Complex mutual information-theoretic stock networks
Başlık çevirisi mevcut değil.
- Tez No: 719642
- Danışmanlar: DR. KHALDOUN KHASHANAH
- Tez Türü: Doktora
- Konular: İstatistik, Statistics
- Anahtar Kelimeler: Belirtilmemiş.
- Yıl: 2019
- Dil: İngilizce
- Üniversite: Stevens Institute of Technology
- Enstitü: Yurtdışı Enstitü
- Ana Bilim Dalı: Belirtilmemiş.
- Bilim Dalı: Belirtilmemiş.
- Sayfa Sayısı: 133
Özet
Özet yok.
Özet (Çeviri)
Financial markets can be characterized as complex systems because of interactions between heterogeneous components and existing nonlinearities. Network theory can be used to model the financial market in which nodes can be stocks, commodities or currencies. The current state of the art is to measure distances between nodes using Pearson correlations. In this thesis, mutual information (MI) is applied to describe similarities between assets. MI is a general measure of dependency and can detect linear and non-linear relationships whereas Pearson correlation fails to detect the nonlinear relationships. This dissertation consists of three essays. In essay 1, we propose a comprehensive comparison approach between the mutual information (MI) metric and the Pearson correlation metric. We find that networks constructed by these two measures become very different during depending on market regimes and network topological structure. We pay special attention to the cases where two measures are strongly mismatched and examine the reasons. Relationship between the entropy and the moments of daily stock return distributions are investigated and we find very strong relationship between entropy and the kurtosis. We compare the performance of MI and correlation techniques in terms of identification of coherent and well-separated stock communities and results show that MI approaches perform better in crisis and non-crisis periods. In essay 2, we analyzed how local, mesoscopic and global topological properties of mutual information based stock networks evolve annually. To detect and quantify the impact of a major crisis on the market network structure, we propose to use iv the information-theoretic quantifiers. We observe that the entropy of the system relatively reduces during crisis regimes. We propose to use classic internal cluster indices and information-theoretic quantifiers to capture the topological evolution of sectors by treating them as fixed communities, i.e. labels of stocks do not change over time in the system. We find that the structural changes of the financial sector during the subprime crisis and it has the strongest correlation with markets in terms of structural uncertainty. In essay 3, we analyze how the homogeneity in each aggregation level of Global Industry Classification System (GICS) scheme changes over time and identify the industries which have more homogeneous structure than others in terms of stock returns comovement. We propose a mesoscopic approach in terms of network-theoretic framework to describe the relationship between industry groups. We investigate the time evolution of the interaction structure of the industry groups and identify the important ones in the market. Our analysis reveals that during a crisis period, banks, as an industry group, become more important in the market and the local interaction structure of industry groups becomes much simpler or a star network topology. In conclusion, we find that some asset pairs have non-linear relationships and MI provides a better alternative measure to define the links in financial networks. MI network and Pearson network become very different at local and global topological scales during various regimes. Community detection algorithms yield well-separated stock communities with MI networks comparing Pearson ones. Besides the classic network measures, information-theoretic measures provide an information advantage in revealing nonlinear dependencies to quantify and detect financial crises and regime switching.
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