论文标题
金融资产价格预测的机器学习算法
Machine Learning Algorithms for Financial Asset Price Forecasting
论文作者
论文摘要
本研究论文探讨了可用于财务资产价格预测的机器学习（ML）算法和技术的性能。对于定量金融和从业者来说，对资产价格和回报的预测和预测仍然是最具挑战性和令人兴奋的问题之一。近年来生成和捕获的数据的大量增加为利用机器学习算法提供了机会。这项研究直接将现代机器学习算法（HPC）基础架构与美国股票数据中传统且富有流行的资本资产定价模型（CAPM）相比，现代机器学习算法（HPC）基础架构的最新实现并进行了对比。实施的机器学习模型 - 对整个库存宇宙的时间序列数据进行培训（除了外源宏观经济变量之外）大大优于样本外（OOS）测试数据的CAPM。
This research paper explores the performance of Machine Learning (ML) algorithms and techniques that can be used for financial asset price forecasting. The prediction and forecasting of asset prices and returns remains one of the most challenging and exciting problems for quantitative finance and practitioners alike. The massive increase in data generated and captured in recent years presents an opportunity to leverage Machine Learning algorithms. This study directly compares and contrasts state-of-the-art implementations of modern Machine Learning algorithms on high performance computing (HPC) infrastructures versus the traditional and highly popular Capital Asset Pricing Model (CAPM) on U.S equities data. The implemented Machine Learning models - trained on time series data for an entire stock universe (in addition to exogenous macroeconomic variables) significantly outperform the CAPM on out-of-sample (OOS) test data.
