High-frequency financial econometrics and low frequency investment management

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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.