Oh buoy! How data science improves shipping intelligence for hedge funds
Abraham Thomas demonstrates how maritime data can be used to predict physical commodity flows, in a case study that covers every stage of the data lifecycle, from raw data acquisition, data cleansing and structuring, and machine learning and probabilistic modeling to conversion to tractable format, packaging for final audience, and commercialization and distribution.
| Talk Title | Oh buoy! How data science improves shipping intelligence for hedge funds |
| Speakers | Abraham Thomas (Quandl) |
| Conference | Strata Data Conference |
| Conf Tag | Make Data Work |
| Location | New York, New York |
| Date | September 26-28, 2017 |
| URL | Talk Page |
| Slides | Talk Slides |
| Video | |
Commodity traders and hedge funds have long used physical commodity flows as a critical input to their pricing and risk models. New advances in data and analytics now enable traders to predict these flows even before they happen. Using a combination of public and proprietary data, machine learning techniques, custom models spanning multiple domains, and human-in-the-loop data collection and collation, it is now possible to predict commodity shipments two to three weeks in advance of published import-export figures. Abraham Thomas demonstrates how maritime data can be used to predict physical commodity flows, in a case study that covers every stage of the data lifecycle, from raw data acquisition, data cleansing and structuring, and machine learning and probabilistic modeling to conversion to tractable format, packaging for final audience, and commercialization and distribution.
