Analysis and Prediction of Fluctuations for Sector Price Indices with Cross-Correlation and Association Mining Based Networks: Tehran Stock Exchange Case

(ProQuest: ... denotes formulae omitted.)1. IntroductionNetwork science has shown enormous applications in many interdisciplinary fields in the recent decade. Any structure or natural phenomena that can be modeled as a network of nodes and edges can be studied by the means of graph theory and network science. This study is about using networks in the field of finance and economy, in particular, analysis of Tehran Stock Exchange (TSE) market and fluctuation prediction of different market sectors price-indices. In this study, association rules are used in an extra-ordinary way to study fluctuation patterns of the market.There are many previous studies done mostly in recent decade that focus on analysis and prediction of financial markets with complex networks. The most cited research that was done in 2003, by Giovanni et al. was about studying New York stock market with help of correlation to make networks of stock prices time series and finding minimal spanning trees to have a better understanding of the topology of stock market [1]. Complex networks analysis is a new tool for understanding many different aspects of financial markets that could not be fully understood before and makes an important role in new studies of financial markets. The importance of this tool was discussed in a research by Gatti et al. in 2010 [2].In a study in 2004, various applications of network science in finance were presented by Caldarelli et al. [3]. This study showed some applications of graph theory methods that could be useful in finance and economy. With the spread of using complex networks in finance and economy, many studies tried to focus on different aspects of markets for building different kinds of networks. By studying these networks, the researchers found out a better knowledge of the financial markets in many aspects. Some studies are presented in this section as examples of how the researchers made networks out of financial data and what they found out with the help of network science.In a research in 2005 by Garlaschelli et al., a network description of large market investments was proposed where stocks and shareholders were vertices and the edges of the network were weighted and corresponded to shareholdings [4].In 2007, another study by Naylor et al. used two hierarchical methods, to develop a topological influence map for some currencies from a distance matrix. They used minimal spanning trees and ultra-metric hierarchical trees to understand the topology of complex networks for foreign exchange market and discussed the scale-free structures found out in the networks made [5]. Correlation matrices of stock returns over time in New York Stock Exchange were analyzed using spectral and network methods in a research in by Heimo et al. [5]. In a study of Hank Seng stock market of Hong Kong, Li and Wang extracted the hidden fluctuation patterns of the stock index from a directed network topology. They used betweenness and inverse participation ratio of the nodes of the network to analyze the fluctuations of the stocks [6]. A review of the literature on small-world networks used in management and social science was done by Uzzi et al. where they showed different interdisciplinary applications of small-world networks as previously discussed by Milgram in other fields of science [7].In 2008, Yang & Yang presented a reliable procedure to build networks from correlation matrix of different time series. They used the correlations between time series to build adjacency matrix based on different thresholds [8]. Kwon and Yang used transfer entropy to show direction and strength of information flow between stock indices time series [9]. It was a new way of building directed networks out of time series. As it will be seen later in this study, using statistical correlation can only make undirected networks. In this study, a new method of building directed networks out of financial data has been used by finding association rules between the fluctuation patterns to extract a directed network. …