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 and J.E.V. Johnson

No 2019-023, IRTG 1792 Discussion Papers from Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"

Abstract: 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.

Keywords: risk management; retail finance; forecasting; deep learning (search for similar items in EconPapers)
JEL-codes: C00 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:zbw:irtgdp:2019023

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