Borsalar Arası İlişkilerin Özdüzenleyici Haritalarla Kümelendirilmesi

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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