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Journals / Ekonomi İşletme ve Maliye Araştırmaları Dergisi / 2026 / Cilt: 7 Sayı: 2

Forecasting and Strategy Design in Financial Decision-Making Using Machine Learning

Finansal Karar Süreçlerinde Makine Öğrenimi Tabanlı Tahmin ve Strateji Geliştirme Yaklaşımları

Mahmut Emin Çevik(İSTANBUL ÜNİVERSİTESİ, SOSYAL BİLİMLER ENSTİTÜSÜ, FİNANS (DR))0000-0003-4247-7459
Bengü Vuran(İSTANBUL ÜNİVERSİTESİ, İŞLETME FAKÜLTESİ, İŞLETME BÖLÜMÜ, FİNANS ANABİLİM DALI)0000-0002-2428-1543
Pages
213–237
Publication Date
2025-08-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Nevertheless, it should be noted that these findings are contingent upon the specific dataset and modeling configurations employed, and may vary across different market environments and data regimes.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Owing to the inherent uncertainty of financial markets, investment decisions based solely on intuition involve substantial risk.
📊
Key Findings & Data:In the comparative analysis conducted with Random Forest, XGBoost, and LSTM models, the Random Forest model demonstrated superior performance, yielding a lower mean absolute error (MAE) of approximately 24 index points and generating more consistent predictive signals.
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Conclusion & Impact:Nevertheless, it should be noted that these findings are contingent upon the specific dataset and modeling configurations employed, and may vary across different market environments and data regimes.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Finansal zaman serisi · Makine öğrenimi · Random Forest

Abstract

Owing to the inherent uncertainty of financial markets, investment decisions based solely on intuition involve substantial risk. Accordingly, this study investigates the integration of machine learning-based forecasting techniques into investment decision-making processes. In the comparative analysis conducted with Random Forest, XGBoost, and LSTM models, the Random Forest model demonstrated superior performance, yielding a lower mean absolute error (MAE) of approximately 24 index points and generating more consistent predictive signals. When these signals were translated into trading strategies, the model exhibited substantial improvements not only in predictive accuracy but also in risk-adjusted performance, achieving a total return of 95.51% and a Sharpe ratio of 8.06. Nevertheless, it should be noted that these findings are contingent upon the specific dataset and modeling configurations employed, and may vary across different market environments and data regimes.

Özet

Finansal piyasaların doğasında bulunan belirsizlik nedeniyle, yalnızca sezgilere dayalı yatırım kararları önemli riskler taşımaktadır. Bu nedenle, bu çalışma, makine öğrenimi tabanlı tahmin tekniklerinin yatırım karar süreçlerine nasıl entegre edilebileceğini araştırmayı amaçlamaktadır. Random Forest, XGBoost ve LSTM modellerini içeren karşılaştırmalı analizimizde, Random Forest modeli, yaklaşık 24 endeks puanlık ortalama mutlak hata (MAE) ile daha düşük tahmin hatası ve daha tutarlı sinyallerle öne çıkmıştır. Bu sinyaller yatırım stratejilerine dönüştürüldüğünde, yalnızca tahmin doğruluğunda değil, aynı zamanda %95,51 toplam getiri ve 8,06 Sharpe oranı gibi risk-ayarlı getirilerde de anlamlı iyileşmeler gözlenmiştir. Ancak, bu bulguların çalışmada kullanılan veri seti ve modelleme seçimlerine özgü olduğu ve farklı piyasa koşullarında veya veri rejimlerinde sonuçların değişebileceği vurgulanmalıdır.

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Cite This Article

Çevik, M. E., Vuran, B. (2025). Forecasting and Strategy Design in Financial Decision-Making Using Machine Learning. Ekonomi İşletme ve Maliye Araştırmaları Dergisi, Cilt: 7 Sayı: 2, 213–237. https://doi.org/10.38009/ekimad.1710303

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