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Much has been said in the data world about ideal states: “Data at your fingertips”, “Plug-and-play insights”, “Empower your whole organization with data”, and “Democratizing insights from data”. This list is long, and to be honest, does sound great. There is value in setting such big-picture goals in an organization, but often the route to go from here to there is a lot harder than initially considered. Data Warehouses such as Snowflake are often proposed as the solution that ushers in this step-change in organizational performance. If only your data was one well-defined, easy-to-understand, definitely maintained query away, everyone would be able to incorporate it into their day-to-day decisions. At Eagle Alpha, we think Data Warehouses can be useful but often find ourselves in a chicken-and-egg situation with them. As mentioned, Data Warehouses do their best when the data is organized, but who is supposed to do that organizing and how? How do you know what to set up so that data users in your organization can hit the ground running when all you have are 300,000 poorly named CSV files of unknown content scattered across 1500 sub-directories of sub-directories? The ideal starts to drift pretty far away when that is the reality – a reality that many organizations face even with the internal data that they nominally can control the production of. Add in the requirement to incorporate external third-party sources of data, and soon addressing the ambiguity and confusion that stems from this data circus becomes the all-consuming job of many data engineering teams.

Exabel and Eagle Alpha’s partnership takes on this challenge end to end. By combining Eagle Alpha’s deep knowledge and expertise in the alternative data ecosystem with Exabel’s powerful analysis platform, investment teams can finally tackle these operational and technical challenges holistically, delivering efficient data evaluation, at scale.

As asset managers invest resources into building out alternative data initiatives, data sourcing is seen as an integral part of the process, with in-house and outsourced professionals finding and introducing datasets and data vendors. With datasets rapidly growing in price and impacting the growth in spending of firms buying alternative data, the data sourcing function is now more critical than ever before. These results are based on our first-of-its-kind survey of 24 buyside funds with varying levels of alternative data experience.

The recent rally off the June 16 lows (S&P 500 +5.36%, Nasdaq +7.57%, Russell 3000 +5.42%) has made some crowded shorts more squeezable. We have seen the number of highly squeezable stocks in our Squeeze metric since June 15 more than double, and our average squeeze score has increased by over 250% (from 12.22 to 31.48). The Consumer Discretionary (primarily auto) and HealthCare (Biotech) sectors have the most constituents in the top 25 most squeezable stocks.

Alternative data is increasingly being used in private markets, including private equity, real estate, credit, and infrastructure, across deal origination, due diligence, and post-acquisition value creation and risk mitigation. Initially, interest in alternative data began when companies wanted additional information around digital market spend comparisons and reputation and social media review comparisons.

Employment data has been the 4th most popular alternative data category on the Eagle Alpha data discovery platform over the past 12 months, seeing a 40% YoY increase in the number of employment datasets onboarded. Job listings data provide asset managers holistic information on total employee count, job functions, skills, gender, and tenure which can then be used to measure firm performance, hiring strategy, employee turnover, headcount growth, and estimate expenses. Employment data can also take the form of employee profiles and company reviews which can help measure employee perception and sentiments, thus providing a good use case not only for firms dealing with public equities but also for private equity in the form of deal origination analysis and due diligence.

Cryptocurrency, or “crypto” as it has become widely referred to, has been one of the quickest moving trends in the investment space of late, with retail and institutional investors throwing their hats in the ring. While traditional markets must obtain all their information from external entities, through conventional channels like Bloomberg or via alternative sources such as satellite and geolocation, cryptocurrency provides the opportunity to also look at global internal data. Thanks to blockchain technology, you can get real-time statistics for anything on-chain.

ESG is the hottest topic in the data world right now. Regulators and asset owners are putting increasing pressure on funds to invest in a more environmentally friendly and socially responsible way. This provides a great opportunity to funds who offer ESG solutions and we have seen massive growth in the AUM directed to funds claiming to incorporate ESG factors into their investment process.

Emerging Markets Asia, and China, in particular, have become a hotbed of activity for alternative data. At Eagle Alpha, we are seeing rocketing demand for data from the region. Initially, this demand was from international managers looking to better understand dynamics on the ground in the region, but increasingly we are seeing local managers show more interest in alternative data. We are seeing the supply side of the market wakening up to this increased thirst for data. The data market in EM Asia is growing in size and sophistication.

Inflation forecasts for 2022 saw a steady increase across Europe last year, driven in part by bottlenecks in supply chains, and rising energy and food prices. For EPFR-tracked Europe Regional bond funds, the extent of this peak in consumer price expectations across European countries has been reflected by a more pronounced downturn in Active vs Passive fund positioning. Active fund manager sentiment towards countries with higher forecasts, such as the UK, has become more bearish compared against 2020 end-of year allocations. In this example, the spread between these fund groups dropped from +2.9%to -10.5%.