At Neudata we have seen increased interest in alternative datasets that offer insights into a given company’s Intellectual Property. We explain 1) why we think this is, 2) why we believe trademark data is particularly interesting, 3) empirical studies that support this view, and 4) recommended providers to use in order to access trademark data.

Companies are more likely to share information to the public which demonstrates a high commitment to ESG standards. The same companies tend to avoid the disclosure of information that could reflect poorly on corporate practices. As an investor, one can overcome this issue by adopting an ‘outside-in’ approach through the use of alternative data sources.

Using data sourced from a transportation alternative data provider, we provide an overview of key correlations between railroad commodity volumes data and a number of economic indicators.

We offer our advice on the following three issues for: 1) challenges and pitfalls to be aware of when using e-receipt datasets 2) how different e-receipt data providers compare, and 3) what to look for ahead of selecting an alternative data provider

We summarise backtesting results directly sourced from one of our data providers, explaining how South Korean export data can be used as a leading indicator of company revenue. Specifically, this alternative dataset identifies correlations for companies that export products with a South Korean dominance.

Many investment managers question the link between ESG performance and company earnings. To compound the issue, at Neudata we have noticed that ESG research agencies often find conflicting results in their analysis. So why are there discrepancies? And will finding the cause of polarised research outcomes provide us with a more accurate picture of what ESG is good for? We also outline several applicable alternative data offerings.

We take one of the most high-profile methane gas leaks as a use case example to establish whether access to such alternative data can be used ahead of major share price moves. The example demonstrates the potential alpha in detecting unintended methane emissions that have financially material consequences.

We summarise findings directly sourced from one of our alternative data providers explaining how product pricing and markdown data anticipated the difficulties the Tod's brand was having in 2017. Specifically, this dataset identifies 1) low growth in product lines other than footwear, 2) a high level of promotions on products, and 3) brand value dilution due to a vast presence in off-price channels.

Across Neudata's coverage universe we evaluate which alternative data providers could have helped investors detect the group’s collapse. We describe six very different offerings and the read-across between the output from their data and Carillion. Where possible, we have also asked each data provider for their own thoughts on this specific case study.

Using alternative data sourced from a provider on Neudata's platform, this study analyses foot traffic across the top 7 US retail stores over the 2017 holiday period (Walmart, Target, The Home Depot, Lowe’s Home Improvement, Verizon Wireless, Sprint and Best Buy). We identify what we believe to be the most interesting conclusions and limitations of the study.