Abstract
In this article, we review several techniques to extract information from large-scale stock market data. We discuss recurrence analysis of time series, decomposition of aggregate correlation matrices to study co-movements in financial data, stock level partial correlations with market indices, multidimensional scaling, and minimum spanning tree. We apply these techniques to daily return time series from the Indian stock market. The analysis allows us to construct networks based on correlation matrices of individual stocks on one hand, and on the other, we discuss dynamics of market indices. Thus, both microlevel and macrolevel dynamics can be analyzed using such tools. We use the multidimensional scaling methods to visualize the sectoral structure of the stock market and analyze the co-movements among the sectoral stocks. Finally, we construct a mesoscopic network based on sectoral indices. Minimum spanning tree technique is seen to be extremely useful in order to group technologically related sectors, and the mapping corresponds to actual production relationship to a reasonable extent.
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Acknowledgements
This research was partially supported by the institute grant, IIM Ahmedabad. KS thanks University Grants Commission (Ministry of Human Research Development, Govt. of India) for her junior research fellowship. AC acknowledges financial support from the institutional research funding IUT (IUT39-1) of the Estonian Ministry of Education and Research, grant number BT/BI/03/004/2003(C) of Govt. of India, Ministry of Science and Technology, Department of Biotechnology, Bioinformatics division, and University of Potential Excellence-II grant (Project ID-47) of the Jawaharlal Nehru University, New Delhi, India.
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Sharma, K., Shah, S., Chakrabarti, A.S., Chakraborti, A. (2017). Sectoral Co-movements in the Indian Stock Market: A Mesoscopic Network Analysis. In: Aruka, Y., Kirman, A. (eds) Economic Foundations for Social Complexity Science. Evolutionary Economics and Social Complexity Science, vol 9. Springer, Singapore. https://doi.org/10.1007/978-981-10-5705-2_11
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