We previously highlighted a study which found that institutional investors are using Freedom of Information Act (FOIA) requests – specifically, FDA FOIA requests - to generate abnormal returns. In this piece, we provide three examples of how investors have successfully utilised this FOIA technique to obtain early access to stock-moving information.

It is no secret that the current alternative data market heavily services public equity investors. Of course, these are some of the biggest alt data spenders, so such preferential treatment isn’t surprising. However, this bias leads us to believe that non-equity investors may assume that alt data isn’t relevant to their strategies. Unsurprisingly, we believe otherwise. In this report we take a look at the alternative data options for real estate investors. Specifically, we address 1) issues with using traditional datasets and 2) which alternative datasets could help provide more accurate KPIs.

The role of retail investors in the financial markets cannot be understated or underappreciated. In the following Intelligence report, we demonstrate how retail investor activity, sentiment, and attention can be a proxy for institutional investors to gauge future asset prices, stock liquidity, market returns, and market efficiency. The report will also go on to discuss several alternative datasets on retail investors that may be used by readers in creating such a leading indicator.

Don’t hate the ESG rater, hate the very subjective ESG game. With the upcoming regulatory changes for investors to integrate ESG, we outline the specific issues investors should pay attention to. We also summarize the current state of ESG data, trends impacting the industry and the meaning of ESG.

A narrative dominates the media and news that we in the midst of a turbulent and unpredictable epoch. In other words, we are living through ‘uncertain times.’ Many people, academics, alt data providers, and investors alike, are asking themselves whether such text-based narratives can be distilled into a quantitative signal. In this intelligence piece, we demonstrate how economic indicators derived from unstructured news data have been variously used to assess Chinese labour market conditions and forecast Japanese inflation.

Following on from Part II in our healthcare series, which explored the application of machine-learning models to generate phase transition and approval predictions, we provide our view on the alternative datasets that pharma and biotech investors can use to augment their R&D pipeline analysis.

Slack, CrowdStrike, and PagerDuty – three SaaS companies have already gone public this year, and we are only halfway through 2019. For institutional investors that are active in this market, or simply curious about the space, we discuss some alternative approaches (and, of course, alternative datasets!) that could shed light on adoption, churn rates, recurring revenue, and acquisition costs of SaaS providers.

The growth of e-commerce has facilitated inflation tracking on a daily basis, namely from aggregating online pricing data. Moreover, the ubiquity of e-commerce in-itself is changing aggregate inflation dynamics. We believe product pricing datasets have a particularly potent use case in generating early inflation signals in opaque markets, such as LatAm, where official statistics are challenging to obtain. In this report, we review the academic literature that examines the behavior of online prices and its response to aggregate national shocks. We also outline where to find alternative sources of inflation data.

Cybersecurity risk ratings have recently drawn attention as a new player in the alt data arena. In this piece, we summarise the current academic status quo regarding cybersecurity breach incidents and financial market impact, before discussing the usability of relevant datasets from an event arbitrage and risk management perspective.

How much does alternative data cost? The million dollar question. We leverage our unique position in the industry and proprietary data on pricing to shed light on how much datasets really cost, which factors influence the price of a dataset, and whether funds are willing to pay the price for valuable datasets.