Humboldt-Universität zu Berlin - High Dimensional Nonstationary Time Series

IRTG1792DP2019 023

Can Deep Learning Predict Risky Retail Investors? A Case Study in Financial Risk
Behavior Forecasting

A. Kolesnikova
Y. Yang
S. Lessmann
T. Ma
M.-C. Sung
J.E.V. Johnson

The paper examines the potential of deep learning to produce decision support
models from structured, tabular data. Considering the context of financial risk
management, we develop a deep learning model for predicting whether individual
spread traders are likely to secure profits from future trades. This 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 the behavior of traders is challenging. Deep
learning promises a remedy. Learning hierarchical distributed representations of
the raw 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.

risk management, retail finance, forecasting, deep learning

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