Machine Learning in Finance

Trends, Developments and Business Practices in the Financial Sector
Erscheinungsjahr 2025
ISBN: 978-3-031-83265-9
Verlag: Springer Nature Switzerland

Buch, Englisch, 199 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 487 g

Reihe: Contributions to Finance and Accounting

This book discusses the evolution of technical features in decentralized finance and focuses on machine-learning finance in emerging economies. As technological advancement evolves at an unpredictable pace, the financial industry, like every other sector, must adapt accordingly. Furthermore, the rapid expansion of diverse financial products and services is creating new applications and markets. Alongside technological progress, the exploration of complex patterns in vast amounts of data, known as big data, is facilitated by its commonly acknowledged characteristics: volume, variety, veracity, value, and velocity.

Overall, machine learning has become crucial in the financial industry, allowing businesses to automate operations, gain insights from data, and make more informed decisions in real time. This edited book covers algorithmic trading, risk management, fraud detection, customer service and personalization, portfolio management, credit scoring, sentiment analysis, and algorithmic pricing. The book connects theoretical concepts with practical real-world applications, benefiting professionals looking to enhance their proficiency in using these methods efficiently. It offers insightful guidance for theorists, market participants, and policymakers by exploring financial theories and practices in light of contemporary machine-learning approaches, with a special emphasis on emerging economies.

Zielgruppe

Research

Autoren/Hrsg.

Kartal, Burcu

Gün, Musa

Fachgebiete

• Wirtschaftswissenschaften Finanzsektor & Finanzdienstleistungen Finanzsektor & Finanzdienstleistungen: Allgemeines
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Wirtschaftswissenschaften Betriebswirtschaft Management Forschung & Entwicklung (F&E), Innovation

Inhaltsverzeichnis

Chapter 1. Machine Learning in Finance: Transformation of Financial Markets (Musa Gün).- Chapter 2. Digital Currencies and Financial Transformation (Bilal Bagis).- Chapter 3. A Hybrid ARIMA-LSTM/GRU Model for Forecasting Monthly Trends in Turkey’s Gold and Currency Markets with a Macro-Economic Data-Driven Approach (Mehmet Fatih Sert).- Chapter 4. Predicting The Environmental Impact of Financial Development with Machine Learning Algorithms (Burcu Kartal).- Chapter 5. A Comparative Analysis of Artificial Neural Networks and Time Series Models in Exchange Rate Forecasting (Emre ÜRKMEZ).- Chapter 6. The Imbalanced Data Problem: Investigating Factors Affecting Financial Freedom Using Data Mining Techniques with SMOTE Method (Abdurrahman Coskuner).- Chapter 7. The Impact of ESG Factors on the Propensity for Dividends for European Firms: A Machine Learning Approach (Önder DORAK).- Chapter 8. Machine Learning Algorithms to Study the Impact of Sustainability on Financial Success: Evidence from US Stock Market (Merve Dogruel).- Chapter 9. Predicting Borsa Istanbul Banking and Finance Stocks Using Turkish Social Media Sentiment with Machine and Deep Learning (Deniz Sevinç).- Chapter 10. Portfolio Management Through Algorithmic Trading (Ahmet AKUSTA).- Chapter 11. Assessing Bitcoin Return Extrema in the Context of Extreme Value Theory (Erhan Uluceviz).- Chapter 12. Machine Learning in Portfolio Optimization (Diler TÜRKOGLU).

Über Autor(innen)

Musa Gün is Associate Professor for Finance at the Recep Tayyip Erdogan University in Rize (Türkiye). His research focuses on asset pricing models, market anomalies, credit risk management, investment valuations, and financial technologies.

Burcu Kartal is Assistant Professor in the Department of Quantitative Methods at the Recep Tayyip Erdogan University in Rize (Türkiye). Her research interests include data mining, machine learning, text mining, qualitative methods, and metaheuristics.