Investors may want to pay more attention to intangible assets. As a percentage of total assets on S&P 500 constituents’ balance sheets, intangibles have risen from 17% in 1975 to over 84% today. Many datasets that measure intangibles are relatively unknown and under-utilized – perhaps offering a degree of edge to some investors. In this report we outline where to find alternative data sources that measure 3 types of intangibles: 1) brand value, 2) human capital and 3) intellectual property.

Sentiment can be found in places other than text-based language. We introduce an academic paper that correlates market sentiment with news article photos and consider whether or not multimodal sentiment analysis (derived from audio, images, video) has a future in the landscape of alternative data.

Drug development pipelines are the foundation on which pharma and biotech companies build their future success. Consequently, share price volatility is commonplace around the announcement of clinical trial results. In this report, we expand on our previous work - Using Alt Data to Evaluate Pharma Pipelines – by exploring a) the impact of clinical trial outcomes on the market value of pharma and biotech companies and b) predictive factors that could be used to estimate clinical trial outcomes.

2019 marks the start of the next generation of communication: 5G. While the US and China rollout the technology using existing 4G networks and infrastructure, we look forward to 2025 - when 5G is expected to be operating without any reliance on its predecessor. In this Intelligence report, we use alternative data to try to answer 1) which telco operators are likely to dominate the market six years from now, and 2) how much they stand to earn with their innovations.

A growing amount of literature examines the relationship between financial performance and employee sentiment, as measured by crowdsourced company ratings and reviews. In particular, recent studies assess the ability of these datasets to forecast stock returns and earnings surprises, since changes in employee satisfaction levels can signal shifts in the economic condition of the firm. In this report we 1) highlight the predictive characteristics of employee feedback data, 2) discuss the limitations of these datasets and offer possible solutions, and 3) provide a market summary of crowdsourced employee review datasets.

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.