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For more than a decade, regulators and policymakers alike struggled to articulate and implement disclosure expectations for public companies associated with climate risk. Although in 2010 the Securities and Exchange Commission (SEC) issued a guidance reminding that US-traded companies are required to disclose material climate risks in their public filings, the guideline is notably infirm in providing an organised approach for making such disclosures, instead leaving it largely in the issuers’ discretion whether, how and in what form to disclose climate-related risks. The resulting landscape is a disorienting array of disclosures scattered across dozens of types of securities filings, wherein the process of benchmarking them to other filings and standards underlying the majority of the US securities regulation is a challenging task. Although some efforts exist to identify climate risk disclosures, they have been limited in scope.
In this post, we present machine learning tools aimed both at extracting climate risk disclosures and inferring to what extent issuers’ characteristics affect the likelihood of submission.
In 2010, the SEC approved a notorious directive requiring issuers to disclose information about the potential effects of physical climate change events and related regulations on business operations and financial positions. As afore-mentioned, the SEC’s guidance did not prescribe the formal requirements to abide by for fulfilling the disclosure, nor did it provide any pertinent templates guiding issuers, thereby triggering a trade-off. On one hand, empowering market forces to shape the disclosure process relieves issuers’ regulatory burden. On the other hand, the lack of procedural consistency entails high coordination costs. And those costs are clearly apparent. In 2018, the General Accounting Office (GAO) published an analysis of firms performances, highlighting a lack of transparency in the climate risk disclosure process and consequent hurdles facing regulators when deprived of the parameters assessing to what extent disclosures are appropriate.
To address these issues, our project deploys machine learning tools identifying the set of public companies that have submitted climate risk disclosures in compliance with the SEC’s interpretive guidance. We begin by querying company disclosures from the EDGAR database. To identify parts of the documents that may contain a climate risk disclosures, we use a suitable set of keywords and -phrases, containg, for instance, climate, global warming, and greenhouse gas. The keywords are intentionally broad, leading to an overinclusive set on candidate disclosures. In a next step, we label a subset of these candidate disclosures by hand for whether they actually contain climate risk disclosures or not. Finally, using these labelled texts, we train and cross-validate a machine learning classifier. The classifier is able to determine the presence of a climate risk disclosure with exceptionally high accuracy and we use it extract climate risk disclosures from the complete set of all company filings.
Examining the identified climate risk disclosures shows that ‘cookie cutter’ disclosures are common—whereby many issuers submit nearly identical ‘boilerplate’ disclosures that lack firm-specific nuance. At the same time, a variety of issuers appear to draft tailored types of disclosures that do not follow a standard template. Such bespoke disclosures are more likely to be highly informative for investors.
Further, the model provides empirical evidence that submitting climate risk disclosures often goes viral in industries. Once a few issuers start submitting, the practice gains momentum within a particular sector. In extraction, construction, transportation, and energy industries the overall trend is to submit disclosures more frequently than in others (eg finance), which coheres with broader intuitions and data about carbon footprints—though crypto-currency mining may eventually turn to be sensitive for the financial industry as well.....
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