Can deep learning predict risky retail investors? A case study in financial risk behavior forecasting
- Submitting institution
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University of Edinburgh
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 165334410
- Type
- D - Journal article
- DOI
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10.1016/j.ejor.2019.11.007
- Title of journal
- European Journal of Operational Research
- Article number
- -
- First page
- 217
- Volume
- 283
- Issue
- 1
- ISSN
- 0377-2217
- Open access status
- Access exception
- Month of publication
- November
- Year of publication
- 2019
- URL
-
-
- Supplementary information
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-
- 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
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5
- Research group(s)
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B - Data Science and Artificial Intelligence
- Citation count
- 7
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper has been selected by the EJOR (ABS 4*) editor highlight 2020. It is the first paper to apply deep learning to investors risk-taking behaviour prediction – an important issue for trading exchanges’ risk management. The project achieved "Certificate of Excellence” the highest outstanding outcome awarded by Innovate UK. The project RA has been nominated for excellent KTP associate and has been hired by Santander Ltd. The project has also attracted 2 EPSRC Case scholarships and one ESRC IAA grant. One PhD has been hired by AIG Ltd. The paper’s work facilitated a start-up.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -