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.
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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