International Congress on Eurasian Economies
26-28 June 2024 – Bishkek, KYRGYZSTAN
Paper properties
Paper ID : 2943
Status : Paper accepted
Language : English
Topic : Finance and Financial Crises
Presenter: Asst. Prof. Dr. Naime İrem Duran
Session : 3A Finance
A Review of Machine Learning Models in Finance: Evidence from Academic Research in Turkey
A Review of Machine Learning Models in Finance: Evidence from Academic Research in Turkey
- Asst. Prof. Dr. Naime İrem Duran (Istanbul Beykent University, Türkiye)
Abstract
In recent years, the application of machine learning models in finance has attracted great attention due to its potential to improve decision-making processes and risk management strategies. The aim of this article is to present a comprehensive review of academic research conducted in Turkey on the use of machine learning models in finance. It aims to identify machine learning techniques commonly used in Turkish finance studies, evaluate their effectiveness, and provide insights into successful applications. The findings reveal that regression analysis is widely used in predicting financial variables such as stock prices and exchange rates. Clustering techniques have been effective in customer segmentation and market basket analysis. Decision trees are frequently used in credit scoring and fraud detection tasks due to their interpretability and ease of implementation. Moreover, artificial neural networks, especially deep learning algorithms; It has shown promising results in complex financial tasks such as sentiment analysis, anomaly detection, and algorithmic trading. In conclusion, this review underlines the significant potential of machine learning models in finance in Turkey. A few suggestions can be made regarding machine learning in finance in Turkey to identify future research areas. These may include developing customized machine learning models for specific financial applications that require more in-depth analysis, improving the quality and size of datasets, and investigating new techniques outside of existing models. There is also a need for more studies to provide practical guidance on how machine learning techniques are applied by financial institutions and how these applications can be improved.
JEL codes: G17, C10, C13
