基于机器学习的量化择时与量化选股方法研究
A Study of Quantitative Timing and Quantitative Stock Selection Based on machine learning
Abstract
自我国健全金融市场以来，量化投资凭借大数据统计和科学的投资管理受到了越来越多的关注。但相比于西方市场，我国的量化投资还处于起步阶段，发展前景巨大。传统的量化投资模型多依赖于研究者的金融知识和经验所设计的指标并配合经济学模型或策略，金融知识和经验在量化投资模型的构建过程中起到了主要作用，因而模型的开发和更新的效率较低，人的影响因素较大。而机器学习是一种主要受数据驱动的建模方式，且在处理高噪声的非线性问题上具有天然的优势。在金融数据不断爆炸的大环境下，将机器学习理论运用于量化投资领域具有重要的意义和前景。 本文基于机器学习理论对量化投资中的量化择时和量化选股这两种投资类型分别进行研究，并提出相应... Since China's sound financial market, quantitative investment with large data statistics and scientific investment management has been more and more attention. But compared to the Western market, China's quantitative investment is still in its infancy, the development prospects are huge. The traditional quantitative investment model mainly relies on the researcher's financial knowledge and experie...