Physics
Prof. Anirban Chakraborti
Jawaharlal Nehru University, New Delhi
A financial market is a striking example of a complex socio-economic system. We will present the reults of the application of “mesoscopic network” in establishing an empirical linkage between the nominal financial networks and the underlying economic fundamentals across countries [1]. We construct the nominal return correlation networks from daily data to encapsulate sector-level dynamics and figure the relative importance of the sectors in the nominal network through a measure of centrality and clustering algorithms [2]. The eigenvector centrality robustly identifies the backbone of the minimum spanning tree defined on the return networks as well as the primary cluster in the multidimensional scaling map. We show that the sectors that are relatively large in size, defined with the metrics market capitalization, revenue and number of employees, constitute the core of the return networks, whereas the periphery is mostly populated by relatively smaller sectors. Therefore, sector level nominal return dynamics is anchored to the real size effect, which ultimately shapes the optimal portfolios for risk management. We will also present the study of “multiplex networks” constructed from the financial indices of different countries, international trade (exports and imports) and foreign direct investments (investments made by companies or individuals of one country in business interests in another country), and the correlations among them.
References
[1] K. Sharma, B. Gopalakrishnan, A.S. Chakrabarti and A. Chakraborti, “Co-movements in financial fluctuations are anchored to economic fundamentals: A mesoscopic mapping”, arXiv: 1612.05952v2 (2017).
[2] K. Sharma, S. Shah, A.S. Chakrabarti and A. Chakraborti, “Sectoral co-movements in the Indian stock market: A mesoscopic network analysis”. In Y. Aruka and A. Kirman (Eds.), Economic Foundations for Social Complexity Science: Theory, Sentiments, and Empirical Laws (Springer, 2017); arXiv: 1607.05514 (2016).
