183

Abstract: 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.

Keywords: Profitability Prediction; Artificial Neural Network; Panel Data (search for similar items in EconPapers)
JEL-codes: C33 C45 G17 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:ahs:journl:v:10:y:2025:i:1:p:160-183

DOI: 10.30784/epfad.1602204

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