MAKİNE ÖĞRENMESİ İLE YATAY KESİT HİSSE SENEDİ GETİRİLERİNİN TAHMİN EDİLEBİLİRLİĞİ: KÜRESEL PİYASALARDA AMPİRİK BİR ANALİZ

Abstract

Bu çalışma, küresel hisse senedi piyasa verilerini kullanarak makine öğrenimi modelleriyle incelemektedir. 63 Firma karakteristiği hesaplayarak, makine öğrenme yöntemlerini uyguladığımızda modelimizin ekonomik kazanım ve performans açısından daha iyi sonuçlar göstermiştir. Derin öğrenme modelleriyle karşılaştırıldığında gradient-boosted regresyon ağaçları, derin öğrenme modellerine kıyasla, büyük olasılıkla düşük sinyal-gürültü oranı, derin modellerinin hiper-parametrelere karşı yüksek hassasiyet göstermesi daha tutarlı ve güvenilir sonuçlar vermektedir. Sonuçlar makine öğrenmesi yöntemlerinin başarılı portföyler oluşturmak için de kullanılabileceğini göstermektedir. Ayrıca, model karmaşıklığını artırmanın Sharpe oranlarında iyileşmeler gibi ekonomik faydalar sağladığı gösterilmektedir. Sonuçlar, makine öğrenimi modellerinin tutarlılığını ve genelleme yeteneğini vurgulayarak, modern finansal sistemde makine öğreniminin önemini ortaya koymaktadır. Bu çalışma, sadelik ilkesine dair geleneksel anlayışları sorgulamakta ve belirli bir karmaşıklık derecesine dayalı olarak stratejik ekonomik kazançlar gösterdiğini ortaya koymaktadır.

Keywords

• Makine Öğrenimi
• Varlık Fiyatlaması
• Hisse Senedi Risk Primi
• Tahminsel Modelleme
• Getirilerin Tahmin Edilebilirliği
• Finansal Analiz

MACHINE LEARNING FOR CROSS-SECTIONAL RETURN PREDICTABILITY: EVIDENCE FROM GLOBAL STOCK MARKETS

Abstract

This work examines cross-sectional stock returns with machine learning models using global stock market data. By calculating 63 firm level characteristics, we find that our model outperforms linear models in terms of both economic and statistical performance. Shallow models, such as gradient boosted decision trees, provides more consistent and reliable performance compared to deeper ones in the context of asset pricing, likely due to a low signal-to-noise ratio and sensitivity to parameters. The results revealed that machine learning models can be developed into effective portfolios, complexity is welcomed when it enhances performance such as Sharpe ratios. Taken together, these results demonstrate the relative importance of machine learning for a modern financial system, and specifically, the ability to synthesize information from various characteristics that impact stock returns. This study challenges traditional notions of a preference for parsimony and, based on certain degrees of complexity, demonstrates strategic economic gains.

Keywords

• Machine Learning
• Asset Pricing
• Equity Risk Premium
• Predictive Modeling
• Return Predictability
• Financial Analysis

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