For decades, financial firms have been able to maintain a competitive edge with platforms that provide live data feeds and the ability to trade with a single click. However, just as Paul Reuter’s telegrams replaced carrier pigeons, alternative data sources are disrupting the monopoly that platforms such as the Bloomberg terminal and Reuter’s Ekion currently have. Fund managers are expected to increase spending on alternative data by 4.2 times from $400 M in 2017 to $1.7 B in 2020. As the use of alternative data becomes mainstream, what is currently a competitive advantage will soon become a necessary requirement to compete in the financial world.
We are in a second data revolution where speed is not the only requirement to keep pace with the market, rather operating with new, unstructured, and uncommon data, such as trends, news, articles or rumours are gradually becoming a necessity. Alternative data helps fund managers obtain multiple levels of insights across investment opportunities; insights that could not be found via traditional analytics on financial data. Hedge Funds are leading the charge when it comes to adopting this, but as the data available grows and adoption rates increase, there will be no alternative than to use alternative data.
“More than half of hedge fund managers are already using alternative data to gain a competitive edge” – AIMA and SS&C
Traditional financial players struggle with data that is not financial, they don’t leverage best in class technologies and innovation is not at the forefront of their priorities, as a result they risk falling behind. Failing to adapt to the market needs significantly impacts the bottom line of businesses, a theme that is pertinent to every industry. Some players like Kodak failed this test, they could not keep up with the digital revolution, while others have succeeded, won market share, and became leaders in their area. Reuters, for example, made the first steps in a field that revolutionised the world of finance: they began development of a computer terminal displaying FOREX rates. This platform was soon augmented with the ability to make electronic transactions and was the forefather of Refinitiv Eikon, a platform built for trade innovation and the worlds largest directory to connect with verified finance professionals. It is imperative that players within the investment landscape adopt alternative data to support decision making and finding alpha.
There are 2.5 quintillion bytes of data created every day and leveraging this data for financial gains sounds like a no brainer. However, identifying what actually matters is a much more complicated process. The digital economy is the key proponent to the creation and rise of alternative data. There is a big difference between collecting as much data as you can vs actually finding and using the data that helps power your decision making. The true value of alternative data comes from its use, answering the question, “what are we really achieving from this data?”.
Signals are trends you can extract from a data set that provide meaningful information, meanwhile the noise is a random fluctuation that makes the signal difficult to catch. Detecting a signal in all the noise and its extraction, is a highly prized assets in the financial industry.
There are companies that have specialised in collecting certain types of data and have used it to enhance their product offerings, draw new customer profiles and generate new revenue streams. One such company is SpaceKnow, they leverage the images produced by over 300 satellites currently photographing the Earth and implement image recognition AI algorithms to create various indices capturing industrial activity. They use these indices as economic indicators where traditional reporting and data sources cannot be trusted. On one end of the spectrum, publicly available data, which tends to be free such as the datasets on Google Cloud Platform can help uncover insights that can help business outside the investment landscape make decisions. The benefits of of alternative data are not only limited to those businesses making investment decisions.
In a fast-paced ever-changing environment, having best-in-class information with the right level of granularity is a competitive asset. Any edge, no matter how small, can result in large gains within the industry. At Ekimetrics, when using alternative data, relevance and reliability are foremost on our agenda. Not only have we enabled our clients to adopt and facilitate the use of alternative data assets, but we have also provided methods of analysis to keep pace with the cutting-edge advancements within the investment landscape. Some of the successes we have had with our clients are summarised below:
1. Power up revenue strategy via first-mover approach
The leadership team of an Investment group wanted to build a fresh series of industry-leading thematic fund, a first for them. Besides growth of assets under management (AUM), they also wanted to attract a new breed of investors and especially first time customers who might have previously been turned off by traditional financial products. With innovation at the heart of this endeavour, the investment group was looking for a pure data play when creating an investment universe rather than than using their portfolio managers.
We were brought in to help build this fund. Our approach was a back-tested and optimised intersection of 3 dimensions:
a) Customized thematic to which a universe of target companies was mapped. We leveraged AI to map companies across segmented themes (EdTech, MedTech etc.), each with its own score and rank. Companies over-indexing on performance and in the top themes were selected to form the investment universe of the fund. This universe was unbiased and provided a global view of how the market was performing rather that the views of a particular person.
b) Distinct sentiment dimensions indicating the strength of signal associated with the theme. Our algorithm listened to the market, news sources and social media, to detect “trends” and “buzz” across several core sentiment dimensions. This sentiment analysis firstly helped establish if the theme was trending in a positive or negative direction and secondly to compare the local sentiments of the selected companies to the relevant themes.
c) Financial dimensions used to back-test performance and risk across various time horizons. The algorithm selected companies were then added to the thematic fund if they met the investment principles that the investment group had.
Post launch performance was up +15% to +80% against the benchmark, with further acceleration during Covid market period. This thematic fund outperformed similar funds and performed significantly better than the average fund in the group’s portfolio. The added features of theme selection and the data-first methodology also helped attract and bring in a new type of customer to the investment group.
2. Empower and enhance research capabilities by sourcing, pre-linking, and visualizing data across multiple relevant domains impact.
We worked with a debt management services provider that was trying to transform their unit economics in terms of both costs and time-to-decision. They would, in bulk, buy debt of credit providers and would go through account by account to see where they could recoup their investments. We identified that one of the major pain points was how time-consuming it was for an agent to investigate an account. They would need to collect all the relevant data before being able to make a decision on whether a specific account was legitimate or not. An agent would have to manually search through multiple internal & external data sources to get an appropriate understanding of an account.
This process would be repeated for every single account the agent worked on. Due to the uniqueness of their business model, leveraging alternative data had the potential to improve their efficiency drastically. We built a tool that brought together broad sources of data, it pre-linked the data from public sphere and gave the agent the ability to easily consume the information through visual queues. The data sources included internal data as well as free and paid external sources such as public datasets, government data, google etc. Network graphs were used to visualise the connectivity and create synergies between those sources, with each node representing information about an account found via alternative data.
The tool we built reduced the time taken to investigate an account by over 10x and enabled deeper insight within the investigations. Not only were there the immediate short term gains of improved efficiency and a higher quality of output but the saved resources due to this were reallocated to drive wider institutional change and to introduce advanced analytics into the organisation.
Companies that fail to build the right capabilities to incorporate alternative data such as trends, news or rumours run the risk of becoming obsolete in the long term. In industries where success is so highly dependent on speed and timing, processing, and assimilating alternative sources of data will be crucial for firms seeking to maintain a competitive advantage. As more players in the industry start to develop and adopt alternative data strategies, the market starts reacting faster and can increasingly anticipate trends from traditional data sources. The classical discretionary style of decision making will transition towards a more quantitative style of decision making across the industry as firms begin to find alpha with the use of alternative data. Developing alternative data capabilities has not only enabled our clients to power up their revenue strategy and operational model, but also develop innovative products and acquire a new type of customer.
Thought Leadership
Thought Leadership
