Measuring Complexity in Financial Data

Frontiers in Physics

Anindya S. Chakrabarti¹  Gaurang Singh Yadav²  Apratim Guha³ 

[1] Economics Area, Indian Institute of Management Ahmedabad, Ahmedabad, India;Indian Institute of Management Ahmedabad, Ahmedabad, India;Production and Quantitative Methods Area, Indian Institute of Management Ahmedabad, Ahmedabad, India;Production, Operations and Decision Sciences Area, XLRI, Xavier School of Management, Jamshedpur, India;

关键词: complex systems;    networks;    spectral analysis;    mutual information;    interaction;    Granger causality;   

DOI  :  10.3389/fphy.2020.00339

来源: DOAJ

【 摘 要 】

The stock market is a canonical example of a complex system, in which a large number of interacting agents lead to joint evolution of stock returns and the collective market behavior exhibits emergent properties. However, quantifying complexity in stock market data is a challenging task. In this report, we explore four different measures for characterizing the intrinsic complexity by evaluating the structural relationships between stock returns. The first two measures are based on linear and non-linear co-movement structures (accounting for contemporaneous and Granger causal relationships), the third is based on algorithmic complexity, and the fourth is based on spectral analysis of interacting dynamical systems. Our analysis of a dataset comprising daily prices of a large number of stocks in the complete historical data of NASDAQ (1972–2018) shows that the third and fourth measures are able to identify the greatest global economic downturn in 2007–09 and associated spillovers substantially more accurately than the first two measures. We conclude this report with a discussion of the implications of such quantification methods for risk management in complex systems.

【 授权许可】

Unknown