SOURCE-LINKED INTELLIGENCE

Hidden Cascade Modelling for the Identification of Hidden Tip Chains in Stock Markets

CORDIS · observation · Publication date unknown

e of agent states from indirect observations, has received limited attention in the existing literature. This proposal, ""HiddenTipChains"", introduces approaches based on Topological Data Analysis, Machine Learning, and probabilistic modeling to reveal hidden information transfer through investors social connections and to detect suspicious trading based on insider information. This supports European Commission goals for financial stability and investor protection. In a broader context, the methods will enable us to analyse hidden cascade models when direct observations of agents' true states are not directly observable. I expect that in future research, these approaches can find application in addressing hidden cascade challenges across various fields, such as epidemiology, climate science, and information security. The empirical part of the project is based on world-wide unique and exceptionally extensive datasets, providing us with full access to investors full market-wide trading history on all the securities they traded. Additionally, we have observable social connections amo

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recordType

award

status

SIGNED

region

EU

value

199694.4

unit

EUR