英文标题：
《Measuring Systematic Risk with Neural Network Factor Model》
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作者：
Jeonggyu Huh
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最新提交年份：
2018
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英文摘要：
  In this paper, we measure systematic risk with a new nonparametric factor model, the neural network factor model. The suitable factors for systematic risk can be naturally found by inserting daily returns on a wide range of assets into the bottleneck network. The network-based model does not stick to a probabilistic structure unlike parametric factor models, and it does not need feature engineering because it selects notable features by itself. In addition, we compare performance between our model and the existing models using 20-year data of S&P 100 components. Although the new model can not outperform the best ones among the parametric factor models due to limitations of the variational inference, the estimation method used for this study, it is still noteworthy in that it achieves the performance as best the comparable models could without any prior knowledge.
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中文摘要：
本文采用一种新的非参数因子模型——神经网络因子模型来度量系统风险。通过将大量资产的每日收益插入瓶颈网络，自然可以找到系统风险的合适因素。与参数因子模型不同，基于网络的模型不遵循概率结构，并且不需要特征工程，因为它自己选择显著的特征。此外，我们还利用标准普尔100指数成分股的20年数据，比较了我们的模型与现有模型之间的性能。尽管由于变分推理的局限性，新模型的性能无法超过参数因子模型中的最佳模型，但本研究所用的估计方法仍然值得注意，因为它在没有任何先验知识的情况下达到了可比模型的最佳性能。
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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：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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