英文标题：
《Can Deep Learning Predict Risky Retail Investors? A Case Study in
  Financial Risk Behavior Forecasting》
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作者：
Yaodong Yang, Alisa Kolesnikova, Stefan Lessmann, Tiejun Ma,
  Ming-Chien Sung, Johnnie E.V. Johnson
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最新提交年份：
2019
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英文摘要：
  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.
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中文摘要：
本文探讨了深入学习支持金融风险管理决策的潜力。我们开发了一个深度学习模型，用于预测个别利差交易者是否能从未来交易中获得利润。此任务体现了风险和行为预测中面临的典型建模挑战。传统的机器学习需要代表特征-目标关系的数据，并且依赖于通常成本高昂的手工特征开发、维护和修订。因此，对交易者行为等高度可变、异构的模式进行建模是一项挑战。深度学习有望得到补救。以自动方式学习数据的分层分布式表示（如风险承担行为），它揭示了决定目标的生成性特征（如交易者的盈利能力），避免了手动特征工程，并且对变化（如动态市场条件）更为稳健。使用深度网络进行操作风险预测的结果证实了深度学习的特征学习能力，为设计合适的网络架构提供了指导，并证明了深度学习优于机器学习和基于规则的基准测试。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Risk Management        风险管理
分类描述：Measurement and management of financial risks in trading, banking, insurance, corporate and other applications
衡量和管理贸易、银行、保险、企业和其他应用中的金融风险
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一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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