Çapraz Sermaye Akımları ile Borsa Endeksi Arasındaki İlişkinin Makine Öğrenmesi ile Analizi

Yıl 2024, , 244 - 263, 25.03.2024

Ahmet Akusta

https://doi.org/10.11616/asbi.1393570

Öz

Bu çalışma, çapraz sermaye akımları analizini kullanarak BIST100 hisse senedi endeksinin tahminini araştırmaktadır. Finansal zaman serilerinin tahminindeki karmaşıklıkları ele almak için öznitelik mühendisliği ve Orthogonal Matching Pursuit (OMP) modeli kullanılmıştır. Önyargısız bir model sağlamak için gecikmeli değerler, hareketli ortalamalar ve volatilite ölçümleri gibi öznitelikler titizlikle seçilmiş ve normalize edilmiştir. OMP modeli, finansal verilerin çok boyutluluğu sorununu çözmek için optimize edilmiş ve seyreklik kısıtı aracılığıyla aşırı uyumdan kaçınılmıştır. Bu yaklaşımla, endeks varyansını yakalama yeteneğini gösteren 0.88 R-kare puanı elde edilmiştir. Gerçek ve tahmin edilen değerler arasındaki görsel karşılaştırmalar, modelin doğruluğunu teyit etmektedir. Bu makale, karmaşık örüntüleri ayırt edebilen ve yatırım stratejileri için değerli içgörüler sunan modeller geliştirmede metodolojik hassasiyetin önemini vurgulamaktadır. Çalışmanın sonuçları, sermaye hareketleri ve makroekonomik değişkenlerin, finansal piyasaların karmaşıklığına rağmen Borsa Endeksi tahmini için makine öğrenimi ile iyi bir uyumlu olduğunu göstermektedir.

Anahtar Kelimeler

Çapraz Sermaye Akımları, Hisse Senedi Endeksi Tahmini, Finansal Ekonometri, Zaman Serisi Analizi, Tahmine Dayalı Modelleme

Analysis of the Relationship Between Cross Capital Flows and Stock Exchange Index with Machine Learning

Öz

This paper investigates forecasting the BIST100 stock index using cross-capital flow analysis. It employs feature engineering and the Orthogonal Matching Pursuit (OMP) model to navigate the intricacies of financial time series prediction. The study meticulously selects features such as lagged values, moving averages, and volatility metrics, normalized to ensure unbiased model impact. The OMP model is carefully optimized to handle the dimensionality of financial data, avoiding overfitting through a sparsity constraint. This approach yields an R-squared score of 0.88, indicating a solid capability to capture index variance. Visual comparisons between actual and predicted values further validate the model's accuracy. The paper highlights the importance of methodological precision in developing models capable of discerning complex patterns, offering valuable insights for investment strategies. Implications of the study show that cross-capital movements and macroeconomic variables are a good fit with ML to predict the Stock Market despite the complexity of financial markets.

Anahtar Kelimeler

Cross-Capital Flows, Stock Index Forecasting, Financial Econometrics, Time Series Analysis, Predictive Modeling

Kaynakça

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