FORECASTING THE EXCHANGE RATE OF THE UKRAINIAN HRYVNIA USING MACHINE LEARNING METHODS
DOI:
https://doi.org/10.31891/csit-2023-1-10Keywords:
exchange rate, gradient boosting, regression analysis, machine learning, forecasting, Ukrainian hryvnia, Data ScienceAbstract
This article describes the concept of currency exchange rate and the typology of various factors that influence it. A multifactor regression model was constructed to investigate the influence of factors on the exchange rate of the Ukrainian hryvnia and to forecast the dynamics of this rate based on the studied factors using Data Science technologies.
The purpose of this work is to study the peculiarities of the formation of the exchange rate of the Ukrainian hryvnia, the characteristics of the influence of various external factors on this rate, and the creation of an effective forecasting model of the Ukrainian national currency rate, based on a certain number of fundamental financial and economic factors that influence this rate.
Macroeconomic indicators that theoretically have an impact on the dynamics of the currency exchange rate were chosen to build the model. Data on the exchange rate of the Ukrainian hryvnia to the US dollar and economic indicators for selected factors were collected from 2010 to September 2022. During the implementation of the task, the collected data was processed, brought into a uniform form, and normalized. Machine learning methods were used for regression modeling, specifically the XGBoost gradient boosting method.
As a result, a retrospective forecast of the Ukrainian hryvnia exchange rate was obtained, based on factor variables, and an estimate of the impact of each selected feature on the currency exchange rate was calculated. The scientific novelty of this work lies in the application of modern machine learning methods and technologies for the analysis, modeling, and forecasting of the exchange rate of the Ukrainian national currency.
The practical significance of this article lies in the possibility of using the proposed approaches to forecasting the exchange rate of the Ukrainian hryvnia with the use of machine learning methods by all interested parties, including financial institutions of Ukraine, to achieve stability of the national currency, which in turn will affect the development of the national economy as a whole and the welfare of the population of the country.
