High-frequency financial econometrics and low frequency investment management

高频金融计量与低频投资管理

• 批准号: 1892829
• 金额: --
• 依托单位: Lancaster University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1892829
• 关键词: frequency; financial; econometrics; low; investment

The current low yield environment poses considerable challenges to many institutional investors.Hence some investors loosen investment constraints to allow for more risky investments.Nevertheless, speculating on higher risk levels also demands for stricter risk control to comply withpredefined investment targets. In addressing these needs, a systematic approach as followed withinquantitative investment management (QIM) is highly suitable. QIM originates in asset pricing theoryand is concerned about modeling and forecasting the relevant drivers of assets' risk and return usingeconometric techniques. Consequently, QIM is a highly technical, empirical and data-drivendiscipline. Acknowledging the adaptive nature of capital markets Invesco Quantitative Strategies(IQS) is currently sourcing new data on high-frequency (HF) news analytics that will be available forthe candidate for a more precise modelling of capital markets. While quantitative investmentmanagers are already analyzing large amounts of data, the analysis of huge data sets poses severalchallenges rendering HF econometrics one of the most active research areas in finance: Not only isthe data large in size, it is usually fairly complex, arrives at high speed at high resolution, and issubject to noise. In particular, our proposed study aims to investigate the use of news flow data aswell as HF data for low-frequency QIM. As for news flow data, we will consider the provision of realtimenews analysis services. These services collect news in real time and classify them based on amapping of key words, and phrases to pre-defined sentiment values. Therefore, our proposed studywill investigate the relevance of these new data sets for QIM by building on techniques fromeconometrics, computer science, and data science alike.

目前的低收益环境对许多机构投资者构成了相当大的挑战。因此，一些投资者放松了投资限制，允许进行风险更高的投资。然而，投机风险更高，也需要更严格的风险控制，以符合预定的投资目标。为了满足这些需求，量化投资管理（QIM）中遵循的系统方法非常合适。QIM起源于资产定价理论，关注的是使用计量经济学技术对资产风险和收益的相关驱动因素进行建模和预测。因此，QIM是一个高度技术性，经验性和数据驱动的学科。Invesco Quantitative Strategies（IQS）承认资本市场的适应性，目前正在寻找关于高频（HF）新闻分析的新数据，这些数据将用于更精确的资本市场建模。虽然量化投资经理已经在分析大量的数据，但对巨大数据集的分析带来了一些挑战，使HF计量经济学成为金融领域最活跃的研究领域之一：不仅数据量大，而且通常相当复杂，以高分辨率高速到达，并且受到噪音的影响。特别是，我们提出的研究的目的是调查使用的新闻流数据以及高频数据的低频QIM。至于新闻流数据方面，我们会考虑提供实时新闻分析服务。这些服务收集真实的新闻，并根据关键词和短语对预定义的情感值进行分类。因此，我们提出的研究将通过建立计量经济学，计算机科学和数据科学等技术来研究这些新数据集与QIM的相关性。