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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
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.
0377-2217
Kim, A.
47ca4216-19e4-48de-967b-d415b5406e00
Yang, Y.
0c661323-7e23-41c6-a9a2-b4479fd74ef1
Lessman, S.
0c3ad234-5ec4-44f5-a0ea-d263668cc643
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.
47ca4216-19e4-48de-967b-d415b5406e00
Yang, Y.
0c661323-7e23-41c6-a9a2-b4479fd74ef1
Lessman, S.
0c3ad234-5ec4-44f5-a0ea-d263668cc643
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., Lessman, S., Ma, Tiejun, Sung, Ming-Chien and Johnson, Johnnie E.V. (2019) Can deep learning predict risky retail investors? A case study in financial risk behavior forecasting. European Journal of Operational Research. (In Press)

Record type: Article

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
Restricted to Repository staff only until 1 November 2021.
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Accepted/In Press date: 1 November 2019

Identifiers

Local EPrints ID: 435401
URI: https://eprints.soton.ac.uk/id/eprint/435401
ISSN: 0377-2217
PURE UUID: c36d858f-7699-491b-8856-f67378e84334
ORCID for Ming-Chien Sung: ORCID iD orcid.org/0000-0002-2278-6185

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Date deposited: 05 Nov 2019 17:30
Last modified: 20 Nov 2019 01:36

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