SEC 10-K Ownership Data Extraction Modeling#

Overview#

Utilities are often part of a nested hierarchy of holding companies and their subsidiaries which makes it difficult to understand the complex web of political and economic incentives they are responding to. These subsidiary relationships are reported in the SEC’s Form 10-K, via an attachment called Exhibit 21, however the format and contents of this attachment is neither standardized nor structured. With support from The Mozilla Foundation, Catalyst built a machine learning model to extract ownership data from this Ex. 21 attachment and structures it into a table along with information about the 10-K filing companies. We also used probabilistic record linkage to connect these owner and subsidiary companies to utility companies that file with EIA. This connection in turn enables a connection to data reported to FERC and EIA contained in PUDL. We only conducted an initial round of modeling, so this dataset is a beta version and its contents and connections to other datasets are probabilistic in nature.

The following output tables are created from this process:

Extracting Ownership Data From Exhibit 21 Attachments#

The ownership tables contained in the Exhibit 21 attachments are not consistently formatted and lack standardization in content and layout. Extracting this information required a machine learning model to generalize across the many different layouts. We used LayoutLMv3, a pre-trained model from the Hugging Face Transformers library designed for document layout-aware information extraction. We fine-tuned LayoutLM for named entity recognition on 160 labeled Exhibit 21 documents, classifying each word in the documents as one of four labels: subsidiary name, location of incorporation, ownership percentage, or other. After classifying the word tokens, we applied a set of heuristics to organize the data into structured tables. This rules-based model leveraged the position of bounding box coordinates and the order of entity tags assigned by LayoutLM. For example, one heuristic enforces that each row in the extracted table contains only one subsidiary name, location, and ownership fraction, so if two subsidiary names are adjacent in the extracted tokens, then they are placed on different rows of the output table. We validated the final extracted tables against a validation set of 72 manually transcribed tables. The extracted data is structured into the core_sec10k__quarterly_exhibit_21_company_ownership table, and subsequently attributes about the subsidiary and parent companies are merged on in the out_sec10k__parents_and_subsidiaries table.

Assigning subsidiary_company_id_sec10k to Extracted Subsidiary Companies#

To track subsidiaries over time, we assign a unique identifier called subsidiary_company_id_sec10k to each subsidiary extracted from an Exhibit 21 filing and reported in the core_sec10k__quarterly_exhibit_21_company_ownership table. This identifier is constructed from three components: the filer company’s Central Index Key (CIK), the subsidiary company name, and the subsidiary’s location of incorporation.

As a result, the same subsidiary reported by the same filer across multiple years will have a consistent subsidiary_company_id_sec10k. However, if the same subsidiary appears in filings from different parent companies, it will receive a different identifier in each context.

In short, subsidiary_company_id_sec10k tracks subsidiaries consistently within a single filer’s history but should not be used to link the same subsidiary across different filers.

Matching Subsidiary Companies to a Central Index Key#

Some subsidiary companies reported in Ex. 21 attachments also file their own 10-K filing and thus have an implicit central index key. This CIK is not reported in the Ex. 21 attachments, so we connected the subsidiary companies in core_sec10k__quarterly_exhibit_21_company_ownership to the filing companies in core_sec10k__quarterly_company_information.

To do this, we match the subsidiary companies to 10-K filers on company name. If there are multiple matches with the same company name we choose the pair with the most overlap in location of incorporation and then nearest report years. This is a fairly conservative matching process, meaning that many subsidiaries are not matched to their CIK, but there are unlikely to be subsidiaries that are erroneously matched to a CIK. This process produces the core_sec10k__assn_exhibit_21_subsidiaries_and_filers table.

In this table, 2% of unique subsidiary_company_id_sec10k are matched to 19% of the filers.

The fact that on average there are ~3.5 subsidiary_company_id_sec10k mapping to each central_index_key is due to the subsidiary_company_id_sec10k tracking the same company across time, but not across different owners. Multiple subsidiary_company_id_sec10k are assigned to the same subsidiary under different owners. Due to the ambiguities of subsidiary_company_id_sec10k, these percentages point to a couple interpretations:

  • most subsidiary companies don’t file their own 10-K filing (due to their relatively small size), so we have little information about subsidiary companies to work with in general.

  • and/or a relatively high fraction of SEC 10-K filers are subsidiaries of other SEC 10-K filers – 1/5 of all filers are showing up in another company’s Ex. 21.

Matching SEC Filing Companies to EIA Utilities#

The core_sec10k__quarterly_company_information table contains attributes about SEC 10-K filing companies, such as address information, that can be used to connect these companies to the companies that report to EIA using probabilistic record linkage We use a model built with the Python package Splink to connect the core_sec10k__quarterly_company_information to the out_eia__yearly_utilities table. The match between central_index_key and utility_id_eia is one-to-one and is not allowed to change over time. In cases where there were multiple candidate matches, the match with the highest probability is selected. This result of this match can be found in the core_sec10k__assn_sec10k_filers_and_eia_utilities table.

Matching SEC Subsidiary Companies to EIA Utilities#

After constructing the core_sec10k__assn_sec10k_filers_and_eia_utilities table, we take the remaining EIA utilities which have not been matched to an SEC filer and match them to subsidiary companies reported in Ex. 21 attachments. We don’t have all the additional attributes about these subsidiaries that we have about the filers, so we do this match based solely on shared company names. This matches an additional 1703 EIA utilities to Ex. 21 subsidiaries. .04% of subsidiary_company_id_sec10k are matched to an EIA utility in this table.

Assumptions#

Over the course of this process, we make several assumptions about the data:

  • The filer company of an SEC 10-K filing is the parent company of the subsidiary companies listed in that filing’s Ex. 21. Several sets of information about companies may be reported in the header of a 10-K filing, as the filer may report one 10-K with other companies under its umbrella. General Instruction I(2)(b) of Form 10-K seems to back up this assumption that the subsidiaries reported in the Ex. 21 are those of the filing company. Records across many tables can be traced back to a unique filename, so we can see the filing-level information that’s associated with it in the out_sec10k__quarterly_filings table, including the CIK of the filer.

  • When constructing core_sec10k__quarterly_company_information we assume that each block of company information in a 10-K header refers to a different company, and there should not be two different blocks of information about the same company within one 10-K filing. We have to drop 10 blocks of information (.002% of the data) which refer to the same company within one 10-K filing when creating this table. These duplicates have very minor differences (i.e. two blocks are exactly the same except they differ in their film_number attribute) and are interpreted as filing errors.

Future Improvements#

  • Due to limits with memory, record linkage between SEC 10-K filers and EIA utilities was initially only conducted on the most recent year of data. Future work will conduct the match on all years of data.

  • The information extracted from Ex. 21 attachments is structured into tabular form using a set of heuristics. Future work will use a more generalized, robust model to structure this data into tabular form.

  • While fine-tuning LayoutLMv3 doesn’t require a large corpus of labeled documents for training and we got an accuracy score of 95% fine-tuning with 160 labeled documents, labeling more documents may improve the performance of the word token classification model.

  • We didn’t extract ownership data for the years 2018-2022 and plan to run the model to capture these years in the future.