Please use this identifier to cite or link to this item: http://hdl.handle.net/11718/25333
Full metadata record
DC FieldValueLanguage
dc.contributor.authorYadav, Gaurang Singh
dc.contributor.authorGuha, Apratim
dc.contributor.authorChakrabarti, Anindya S.
dc.date.accessioned2022-02-11T10:15:24Z-
dc.date.available2022-02-11T10:15:24Z-
dc.date.issued2020
dc.identifier.citationYadav, G. S., Guha, A., & Chakrabarti, A. S. (2020). Measuring Complexity in Financial Data. Frontiers in Physics, 8. https://doi.org/10.3389/fphy.2020.00339
dc.identifier.issn2296-424X
dc.identifier.urihttps://www.doi.org/10.3389/fphy.2020.00339
dc.identifier.urihttp://hdl.handle.net/11718/25333-
dc.description.abstractThe 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.
dc.description.sponsorshipIIM Ahmedabad
dc.language.isoen_US
dc.publisherFRONTIERS MEDIA SA
dc.relation.ispartofFrontiers in Physics
dc.subjectcomplex systems
dc.subjectnetworks
dc.subjectspectral analysis
dc.subjectmutual information
dc.subjectinteraction
dc.subjectGranger causality
dc.subjectalgorithmic complexity
dc.titleMeasuring Complexity in Financial Data
dc.typeArticle
dc.rights.licenseCC BY
dc.contributor.institutionauthorIndian Institute of Management Ahmedabad, Vastrapur, Ahmedabad, Gujarat 380015, India
dc.description.wosidWOS:000586009200001
dc.identifier.doi10.3389/fphy.2020.00339
dc.identifier.volume8
Appears in Collections:Open Access Journal Articles

Files in This Item:
File SizeFormat 
measuring_complexity_in_2020.pdf842.21 kBAdobe PDFView/Open


Items in IIMA Institutional Repository are protected by copyright, with all rights reserved, unless otherwise indicated.