Can deep learning predict risky retail investors? A case study in financial risk behavior forecasting
Can deep learning predict risky retail investors? A case study in financial risk behavior forecasting
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.
Deep learning, Forecasting, Retail finance, Risk management
217-234
Kim, A.
47ca4216-19e4-48de-967b-d415b5406e00
Yang, Y.
0c661323-7e23-41c6-a9a2-b4479fd74ef1
Lessmann, S
6403b880-a3d3-4947-80f1-27c6fa64da9e
Ma, Tiejun
1f591849-f17c-4209-9f42-e6587b499bae
Sung, Ming-Chien
2114f823-bc7f-4306-a775-67aee413aa03
Johnson, Johnnie E.V.
6d9f1a51-38a8-4011-a792-bfc82040fac4
16 May 2020
Kim, A.
47ca4216-19e4-48de-967b-d415b5406e00
Yang, Y.
0c661323-7e23-41c6-a9a2-b4479fd74ef1
Lessmann, S
6403b880-a3d3-4947-80f1-27c6fa64da9e
Ma, Tiejun
1f591849-f17c-4209-9f42-e6587b499bae
Sung, Ming-Chien
2114f823-bc7f-4306-a775-67aee413aa03
Johnson, Johnnie E.V.
6d9f1a51-38a8-4011-a792-bfc82040fac4
Kim, A., Yang, Y., Lessmann, S, Ma, Tiejun, Sung, Ming-Chien and Johnson, Johnnie E.V.
(2020)
Can deep learning predict risky retail investors? A case study in financial risk behavior forecasting.
European Journal of Operational Research, 283 (1), .
(doi:10.1016/j.ejor.2019.11.007).
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.
Text
DNN Spread Trading R3 main body
- Accepted Manuscript
More information
Accepted/In Press date: 1 November 2019
e-pub ahead of print date: 26 November 2019
Published date: 16 May 2020
Additional Information:
Funding Information:
We thank the editor, Prof. Teunter, for his efforts in handling our paper and are thankful to three anonymous reviewers whose feedback has helped tremendously to improve earlier versions of the paper. We are especially grateful to J.C. Moreno Paredes for his invaluable help with data preparation.
Publisher Copyright:
© 2019 Elsevier B.V.
Keywords:
Deep learning, Forecasting, Retail finance, Risk management
Identifiers
Local EPrints ID: 435401
URI: http://eprints.soton.ac.uk/id/eprint/435401
ISSN: 0377-2217
PURE UUID: c36d858f-7699-491b-8856-f67378e84334
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Date deposited: 05 Nov 2019 17:30
Last modified: 29 Oct 2022 04:02
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Author:
A. Kim
Author:
Y. Yang
Author:
S Lessmann
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