smjs_filing_types_info.txt

(extract from https://github.com/aws/sagemaker-jumpstart-industry-pack/blob/main/docs/source/notebooks/finance/notebook1/SEC_Retrieval_Summarizer_Scoring.rst) 

SEC Forms 10-K/10-Q
~~~~~~~~~~~~~~~~~~~

10-K/10-Q forms are quarterly reports required to be filed by companies.
They contain full disclosure of business conditions for the company and
also require forward-looking statements of future prospects, usually
written into a section known as the “Management Discussion & Analysis”
section. There also can be a section called “Forward-Looking
Statements”. For more information, see `Form
10-K <https://www.investor.gov/introduction-investing/investing-basics/glossary/form-10-k>`__
in the *Investor.gov webpage*.

Each year firms file three 10-Q forms (quarterly reports) and one 10-K
(annual report). Thus, there are in total four reports each year. The
structure of the forms is displayed in a table of contents.

The SEC filing retrieval supports the downloading and parsing of 10-K,
10-Q, 8-K, 497, 497K, S-3ASR and N-1A, seven form types for the tickers
or CIKs specified by the user. The following block of code will download
full text of the forms and convert it into a dataframe format using a
SageMaker session. The code is self-explanatory, and offers customized
options to the users.

**Technical notes**:

1. The data loader accesses a container to process the request. There
   might be some latency when starting up the container, which accounts
   for a few initial minutes. The actual filings extraction occurs after
   this.
2. The data loader only supports processing jobs with only one instance
   at the moment.
3. Users are not charged for the waiting time used when the instance is
   initializing (this takes 3-5 minutes).
4. The name of the processing job is shown in the run time log.
5. You can also access the processing job from the `SageMaker
   console <https://console.aws.amazon.com/sagemaker>`__. On the left
   navigation pane, choose Processing, Processing job.

Users may update any of the settings in the ``data_loader`` section of
the code block below, and in the ``dataset_config`` section. For a very
long list of tickers or CIKs, the job will run for a while, and the
``...`` stream will indicate activity as it proceeds.

| **NOTE**: We recommend that you use CIKs as the input. The tickers are
  internally converted to CIKs according to this `mapping
  file <https://www.sec.gov/include/ticker.txt>`__.
| One ticker can map to multiple CIKs, but this solution supports only
  the latest ticker to CIK mapping. Make sure to provide the old CIKs in
  the input when you want historical filings.

The following code block shows how to use the SEC Retriever API. You
specify system resources (or just choose the defaults below). Also
specify the tickers needed, the SEC forms needed, the date range, and
the location and name of the file in S3 where the curated data file will
be stored in CSV format. The output will shows the runtime log from the
SageMaker processing container and indicates when it is completed.

   **Important**: This example notebook uses data obtained from the SEC
   EDGAR database. You are responsible for complying with EDGAR’s access
   terms and conditions located in the `Accessing EDGAR
   Data <https://www.sec.gov/os/accessing-edgar-data>`__ page.

.. code:: ipython3

    %%time

    dataset_config = EDGARDataSetConfig(
        tickers_or_ciks=['amzn','goog', '27904', 'FB'],  # list of stock tickers or CIKs
        form_types=['10-K', '10-Q'],                     # list of SEC form types
        filing_date_start='2019-01-01',                  # starting filing date
        filing_date_end='2020-12-31',                    # ending filing date
        email_as_user_agent='test-user@test.com')        # user agent email

    data_loader = DataLoader(
        role=sagemaker.get_execution_role(),    # loading job execution role
        instance_count=1,                       # instances number, limit varies with instance type
        instance_type='ml.c5.2xlarge',          # instance type
        volume_size_in_gb=30,                   # size in GB of the EBS volume to use
        volume_kms_key=None,                    # KMS key for the processing volume
        output_kms_key=None,                    # KMS key ID for processing job outputs
        max_runtime_in_seconds=None,            # timeout in seconds. Default is 24 hours.
        sagemaker_session=sagemaker.Session(),  # session object
        tags=None)                              # a list of key-value pairs

    data_loader.load(
        dataset_config,
        's3://{}/{}/{}'.format(bucket, sec_processed_folder, 'output'),     # output s3 prefix (both bucket and folder names are required)
        'dataset_10k_10q.csv',                                              # output file name
        wait=True,
        logs=True)

Output
^^^^^^

The output of the DataLoader processing job is a dataframe. This job
includes 32 filings (4 companies for 8 quarters). The CSV file is
downloaded from S3 and then read into a dataframe, as shown in the
following few code blocks.

The filing date comes within a month of the end date of the reporting
period. The filing date is displayed in the dataframe. The column
``"text"`` contains the full plain text of the filing but the tables are
not extracted. The values in the tables in the filings are balance-sheet
and income-statement data (numeric and tabular) and are easily available
elsewhere as they are reported in numeric databases. The last column
(``"mdna"``) of the dataframe comprises the Management Discussion &
Analysis section, which is also included in the ``"text"`` column.

.. code:: ipython3

    client = boto3.client('s3')
    client.download_file(bucket, '{}/{}/{}'.format(sec_processed_folder, 'output', 'dataset_10k_10q.csv'), 'dataset_10k_10q.csv')
    data_frame_10k_10q = pd.read_csv('dataset_10k_10q.csv')
    data_frame_10k_10q

As an example of a clean parse, print out the text of the first filing.

.. code:: ipython3

    print(data_frame_10k_10q.text[0])

To read the MD&A section, use the following code to print out the
section for the second filing in the dataframe.

.. code:: ipython3

    print(data_frame_10k_10q.mdna[1])

SEC Form 8-K
~~~~~~~~~~~~

This form is filed for material changes in business conditions. This
`Form 8-K
page <https://www.sec.gov/fast-answers/answersform8khtm.html>`__
describes the form requirements and various conditions for publishing a
8-K filing. Because there is no set cadence to these filings, several
8-K forms might be filed within a year, depending on how often a company
experiences material changes in business conditions.

The API call below is the same as for the 10-K forms; simply change the
form type ``8-K`` to ``10-K``.

.. code:: ipython3

    %%time

    dataset_config = EDGARDataSetConfig(
        tickers_or_ciks=['amzn','goog', '27904', 'FB'],  # list of stock tickers or CIKs
        form_types=['8-K'],                              # list of SEC form types
        filing_date_start='2019-01-01',                  # starting filing date
        filing_date_end='2020-12-31',                    # ending filing date
        email_as_user_agent='test-user@test.com')        # user agent email

    data_loader = DataLoader(
        role=sagemaker.get_execution_role(),    # loading job execution role
        instance_count=1,                       # instances number, limit varies with instance type
        instance_type='ml.c5.2xlarge',          # instance type
        volume_size_in_gb=30,                   # size in GB of the EBS volume to use
        volume_kms_key=None,                    # KMS key for the processing volume
        output_kms_key=None,                    # KMS key ID for processing job outputs
        max_runtime_in_seconds=None,            # timeout in seconds. Default is 24 hours.
        sagemaker_session=sagemaker.Session(),  # session object
        tags=None)                              # a list of key-value pairs

    data_loader.load(
        dataset_config,
        's3://{}/{}/{}'.format(bucket, sec_processed_folder, 'output'),    # output s3 prefix (both bucket and folder names are required)
        'dataset_8k.csv',                                                  # output file name
        wait=True,
        logs=True)

.. code:: ipython3

    client = boto3.client('s3')
    client.download_file(bucket, '{}/{}/{}'.format(sec_processed_folder, 'output', 'dataset_8k.csv'), 'dataset_8k.csv')
    data_frame_8k = pd.read_csv('dataset_8k.csv')
    data_frame_8k

As noted, 8-K forms do not have a fixed cadence, and they depend on the
number of times a company changes the material. Therefore, the number of
forms varies over time.

Next, print the plain text of the first 8-K form in the dataframe.

.. code:: ipython3

    print(data_frame_8k.text[0])

Other SEC Forms
~~~~~~~~~~~~~~~

We also support SEC forms 497, 497K, S-3ASR, N-1A, 485BXT, 485BPOS,
485APOS, S-3, S-3/A, DEF 14A, SC 13D and SC 13D/A.

SEC Form 497
^^^^^^^^^^^^

Mutual funds are required to file Form 497 to disclose any information
that is material for investors. Funds file their prospectuses using this
form as well as proxy statements. The form is also used for Statements
of Additional Information (SAI). The forward-looking information in Form
497 comprises the detailed company history, financial statements, a
description of products and services, an annual review of the
organization, its operations, and the markets in which the company
operates. Much of this data is usually audited so is of high quality.
For more information, see `SEC Form
497 <https://www.investopedia.com/terms/s/sec-form-497.asp>`__.

SEC Form 497K
^^^^^^^^^^^^^

This is a summary prospectus. It describes the fees and expenses of the
fund, its principal investment strategies, principal risks, past
performance if any, and some administrative information. Many such forms
are filed for example, in Q4 of 2020 a total of 5,848 forms of type 497K
were filed.

SEC Form S-3ASR
^^^^^^^^^^^^^^^

The S-3ASR is an automatic shelf registration statement which is
immediately effective upon filing for use by well-known seasoned issuers
to register unspecified amounts of different specified types of
securities. This Registration Statement is for the registration of
securities under the Securities Act of 1933.

SEC Form N-1A
^^^^^^^^^^^^^

This registration form is required for establishing open-end management
companies. The form can be used for registering both open-end mutual
funds and open-end exchange traded funds (ETFs). For more information,
see `SEC Form
N-1A <https://www.investopedia.com/terms/s/sec-form-n-1a.asp>`__.