The public narrative around Brexit has reached the point where it is now impossible to see the woods for the trees. In the following piece, we ask 1) What even is Brexit? 2) How can an investor better gauge their exposure to it? 3) How can we obtain higher-frequency, lower-latency macro indicators? 4) Is it possible to preempt corporate reactions?

There is an ever-expanding supply of alternative data sources that can be utilised by pharma, biotech, and medtech investors. In this piece, we turn our attention away from “traditional” clinical trial data towards Real-World Data (RWD). Namely, data that is generated in real-world medical settings. In the first of a series of healthcare-focused Neudata intelligence articles, we provide a primer on the RWD landscape, including 1) an introduction to healthcare data and how RWD is defined, 2) the different types and sources of RWD, and 3) regional differences in the collection and availability of RWD.

In this Intelligence report, we examine how public procurement data could have flagged the construction giant, Interserve’s, demise ahead of time. We also highlight a few suitable alternative data providers for readers interested in incorporating this type of data in their analysis.

We highlight key findings from a Harvard Business School study that combines traditional ESG scores with alternative data sources that measure public sentiment momentum on corporate sustainability performance. The study finds that an ESG factor that goes long on firms with high or improving ESG performance and negative sentiment momentum, and short on firms with low or decreasing ESG performance and positive sentiment momentum delivers significant positive alpha.

As the use of alternative data within the investment community grows more prevalent, this prompts questions on dataset crowding and its implications for alpha generation. While opinions may be divided on the significance of buy-side client numbers in evaluating a dataset, an analysis of dataset crowding serves as an interesting source of insight into the interplay between supply and demand for different data types. In the following analysis, we explore: (1) levels of dataset crowding across different dataset types, (2) supply and demand for four dataset types: web scraping, web & app tracking, ESG, and location, (3) the types of data that will be most valued by investors in the future.

Although alternative data providers are united on the front that mobile gaming is a steadily growing market, they are, however, divided on its exact size. In this Intelligence report, we historically compare predictions made by three mobile gaming data vendors, and take a closer look as to why there is such a divergence in their estimates.

The past years have seen an emergence of numerous providers offering either signal outputs or analytics tools that rely on Natural Language Processing. In this Neudata Intelligence piece, we introduce several basic tenants of NLP, a selection of ‘alt data’ applications, and finally ask – how can a prospective user evaluate such products in the pre-testing phase?