Over the past two years, alternative data sources have proven to offer hugely effective and timely insights for companies wanting to understand the impact of Covid-19. Coming into the winter holiday season, and following on from Black Friday and Thanksgiving, the cracks in the global supply chain have proven to have a devastating impact on commercial sectors, but also disrupting other sectors via a knock-on-effect. Due to this, many industries are now turning to alternative data to further understand this impact.
The Covid-19 pandemic has brought about a variety of interesting trends, some of which have created both opportunities and challenges for many organizations globally. Alternative data sources have proven to offer hugely impactful and timely insights for companies wanting to understand the impact of Covid-19 on verticals such as eCommerce (consumer transaction data, social media, etc.), travel, and hospitality (mobility data, geolocation, etc.), among many others. However, two years on, and as we enter the holiday season, organizations are relying on alternative data sources to understand how the global supply chain will be affected this year. This paper outlines the importance of alternative data sources in the understanding of global supply chains, considering some of the more unique and unstructured datasets that are available to global teams.
Welcome to our “2021 Year In Review”. Given Eagle Alpha’s role as the leading alternative data aggregator and advisor, this year’s review will be both insightful into the major themes we observed in 2021 and, as importantly, be inspirational and thought-provoking in how you approach alternative data in 2022.
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.
Coming December 9th! Our 1st Annual Alternative Data Report: 2021 Year In Review. We are delighted to announce that we will be publishing our first annual Alternative Data Report which will provide an overview on What we have seen in 2021, Updates on 2021 predictions, Hot topics (ESG, Legal and Compliance, Inflation, China, App Annie), Data Strategy report summaries, Tech updates, Predictions for 2022.
Available to all readers. Pre-order your copy today!
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.
At our 4th Hackathon this year, Team Quantagon, led by Alpha tester Seth Leonard, Founder of OttoQuant took the win, for the second time in a row! Congratulations Team Quantagon!
We will be publishing the full analysis in a white paper coming soon. Please register your interest to receive this paper. Through using and blending data from Ascential, Causality Link, LinkUp, Revelio Labs and SpaceKnow Inc., the predictions from OttoQuant at Eagle Alpha’s Alternative Inflation Data Hackathon outperformed both the consensus forecast (0.3%) and Cleveland Fed Nowcast (0.36%) for month-on-month inflation in September, with a nowcast of 0.37% using aggregated alternative series. Interestingly, the best performing model was that which used 138 disaggregated alternative data series, with a prediction of 0.43%. The true month-on-month change in the CPI was 0.41%.
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.
This blog is based on a webcast hosted in partnership with CAIA Association, where Eagle Alpha’s Director of Dara Strategy and Analytics, Ronan Crosson, spoke alongside Tim Kiely, Lead Data Scientist, at American Securities about using alternative data for...