Artificial Intelligence in Financial Forecasting: Analyzing the Suitability of AI Models for Dollar/TL Exchange Rate Predictions

TL;DR

This study evaluates various AI models for predicting Dollar/TL exchange rates, finding that neural network and linear regression models are suitable, while other AI models are not effective for this task.

Contribution

It provides a comparative analysis of multiple AI models' effectiveness in exchange rate prediction, highlighting the suitability of multilayer perceptron and linear regression.

Findings

MLP and linear regression models are suitable for exchange rate prediction.

Other AI models like random forest and SVM are not suitable.

Model suitability remained consistent despite economic changes in Turkey.

Abstract

The development of artificial intelligence has made significant contributions to the financial sector. One of the main interests of investors is price predictions. Technical and fundamental analyses, as well as econometric analyses, are conducted for price predictions; recently, the use of AI-based methods has become more prevalent. This study examines daily Dollar/TL exchange rates from January 1, 2020, to October 4, 2024. It has been observed that among artificial intelligence models, random forest, support vector machines, k-nearest neighbors, decision trees, and gradient boosting models were not suitable; however, multilayer perceptron and linear regression models showed appropriate suitability and despite the sharp increase in Dollar/TL rates in Turkey as of 2019, the suitability of valid models has been maintained.