Project description
Understanding and overcoming hidden cascade scenarios
The financial market is a turbulent sector with profound impacts on economies and global well-being. Insider trading exacerbates this instability, granting certain investors an unfair advantage while jeopardising others. Hidden cascade scenarios occur when investors clandestinely share insider information within social circles, evading detection and monitoring. With this in mind, the MSCA-funded HiddenTipChains project aims to address this issue by focusing on hidden cascade scenarios, an area currently lacking in research. Leveraging machine learning, topological data analysis, and probabilistic modelling, the project seeks to unveil and detect these scenarios, aligning with the European Commission's objectives for investor protection.
Objective
"Consider a scenario in a financial market where investors privately share important insider information within their social circles. Although financial authorities could monitor investors' transactions, it remains challenging to identify which investors have received and are exploiting insider information. I term this scenario a hidden cascade problem, in which the true states of agents cannot be directly observed. This particular form of cascade modelling, involving the inference 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 among insider investors, allowing us to track how news tips about future events propagate within this network. The approached developed in this project will be verified with synthetic data with extensive Monte-Carlo experiments. The project contributes to the Computer Science literature by developing techniques for hidden cascade problems and within the Quantitative Finance domain for identifying market abuse.
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Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
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Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
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Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships
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Call for proposal
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Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) HORIZON-MSCA-2023-PF-01
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33100 Tampere
Finland
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