Speaker: Prof. Anirban Chakraborti, School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi

Date & Hour: 04 September 2019, 4:15 pm

Venue: Seminar Hall 1

Title: Predicting the unpredictable: A case study of financial market crashes

Abstract: Catastrophic events, though rare, do occur and when they occur, they have devastating effects. The study of the critical dynamics in complex systems is always interesting yet challenging. We choose financial market as an example of a complex system, and do the analysis of the S&P 500 (USA) stock market based on the evolution of cross-correlation structure patterns, and using tools from random matrix theory and data science. We identify “market states” as clusters of similar correlation structures, which occur more frequently than by pure chance (randomness). Power mapping method from the random matrix theory is used to suppress the noise on correlation patterns, and an adaptation of the intra-cluster distance method is used to obtain the optimum number of market states, and also identify the “precursors” to the crashes. The dynamics of the transitions between the states are studied. The resulting “emerging spectrum” of eigenvalues near zero have the intriguing property of reflecting the market crashes or crises.