英文标题：
《Deeply Learning Derivatives》
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作者：
Ryan Ferguson and Andrew Green
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最新提交年份：
2018
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英文摘要：
  This paper uses deep learning to value derivatives. The approach is broadly applicable, and we use a call option on a basket of stocks as an example. We show that the deep learning model is accurate and very fast, capable of producing valuations a million times faster than traditional models. We develop a methodology to randomly generate appropriate training data and explore the impact of several parameters including layer width and depth, training data quality and quantity on model speed and accuracy.
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中文摘要：
本文利用深度学习对衍生品进行估值。该方法具有广泛的适用性，我们以一篮子股票的看涨期权为例。我们表明，深度学习模型准确且速度非常快，能够产生比传统模型快一百万倍的估值。我们开发了一种随机生成适当训练数据的方法，并探讨了层宽和层深、训练数据质量和数量等参数对模型速度和精度的影响。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Computational Finance        计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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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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