英文标题：
《High-Dimensional Estimation, Basis Assets, and the Adaptive Multi-Factor
  Model》
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作者：
Liao Zhu, Sumanta Basu, Robert A. Jarrow, Martin T. Wells
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最新提交年份：
2021
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英文摘要：
  The paper proposes a new algorithm for the high-dimensional financial data -- the Groupwise Interpretable Basis Selection (GIBS) algorithm, to estimate a new Adaptive Multi-Factor (AMF) asset pricing model, implied by the recently developed Generalized Arbitrage Pricing Theory, which relaxes the convention that the number of risk-factors is small. We first obtain an adaptive collection of basis assets and then simultaneously test which basis assets correspond to which securities, using high-dimensional methods. The AMF model, along with the GIBS algorithm, is shown to have a significantly better fitting and prediction power than the Fama-French 5-factor model.
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中文摘要：
本文针对高维金融数据提出了一种新的算法——分组可解释基选择（GIBS）算法，以估计一种新的自适应多因素（AMF）资产定价模型，该模型是由最近发展起来的广义套利定价理论所隐含的，它放松了风险因素数量较少的惯例。我们首先获得基础资产的自适应集合，然后使用高维方法同时测试哪些基础资产对应于哪些证券。AMF模型以及GIBS算法被证明比Fama-French 5因子模型具有更好的拟合和预测能力。
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分类信息：

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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一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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