GRAFİKSEL LASSO İLE PORTFÖY OPTİMİZASYONU VE BORSA İSTANBUL’DA BİR UYGULAMA

Öz

Grafiksel Lasso (least absolute shrinkage and selection operator) algoritması son yıllarda makine öğrenmesi alanında popüler bir araç oldu. Genel olarak sınıflandırma problemlerinin özellik seçimi için kullanılıyor olsa da aynı zamanda kovaryans matris tahmininde de başvurulur hale geldi. Ortalama-varyans portföy optimizasyonu, portföy riskinin hesaplanmasında tarihi verilerden yararlanılarak oluşturulan kovaryans matrisini kullanmaktadır. Bu aynı zamanda ortalama-varyans portföy optimizasyonu metodunun en çok eleştiri aldığı konudur. Bu çalışmanın amacı farklı L1 ceza faktörleri kullanarak grafiksel Lasso algoritmasının kovaryans matris tahminine ve bunun portföy optimizasyon performansına olan etkilerini göstermektir. Çalışmada ortalama-varyans portföy optimizasyonu amprik ve tahmini kovaryans matrisleri kullanılarak BIST 30 endeksine uygulanmakta ve sonuçlar karşılaştırılmaktadır.

Anahtar Kelimeler

• Grafiksel Lasso
• Portföy Optimizasyonu
• Kovaryans Matrisi

PORTFOLIO OPTIMIZATION WITH GRAPHICAL LASSO AND AN APPLICATION IN BORSA ISTANBUL

Öz

Graphical Lasso (Least absolute shrinkage and selection operator) has become a popular tool in the field of machine learning in recent years. Although it has been deployed mainly for feature selection in classification problems, it is also used for covariance matrix estimation. Mean-variance portfolio optimization relies on sample covariance matrix for the calculation of the portfolio’s risk, whereas it has been most hardly criticized. The aim of this study is to demonstrate the effect of the covariance matrix estimation by Graphical Lasso algorithm with varying L1 penalty factors. Mean-variance portfolio optimization using empirical and estimated covariance matrices are applied to BIST 30 index and the results are compared.

Anahtar Kelimeler

• Graphical Lasso
• Portfolio Optimization
• Covariance Matrix

Kaynakça

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