Hierarchical clustering

Nowadays, financial markets in Iran have attracted the attention of many managers, investors and financial policymakers. Therefore, in order to make the optimal decision and reduce the risks in such a market, it is important to identify and analyze the network behavior of the financial markets at different times to obtain the optimal decision. The current study aims to answer the following research question; how is it possible to use the minimum spanning tree and hierarchical clustering in the network analysis of the Tehran Stock Exchange? The period examined was 2013 to 2018. The population consisted of all the companies accepted in Tehran Stock Exchange. The sampling was selected purposefully and contained the companies which had at least one trading day in the time span from the beginning of 2013 to the end of 2018. The stock of the investigated companies was considered as the vertexes of one graph and the coherent information criterion was considered as the weight of the edge. First, the minimum spanning tree of the graph was calculated. The results revealed that the stocks of DarooAbuReihan, DarooPakhsh and Alborzdaroo had a high influence on directing the prices of the other stocks. Furthermore, the results of hierarchical clustering classified the stocks of the companies into 8 clusters. This study presents a viewpoint about the modern method designed for the analysis of complex financial networks. Moreover, the study offers an analysis of Iran's stock market structure which can be the center of finance researchers and analysts' attention.

The purpose of this paper is investigating the effect of JCPOA on the network behavior analysis of Tehran Stock Exchange indexes using the minimum spanning tree (MST) and hierarchical clustering. By simplifying a complex system, network analysis allows for the extraction of important and essential information from that system. In this paper, using network analysis the simultaneous behavior of 38 industry indexes in Tehran Stock Exchange in manufacturing, service and invest-ment sectors during 2012-2017 was investigated. These analysis included identi-fying the main indexes in the direction of moving other indexes using the MST, providing a classification using hierarchical clustering for the behavioral similarity of the indexes as well as examining the degree of integration (behavioral similarity) of market indexes over time. The results showed that investment, automobile, industry and medicine indexes in the research period had a major role in guiding other indexes and indexes can be classified into six groups in terms of behavioral similarity. The market has also been moving toward integration of indexes since early 2015 and beginning the executive steps of Joint Comprehensive Plan of Action (JCPOA). This reflects the investors' hope for the promotion of all indexes.

Data mining is known as one of the powerful tools in generating information and knowledge from raw data, and Clustering as one of the standard methods in data mining is a suitable method for grouping data in different clusters that helps to understand and analyze relationships. It is one of the essential issues in the field of investment, so by using stock market clustering, helpful information can be obtained to predict changes in stock prices of different companies and then on how to decide the correct number and shares in the portfolio to private investors and financial professionals' help. The purpose of this study is to cluster the companies listed on the Tehran stock exchange using three methods of K-means Clustering, Hierarchical clustering, and Affinity propagation clustering and compare these three methods with each other. To conduct this research, the adjusted price of 50 listed companies for the period 2019-07-01 to 2020-09-29 has been used.  The evaluation results show that the obtained silhouette coefficient for K-means Clustering is higher and, therefore, better than other methods for stock exchange data. In the continuation of the research, calculating the co-integration of stock pairs that have the same co-movement with each other were identified, and finally, clusters were compiled using the t-SNE method.

Purpose: The research aims to visualize and analyze co-word network, and thematic clusters of the intellectual structure in the field of digital content management during 2010-2020. Method: The study is applied research with a descriptive approach which is conducted by techniques of co-word, and social network analysis. Data analysis and visualization of the co-word network were represented by SPSS, UCINet, and Python programming language. Findings: 8 main clusters are identified. The cluster multimedia content management & retrieval is the most mature and central thematic cluster. The USA and various sub-categories of Computer Science are located in the top ranks of WOS in the field. Most productions were published in 2020. Generally, the Clusters were labeled in two contexts of health and LAM (Libraries, Archives, Museums, and cultural heritage). Conclusion: Content-based management and retrieval are focused on artificial intelligence, decision-supported, knowledge-based and ontological techniques which are conducted as novel approaches and underlying trends in the field.