Predicting the direction of financial dollarization movement with genetic algorithm and machine learning algorithms: The case of Turkey

Abstract

Financial dollarization has many implications on the economy and the banking sector of developing countries. High level of financial dollarization causes fragilities on the balance sheet of the banks. Due to the negative effects of dollarization on the banks, predicting the financial dollarization rate for the following periods becomes very crucial for the soundness of the banking sector. This study aims to predict the weekly movement of the financial dollarization rate in the Turkish banking sector. The dataset includes asset dollarization rate series for 848 weeks between 30/12/2005 and 25/03/2022. Four different machine learning algorithms (K-Nearest Neighbor, Decision Tree, Naïve Bayes, and Support Vector Machine) are utilized to predict the next week’s financial dollarization rate movement. The parameters of the machine learning algorithms are optimized with a genetic algorithm. The parameters are divided into two groups as common parameters and model-specific parameters. Common parameters are the parameters utilized in all machine learning models and include the score transform method, standardization choice, the values on the cost matrix (used to reduce the misclassification rate), and autoregressive degree. The overall dataset is divided into four sub-periods and three different predicting schemes are utilized in each sub-period and overall period. According to the results of the analysis, the prediction performance in the overall dataset, which covers a wider period, was up to 73 % and the prediction performance was up to 90 % in sub-period datasets where the economy was relatively stable.