In the ESG landscape, some of the pain points for asset managers, are a lack of comparable, high-quality, high-frequency raw data, a lack of standardized definitions of sustainable activities, conflicting ESG taxonomies, and divergence in scoring methodologies across traditional ESG data vendors. This conundrum remains despite a proliferation of ESG data vendors since current ESG scoring methodologies are limited
by voluntary and sparse non-financial input data and must be supplemented by alternative datasets such as events data, real-time data, satellite imagery data, traditional and social media data, all of which capture material issues and events not reflected in a company’s financial and regulatory filings. Extracting, evaluating, and standardizing these alternative datasets require significant manual effort, creating strong incentives for the use of machine learning (ML), artificial intelligence (AI), and natural language processing (NLP) techniques.
To evaluate all the components of the S-Pillar, the paper will review social data vendors in 3 parts: Part I: Social Capital, Part 2: Human Capital and Part 3: AI & NLP for Social Analysis. There is no one vendor that covers all categories of social data in detail. This report aims to help readers identify and understand which vendors can cover each of the social categories, what regions they cover, the granularity of the datasets and other key metrics of these data vendors’ datasets. With the introduction of new sustainable finance disclosure regulations and taxonomies from the UN and across the EU, North America, and Asia, it is more important now than ever to understand ESG data and where investors can source reliable ESG data.
Mandatory and non-mandatory ESG reporting obligations vary massively by region. Researchers have highlighted 1750 sustainability reporting provisions across 60 countries worldwide. And these reporting regulations are in constant flux. Just this year both the EU and the SEC in the US have signalled their intention to tighten non-financial disclosure requirements, though we are some way off a single harmonized global system. The shifting and inconsistent requirements have increased the importance of alternative datasets from independent third parties when assessing the ESG credentials of an investment.
Defined as non-traditional data that can be used in the investment process, alternative data has gained increased attention in the investment industry over the last decade. Essentially alternative data has come to describe data outside of market data such as market price data, tick data, volume and fundamental data. Alternative data has been gaining popularity over the last decade with surveys suggesting up to half of buyside funds are using it.
Alternative data users were once limited to quant funds, but it is not just buyside firms that stand to benefit from the surging availability of alternative data sources. Very soon alternative data users included other financial services firms and verticals including private equity and corporates (the latter refer to it as “external data”).
Alternative data opens the firm to new opportunities and sources of alpha or insight, but it also potentially opens the firm to new risks. To come to grips with this surging demand, legal and compliance teams on the buyside have had to upskill and educate themselves on considerations unique to alternative data. Compliance teams now need to place much greater emphasis on concepts such as data provenance and anonymization techniques.
This paper encapsulates Eagle Alpha’s eight-plus years in the alternative data industry, and particularly the last two years of monthly webinars and regular articles in partnership with our legal partners, New York law firm Lowenstein Sandler. The paper is underpinned by content in Eagle Alpha’s vast legal and compliance library which we highlight at the end of each section. Footnotes are provided for any third-party content used in the report.
24 buyside funds with varying levels of alternative data experience completed detailed surveys over a three-week period in May 2021. This report breaks down the responses by Spend and Growth, Innovation, ROI, Dataset trials, Challenges and more…
Mobile apps provide a wide range of valuable insights surrounding the success of a company through the data points collected on its usage. App data has rapidly become an essential tool for companies to understand how their apps are succeeding or failing, while also providing a benchmark against the greater market. Consumers today spend an average of 4.2 hours per day using mobile apps, verifying the novel value to companies for product optimization, advertising, stock market trading and more. According to Smart Insights, consumers now spend 9 out of 10 mobile minutes on apps.
As part of our Eagle Alpha Spotlight series, we will be exploring different alternative data sources and speaking with data vendors for first-hand insights. This paper dives into mobile app data and its benefits for all data buyers. The information included is sourced from proprietary and third-party content, and from interviews with leading mobile app data vendors.
What is ‘ESG data’? We hear the term ESG Data in the financial vertical a lot but what does ESG Data actually consist of? Over a series of three papers, Eagle Alpha is going the break down each of the three ESG pillars. We will also publish a fourth paper exploring ESG natural language processing (NLP) providers and other providers of ESG scoring datasets.
With the introduction of new sustainable finance disclosure regulations and taxonomies from the UN and across the EU, North America, and Asia, it is more important now than ever to understand ESG data and where investors can source reliable ESG data so that they can understand how the make-up of their portfolios and offerings are impacting their sustainable investing goals. If the raw environmental data that you plan to incorporate is not accurate this might be more detrimental than no raw environmental data at all. With the ever-increasing ‘E’ datasets, it is vital to source science-based evidence that the dataset you wish to incorporate is accurate and fits your intended use case.
In this report we summarize the major developments in the alternative data industry in 2020 based on Eagle Alpha’s proprietary data and insights. Many of these trends and themes will have relevance into 2021 and beyond.
This paper pulls together publicly available data along with insights from vendor interviews and Eagle Alpha’s proprietary articles and webinars. Furthermore, the paper also leans heavily on insights from our legal partners, New York law firm Lowenstein Sandler, who are regular contributors to our content as well as several data providers who offered additional perspectives.
We have grouped our insights into 5 key sections:
• Major data privacy trends affecting alternative data,
• Data sources in the regulatory spotlight and the legal cases that surround them,
• A section that looks beyond regulation to how companies are self-regulating and restricting the way data is tracked and collected,
• Considerations beyond regulation, such as the developing perceptions of consumers and how “gatekeepers” are impacting data collection and availability.
• An in-depth analysis on important developments being introduced by “gatekeepers” like Apple and Google, and how these changes are impacting the mobile app ecosystem in particular.