21 May 2019
Magnus Spence Head of Investments, Alternatives
In a never-ending quest to find an ‘edge’ over their peers, alternative data may prove the holy grail for some fund managers, says Magnus Spence, Head of Investments, Alternatives.
It is well-known that active investment managers rely on finding new information or insights about a company to beat benchmarks and competition. And over the last 30 years, the amount of information available to fund managers has grown exponentially. From annual reports and trading updates produced by company management to sell side fundamental research and tick by tick price information, the sheer volume of information is bewildering. And in the never-ending quest to find an ‘edge’, some fund managers are now turning their attention to a new source of information. Welcome to the world of ‘Alternative Data’.
Imagine you decide to visit a Costa Coffee for lunch today and use your mobile phone to look at the opening times of Legoland over the half term break. Behind the scenes, data will be collected on your location (via the geo-location software on your phone), your spending and on the online search for Legoland. This information is anonymised and aggregated with data from thousands of other people carrying out similar searches in similar situations. This data can then be packaged up and made available to organisations or individuals like fund managers who can use it to predict the number of people visiting Costa and the likely number of visitors to Legoland. This is an example of alternative data and it is becoming more and more commonplace in the world of fund management.
There are approximately 1600 alternative datasets which can be purchased by fund managers today providing information on a huge variety of subjects such as shopping habits, cargo movements, satellite images, attitudes and sentiments, factory production and carbon emissions. In theory, this new source of information should give a fund manager a big advantage and enable them to glean valuable insights before others. But in practice, it’s not a simple as that. Firstly, the data is not cheap with some datasets selling for millions of pounds. Secondly, the data is often ‘raw’ and needs to be scrubbed before it can be used by undertaking tasks such as filling in missing values or removing outliers. Then, a data scientist is needed to pre-process, feature engineer and model build to try and help extract the important trends from it.
Jupiter is already exploring the potential use of alternative datasets in fund management. In the second half of 2018, Jupiter employees exploring this new area attended a number of external conferences, allowing us to meet over 50 providers in a short space of time. We then picked the most relevant providers and invited them to present their data to us in-house. The data sets covered a variety of topics from employment data to patent filings, and from sentiment measures about Asian companies based on neuro-linguistic analyses of web-based Chinese text to geo-location data on UK consumers.
Unsurprisingly, the sheer volume of data coupled with the large number of sources can be a challenge. Quant firms will tend to have existing data science teams, enabling them to ingest vast quantities of data, but discretionary firms are having to build these teams from scratch. A significant number of alternative data providers are based in America, which can result in highly US-centric coverage and potential concerns around ethical best practice differing across jurisdictions.
Jupiter has already started to meet its first challenge by building out a data science team. Last month, the team began trials on alternative datasets and aim to actively use several throughout 2019. Is alternative data the holy grail of investing? We don’t really know at the moment, but we are hopeful that it may help our fund managers keep ahead of the competition with this new and exciting potential source of ‘edge’.
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