# Analyzing SEC Administrative Proceedings in Python

> Access and analyze structured data of SEC administrative proceedings from 1995 to present, revealing trends in orders, requested reliefs, penalties and more.

Source: https://sec-api.io/docs/sec-administrative-proceedings-database-api/python-example

**On this page:**

- [Quick Start](#Quick-Start)
- [Data Loading](#Data-Loading)
- [Proceedings by Year and Month](#Proceedings-by-Year-and-Month)
  - [Proceedings by Year and Settlement Type](#Proceedings-by-Year-and-Settlement-Type)
  - [Proceedings by Year And Count of Agreements to Pay Penalties](#Proceedings-by-Year-And-Count-of-Agreements-to-Pay-Penalties)
- [Penalty Amount Analysis](#Penalty-Amount-Analysis)
  - [Penalty Amount by Year](#Penalty-Amount-by-Year)
  - [Top 10 Penalty Amounts](#Top-10-Penalty-Amounts)
  - [Penalties by Type of Defendant](#Penalties-by-Type-of-Defendant)
- [Proceedings by Category](#Proceedings-by-Category)
- [Requested Reliefs by Category](#Requested-Reliefs-by-Category)
- [Violated Securities Laws](#Violated-Securities-Laws)

This tutorial provides a step-by-step guide to accessing and analyzing SEC administrative proceedings using Python and the [Administrative Proccedings API](https://sec-api.io/docs/sec-administrative-proceedings-database-api). The dataset for this tutorial contains all SEC administrative proceedings from 1995 to 2024, including parties involved, order types, penalties, and more.

The analysis will cover the following topics:

- Data loading, cleaning, and preprocessing
- Proceedings published per year and month
- Top 10 most common order types
- Penalty amounts per year and respondent type
- And more

## Quick Start

```python
!pip install sec-api
```

```python
# NOTE: Replace with your own API key
API_KEY_SEC_API = "YOUR_API_KEY"
```

```python
from sec_api import SecAdministrativeProceedingsApi

adminProceedingsApi = SecAdministrativeProceedingsApi(API_KEY_SEC_API)

searchRequest = {
    "query": "releasedAt:[2024-01-01 TO 2024-12-31]",
    "from": "0",
    "size": "50",
    "sort": [{"releasedAt": {"order": "desc"}}],
}

response = adminProceedingsApi.get_data(searchRequest)
proceedings = response["data"]

print(f"Loaded first {len(proceedings)} proceedings")
```

```
Loaded first 50 proceedings
```

```python
import pandas as pd

df = pd.DataFrame(proceedings)
df.head()
```

Out[3]:

|  | id | releasedAt | releaseNo | fileNumbers | respondents | respondentsText | resources | title | summary | tags | ... | parallelActionsTakenBy | hasAgreedToSettlement | hasAgreedToPayPenalty | penaltyAmounts | requestedRelief | violatedSections | orders | investigationConductedBy | litigationLedBy | otherAgenciesInvolved |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 0 | 0ab80b58b2fcf40e7497aa0000759a37 | 2024-12-31T12:19:45-05:00 | [34-102060, AAER-4554] | [3-22386] | [{'name': 'Accell Audit & Compliance, PA', 'ty... | Accell Audit & Compliance, PA | [{'label': 'primary', 'url': 'https://www.sec.... | ORDER INSTITUTING PUBLIC ADMINISTRATIVE PROCEE... | The SEC has instituted public administrative p... | [fraudulent financial reporting, accounting mi... | ... | [] | True | True | [{'penaltyAmount': '75000', 'penaltyAmountText... | [] | [Section 10(b) of the Exchange Act, Rule 10b-5] | [Accell is suspended from appearing or practic... | [] | [] | [] |
| 1 | 0dbf2331563447a91e64b78077239115 | 2024-12-30T13:02:35-05:00 | [IA-6806] | [3-22385] | [{'name': 'Steven J. Susoeff', 'type': 'indivi... | Steven J. Susoeff | [{'label': 'primary', 'url': 'https://www.sec.... | ORDER INSTITUTING ADMINISTRATIVE PROCEEDINGS P... | The SEC has instituted administrative proceedi... | [fraudulent cherry-picking, investment adviser... | ... | [] | True | False | [] | [] | [Sections 17(a) of the Securities Act of 1933,... | [Respondent Susoeff is barred from association... | [] | [] | [] |
| 2 | 8ad2aaf148601d33de3460509dd08cec | 2024-12-27T17:12:33-05:00 | [34-102047, AAER-4553] | [3-22384] | [{'name': 'Christopher Hiestand', 'type': 'ind... | Christopher Hiestand, CPA | [{'label': 'primary', 'url': 'https://www.sec.... | ORDER INSTITUTING PUBLIC ADMINISTRATIVE PROCEE... | The SEC has instituted public administrative p... | [fraudulent financial reporting, accounting mi... | ... | [] | True | True | [{'penaltyAmount': '50000', 'penaltyAmountText... | [] | [Section 10(b) of the Exchange Act, Rule 10b-5] | [Hiestand is suspended from appearing or pract... | [] | [] | [] |
| 3 | 804947f56e7146f94b3ce9bf64d8012a | 2024-12-23T14:42:30-05:00 | [34-102031] | [3-22383] | [{'name': 'Richard Brown', 'type': 'individual... | Richard Brown | [{'label': 'primary', 'url': 'https://www.sec.... | ORDER INSTITUTING ADMINISTRATIVE PROCEEDINGS P... | The SEC has instituted administrative proceedi... | [securities fraud, disclosure fraud] | ... | [United States District Court for the Eastern ... | True | False | [] | [disgorgement of profits, civil penalties] | [Section 17(a) of the Securities Act of 1933, ... | [Respondent Brown is barred from association w... | [] | [] | [] |
| 4 | 91a2c34129c53e911759a959be1aa829 | 2024-12-20T17:51:48-05:00 | [33-11349] | [3-22382] | [{'name': 'Tai Mo Shan Limited', 'type': 'comp... | Tai Mo Shan Limited | [{'label': 'primary', 'url': 'https://www.sec.... | ORDER INSTITUTING CEASE-AND-DESIST PROCEEDINGS... | The SEC has instituted cease-and-desist procee... | [unregistered securities, crypto, misleading c... | ... | [] | True | True | [{'penaltyAmount': '73452756', 'penaltyAmountT... | [disgorgement of profits, civil penalties] | [Sections 5(a) and 5(c) of the Securities Act,... | [Respondent Tai Mo Shan cease and desist from ... | [] | [] | [] |

5 rows × 22 columns

```python
print(df.info())
```

```
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 50 entries, 0 to 49
Data columns (total 22 columns):
 #   Column                    Non-Null Count  Dtype
---  ------                    --------------  -----
 0   id                        50 non-null     object
 1   releasedAt                50 non-null     object
 2   releaseNo                 50 non-null     object
 3   fileNumbers               50 non-null     object
 4   respondents               50 non-null     object
 5   respondentsText           50 non-null     object
 6   resources                 50 non-null     object
 7   title                     50 non-null     object
 8   summary                   50 non-null     object
 9   tags                      50 non-null     object
 10  entities                  50 non-null     object
 11  complaints                50 non-null     object
 12  parallelActionsTakenBy    50 non-null     object
 13  hasAgreedToSettlement     50 non-null     bool
 14  hasAgreedToPayPenalty     50 non-null     bool
 15  penaltyAmounts            50 non-null     object
 16  requestedRelief           50 non-null     object
 17  violatedSections          50 non-null     object
 18  orders                    50 non-null     object
 19  investigationConductedBy  50 non-null     object
 20  litigationLedBy           50 non-null     object
 21  otherAgenciesInvolved     50 non-null     object
dtypes: bool(2), object(20)
memory usage: 8.0+ KB
None
```

## Data Loading

```python
import os
import json
import time
import random
import sys
from concurrent.futures import ThreadPoolExecutor
import pandas as pd
from sec_api import SecAdministrativeProceedingsApi

adminProceedingsApi = SecAdministrativeProceedingsApi(API_KEY_SEC_API)

YEARS = range(2024, 1994, -1) # from 2024 to 2004
TEMP_FILE_TEMPLATE = "./temp_file_{}.jsonl"
TARGET_FILE = "./sec-administrative-proceedings.jsonl"

def process_year(year):
    backoff_time = random.randint(10, 800) / 1000
    print(f"Starting year {year} with backoff time {backoff_time:,}s"); sys.stdout.flush()
    time.sleep(backoff_time)

    tmp_filename = TEMP_FILE_TEMPLATE.format(year)
    tmp_file = open(tmp_filename, "a")

    for month in range(12, 0, -1):
        search_from = 0
        month_counter = 0

        while True:
            query = f"releasedAt:[{year}-{month:02d}-01 TO {year}-{month:02d}-31]"
            searchRequest = {
                "query": query,
                "from": search_from,
                "size": "50",
                "sort": [{"releasedAt": {"order": "desc"}}],
            }

            response = None
            try:
                response = adminProceedingsApi.get_data(searchRequest)
            except Exception as e:
                print(f"{year}-{month:02d} error: {e}"); sys.stdout.flush()
                continue

            if response == None or len(response["data"]) == 0:
                break

            search_from += 50
            month_counter += len(response["data"])
            jsonl_data = "\n".join([json.dumps(entry) for entry in response["data"]])
            tmp_file.write(jsonl_data + "\n")

        print(f"Finished loading {month_counter} proceedings for {year}-{month:02d}")
        sys.stdout.flush()

    tmp_file.close()

    return year

if not os.path.exists(TARGET_FILE):
    with ThreadPoolExecutor(max_workers=4) as pool:
        processed_years = list(pool.map(process_year, YEARS))
    print("Finished processing all years.", processed_years)

    # merge the temporary files into one final file
    with open(TARGET_FILE, "a") as outfile:
        for year in YEARS:
            temp_file = TEMP_FILE_TEMPLATE.format(year)
            if os.path.exists(temp_file):
                with open(temp_file, "r") as infile:
                    outfile.write(infile.read())
else:
    print("File already exists. Skipping download.")
```

```
File already exists. Skipping download.
```

```python
# load JSONL into dataframe
df = pd.read_json(TARGET_FILE, lines=True)

# convert "releasedAt" to datetime and to EST timezone
df["releasedAt"] = pd.to_datetime(df["releasedAt"], utc=True)
df["releasedAt"] = df["releasedAt"].dt.tz_convert("US/Eastern")
df["releasedAtYear"] = df["releasedAt"].dt.year
df["releasedAtMonth"] = df["releasedAt"].dt.month
df["releasedAtYearMonth"] = df["releasedAt"].map(lambda x: x.strftime("%Y-%m"))
# Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday
df["releasedAtDay"] = df["releasedAt"].dt.day_name()
df["hasAgreedToSettlement"] = df["hasAgreedToSettlement"].astype(bool)
df["hasAgreedToPayPenalty"] = df["hasAgreedToPayPenalty"].astype(bool)
first_year = df["releasedAtYear"].min(); last_year = df["releasedAtYear"].max()

print(f"Loaded {len(df):,.0f} SEC administrative proceedings from {first_year} to {last_year}\n")
df.head()
```

```
Loaded 17,987 SEC administrative proceedings from 1995 to 2024
```

Out:

|  | id | releasedAt | releaseNo | fileNumbers | respondents | respondentsText | resources | title | summary | tags | ... | requestedRelief | violatedSections | orders | investigationConductedBy | litigationLedBy | otherAgenciesInvolved | releasedAtYear | releasedAtMonth | releasedAtYearMonth | releasedAtDay |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 0 | 0ab80b58b2fcf40e7497aa0000759a37 | 2024-12-31 12:19:45-05:00 | [34-102060, AAER-4554] | [3-22386] | [{'name': 'Accell Audit & Compliance, PA', 'ty... | Accell Audit & Compliance, PA | [{'label': 'primary', 'url': 'https://www.sec.... | ORDER INSTITUTING PUBLIC ADMINISTRATIVE PROCEE... | The SEC has instituted public administrative p... | [fraudulent financial reporting, accounting mi... | ... | [] | [Section 10(b) of the Exchange Act, Rule 10b-5] | [Accell is suspended from appearing or practic... | [] | [] | [] | 2024 | 12 | 2024-12 | Tuesday |
| 1 | 0dbf2331563447a91e64b78077239115 | 2024-12-30 13:02:35-05:00 | [IA-6806] | [3-22385] | [{'name': 'Steven J. Susoeff', 'type': 'indivi... | Steven J. Susoeff | [{'label': 'primary', 'url': 'https://www.sec.... | ORDER INSTITUTING ADMINISTRATIVE PROCEEDINGS P... | The SEC has instituted administrative proceedi... | [fraudulent cherry-picking, investment adviser... | ... | [] | [Sections 17(a) of the Securities Act of 1933,... | [Respondent Susoeff is barred from association... | [] | [] | [] | 2024 | 12 | 2024-12 | Monday |
| 2 | 8ad2aaf148601d33de3460509dd08cec | 2024-12-27 17:12:33-05:00 | [34-102047, AAER-4553] | [3-22384] | [{'name': 'Christopher Hiestand', 'type': 'ind... | Christopher Hiestand, CPA | [{'label': 'primary', 'url': 'https://www.sec.... | ORDER INSTITUTING PUBLIC ADMINISTRATIVE PROCEE... | The SEC has instituted public administrative p... | [fraudulent financial reporting, accounting mi... | ... | [] | [Section 10(b) of the Exchange Act, Rule 10b-5] | [Hiestand is suspended from appearing or pract... | [] | [] | [] | 2024 | 12 | 2024-12 | Friday |
| 3 | 804947f56e7146f94b3ce9bf64d8012a | 2024-12-23 14:42:30-05:00 | [34-102031] | [3-22383] | [{'name': 'Richard Brown', 'type': 'individual... | Richard Brown | [{'label': 'primary', 'url': 'https://www.sec.... | ORDER INSTITUTING ADMINISTRATIVE PROCEEDINGS P... | The SEC has instituted administrative proceedi... | [securities fraud, disclosure fraud] | ... | [disgorgement of profits, civil penalties] | [Section 17(a) of the Securities Act of 1933, ... | [Respondent Brown is barred from association w... | [] | [] | [] | 2024 | 12 | 2024-12 | Monday |
| 4 | 91a2c34129c53e911759a959be1aa829 | 2024-12-20 17:51:48-05:00 | [33-11349] | [3-22382] | [{'name': 'Tai Mo Shan Limited', 'type': 'comp... | Tai Mo Shan Limited | [{'label': 'primary', 'url': 'https://www.sec.... | ORDER INSTITUTING CEASE-AND-DESIST PROCEEDINGS... | The SEC has instituted cease-and-desist procee... | [unregistered securities, crypto, misleading c... | ... | [disgorgement of profits, civil penalties] | [Sections 5(a) and 5(c) of the Securities Act,... | [Respondent Tai Mo Shan cease and desist from ... | [] | [] | [] | 2024 | 12 | 2024-12 | Friday |

5 rows × 26 columns

## Proceedings by Year and Month

```python
import matplotlib.pyplot as plt
import matplotlib.style as style

style.use("default")

params = {
    "axes.labelsize": 8, "font.size": 8, "legend.fontsize": 8,
    "xtick.labelsize": 8, "ytick.labelsize": 8, "font.family": "sans-serif",
    "axes.spines.top": False, "axes.spines.right": False, "grid.color": "grey",
    "axes.grid": True, "axes.grid.axis": "y", "grid.alpha": 0.5, "grid.linestyle": ":",
}

plt.rcParams.update(params)
```

```python
# piv table with years = index, months = columns, values = count of proceedings
unique_proceedings = df.explode('fileNumbers').drop_duplicates(subset=['fileNumbers'], keep='last')

df_year_month = unique_proceedings.pivot_table(
# df_year_month = df.pivot_table(
    index="releasedAtYear",
    columns="releasedAtMonth",
    values="id",
    aggfunc="count",
    fill_value=0,
)

total_col = df_year_month.sum(axis=1)
mean_col = round(df_year_month.mean(axis=1), 0)
median_col = round(df_year_month.median(axis=1), 0)

df_year_month["total"] = total_col
df_year_month["mean"] = mean_col
df_year_month["median"] = median_col

total_row = df_year_month.sum(axis=0)
mean_row = round(df_year_month.mean(axis=0), 0)
median_row = round(df_year_month.median(axis=0), 0)

df_year_month.loc["total"] = total_row
df_year_month.loc["mean"] = mean_row
df_year_month.loc["median"] = median_row

df_year_month = df_year_month.astype(int)

print("SEC Administrative Proceedings by Year and Month")
df_year_month
```

```
SEC Administrative Proceedings by Year and Month
```

Out[8]:

| releasedAtMonth | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | total | mean | median |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| releasedAtYear |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 1995 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 11 | 9 | 7 | 28 | 2 | 0 |
| 1996 | 25 | 27 | 15 | 9 | 14 | 14 | 11 | 8 | 66 | 19 | 8 | 12 | 228 | 19 | 14 |
| 1997 | 17 | 19 | 15 | 17 | 19 | 13 | 10 | 16 | 75 | 6 | 5 | 25 | 237 | 20 | 16 |
| 1998 | 15 | 3 | 14 | 19 | 13 | 20 | 21 | 17 | 56 | 16 | 8 | 14 | 216 | 18 | 16 |
| 1999 | 19 | 15 | 20 | 24 | 27 | 11 | 10 | 49 | 61 | 14 | 15 | 15 | 280 | 23 | 17 |
| 2000 | 4 | 15 | 15 | 19 | 13 | 32 | 9 | 13 | 64 | 12 | 10 | 28 | 234 | 20 | 14 |
| 2001 | 8 | 18 | 16 | 10 | 31 | 28 | 14 | 16 | 39 | 15 | 16 | 18 | 229 | 19 | 16 |
| 2002 | 20 | 19 | 33 | 25 | 13 | 27 | 36 | 23 | 24 | 18 | 30 | 39 | 307 | 26 | 24 |
| 2003 | 23 | 20 | 21 | 20 | 39 | 26 | 30 | 35 | 49 | 33 | 19 | 23 | 338 | 28 | 24 |
| 2004 | 12 | 27 | 31 | 19 | 29 | 24 | 35 | 53 | 56 | 32 | 24 | 35 | 377 | 31 | 30 |
| 2005 | 18 | 30 | 15 | 3 | 1 | 2 | 1 | 2 | 15 | 2 | 2 | 8 | 99 | 8 | 2 |
| 2006 | 3 | 10 | 3 | 2 | 4 | 3 | 5 | 4 | 13 | 0 | 5 | 2 | 54 | 4 | 4 |
| 2007 | 4 | 1 | 1 | 2 | 3 | 3 | 4 | 7 | 12 | 0 | 1 | 2 | 40 | 3 | 2 |
| 2008 | 1 | 7 | 8 | 5 | 3 | 4 | 6 | 4 | 13 | 3 | 1 | 2 | 57 | 5 | 4 |
| 2009 | 7 | 4 | 3 | 14 | 19 | 44 | 36 | 38 | 27 | 33 | 28 | 31 | 284 | 24 | 28 |
| 2010 | 42 | 26 | 45 | 41 | 42 | 34 | 37 | 39 | 57 | 20 | 36 | 28 | 447 | 37 | 38 |
| 2011 | 34 | 62 | 32 | 36 | 47 | 39 | 43 | 29 | 48 | 35 | 36 | 31 | 472 | 39 | 36 |
| 2012 | 40 | 56 | 38 | 34 | 39 | 33 | 35 | 33 | 47 | 28 | 28 | 44 | 455 | 38 | 36 |
| 2013 | 32 | 24 | 38 | 50 | 38 | 18 | 36 | 44 | 97 | 53 | 28 | 33 | 491 | 41 | 37 |
| 2014 | 51 | 51 | 41 | 38 | 36 | 54 | 42 | 48 | 131 | 38 | 58 | 33 | 621 | 52 | 45 |
| 2015 | 35 | 47 | 47 | 55 | 37 | 101 | 45 | 55 | 104 | 53 | 38 | 57 | 674 | 56 | 50 |
| 2016 | 43 | 57 | 48 | 46 | 23 | 57 | 35 | 149 | 85 | 43 | 41 | 53 | 680 | 57 | 47 |
| 2017 | 58 | 42 | 29 | 46 | 45 | 47 | 28 | 52 | 95 | 28 | 18 | 33 | 521 | 43 | 44 |
| 2018 | 23 | 26 | 33 | 41 | 49 | 49 | 46 | 85 | 152 | 15 | 24 | 52 | 595 | 50 | 44 |
| 2019 | 13 | 21 | 105 | 24 | 31 | 33 | 67 | 104 | 158 | 17 | 14 | 20 | 607 | 51 | 28 |
| 2020 | 51 | 35 | 20 | 35 | 31 | 30 | 51 | 44 | 157 | 22 | 14 | 34 | 524 | 44 | 34 |
| 2021 | 14 | 18 | 16 | 17 | 76 | 27 | 59 | 63 | 98 | 13 | 20 | 28 | 449 | 37 | 24 |
| 2022 | 32 | 56 | 19 | 25 | 33 | 41 | 18 | 68 | 142 | 17 | 14 | 16 | 481 | 40 | 28 |
| 2023 | 13 | 12 | 17 | 43 | 71 | 27 | 26 | 57 | 177 | 11 | 14 | 15 | 483 | 40 | 22 |
| 2024 | 17 | 38 | 17 | 25 | 25 | 28 | 3 | 53 | 178 | 57 | 48 | 50 | 539 | 45 | 33 |
| total | 674 | 786 | 755 | 744 | 851 | 869 | 799 | 1208 | 2297 | 664 | 612 | 788 | 11047 | 920 | 757 |
| mean | 22 | 26 | 25 | 25 | 28 | 29 | 27 | 40 | 77 | 22 | 20 | 26 | 368 | 31 | 25 |
| median | 18 | 22 | 20 | 24 | 30 | 28 | 29 | 38 | 62 | 18 | 17 | 28 | 412 | 34 | 26 |

```python
fig, ax = plt.subplots(figsize=(5, 3))

df_year_month.loc[1996:2024]["total"].plot(
    kind="line",
    ax=ax,
    marker="o",
    markersize=3,
    linewidth=1,
)
ax.set_title("Administrative Proceedings with \nUnique File Numbers per Year")
ax.set_xlabel("Year")
ax.set_ylabel("Number of Proceedings")
plt.tight_layout()
plt.grid(axis="x")
ax.set_axisbelow(True)
plt.show()
```

### Proceedings by Year and Settlement Type

```python
# piv table for hasAgreedToSettlement
# col = hasAgreedToSettlement (true, false), index = year, values = count of cases
df_year_settlement = unique_proceedings.pivot_table(
    index="releasedAtYear",
    columns="hasAgreedToSettlement",
    values="id",
    aggfunc="count",
    fill_value=0,
)

total_col = df_year_settlement.sum(axis=1)
mean_col = round(df_year_settlement.mean(axis=1), 0)
median_col = round(df_year_settlement.median(axis=1), 0)

total_row = df_year_settlement.sum(axis=0)
mean_row = round(df_year_settlement.mean(axis=0), 0)
median_row = round(df_year_settlement.median(axis=0), 0)

df_year_settlement.loc["total"] = total_row
df_year_settlement.loc["mean"] = mean_row
df_year_settlement.loc["median"] = median_row

df_year_settlement = df_year_settlement.astype(int)

df_year_settlement
```

Out[10]:

| hasAgreedToSettlement | False | True |
| --- | --- | --- |
| releasedAtYear |  |  |
| 1995 | 3 | 25 |
| 1996 | 56 | 172 |
| 1997 | 46 | 191 |
| 1998 | 45 | 171 |
| 1999 | 71 | 209 |
| 2000 | 41 | 193 |
| 2001 | 34 | 195 |
| 2002 | 56 | 251 |
| 2003 | 69 | 269 |
| 2004 | 95 | 282 |
| 2005 | 25 | 74 |
| 2006 | 13 | 41 |
| 2007 | 6 | 34 |
| 2008 | 13 | 44 |
| 2009 | 120 | 164 |
| 2010 | 192 | 255 |
| 2011 | 207 | 265 |
| 2012 | 219 | 236 |
| 2013 | 212 | 279 |
| 2014 | 245 | 376 |
| 2015 | 287 | 387 |
| 2016 | 201 | 479 |
| 2017 | 170 | 351 |
| 2018 | 174 | 421 |
| 2019 | 176 | 431 |
| 2020 | 178 | 346 |
| 2021 | 168 | 281 |
| 2022 | 209 | 272 |
| 2023 | 151 | 332 |
| 2024 | 131 | 408 |
| total | 3613 | 7434 |
| mean | 120 | 248 |
| median | 126 | 260 |

```python
# stacked bar chart for hasAgreedToSettlement
fig, ax = plt.subplots(figsize=(6, 4))

df_year_settlement.drop(["total", "mean", "median"]).plot(
    kind="bar", stacked=False, ax=ax, color=["#1f77b4", "#ff7f0e"]
)

plt.title("SEC Administrative Proceedings by Year and Settlement")
plt.xlabel("Year")
plt.ylabel("Count")
plt.legend(["No Settlement", "Settlement"], loc="upper right")
plt.grid(axis="x")
ax.set_axisbelow(True)
plt.tight_layout()
plt.show()
```

### Proceedings by Year And Count of Agreements to Pay Penalties

```python
# piv table for hasAgreedToPayPenalty
# col = hasAgreedToPayPenalty (true, false), index = year, values = count of cases
df_year_penalty = unique_proceedings.pivot_table(
    index="releasedAtYear",
    columns="hasAgreedToPayPenalty",
    values="id",
    aggfunc="count",
    fill_value=0,
)

total_col = df_year_penalty.sum(axis=1)
mean_col = round(df_year_penalty.mean(axis=1), 0)
median_col = round(df_year_penalty.median(axis=1), 0)

total_row = df_year_penalty.sum(axis=0)
mean_row = round(df_year_penalty.mean(axis=0), 0)
median_row = round(df_year_penalty.median(axis=0), 0)

df_year_penalty.loc["total"] = total_row
df_year_penalty.loc["mean"] = mean_row
df_year_penalty.loc["median"] = median_row

df_year_penalty = df_year_penalty.astype(int)

df_year_penalty
```

Out[12]:

| hasAgreedToPayPenalty | False | True |
| --- | --- | --- |
| releasedAtYear |  |  |
| 1995 | 22 | 6 |
| 1996 | 179 | 49 |
| 1997 | 184 | 53 |
| 1998 | 158 | 58 |
| 1999 | 212 | 68 |
| 2000 | 167 | 67 |
| 2001 | 170 | 59 |
| 2002 | 238 | 69 |
| 2003 | 270 | 68 |
| 2004 | 263 | 114 |
| 2005 | 63 | 36 |
| 2006 | 35 | 19 |
| 2007 | 24 | 16 |
| 2008 | 37 | 20 |
| 2009 | 210 | 74 |
| 2010 | 355 | 92 |
| 2011 | 374 | 98 |
| 2012 | 375 | 80 |
| 2013 | 369 | 122 |
| 2014 | 396 | 225 |
| 2015 | 427 | 247 |
| 2016 | 420 | 260 |
| 2017 | 313 | 208 |
| 2018 | 326 | 269 |
| 2019 | 421 | 186 |
| 2020 | 309 | 215 |
| 2021 | 264 | 185 |
| 2022 | 313 | 168 |
| 2023 | 249 | 234 |
| 2024 | 223 | 316 |
| total | 7366 | 3681 |
| mean | 246 | 123 |
| median | 256 | 86 |

```python
# stacked bar chart for hasAgreedToSettlement
fig, ax = plt.subplots(figsize=(6, 3.5))

df_year_penalty.drop(["total", "mean", "median"]).plot(
    kind="bar", stacked=False, ax=ax, color=["#1f77b4", "#ff7f0e"]
)

plt.title("SEC Administrative Proceedings by Year and Penalty")
plt.xlabel("Year")
plt.ylabel("Count")
plt.legend(["Did not agree to penalty", "Agreed to penalty"], loc="upper left")
plt.grid(axis="x")
ax.set_axisbelow(True)
plt.tight_layout()
plt.show()
```

## Penalty Amount Analysis

```python
all_penalties = []
file_numbers = {}

# iterate over all rows, extract penalties and append to all_penalties
for i, row in unique_proceedings.iterrows():
    penaltyAmounts = row["penaltyAmounts"]

    if isinstance(penaltyAmounts, list):
        for penalty in penaltyAmounts:
            if "penaltyAmount" in penalty:
                # find entity with "name" == "imposedOn"
                entity = list(filter(lambda x: x["name"] == penalty["imposedOn"], row["entities"]))
                entity_type = entity[0]["type"] if entity else None

                all_penalties.append(
                    {
                        # "caseCitation": row['caseCitation'],
                        "fileNumber": row["fileNumbers"],
                        "releaseNo": row["releaseNo"],
                        "releasedAt": row["releasedAt"],
                        "releasedAtYear": row["releasedAtYear"],
                        "releasedAtMonth": row["releasedAtMonth"],
                        "amount": penalty["penaltyAmount"],
                        "imposedOn": penalty["imposedOn"],
                        "imposedOnType": entity_type,
                        # "url": row["url"],
                    }
                )

all_penalties_df = pd.DataFrame(all_penalties)
all_penalties_df["amount"] = all_penalties_df["amount"].astype(float)

# remove all doubles by caseCitation and imposedOn
all_penalties_df = all_penalties_df.drop_duplicates(
    subset=["fileNumber", "imposedOn", "amount"], keep="last"
)

all_penalties_df
```

Out[14]:

|  | fileNumber | releaseNo | releasedAt | releasedAtYear | releasedAtMonth | amount | imposedOn | imposedOnType |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 0 | 3-22386 | [34-102060, AAER-4554] | 2024-12-31 12:19:45-05:00 | 2024 | 12 | 75000.00 | Accell Audit & Compliance, PA | company |
| 1 | 3-22384 | [34-102047, AAER-4553] | 2024-12-27 17:12:33-05:00 | 2024 | 12 | 50000.00 | Christopher Hiestand | individual |
| 2 | 3-22382 | [33-11349] | 2024-12-20 17:51:48-05:00 | 2024 | 12 | 73452756.00 | Tai Mo Shan Limited | company |
| 3 | 3-22382 | [33-11349] | 2024-12-20 17:51:48-05:00 | 2024 | 12 | 12916153.00 | Tai Mo Shan Limited | company |
| 4 | 3-22382 | [33-11349] | 2024-12-20 17:51:48-05:00 | 2024 | 12 | 36726378.00 | Tai Mo Shan Limited | company |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 5933 | 3-8865 | [34-36338] | 1995-10-05 09:11:20-04:00 | 1995 | 10 | 10000.00 | John Laurienti | individual |
| 5934 | 3-8865 | [34-36338] | 1995-10-05 09:11:20-04:00 | 1995 | 10 | 50000.00 | Dickinson & Co. | company |
| 5935 | 3-8864 | [34-36334] | 1995-10-04 09:11:20-04:00 | 1995 | 10 | 11402.50 | Lewco Securities Corporation | company |
| 5936 | 3-8863 | [34-36333] | 1995-10-04 09:11:20-04:00 | 1995 | 10 | 107000.00 | French American Banking Corporation | company |
| 5937 | 3-8863 | [34-36333] | 1995-10-04 09:11:20-04:00 | 1995 | 10 | 15918.84 | French American Banking Corporation | company |

5909 rows × 8 columns

### Penalty Amount by Year

```python
# aggregate amount by year
penalties_year = all_penalties_df.groupby("releasedAtYear")["amount"].sum()
penalties_year = penalties_year.astype(int)
penalties_year = pd.DataFrame(penalties_year)
penalties_year["amount"] = round(penalties_year["amount"] / 1_000_000, 2)
print("Total Penalties in Million USD by Year")
penalties_year
```

```
Total Penalties in Million USD by Year
```

Out[15]:

|  | amount |
| --- | --- |
| releasedAtYear |  |
| 1995 | 5.22 |
| 1996 | 30.19 |
| 1997 | 20.13 |
| 1998 | 20.49 |
| 1999 | 54.28 |
| 2000 | 131.97 |
| 2001 | 635.92 |
| 2002 | 60.40 |
| 2003 | 431.53 |
| 2004 | 1602.46 |
| 2005 | 752.19 |
| 2006 | 317.54 |
| 2007 | 31.34 |
| 2008 | 681.46 |
| 2009 | 243.64 |
| 2010 | 1566.58 |
| 2011 | 1011.27 |
| 2012 | 319.56 |
| 2013 | 889.66 |
| 2014 | 4464.57 |
| 2015 | 1254.10 |
| 2016 | 1787.28 |
| 2017 | 887.40 |
| 2018 | 1845.09 |
| 2019 | 944.43 |
| 2020 | 3181.67 |
| 2021 | 792.16 |
| 2022 | 1557.67 |
| 2023 | 1478.46 |
| 2024 | 2158.77 |

```python
fig, ax = plt.subplots(figsize=(5, 3))

penalties_year["amount"].plot(
    kind="line",
    ax=ax,
    marker="o",
    markersize=3,
    linewidth=1,
)

ax.get_yaxis().set_major_formatter(plt.FuncFormatter(lambda x, loc: "{:,}".format(int(x))))
ax.set_title("Penalties per Year")
ax.set_xlabel("Year")
ax.set_ylabel("Penalty Amount\nin Million USD")
plt.tight_layout()
plt.grid(axis="x")
ax.set_axisbelow(True)
plt.show()
```

### Top 10 Penalty Amounts

```python
# sort all penalties by amount, show top 10
top_10_penalties = all_penalties_df.sort_values("amount", ascending=False).head(10)
top_10_penalties["amount"] = round(top_10_penalties["amount"] / 1_000_000, 2)
top_10_penalties["amount"] = top_10_penalties["amount"].map("{:,.1f}".format)
print("Top 10 SEC Penalties in Million USD between 1995 and 2024")
top_10_penalties[['amount', 'imposedOn', 'releasedAt', 'releaseNo']]
```

```
Top 10 SEC Penalties in Million USD between 1995 and 2024
```

Out[17]:

|  | amount | imposedOn | releasedAt | releaseNo |
| --- | --- | --- | --- | --- |
| 3808 | 900.0 | S.A.C. Capital Advisors, LLC | 2014-06-27 09:21:21-04:00 | [IA-3864] |
| 3809 | 900.0 | CR Intrinsic Investors, LLC | 2014-06-27 09:21:21-04:00 | [IA-3864] |
| 3810 | 900.0 | Sigma Capital Management, LLC | 2014-06-27 09:21:21-04:00 | [IA-3864] |
| 3807 | 900.0 | S.A.C. Capital Advisors, L.P. | 2014-06-27 09:21:21-04:00 | [IA-3864] |
| 2027 | 853.2 | Petróleo Brasileiro S.A. – Petrobras | 2018-09-27 09:24:56-04:00 | [33-10561, 34-84205, AAER-3989] |
| 944 | 675.0 | Allianz Global Investors U.S. LLC | 2022-05-17 09:28:19-04:00 | [34-94927, IA-6027] |
| 1345 | 606.3 | The Goldman Sachs Group, Inc. | 2020-10-22 09:26:36-04:00 | [34-90243, AAER-4191] |
| 5341 | 606.0 | Republic New York Securities Corp. | 2001-12-17 08:12:59-05:00 | [34-45157] |
| 4329 | 583.5 | Barry J. Minkow | 2011-11-22 08:18:30-05:00 | [IA-3320] |
| 1449 | 556.7 | Herbalife Nutrition Ltd. | 2020-08-28 09:26:48-04:00 | [34-89704, AAER-4165] |

### Penalties by Type of Defendant

```python
# create piv table across years and imposedOnType, with values = sum of penalties
penalties_entity_type = all_penalties_df.pivot_table(
    index="releasedAtYear",
    columns="imposedOnType",
    values="amount",
    aggfunc="sum",
    fill_value=0,
)

total_col = penalties_entity_type.sum(axis=1)
mean_col = round(penalties_entity_type.mean(axis=1), 0)
median_col = round(penalties_entity_type.median(axis=1), 0)

total_row = penalties_entity_type.sum(axis=0)
mean_row = round(penalties_entity_type.mean(axis=0), 0)
median_row = round(penalties_entity_type.median(axis=0), 0)

penalties_entity_type.loc["total"] = total_row
penalties_entity_type.loc["mean"] = mean_row
penalties_entity_type.loc["median"] = median_row

# format to million and , notation
penalties_entity_type = round(penalties_entity_type / 1_000_000, 2)
penalties_entity_type = penalties_entity_type.map("{:,.1f}".format)

print("SEC Penalties in Million USD by Year and Entity Type")

penalties_entity_type = penalties_entity_type[['company', 'individual']]
penalties_entity_type
```

```
SEC Penalties in Million USD by Year and Entity Type
```

Out[18]:

| imposedOnType | company | individual |
| --- | --- | --- |
| releasedAtYear |  |  |
| 1995 | 5.2 | 0.0 |
| 1996 | 21.2 | 8.9 |
| 1997 | 11.6 | 5.8 |
| 1998 | 13.4 | 3.3 |
| 1999 | 51.3 | 1.9 |
| 2000 | 119.7 | 12.2 |
| 2001 | 607.9 | 28.0 |
| 2002 | 13.4 | 46.9 |
| 2003 | 375.4 | 56.0 |
| 2004 | 1,002.4 | 206.5 |
| 2005 | 231.4 | 5.6 |
| 2006 | 297.7 | 2.6 |
| 2007 | 28.3 | 3.1 |
| 2008 | 520.7 | 101.1 |
| 2009 | 106.0 | 12.2 |
| 2010 | 1,120.7 | 39.2 |
| 2011 | 374.0 | 606.1 |
| 2012 | 161.9 | 141.3 |
| 2013 | 741.8 | 145.2 |
| 2014 | 4,128.8 | 51.9 |
| 2015 | 913.4 | 83.5 |
| 2016 | 1,713.4 | 39.7 |
| 2017 | 790.6 | 38.1 |
| 2018 | 1,705.0 | 46.1 |
| 2019 | 802.8 | 78.2 |
| 2020 | 3,121.8 | 23.0 |
| 2021 | 764.8 | 23.8 |
| 2022 | 1,333.2 | 19.0 |
| 2023 | 1,041.8 | 18.5 |
| 2024 | 1,904.9 | 29.7 |
| total | 24,024.4 | 1,877.4 |
| mean | 800.8 | 62.6 |
| median | 564.3 | 28.8 |

```python
fig, ax = plt.subplots(figsize=(5, 3))

data_to_plot = penalties_entity_type.loc[1995:2024].map(
    lambda x: float(x.replace(",", ""))
)

data_to_plot.plot(kind="bar", stacked=False, ax=ax, color=["#1f77b4", "#ff7f0e"])

plt.title("Proceedings Penalties by Year and Entity Type")
plt.xlabel("Year")
plt.ylabel("Penalty Amount\nin Million USD")
plt.legend(["Company", "Individual"], loc="upper right")
plt.grid(axis="x")
ax.set_axisbelow(True)
plt.tight_layout()
plt.show()
```

## Proceedings by Category

```python
all_tags = []

for i, row in unique_proceedings.iterrows():
    tags = row["tags"]
    if isinstance(tags, list):
      all_tags.extend(tags)

all_tags = pd.Series(all_tags)
all_tags = all_tags.value_counts().reset_index()
all_tags.columns = ["tag", "count"]

print("Top 10 Tags in SEC Administrative Proceedings")
all_tags.head(10)
```

```
Top 10 Tags in SEC Administrative Proceedings
```

Out[20]:

|  | tag | count |
| --- | --- | --- |
| 0 | securities fraud | 2115 |
| 1 | delinquent filings | 1676 |
| 2 | disclosure fraud | 1336 |
| 3 | fraud | 894 |
| 4 | accounting fraud | 743 |
| 5 | unregistered securities | 634 |
| 6 | securities violation | 532 |
| 7 | investment adviser | 499 |
| 8 | insider trading | 464 |
| 9 | misrepresentation | 395 |

## Requested Reliefs by Category

```python
# count all unique requestedRelief
all_requested_relief = []

for i, row in unique_proceedings.iterrows():
    requestedRelief = row["requestedRelief"]
    if isinstance(requestedRelief, list):
        all_requested_relief.extend(requestedRelief)

all_requested_relief = pd.DataFrame(all_requested_relief, columns=["requestedRelief"])

all_requested_relief["requestedRelief"] = (
    all_requested_relief["requestedRelief"]
    .str.replace("cease-and-desist order", "cease and desist order")
    .replace("permanent injunctions", "permanent injunction")
)

all_requested_relief = all_requested_relief.value_counts().reset_index()

print("Top 10 Requested Reliefs in SEC Administrative Proceedings")
all_requested_relief.head(10)
```

```
Top 10 Requested Reliefs in SEC Administrative Proceedings
```

Out[21]:

|  | requestedRelief | count |
| --- | --- | --- |
| 0 | civil penalties | 2591 |
| 1 | disgorgement of profits | 2045 |
| 2 | cease and desist order | 1709 |
| 3 | permanent injunction | 913 |
| 4 | suspend or revoke the registration of each cla... | 635 |
| 5 | censure | 466 |
| 6 | civil money penalty | 378 |
| 7 | suspend for a period not exceeding twelve months | 287 |
| 8 | revoke the registration of each class of secur... | 285 |
| 9 | disgorgement | 234 |

## Violated Securities Laws

```python
# count all unique violatedSections
all_violated_sections = []

for i, row in unique_proceedings.iterrows():
    violatedSections = row["violatedSections"]
    if isinstance(violatedSections, list):
        all_violated_sections.extend(violatedSections)

all_violated_sections = pd.Series(all_violated_sections)
all_violated_sections = all_violated_sections.value_counts().reset_index()

all_violated_sections.columns = ["violatedSections", "count"]

print("Top 10 Violated Securities Laws in SEC Administrative Proceedings")
all_violated_sections.head(10)
```

```
Top 10 Violated Securities Laws in SEC Administrative Proceedings
```

Out[22]:

|  | violatedSections | count |
| --- | --- | --- |
| 0 | Rule 10b-5 | 2547 |
| 1 | Exchange Act Section 13(a) | 1701 |
| 2 | Section 10(b) of the Exchange Act | 1445 |
| 3 | Rules 13a-1 and 13a-13 | 1251 |
| 4 | Section 17(a) of the Securities Act of 1933 | 1137 |
| 5 | Section 10(b) of the Securities Exchange Act o... | 699 |
| 6 | Section 17(a) of the Securities Act | 570 |
| 7 | Rules 13a-1 | 528 |
| 8 | Section 13(a) of the Exchange Act | 492 |
| 9 | Rules 13a-13 | 472 |