Adaptive Hidden Markov Model With Anomaly States for Price Manipulation Detection
- Submitting institution
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University of Northumbria at Newcastle
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 22063379
- Type
- D - Journal article
- DOI
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10.1109/TNNLS.2014.2315042
- Title of journal
- IEEE Transactions on Neural Networks and Learning Systems
- Article number
- -
- First page
- 318
- Volume
- 26
- Issue
- 2
- ISSN
- 2162-237X
- Open access status
- Out of scope for open access requirements
- Month of publication
- April
- Year of publication
- 2014
- 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
- 34
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Tackling a major threat to trust in global capital markets, this article proposed and realised an intelligent solution for detecting stock-price manipulation and recognising different forms of market abuse. The research was in collaboration with the London-headquartered Fintech company (www.fidessa.com) who provided stock market data for the verification and validation of the proposed approach. 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). Belatreche was invited to deliver a talk in the industry‐focused Re-Work Deep-Learning in Finance Summit (London, UK https://www.re-work.co/events/deep-learning-in-finance-summit-london-2019/speakers).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -