Borsalar Arası İlişkilerin Özdüzenleyici Haritalarla Kümelendirilmesi

Year 2020, Volume: 8 Issue: 1, 1 - 13, 30.03.2020

Zekai Şenol , Mesut Polatgil

Abstract

Küreselleşme hareketine bağlı olarak borsalar arası
ilişkiler artmıştır. Günümüzün yatırımcıları uluslararası piyasalarda
risk-getiri tercihine bağlı olarak portföyler oluşturmaktadır. Uluslararası
portföylerin çeşitlendirilmesinde borsalar arası ilişkiler dikkate
alınmaktadır. Borsalar arası ilişkilerin artması çeşitlendirme yoluyla risk
azaltmayı önlemektedir. Bu çalışmanın amacı borsalar arası ilişkileri
araştırmaktadır.  Çalışmada verilerine
ulaşılabilen dünyanın değişik yerlerinden, 19 ülke borsasının 3 Ocak 2000-29
Aralık 2017 dönemine ait günlük kapanış endeksleri kullanılmıştır. Endeksler
günlük getiri ve risk değerlerine dönüştürülerek kümeleme işlemi yapılmıştır.
Çalışmada, kümeleme işlemini gerçekleştirmek üzere yapay sinir ağlarının özel
bir çeşidi olan özdüzenleyici haritalar kullanılmıştır. Sonuçlar, gelişmiş ve
gelişmekte olan ülke borsalarının birbirinden ayrıldığını, gelişmiş ülke
borsaları arasındaki ilişkilerin daha yaygın olduğunu ortaya koymuştur. Ayrıca,
küresel kriz döneminde borsalarda birlikte hareket etme niteliğinin azaldığı,
küresel kriz sonrasında ise borsalar arası ilişkilerin arttığı anlaşılmıştır.

Keywords

Özdüzenleyici Haritalar, Borsa Verilerinin Kümelendirilmesi, Portföy Yönetimi, Borsalar

Clustering Relationship of Between Stock Markets with Self Organization Map

Abstract

Due to the globalization movement, the inter-stock
markets relations have increased. Today's investors are creating portfolios
depending on risk-profit preference in international markets. Inter-stock markets
relations are taken into account in international portfolio diversification.
Increased inter-stock markets relations prevent diversification and risk
reduction. The aim of this study is to investigate the relationships between
stock exchanges.  The daily closing
indices of the 19 countries' stock exchange for the period 3 January 2000- 29
December 2017 were used from different parts of the world where data can be
accessed in this study. Clustering is made by converting indices into daily
profit and risk values. In the study, self-organization maps, a special type of
artificial neural networks, were used to perform the clustering process. The
results show that developed and emerging market stock exchanges are seperate
and relationships of developed country stock exchange are more common.
Moreover, it was understood that the feature of moving together in the stock
market during the global crisis diminished and that the relations between the
stock exchanges increased after the global crisis.

Keywords

Self-Organization Maps, Stock Exchange Data Clustering, Portfolio Management, Stock Markets

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