In this Intelligence report, we take a look at the current state of the alt data market for oil and gas investors. Specifically, the report will discuss 1) exactly what alt datasets are appropriate for this sector, 2) use cases for such datasets, and 3) the level of demand we see from investment managers for oil and gas data.

Identifying and tracking insider activity (i.e. when CEOs sell down shares or what they say on earnings calls) can be a useful way to understand what management thinks about the company ’s future performance. Equally, it could also provide irrelevant, unhelpful signals. In this report, we highlight the challenges, literature and use cases on this subject. We also summarise several datasets that may be of use to readers in tracking different types of insider activity.

Historically, environmental performance data has heavily relied upon disclosure information, which companies are not always obligated to report on. However, recent technological developments have enabled us to measure environmental performance in more advanced ways. These datasets can 1) attribute point sources of invisible greenhouse gas emissions via remote satellite technology, and 2) quantify ESG-related intellectual capital through patent filing analysis.

In 2018, we published a piece discussing the potential correlations between trademark registration volume and firm financial performance. But what about the macroeconomic use case? One year later, we investigate whether trademark filing volume can provide an aggregate indicator of business cycle shifts.

The stock market reaction to a single pharmaceutical event, such as a drug approval, repositioning, or adverse event, can have a spillover effect. Beyond the effect on the associated company’s stock price, an event may trigger a latent movement in the stock price of other publicly traded pharmaceutical companies. Research suggests that these sympathetic price movements may be a result of common attributes shared by the companies (beyond economic fundamentals), which are embedded in human language. In this piece, we explore how investors could leverage NLP to detect these ‘hidden relationships’ and generate alpha from pharmaceutical stocks.

In this Intelligence report, we summarise a few alternative datasets that soft commodity investors can leverage in order to predict the prices of crops. Specifically, the report discusses datasets that can offer investors 1) a leading advantage, and/or 2) more visibility into previously opaque regions.