Detecting Wash Trade in Financial Market Using Digraphs and Dynamic Programming
- Submitting institution
-
University of Northumbria at Newcastle
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 22063372
- Type
- D - Journal article
- DOI
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10.1109/TNNLS.2015.2480959
- Title of journal
- IEEE Transactions on Neural Networks and Learning Systems
- Article number
- -
- First page
- 2351
- Volume
- 27
- Issue
- 11
- ISSN
- 2162-237X
- Open access status
- Out of scope for open access requirements
- Month of publication
- October
- Year of publication
- 2015
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
4
- Research group(s)
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E - Intelligent Systems Research Group (iSRG)
- Citation count
- 3
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This article, featured in the top 15 most popular IEEE-Transactions on Neural Networks and Learning Systems articles between May-October 2018 (cis.ieee.org/publications/t-neural-networks-and-learning-systems), proposed and realised a unique solution for modelling and detecting wash trade, one of the main illegal forms of stock market manipulation. The findings supported a successful bid to INVEST-NI (www.investni.com) for a Capital Markets Collaboration Network (CMCN) initiative (www.cmcn.net), which brought together 5 key Fintech companies (Fidessa, Citi, CME-Group, First-Derivatives, SR-Labs). The research was conducted in collaboration with Fidessa, a London-headquartered Fintech company (www.fidessa.com),who provided market data for the verification and validation of the proposed approach.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -