Detecting wash trade in financial market using digraphs and dynamic programming
- Submitting institution
-
Nottingham Trent University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 14 - 697360
- Type
- D - Journal article
- DOI
-
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
- 2162-2388
- 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)
-
A - Computing and Informatics Research Centre
- Citation count
- 3
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper brought a very new approach to the problem of detecting a specific type of illegal trading on the financial markets, namely wash trading. The paper was an outcome of the industry-funded Capital Markets Engineering project supported by five financial technology companies. The paper contributed to a successful project outcome and the project later evolved into the Capital Markets Collaborative Network funded by the same companies and InvestNI – see https://syncni.com/news/2/3536/capital-markets-sector-come-together-to-create-collaborative-network/tab/1356.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
