KRİPTO VARLIK GETİRİLERİNDE BİLGİ AKIŞI VE HİBRİT MODEL KARŞILAŞTIRMASI

Abstract

Blokzincir tabanlı kripto varlıkların fiyat davranışı, yalnızca piyasa içi teknik dinamiklerle değil; haber yoğunluğu, yatırımcı duyarlılığı ve küresel makro-finansal şokların oluşturduğu çok kaynaklı bilgi akışıyla şekillenmektedir. Bu çalışmada BTC, ETH, SOL ve BNB getirileri, 1 Ocak 2022 - 1 Aralık 2025 dönemini kapsayan haftalık verilerle doğrusal (OLS) ve doğrusal olmayan (Random Forest, Gradient Boosting) modeller kullanılarak analiz edilmiştir. Bulgular, ETH ve SOL’un daha yüksek getiri ile birlikte daha yüksek oynaklık sergilediğini, BTC ve BNB’nin ise görece daha istikrarlı bir risk-getiri profiline sahip olduğunu göstermektedir. OLS sonuçları bilgi akışı değişkenlerinin sınırlı doğrusal etkiler ürettiğini ortaya koyarken, doğrusal olmayan modeller daha düşük hata ve daha yüksek yön tahmin başarısı sağlamıştır. Sonuçlar, kripto getirilerinin eşik etkileri ve etkileşimler içerdiğini ve hibrit model yaklaşımlarının üstün performans sunduğunu göstermektedir.

Keywords

• Kripto Paralar
• Makine Öğrenmesi
• Teknik Göstergeler
• Random Forest
• Gradient Boosting
• Regresyon

INFORMATION FLOW AND HYBRID MODEL COMPARISON IN CRYPTO ASSET RETURNS

Abstract

The price behavior of blockchain-based crypto assets is shaped not only by intra-market technical dynamics but also by a multi-source information flow generated by news intensity, investor sentiment, and global macro-financial shocks. In this study, BTC, ETH, SOL, and BNB returns were analyzed using linear (OLS) and non-linear (Random Forest, Gradient Boosting) models with weekly data covering the period from January 1, 2022, to December 1, 2025. The findings show that ETH and SOL exhibit higher volatility along with higher returns, while BTC and BNB have a relatively more stable risk-return profile. OLS results reveal that information flow variables produce limited linear effects, while non-linear models provided lower error and higher directional prediction success. The results demonstrate that crypto returns involve threshold effects and interactions, and that hybrid model approaches offer superior performance.

Keywords

• Cryptocurrencies
• Machine Learning
• Technical Indicators
• Random Forest
• Gradient Boosting
• Regression

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