BAYES AĞ MODELLERİ İLE HİSSE SENEDİ GETİRİLERİNİN KARŞILIKLI DİNAMİK İLİŞKİLERİ

Year 2018, 18. EYI Special Issue, 709 - 720, 20.01.2018

Fatma Busem Hatipoğlu Umut Uyar

https://doi.org/10.18092/ulikidince.346501

Abstract

Modern
portföy teorisinde, portföyde yer alan menkul kıymetler arasındaki ilişkinin
yönünün ve derecesinin riskin azaltılması yönünde etkili olduğu
belirtilmektedir (Markowitz, 1952). Teoride, birbirleriyle yüksek korelasyon
içinde bulunan menkul kıymetlerin aynı portföyde yer almasından
kaçınılmaktadır. Ancak korelasyon katsayısı, iki rassal değişken arasındaki
doğrusal ilişkinin yönünü ve gücünü belirtmektedir. Bayes ağlar kullanılarak
oluşturulan modeller menkul kıymetler arasındaki olasılıksal ilişkiyi görsel
olarak sunabilmekte ve yeni bilgi geldiğinde ağda yer alan menkul kıymet getiri
değerleri eşzamanlı olarak güncellenebilmektedir. Çalışmanın amacı, 2011-2016
dönemleri arasında Borsa İstanbul Ulusal-100 (BIST-100) endeksinde kesintisiz
faaliyet gösteren hisse senetlerine ait getirilerin birbirleri ile olan ilişkilerini
bir makine öğrenmesi olan Bayes ağ modelleri kullanarak araştırmaktır.
Çalışmada Bayes ağ modelleri kullanılarak elde edilecek detaylı ilişkiler ile
yatırımcıların portföy seçimlerinde kullanabilecekleri nitel ve nicel bilgiler
yer almaktadır.

Keywords

Makine Öğrenmesi, Bayes Ağlar, Portföy Seçim Teorisi

THE EXAMINE OF DYNAMIC INTERRELATION OF STOCK RETURNS: BAYESIAN NETWORK MODELS

Abstract

In modern portfolio theory, it is stated that the
relationship between the securities in the portfolio is influenced by the
direction and degree of risk reduction (Markowitz, 1952). In theory, securities
that are highly correlated with each other are avoided from being placed in the
same portfolio. However, the correlation coefficient indicates the direction
and power of the linear relationship between the two random variables. Models
created using Bayesian networks can visually present the probabilistic
relationship between securities, and when new information is available, the
securities return values ​​in the network can be updated simultaneously. The
aim of the study is to investigate the relationships between stocks that have
been operating continuously in the Stock Exchange Istanbul National-100
(BIST-100) index between 2011-2016 by using Bayes network models which are
machine learning. In the study, detailed relationships to be obtained by using
Bayesian network models and qualitative and quantitative information that
investors can use in portfolio selection are included.

Keywords

Machine Learning, Bayesian Networks, Portfolio Selection Theory

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