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.

A structured database of companies that are filing for bankruptcy is a gold mine for institutional investors. Not only does it highlight distressed and/or turnaround investment opportunities, it can also be a valuable leading indicator to identify which other companies may soon share the same fate. For readers that are 1) actively searching for a bankruptcy database, 2) considering alternatives to the database(s) that they are currently subscribed to, or 3) simply curious about the space, we summarise a 2018 academic paper that details how to construct a company bankruptcy database using alternative data.

Using hiring activity data as a signal for future business growth is the most common investment use case for aggregating online job postings. But do other use cases exist for this type of data? For example, can it tell us a) how effective companies are at hiring highly demanded skills, b) whether any of their facilities have suspended operations or even c) a clearer picture of the economic activity in opaque markets? In this piece, we review the landscape of vendors providing job market data, outline possible use cases and discuss its limitations.

With great power comes great responsibility – or not? According to one academic paper, firm-level electricity consumption data provides a novel window into financial misreporting among South Korean firms. We discuss how an investor can take this use case from theory to practice with a number of alternative indicators for firm productivity.

Now Uber and Lyft have both gone public, we take a look at some of the alternative data captured on these companies. There are various sources of insight into performance and prospects for rideshare companies, spanning a number of dataset types. In this piece, we highlight insights derived from ESG and transactional datasets.

It has been nearly six months since we last discussed Net Promoter Scores (NPS) in one of our Intelligence reports - our report initially made the case against NPS. Despite refuting the controversial customer loyalty metric as a leading financial indicator, we at Neudata still continue to receive interest from a significant number of institutional investors for NPS datasets. In response, we revisit the topic and discuss a 2018 academic paper that investigated whether NPS could be predictive of consumer spending and company revenue.

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.

Last week saw the conclusion of Apple and Qualcomm’s much talked about royalties and patent lawsuit. While Qualcomm observed a 23% stock price surge following the lawsuit win, Apple, interestingly, managed to observe a stable stock price despite their loss. In this Intelligence piece, we summarize a 2018 academic paper that investigates the relationship between litigation and stock returns. Specifically, the paper examines whether portfolios consisting of public companies involved in lawsuits could observe positive returns, despite its negative perception.

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.