High-frequency financial econometrics and low frequency investment management
Lead Research Organisation:
Lancaster University
Department Name: Accounting & Finance
Abstract
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 with
predefined investment targets. In addressing these needs, a systematic approach as followed within
quantitative investment management (QIM) is highly suitable. QIM originates in asset pricing theory
and is concerned about modeling and forecasting the relevant drivers of assets' risk and return using
econometric techniques. Consequently, QIM is a highly technical, empirical and data-driven
discipline. 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 for
the candidate for a more precise modelling of capital markets. While quantitative investment
managers are already analyzing large amounts of data, the analysis of huge data sets poses several
challenges rendering HF econometrics one of the most active research areas in finance: Not only is
the data large in size, it is usually fairly complex, arrives at high speed at high resolution, and is
subject to noise. In particular, our proposed study aims to investigate the use of news flow data as
well as HF data for low-frequency QIM. As for news flow data, we will consider the provision of realtime
news analysis services. These services collect news in real time and classify them based on a
mapping of key words, and phrases to pre-defined sentiment values. Therefore, our proposed study
will investigate the relevance of these new data sets for QIM by building on techniques from
econometrics, computer science, and data science alike.
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 with
predefined investment targets. In addressing these needs, a systematic approach as followed within
quantitative investment management (QIM) is highly suitable. QIM originates in asset pricing theory
and is concerned about modeling and forecasting the relevant drivers of assets' risk and return using
econometric techniques. Consequently, QIM is a highly technical, empirical and data-driven
discipline. 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 for
the candidate for a more precise modelling of capital markets. While quantitative investment
managers are already analyzing large amounts of data, the analysis of huge data sets poses several
challenges rendering HF econometrics one of the most active research areas in finance: Not only is
the data large in size, it is usually fairly complex, arrives at high speed at high resolution, and is
subject to noise. In particular, our proposed study aims to investigate the use of news flow data as
well as HF data for low-frequency QIM. As for news flow data, we will consider the provision of realtime
news analysis services. These services collect news in real time and classify them based on a
mapping of key words, and phrases to pre-defined sentiment values. Therefore, our proposed study
will investigate the relevance of these new data sets for QIM by building on techniques from
econometrics, computer science, and data science alike.
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| ES/P000665/1 | 01/10/2017 | 30/09/2027 | |||
| 1892829 | Studentship | ES/P000665/1 | 01/10/2017 | 31/03/2022 | Ananthalakshmi Pallasena Ranganathan |
| Description | Using the G10 universe of currencies, we find evidence in favor of parametric portfolio policies to guide an optimal currency tilting strategy using cross-sectional factor characteristics, but less so an optimal currency timing strategy using time series predictors. While currency carry serves as the main return generator, the two characteristics momentum and value are implicit diversifiers to potentially balance the downside of the FX carry investing in flight-to-quality shifts of FX investors. We examine the parametric portfolio policy's ability to mitigate the downside of the carry trade by incorporating an explicit currency factor timing element. This integrated approach not only outperforms a naive equally weighted benchmark, but also univariate and multivariate parametric portfolio policies. |
| Exploitation Route | This can help in making investment decisions even more those investors that aren't too sophisticated |
| Sectors | Financial Services, and Management Consultancy |
| Description | A part of the findings of this stud has already been implemented at the CASE partner and they have been very appreciative of it |
| First Year Of Impact | 2019 |
| Sector | Financial Services, and Management Consultancy |
| Impact Types | Economic |