Investigating the Influence Relationship Models for Stocks in Indian Equity Market: A Weighted Network Modelling Study

Abstract

The socio-economic systems today possess high levels of both interconnectedness and interdependencies, and such system-level relationships behave very dynamically. In such situations, it is all around perceived that influence is a perplexing power that has an overseeing part in affecting the dynamics and behaviours of involved ones. As a result of the force & direction of influence, the transformative change of one entity has a cogent aftereffect on the other entities in the system. The current study employs directed weighted networks for investigating the influential relationship patterns existent in a typical equity market as an outcome of inter-stock interactions happening at the market level, the sectorial level and the industrial level. The study dataset is derived from 335 constituent stocks of ‘Standard & Poor Bombay Stock Exchange 500 index’ and study period is 1st June 2005 to 30th June 2015. The study identifies the set of most dynamically influential stocks & their respective temporal pattern at three hierarchical levels: the complete equity market, different sectors, and constituting industry segments of those sectors. A detailed influence relationship analysis is performed for the sectorial level network of the construction sector, and it was found that stocks belonging to the cement industry possessed high influence within this sector. Also, the detailed network analysis of construction sector revealed that it follows scale-free characteristics and power law distribution. In the industry specific influence relationship analysis for cement industry, methods based on threshold filtering and minimum spanning tree were employed to derive a set of sub-graphs having temporally stable high-correlation structure over this ten years period.

Stock Network Stability After Crashes Based on Entropy Method

“…Note that the average degree of the network of 180 stocks constructed by the PMFG method is approximately 6. Therefore, to make the entropies of the stock network and of the ER, BA and WS networks comparable, we calculate the entropy of the latter three networks In the stock network, each link weight is defined as the correlation coefficient ρ i,j for stocks i and j [31].…”

Investigating the Evolution of Linkage Dynamics among Equity Markets Using Network Models and Measures: The Case of Asian Equity Market Integration

“…The studies by Kim et al [31,32] and Bhattacharjee et al [30] have evolved a methodology for the construction of weighted equity networks. We adopted a similar methodology for the computation of weights in the weighted networks of Asian market indices.…”

“…In the current study, we adopted a strategy of maximum slope determination for fixing the optimum threshold value. A similar strategy has been adopted by an earlier study by Bhattacharjee et al [30]. This strategy detects a point in the mid-range values in the edge weights wherein maximum variation may occur upon an iterative incremental change in the weight.…”

“…The current study employs an econophysics-derived weighted network modelling approach adapted from the studies by Bhattacharjee et al [30] and Kim et al [31,32]. This is followed by network filtering protocols to generate information-rich, noise-poor sparse graphs possessing relevant connectivity structures.…”