摘要
金融资产配置的关键问题是资产的价格,资产定价是现代金融学的核心内容,揭示资产定价规律一直是金融研究热点之一。文中回顾了机器学习在资产定价领域使用的方法与研究进展,将机器学习资产定价的方法分类为基于特征处理的机器学习方法与端到端处理的深度学习方法;围绕当前机器学习资产定价遇到的主要问题,比较了不同算法在原理和应用场景方面的区别;指出了两类机器学习方法的适用性与局限性;讨论了机器学习资产定价未来可能的研究趋势。
The key problem of financial asset allocation is asset price.Asset pricing is the core content of modern finance,which indicates that asset pricing law has always been one of the hot topics of financial research.This paper reviews the methods used by machine learning in the field of asset pricing and research progresses,classifies machine learning asset pricing method into machine learning method based on the characteristics processing and deep learning method based on end-to-end processing,compares the differences between different algorithms in principle and application scenarios,points out the applicability and limitations of the two kinds of machine learning methods,prospects the research direction on machine learning asset pricing in the future.
作者
许杰
祝玉坤
邢春晓
XU Jie;ZHU Yu-kun;XING Chun-xiao(PBC School of Finance,Tsinghua University,Beijing 100084,China;Beijing National Research Center for Information Science and Technology(BNRist),Tsinghua University,Beijing 100084,China)
出处
《计算机科学》
CSCD
北大核心
2022年第6期276-286,共11页
Computer Science
基金
科技部重点研发计划:现代服务可信交易理论与技术研究(2018YFB1402701)。
关键词
机器学习
资产定价
投资组合
价格预测
深度学习
Machine learning
Asset pricing
Portfolio
Price forecasting
Deep learning
