Detecting wash trade in financial market using digraphs and dynamic programming
- Submitting institution
-
Cardiff University / Prifysgol Caerdydd
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 96989232
- 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
- 2162-237X
- Open access status
- Out of scope for open access requirements
- Month of publication
- October
- Year of publication
- 2015
- URL
-
http://dx.doi.org/10.1109/TNNLS.2015.2480959
- 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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C - Cybersecurity, privacy and human centred computing
- Citation count
- 3
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This collaboration with five financial companies (including two in the Fortune 500) analyses and conceptualises the basic structures of trading collusion in a wash trade by using a directed graph of traders and dynamic programming. The proposed approach is evaluated on NASDAQ and LSE stock datasets to detect wash trade activities and approved by our industrial partners. Li gave invited talks on this work in Chinese Universities (HUST, UESTC).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -