As of 2021, China’s population exceeds 1.4 billion people according to United Nations data, which excludes Hong Kong, Macao, and Taiwan. This number makes up approximately one-fifth of the global population, and with the rapid rise in technology in recent years, it can be concluded that they are also one of the largest producers of data. Historically, there were 4 factors of production known to create economic wealth – land, capital, labour and entrepreneurship, but with an increasing global reliance on technology, data (knowledge) is now considered by many to be the fifth factor. Unlike the initial four factors of production, the pricing models for data are opaque with no ownership models truly in place, and ill definitions surrounding sensitivity and the categorization of data. According to many global experts and firms watching the developments with avid interest, China’s crackdown on Big Tech is seen to safeguard one of the world’s most valuable economic resources and is considered to be the new oil. This paper will provide insight into China’s changing data landscape regarding regulations, always linking back to the connection between alternative data availability, collection, and distribution. The content included in this paper has been collected through both publicly available sources as well as through interviews with data experts and proprietary client-only content available through Eagle Alpha’s Data Strategy solution.
Like most forms of ESG analysis, governance analysis has traditionally relied almost exclusively on self-reported information, or metrics derived from self-report information. This reliance on self-reported information is likely even greater when it comes to the topic of governance given that there is a lot of governance data already available. For the most part, companies are obligated to publish a lot of information that is relevant to governance analysis, such as board composition, executive compensation, shareholder rights, and audit and oversight information. Section 1 discusses datasets for analysing Corporate Governance, and the subtopics of the board, pay, ownership and control, and accounting. Section 2 provides a deep dive on the topic of corporate behavior, encompassing the subtopics of business ethics, anti-competitive practices, corruption and instability, financial system instability, and tax transparency. Finally, in Section 3 we explore other alternative data approaches to governance analysis, most notably environmental risks scoring and AI & NLP techniques for governance analysis.
This paper is a compilation of alternative data insights relevant to engineers and data scientists from Eagle Alpha’s vast library of content on the Data Strategy solution. The Data Strategy solution helps our clients to build and innovate their alternative data strategies and discover new opportunities from alternative data. Through our data Strategy advisory services, live workshops, and proprietary content our clients drive alternative data adoption at their firms, increase the ROI on their alternative data initiative, and they mitigate the risks associated with alternative data.OverviewIn this paper we highlight content that ranges from strategic advice from industry veterans to practical insights from engineers and data scientists with experience working with alternative data. The insights reflect Eagle Alpha’s own perspectives, as well as those of highly respected third parties.
Undoubtedly, alternative data has become more accessible to data buyers of all shapes and sizes, with the most accessible avenue into alternative data being web crawling, or web scraping as it’s also known. Web crawling has been utilized by hedge funds and corporates for several years now and is considered by many to be the “gateway” into the alternative data ecosystem. The reason being that web crawling is a ‘low-risk, high return’ data source. Web crawling offers perfect conditions to achieve buy-in and resourcing and can be implemented with a small team of analysts and engineers.
This Spotlight report explores web crawling by outlining the most pertinent case studies from our vast library of content accessible through the Data Strategy platform, as well as exploring leading web crawled data providers available to view and connect with through our Discovery and Prioritisation solution. We have included case studies for each category to provide some real-world examples of how web crawling can improve the decision-making process for both financial services firms as well as corporates and provide insights that are not readily available through the analysis of traditional forms of data.
Data science and engineering teams at 10 buyside funds completed surveys over a two-week period in August 2021. Although the survey sample is small, there is a great similarity in respondent profiles. This report breaks down the responses by Methodology, Challenges, Storage & Compute Infrastructure, Data Delivery, and ETL & Dashboarding.
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.