Can deep learning predict risky retail investors? A case study in financial risk behavior forecasting

Johnson, J.E.V. ORCID: 0000-0003-3594-4696, Kim, A., Lessmann, S., Yang, Y., Ma, T. and Sung, M., 2019. Can deep learning predict risky retail investors? A case study in financial risk behavior forecasting. European Journal of Operational Research. ISSN 0377-2217

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Abstract

The paper examines the potential of deep learning to support decisions in financial risk management. We develop a deep learning model for predicting whether individual spread traders secure profits from future trades. This task embodies typical modeling challenges faced in risk and behavior forecasting. Conventional machine learning requires data that is representative of the feature-target relationship and relies on the often costly development, maintenance, and revision of handcrafted features. Consequently, modeling highly variable, heterogeneous patterns such as trader behavior is challenging. Deep learning promises a remedy. Learning hierarchical distributed representations of the data in an automatic manner (e.g. risk taking behavior), it uncovers generative features that determine the target (e.g., trader’s profitability), avoids manual feature engineering, and is more robust toward change (e.g. dynamic market conditions). The results of employing a deep network for operational risk forecasting confirm the feature learning capability of deep learning, provide guidance on designing a suitable network architecture and demonstrate the superiority of deep learning over machine learning and rule-based benchmarks.

Item Type: Journal article
Publication Title: European Journal of Operational Research
Creators: Johnson, J.E.V., Kim, A., Lessmann, S., Yang, Y., Ma, T. and Sung, M.
Publisher: Elsevier
Date: 26 November 2019
ISSN: 0377-2217
Identifiers:
NumberType
10.1016/j.ejor.2019.11.007DOI
1210926Other
S0377221719309099Publisher Item Identifier
Divisions: Schools > Nottingham Business School
Depositing User: Jonathan Gallacher
Date Added: 02 Dec 2019 11:19
Last Modified: 28 Apr 2020 14:58
URI: http://irep.ntu.ac.uk/id/eprint/38683

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