BAYES AĞ MODELLERİ İLE FİNANSAL PİYASA DİNAMİKLERİ ÜZERİNE BİR İNCELEME: ELEKTRİK ÜRETİM ŞİRKETLERİ UYGULAMASI

Yatırımcılar tasarruflarını değerlendirmek için finansal varlıklardan oluşan portföyler oluşturmaktadır. Markowitz’e (1952) göre finansal piyasalarda yatırımcılar, belirli bir risk seviyesinde en yüksek getiriyi veya belirli bir getiri düzeyinde en düşük riski sağlayacak portföyler oluşturmayı hedeflerler. Ekonomik, sosyal ve politik açıdan çok sayıda faktörden etkilenen bu piyasalarda, fiyatlarda meydana gelecek değişimler ve yatırım kararları belirsizlik içermektedir. Finans piyasalarında yüksek getiri sağlayacak portföyün seçimi, yatırım kararlarının doğru belirlenmesi ve sonuçların öngörülmesi önemlidir. Bu çalışmanın amacı Arbitraj Fiyatlama Teorisi’ni temel alarak mevcut finansal teorileri ve firmaya özgü dinamiklerin portföy getirisi üzerindeki etkilerini Bayes ağlarla incelemektir. Modelin veri seti makroekonomik faktörler, Borsa İstanbul tüm hisse senetleri endeksi, Borsa İstanbul alt sektör endeksleri ve hisse senedine özgü değişkenlerden oluşmaktadır. Araştırma periyodu Haziran 2001- Ocak 2017 dönemi olarak belirlenmiş ve 188 aylık getiri verilerden oluşturulmuştur. Çalışmada firmaya özgü risklerin portföy getirisi üzerine etkisi detaylı ve farklı açılardan araştırılmış, finans teorisiyle paralel sonuçlar elde edilmiştir.

A REVIEW OF FINANCIAL MARKET DYNAMICS WITH BAYES NETWORK MODELS: THE APPLICATION OF ELECTRICITY GENERATION COMPANIES

To obtain returns, investors form portfolios of financial assets to assess funds held in their hands. According to Markowitz, investors in financial markets are aiming to create portfolios with the highest risk at a given risk level or the lowest risk at a given yield level. In these markets, which are influenced by a number of economic, social and political factors, changes in prices and investment decisions are uncertain. It is important to select the portfolio that will provide high returns in the financial markets, to determine the investment decisions correctly and to predict the results. Based on the objective Arbitrage Pricing Theory of this study, we examine Bayesian networks’ effects of existing financial theories and firm-specific dynamics on portfolio returns. The model’s dataset consists of macroeconomic factors, the Borsa Istanbul total stock index, the stock exchange Istanbul sub-sector indexes, and stock-specific variables, which are 188 months of return for June 2001-January 2017 period. In the study, the effects of firm-specific risks on portfolio turnover were investigated and results were obtained in parallel with finance theory.

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