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

Öz

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.

Anahtar Kelimeler

• 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

Öz

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.

Anahtar Kelimeler

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

Kaynakça

1. Asness, C. S., Moskowitz, T. J., & Pedersen, L. H. (2013). Value and Momentum Everywhere. The Journal of Finance, 68(3), 929–985.
2. Atsalakis, G. S., & Valavanis, K. P. (2009). Surveying stock market forecasting techniques – Part II: Soft computing methods. Expert Systems with Applications, 36(3), 5932–5941. https://doi.org/10.1016/j.eswa.2008.07.006
3. Bali, T. G., Goyal, A., Huang, D., Jiang, F., & Wen, Q. (2020). The Cross-Sectional Pricing of Corporate Bonds Using Big Data and Machine Learning. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3686164
4. Bryzgalova, S., Pelger, M., & Zhu, J. (2020). Forest Through the Trees: Building Cross-Sections of Stock Returns. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3493458
5. Diebold, F., & Mariano, R. (1995). Comparing Predictive Accuracy. Journal of Business & Economic Statistics, 13(3), 253–263.
6. Dillschneider, Y. (2022). A Machine Learning Framework for Asset Pricing (SSRN Scholarly Paper 4097100). https://doi.org/10.2139/ssrn.4097100
7. Drobetz, W., & Otto, T. (2020). Empirical Asset Pricing via Machine Learning: Evidence from the European Stock Market. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3640631
8. Fama, E. F., & French, K. R. (1992). The Cross-Section of Expected Stock Returns. The Journal of Finance, 47(2), 427–465. https://doi.org/10.1111/j.1540-6261.1992.tb04398.x

9. Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1), 3–56. https://doi.org/10.1016/0304-405X(93)90023-5
10. Fama, E. F., & French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116(1), 1–22. https://doi.org/10.1016/j.jfineco.2014.10.010
11. Fama, E. F., & French, K. R. (2017). International tests of a five-factor asset pricing model. Journal of Financial Economics, 123(3), 441–463. https://doi.org/10.1016/j.jfineco.2016.11.004
12. Freund, Y. (1995). Boosting a Weak Learning Algorithm by Majority. Information and Computation, 121(2), 256–285. https://doi.org/10.1006/inco.1995.1136
13. Giglio, S., & Xiu, D. (2021). Asset Pricing with Omitted Factors. Journal of Political Economy, 129(7), 1947–1990. https://doi.org/10.1086/714090
14. Gu, S., Kelly, B., & Xiu, D. (2020). Empirical Asset Pricing via Machine Learning. Review of Financial Studies, 33(5). https://doi.org/10.1093/rfs/hhaa009
15. Harvey, C. R., & Liu, Y. (2021). Lucky factors. Journal of Financial Economics, 141(2), 413–435. https://doi.org/10.1016/j.jfineco.2021.04.014
16. Heaton, J. B., Polson, N. G., & Witte, J. H. (2016). Deep Learning in Finance (Version 3). arXiv. https://doi.org/10.48550/ARXIV.1602.06561
17. Jerome H. Friedman. (2001). Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29(5), 1189–1232. https://doi.org/10.1214/aos/1013203451
18. Kelly, B. T., & Malamud, S. (2021). The Virtue of Complexity in Machine Learning Portfolios. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3984925
19. Kelly, B. T., & Xiu, D. (2023). Financial Machine Learning. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4501707
20. Kozak, S. (2019). Kernel Trick for the Cross Section. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3307895
21. Leippold, M., Wang, Q., & Zhou, W. (2022). Machine learning in the Chinese stock market. Journal of Financial Economics, 145(2). https://doi.org/10.1016/j.jfineco.2021.08.017
22. Moritz, B., & Zimmermann, T. (2016). Tree-Based Conditional Portfolio Sorts: The Relation between Past and Future Stock Returns. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2740751
23. Rasekhschaffe, K. C., & Jones, R. C. (2019). Machine Learning for Stock Selection. Financial Analysts Journal, 75(3). https://doi.org/10.1080/0015198X.2019.1596678
24. Schapire, R. E. (1990). The strength of weak learnability. Machine Learning, 5(2), 197–227. https://doi.org/10.1007/BF00116037
25. Subrahmanyam, A. (2010). The Cross‐Section of Expected Stock Returns: What Have We Learnt from the Past Twenty‐Five Years of Research? European Financial Management, 16(1), 27–42. https://doi.org/10.1111/j.1468-036X.2009.00520.x
26. Swade, A., Hanauer, M. X., Lohre, H., & Blitz, D. (2023). Factor Zoo. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4605976
27. Tobek, O., & Hronec, M. (2021). Does it pay to follow anomalies research? Machine learning approach with international evidence. Journal of Financial Markets, 56, 100588. https://doi.org/10.1016/j.finmar.2020.100588
28. Tukey, J. W. (1961). Box and Jenkins. In Time Series Analysis: Forecasting and Control.