At a Glance: CSRHub ESG Business Intelligence DataFeed
CSRHub utilizes a proprietary Big Data platform to aggregate, normalize, and weight over 600 sources of Corporate Social Responsibility (CSR) data, including ratings from 10 leading ESG analyst databases, to create transparent and comprehensive ESG consensus scores.
CSRHub utilizes a proprietary Big Data platform to aggregate, normalize, and weight over 600 sources of Corporate Social Responsibility (CSR) data, including ratings from 10 leading Environmental, Social, and Governance (ESG) analyst databases, to create transparent and comprehensive ESG consensus scores. The process is powered by patented algorithm and machine learning techniques that harmonize the disparate data sources to produce a stable signal useful for understanding a company’s non-financial performance.
The Coverage and Feed Structure
The CSRHub DataFeed covers over 18,000 public and private companies, government organizations and not-for-profit organizations in 136 industries and 136 countries globally. The DataFeed has monthly history back to 2008.
The DataFeed contains a main table with company level scores. Included are 12 subcategory scores that roll-up to four category scores and a total score following the methodology detailed below.
The CSRHub ESG Business Intelligence DataFeed was specifically designed to overcome the unique set of challenges facing companies and investors hoping to apply Corporate Social Responsibility and ESG/SRI measures to their benchmarking and investment analysis processes. One of the biggest challenges facing investors is the huge number of CSR and ESG/SRI sources which include ESG Analyst Databases, company filing information, news publications, NGOs, industry reports, and more. Compounding the problem is the reality that each source has its own coverage universe, frequency and methodology making it difficult to aggregate everything in a meaningful way.
CSRHub’s ratings system removes the potential for bias and inconsistency by following a robust mechanical process. First, each element of data received from a source is mapped to one or more of 12 subcategories, which in turn, roll up to four top-level categories. Each individual data element is then converted from its raw state to a numeric rating on a 0-100 scale (100 best). The scores from each source are then compared on a company-by-company basis to identify and eliminate biases per source. After making these adjustments, source level data is aggregated by company into subcategory scores. CSRHub weights the score from each source based on its estimate of that source’s credibility and value. Finally, CSRHub will drop ratings for companies without enough information to publish a score. This process results in a broad universe of coverage with stable and meaningful scores.
Example Use Case
CSRHub’s ESG Business Intelligence DataFeed provides users with the market consensus view of a company’s CSR/ESG profile. There tends to be a wide dispersion in company-specific ESG metrics caused by the huge amount and variety of sources providing ESG-relevant information. CSRHub provides an aggregate measure of these sources and their scores are uncorrelated with any individual ESG provider and other measures of company financial performance. This gives investors an orthogonal risk and alpha factor to incorporate in quantitative investment processes.
For fundamental portfolio managers or research analysts, CSRHub’s Business Intelligence DataFeed provides an approachable and easy to understand measure of a company’s ESG performance. This data can be used to understand the quality of a company’s supply chain, how industry peers compare, and to understand a company’s long-range commitment to CSR/ESG principles. This information can be included into Long/Short pair trade strategies, momentum driven approaches, or for positive/negative screening of companies based on ESG profile.