Explaining Indian Stock Market through Geometry of Scale free Networks
Pawanesh Yadav Charu Sharma Niteesh Sahni
This paper presents an analysis of the Indian stock market using a method
based on embedding the network in a hyperbolic space using Machine learning
techniques. We claim novelty on four counts. First, it is demonstrated that the
hyperbolic clusters resemble the topological network communities more closely
than the Euclidean clusters. Second, we are able to clearly distinguish between
periods of market stability and volatility through a statistical analysis of
hyperbolic distance and hyperbolic shortest path distance corresponding to the
embedded network. Third, we demonstrate that using the modularity of the
embedded network significant market changes can be spotted early. Lastly, the
coalescent embedding is able to segregate the certain market sectors thereby
underscoring its natural clustering ability.