Gauging value through data
As various pharmaceutical companies around the world endeavour to find a workable vaccine against the coronavirus, alternative data could help funds take an investment view on where the value lies in this global race.
Nicholas Nolan, Senior Director of Solutions Consulting at SS&C Advent, elaborates: “Funds are analysing the decisions they make on companies working towards a vaccine. They not only need to consider which company will deliver first but, almost more importantly, they need to take into account the sentiment of people in different countries.”
“Most of this is around natural language processing (NLP) to analyse what people are saying on social media platforms. After all, a vaccine will only be successful if it gains public approval and people trust the company producing and distributing it. The NLP is trained to recognise medical language and other words and phrases associated with Covid-19 as well as names of vaccines, therapies and pharma companies involved.”
Alternative data leads to fund managers getting access to information which can help them view and analyse companies from a number of different angles.
“More and more of our funds are turning to alternative data sources to gauge some aspect of a company which isn’t easily analysed or seen in traditional market data. One example of alternative data is ‘sentiment’. This is built by scraping news and comment websites, or by buying feeds of data from social media networks. This data can be used to measure a product’s performance before announcements, employee satisfaction, etc,” Nolan says.
On the investment side alternative data can give managers an edge but operationally, Nolan says there are challenges: “The problem is twofold: What alternative data should be employed (i.e. satellite, sentiment, government data, weather, credit card data, body language tells identified by FBI profilers, etc) and how do firms consume this information and measure it into making investment decisions? Evaluating data quality has been the biggest challenge we have encountered when working with some of our partners.”
Nolan notes how within these alternative data sets, there can be a lot of noise. Managers can find themselves overloaded with information, which may then lead to inaction on their part.
To mitigate the risk of purchasing data having little applicable use, he recommends the following: “One of the approaches firms need to consider is ensuring the data can be corroborated. There is a delicate balance in not having too many data sets and having enough to corroborate one another. For example, if credit card data is telling you that fewer people are spending money at brick and mortar retail stores with no online presence, does satellite information of those retail stores show fewer cars? In this case, these two datasets can support one another. There also needs to be a consistency in the data sets themselves, and the approach when measuring the information.”
The high fees some data sets command is another challenge managers face. The difficulty this raises is finding the balance between the cost of the data and the value it provides. Nolan remarks: “People are quantifying this information; it’s a very bespoke system or model to quantify the value of the data to the point where you can make an investment decision.”
Managers looking to use alternative data to gain a competitive advantage also need to overcome the hurdle of potential lack of expertise in analysing the data. It is no use having access to data which is then not analysed or applied to the manager’s investment process.
If choosing to go down the alternative data path, firms need to employ data scientists and the right kinds of systems and technology to manage and analyse this data. “They need to be very committed to the process since there will be a build out phase and requirements to make investments in time and money,” Nolan advises.
There is a real risk of managers having access to too many data providers. This makes trying to generate a thesis or investment idea and properly analysing all these different data sets even more of a challenge. Nolan remarks: “Investment managers will need to make the right decisions on the types of alternative datasets they want to use to make investment decisions and employ the right teams and infrastructure to support.”
The right framework
Artificial intelligence and machine learning can also support a manager’s efforts in this space. Nolan notes: “Based on large volumes of information systems which can predict trends and patterns across different companies and markets, predictive models of trends and investments are one way we have seen a lot of managers look at generating higher returns. Alternative data can also be a key component.”
In Nolan’s view, having the right technology infrastructure to support analysis and integration is the most critical component to success with alternative data. Nolan comments: “Having a strong and robust framework, in terms of tools and technology, in place to fully support the analytical process is key. Without this, the alternative data is, in most cases, useless.”
Building a technology framework that accommodates alternative data; however, this is not always a simple task. Nolan attests: “There are so many different data sources: satellite, NLP, social media, body language tells identified by FBI profilers… Putting such a diverse data set into a consolidated system is complicated.”
A growing industry
There is no doubt the market for alternative data and data science is here to stay. Nolan makes reference to industry statistics: “There has been a steep increase in the number of providers since around 2010 and 2011. In 2017, there were about 250 alternative data providers and now there are over 350 listed, a number which is increasing every month”.
“Managers are also spending more and more money on alternative data. AlternativeData.org has provided some details on this and found funds with AUM over USD10 billion in 2016 spent USD1.2 million on alternative datasets. Those same funds are now spending USD4.1 million in 2020.”
Senior Director, Solutions Consulting, SS&C Advent
Nicholas Nolan is the Senior Director of Solutions Consulting at SS&C Advent. He primarily works with Hedge Funds, Assets Managers and Fund Administrators as they evaluate new operating models for middle and back office technology and services. Areas of expertise include Complex Derivatives, Bank Loan/Credit and Debt processing, Reconciliation and Asset Servicing. Prior to that he worked at Fidelity Investments in Collateral Risk Management. He is a graduate of Columbia University and currently lives in NYC with his wife and two children.