Analysis of Accounting and Auditing Enforcement Releases

   

On this page:

• Data Loading
• AAER Releases by Year, Month, Day of Week and Time of Day
• Penalty Amounts over Time
• AAERs by Type
• Violated Securities Laws

This guide provides a comprehensive analysis of the Accounting and Auditing Enforcement Releases (AAERs) issued by the U.S. Securities and Exchange Commission (SEC). The analysis spans from the year 1997 to 2025 and covers various aspects of AAERs, including the frequency of releases, the types of violations, and the penalties imposed.

import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.style as style
import json

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)

The data is fetched using the sec-api library, which provides access to the SEC's AAER data via the AAER Database API. The data is then processed and structured into a pandas DataFrame for further analysis. Various attributes of the AAERs, such as the date of release, the types of violations, and the penalties imposed, are extracted and transformed for analysis.

!pip install sec-api

from sec_api import AaerApi

aaerApi = AaerApi("YOUR_API_KEY")

YEARS = range(2025, 1996, -1)  # from 2025 to 1997
TARGET_FILE = "./data/aaer-structured-data.jsonl"

if not os.path.exists(TARGET_FILE):
    for year in YEARS:
        done = False
        search_from = 0
        year_counter = 0

        # fetch all AAERs for the given year
        while not done:
            searchRequest = {
                "query": f"dateTime:[{year}-01-01 TO {year}-12-31]",
                "from": search_from,
                "size": "50",
                "sort": [{"dateTime": {"order": "desc"}}],
            }

            response = aaerApi.get_data(searchRequest)

            if len(response["data"]) == 0:
                break

            search_from += 50
            year_counter += len(response["data"])

            with open(TARGET_FILE, "a") as f:
                for entry in response["data"]:
                    f.write(json.dumps(entry) + "\n")

        print(f"Finished loading {year_counter} AAERs for year {year}")
else:
    print("File already exists, skipping download")

Finished loading 8 AAERs for year 2025
Finished loading 65 AAERs for year 2024
Finished loading 111 AAERs for year 2023
Finished loading 86 AAERs for year 2022
Finished loading 77 AAERs for year 2021
Finished loading 88 AAERs for year 2020
Finished loading 97 AAERs for year 2019
Finished loading 95 AAERs for year 2018
Finished loading 75 AAERs for year 2017
Finished loading 108 AAERs for year 2016
Finished loading 111 AAERs for year 2015
Finished loading 92 AAERs for year 2014
Finished loading 87 AAERs for year 2013
Finished loading 85 AAERs for year 2012
Finished loading 127 AAERs for year 2011
Finished loading 131 AAERs for year 2010
Finished loading 179 AAERs for year 2009
Finished loading 149 AAERs for year 2008
Finished loading 228 AAERs for year 2007
Finished loading 170 AAERs for year 2006
Finished loading 194 AAERs for year 2005
Finished loading 213 AAERs for year 2004
Finished loading 219 AAERs for year 2003
Finished loading 208 AAERs for year 2002
Finished loading 121 AAERs for year 2001
Finished loading 142 AAERs for year 2000
Finished loading 22 AAERs for year 1999
Finished loading 0 AAERs for year 1998
Finished loading 5 AAERs for year 1997

aaers = pd.read_json(TARGET_FILE, lines=True)

# convert data types
bool_cols = ["hasAgreedToSettlement", "hasAgreedToPayPenalty"]
aaers[bool_cols] = aaers[bool_cols].astype(bool)
aaers["dateTime"] = pd.to_datetime(aaers["dateTime"], utc=True)
aaers["dateTime"] = aaers["dateTime"].dt.tz_convert("US/Eastern")
aaers["dateTimeYear"] = aaers["dateTime"].dt.year
aaers["dateTimeMonth"] = aaers["dateTime"].dt.month
aaers["dateTimeYearMonth"] = aaers["dateTime"].dt.to_period("M")
# Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday
aaers["dateTimeDay"] = aaers["dateTime"].dt.day_name()
# dateTimeClass: preMarket (4:00 AM to 9:30 AM), regularMarket (9:30 AM to 4:00 PM), afterMarket (4:00 PM to 8:00 PM), postMarket (8:00 PM to 10:00 PM)
aaers["dateTimeClass"] = aaers["dateTime"].apply(
    lambda x: (
        "preMarket"
        if x.hour < 9 or (x.hour == 9 and x.minute < 30)
        else (
            "regularMarket"
            if x.hour < 16
            else "afterMarket" if x.hour < 20 else "postMarket"
        )
    )
)

print(f"Loaded {len(aaers)} AAERs in total for {YEARS[-1]} to {YEARS[0]}")
print(aaers.info())

Loaded 3293 AAERs in total for 1997 to 2025
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3293 entries, 0 to 3292
Data columns (total 23 columns):
 #   Column                  Non-Null Count  Dtype                     
---  ------                  --------------  -----                     
 0   id                      3293 non-null   object                    
 1   dateTime                3293 non-null   datetime64[ns, US/Eastern]
 2   aaerNo                  3293 non-null   object                    
 3   releaseNo               3293 non-null   object                    
 4   respondents             3293 non-null   object                    
 5   respondentsText         3293 non-null   object                    
 6   urls                    3293 non-null   object                    
 7   summary                 3272 non-null   object                    
 8   tags                    3272 non-null   object                    
 9   entities                3272 non-null   object                    
 10  complaints              3272 non-null   object                    
 11  parallelActionsTakenBy  3272 non-null   object                    
 12  hasAgreedToSettlement   3293 non-null   bool                      
 13  hasAgreedToPayPenalty   3293 non-null   bool                      
 14  penaltyAmounts          3272 non-null   object                    
 15  requestedRelief         3272 non-null   object                    
 16  violatedSections        3272 non-null   object                    
 17  otherAgenciesInvolved   3272 non-null   object                    
 18  dateTimeYear            3293 non-null   int32                     
 19  dateTimeMonth           3293 non-null   int32                     
 20  dateTimeYearMonth       3293 non-null   period[M]                 
 21  dateTimeDay             3293 non-null   object                    
 22  dateTimeClass           3293 non-null   object                    
dtypes: bool(2), datetime64[ns, US/Eastern](1), int32(2), object(17), period[M](1)
memory usage: 521.1+ KB
None

/var/folders/q3/bt7922t52p78qdm75h_8m5yh0000gn/T/ipykernel_81069/795053953.py:10: UserWarning: Converting to PeriodArray/Index representation will drop timezone information.
  aaers["dateTimeYearMonth"] = aaers["dateTime"].dt.to_period("M")

aaers.head()

	id	dateTime	aaerNo	releaseNo	respondents	respondentsText	urls	summary	tags	entities	...	hasAgreedToPayPenalty	penaltyAmounts	requestedRelief	violatedSections	otherAgenciesInvolved	dateTimeYear	dateTimeMonth	dateTimeYearMonth	dateTimeDay	dateTimeClass
0	a1713f9f48aa9129833f02e76cc6eeed	2025-02-04 10:00:21-05:00	AAER-4562	[33-11364, 34-102332]	[{'name': 'Karen J. Smith, CPA', 'type': 'indi...	Karen J. Smith, CPA	[{'type': 'primary', 'url': 'https://www.sec.g...	The SEC has instituted public administrative p...	[accounting fraud, disclosure fraud]	[{'name': 'Karen J. Smith', 'type': 'individua...	...	True	[{'penaltyAmount': '43348.59', 'penaltyAmountT...	[disgorgement of profits, civil penalties, per...	[Sections 17(a)(2) and (3) of the Securities A...	[]	2025	2	2025-02	Tuesday	regularMarket
1	044e225c5b8cc62902c5cba3928b1840	2025-01-29 12:56:13-05:00	AAER-4561	[33-11363, 34-102306]	[{'name': 'Jason M. Boucher, CPA', 'type': 'in...	Jason M. Boucher, CPA	[{'type': 'primary', 'url': 'https://www.sec.g...	The SEC has instituted public administrative p...	[accounting fraud, disclosure fraud]	[{'name': 'Jason M. Boucher', 'type': 'individ...	...	True	[{'penaltyAmount': '20102', 'penaltyAmountText...	[disgorgement of profits, civil penalties, per...	[Sections 17(a)(2) and (3) of the Securities A...	[]	2025	1	2025-01	Wednesday	regularMarket
2	09a3b5479d46cf2be7a1e7368c107182	2025-01-17 09:49:03-05:00	AAER-4556	[33-11354]	[{'name': 'GrubMarket, Inc.', 'type': 'company'}]	GrubMarket, Inc.	[{'type': 'primary', 'url': 'https://www.sec.g...	The SEC has instituted cease-and-desist procee...	[disclosure fraud]	[{'name': 'GrubMarket, Inc.', 'type': 'company...	...	True	[{'penaltyAmount': '8000000', 'penaltyAmountTe...	[cease and desist order, civil penalties]	[Sections 17(a)(2) and 17(a)(3) of the Securit...	[]	2025	1	2025-01	Friday	regularMarket
3	8e91cb6a1df4a724aedcff7d6ed21c37	2025-01-17 09:21:59-05:00	AAER-4560	[34-102231, IA-6828]	[{'name': 'Jeffery Q. Johnson, CPA', 'type': '...	Jeffery Q. Johnson, CPA	[{'type': 'primary', 'url': 'https://www.sec.g...	The SEC has instituted proceedings against Jef...	[auditor independence, improper professional c...	[{'name': 'Jeffery Q. Johnson', 'type': 'indiv...	...	True	[{'penaltyAmount': '30000', 'penaltyAmountText...	[cease and desist order, civil penalties]	[Section 206(4) of the Advisers Act, Rule 206(...	[]	2025	1	2025-01	Friday	preMarket
4	e9f5e13f8812a0cf9bc8ee69c132b8d9	2025-01-17 08:47:55-05:00	AAER-4559	[34-102227]	[{'name': 'Celsius Holdings, Inc.', 'type': 'c...	Celsius Holdings, Inc.	[{'type': 'primary', 'url': 'https://www.sec.g...	The SEC has instituted cease-and-desist procee...	[disclosure fraud, accounting violations]	[{'name': 'Celsius Holdings, Inc.', 'type': 'c...	...	True	[{'penaltyAmount': '3000000', 'penaltyAmountTe...	[cease and desist from committing or causing a...	[Sections 13(a), 13(b)(2)(A), and 13(b)(2)(B) ...	[]	2025	1	2025-01	Friday	preMarket

5 rows × 23 columns

df_year_month = aaers.pivot_table(
    index="dateTimeYear",
    columns="dateTimeMonth",
    values="id",
    aggfunc="count",
    fill_value=0,
)
df_year_month_pretty = df_year_month.copy()
# convert col 1 to 12 to month names, eg 1 => Jan, 2 => Feb, etc
df_year_month_pretty.columns = df_year_month_pretty.columns.map(
    lambda x: pd.to_datetime(str(x), format="%m").strftime("%b")
)

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

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

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

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

df_year_month_pretty = df_year_month_pretty.astype(int)

print("Accounting and Auditing Enforcement Releases by Year and Month")
df_year_month_pretty

Accounting and Auditing Enforcement Releases by Year and Month

dateTimeMonth	Jan	Feb	Mar	Apr	May	Jun	Jul	Aug	Sep	Oct	Nov	Dec	total	mean	median
dateTimeYear
1997	0	0	0	0	0	0	0	0	0	0	1	4	5	0	0
1999	0	0	0	0	0	0	0	0	0	8	11	3	22	2	0
2000	5	15	12	6	11	20	6	8	30	11	9	9	142	12	10
2001	6	8	7	6	13	15	13	5	27	12	1	8	121	10	8
2002	18	6	30	12	12	24	16	17	14	19	22	18	208	17	18
2003	10	16	20	19	21	16	18	19	30	24	10	16	219	18	18
2004	11	16	16	15	26	23	21	22	24	11	18	10	213	18	17
2005	18	14	28	17	10	20	14	21	23	11	8	10	194	16	16
2006	10	20	18	18	10	21	15	8	11	15	12	12	170	14	14
2007	19	18	19	15	9	17	31	13	63	10	5	9	228	19	16
2008	17	13	12	13	17	7	13	15	23	3	7	9	149	12	13
2009	17	11	21	10	14	20	22	12	14	8	8	22	179	15	14
2010	20	3	8	8	6	12	14	16	15	7	15	7	131	11	10
2011	14	13	7	19	10	14	9	7	9	8	6	11	127	11	10
2012	11	7	6	10	3	5	4	6	12	8	4	9	85	7	6
2013	9	4	3	8	5	8	6	4	20	7	2	11	87	7	6
2014	7	10	6	4	4	6	13	4	9	5	6	18	92	8	6
2015	7	15	6	8	4	9	5	8	28	7	1	13	111	9	8
2016	5	13	11	11	3	11	7	5	15	8	11	8	108	9	10
2017	17	3	3	5	3	10	4	6	9	3	6	6	75	6	6
2018	3	5	8	6	3	2	9	11	28	1	2	17	95	8	6
2019	6	5	10	11	3	9	4	9	29	2	3	6	97	8	6
2020	5	3	2	13	9	6	5	10	18	9	1	7	88	7	6
2021	1	4	1	13	3	1	13	7	19	1	6	8	77	6	5
2022	2	6	4	5	4	10	3	13	22	8	8	1	86	7	6
2023	4	9	13	9	8	13	8	20	18	2	3	4	111	9	8
2024	5	6	3	4	5	4	0	3	14	3	7	11	65	5	4
2025	7	1	0	0	0	0	0	0	0	0	0	0	8	1	0
total	254	244	274	265	216	303	273	269	524	211	193	267	3293	272	247
mean	9	9	10	9	8	11	10	10	19	8	7	10	118	10	9
median	7	8	8	10	6	10	8	8	18	8	6	9	110	9	8

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

data_to_plot = df_year_month_pretty.loc[2000:2024].copy()
data_to_plot.index = data_to_plot.index.astype(int)
data_to_plot["total"].plot(kind="line", ax=ax)

ax.set_title("Audit and Accounting Enforcement Releases\nby Year (2000-2024)")
ax.set_xlabel("Year")
ax.set_ylabel("Number of AAERs")
ax.set_axisbelow(True)
ax.set_xticks(data_to_plot.index[::2])
plt.xticks(rotation=90)

for year in data_to_plot.index:
    year_y_max = data_to_plot.loc[year, "total"]
    ax.vlines(year, 0, year_y_max, linestyles=":", colors="grey", alpha=0.5, lw=1)

plt.grid(axis="x")
plt.tight_layout()
plt.show()

fig, ax = plt.subplots(figsize=(3.5, 3))

df_year_month.loc[2000:2024].boxplot(
    ax=ax,
    grid=False,
    showfliers=False,
    flierprops=dict(marker="o", markersize=3),
    patch_artist=True,
    boxprops=dict(facecolor="white", color="tab:blue"),
    showmeans=True,
    meanline=True,
    meanprops={"color": "tab:blue", "linestyle": ":"},
    medianprops={"color": "black"},
    capprops={"color": "none"},
)

ax.set_title("AAERs by Month\n(2000 - 2024)")
ax.set_xlabel("Month")
ax.set_ylabel("AAERs Count")
xticklabels = [pd.to_datetime(str(x), format="%m").strftime("%b") for x in range(1, 13)]
ax.set_xticklabels(xticklabels)
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()

counts_dayOfWeek = (
    aaers[aaers["dateTimeYear"].between(2000, 2024)]
    .groupby(["dateTimeDay"])
    .size()
    .to_frame(name="Count")
).rename_axis("Day of the Week")
counts_dayOfWeek["Pct"] = (
    counts_dayOfWeek["Count"].astype(int) / counts_dayOfWeek["Count"].astype(int).sum()
).map("{:.0%}".format)
counts_dayOfWeek["Count"] = counts_dayOfWeek["Count"].map(lambda x: f"{x:,}")

print(f"AAER disclosures by day of the week (2000 - 2024).")
counts_dayOfWeek.loc[["Monday", "Tuesday", "Wednesday", "Thursday", "Friday"]]

AAER disclosures by day of the week (2000 - 2024).

	Count	Pct
Day of the Week
Monday	574	18%
Tuesday	643	20%
Wednesday	716	22%
Thursday	765	23%
Friday	559	17%

counts_filedAtClass = (
    aaers[aaers["dateTimeYear"].between(2000, 2024)]
    .groupby(["dateTimeClass"])
    .size()
    .sort_values(ascending=False)
    .to_frame(name="Count")
).rename_axis("Publication Time")
counts_filedAtClass["Pct"] = (
    counts_filedAtClass["Count"].astype(int)
    / counts_filedAtClass["Count"].astype(int).sum()
).map("{:.0%}".format)
counts_filedAtClass["Count"] = counts_filedAtClass["Count"].map(lambda x: f"{x:,}")
counts_filedAtClass = counts_filedAtClass.reindex(
    ["preMarket", "regularMarket", "afterMarket"]
)
counts_filedAtClass.index = (
    counts_filedAtClass.index.str.replace("preMarket", "Pre-Market (4:00 - 9:30 AM)")
    .str.replace("regularMarket", "Market Hours (9:30 AM - 4:00 PM)")
    .str.replace("afterMarket", "After Market (4:00 - 8:00 PM)")
)

print(
    f"AAER disclosures by pre-market, regular market hours,\nand after-market publication time (2000 - 2024)."
)
counts_filedAtClass

AAER disclosures by pre-market, regular market hours,
and after-market publication time (2000 - 2024).

	Count	Pct
Publication Time
Pre-Market (4:00 - 9:30 AM)	2,389	73%
Market Hours (9:30 AM - 4:00 PM)	824	25%
After Market (4:00 - 8:00 PM)	45	1%

counts_hasAgreedToPayPenalty = (
    aaers[aaers["dateTimeYear"].between(2000, 2024)]
    .groupby(["hasAgreedToPayPenalty"])
    .size()
    .to_frame(name="Count")
).rename_axis("Has Agreed to Pay Penalty")
counts_hasAgreedToPayPenalty["Pct"] = (
    counts_hasAgreedToPayPenalty["Count"].astype(int)
    / counts_hasAgreedToPayPenalty["Count"].astype(int).sum()
).map("{:.0%}".format)
counts_hasAgreedToPayPenalty["Count"] = counts_hasAgreedToPayPenalty["Count"].map(
    lambda x: f"{x:,}"
)

print(
    f"AAER disclosures by whether the company has agreed to pay a penalty (2000 - 2024)."
)
counts_hasAgreedToPayPenalty

AAER disclosures by whether the company has agreed to pay a penalty (2000 - 2024).

	Count	Pct
Has Agreed to Pay Penalty
False	1,612	49%
True	1,646	51%

penality_amounts = aaers.explode("penaltyAmounts").copy()
penality_amounts["penaltyAmount"] = penality_amounts["penaltyAmounts"].apply(
    lambda x: x["penaltyAmount"] if isinstance(x, dict) else np.nan
)
penality_amounts["penaltyAmount"] = penality_amounts["penaltyAmount"].astype(float)
penality_amounts["penaltyAmount"] = penality_amounts["penaltyAmount"] / 1000
penality_amounts["penaltyAmount"].describe().apply(lambda x: f"{x:,.0f}").to_frame()

	penaltyAmount
count	3,102
mean	8,320
std	74,308
min	0
25%	30
50%	85
75%	500
max	2,250,000

penalties_year = penality_amounts.groupby("dateTimeYear")["penaltyAmount"].sum()
penalties_year = penalties_year.astype(int)
penalties_year = pd.DataFrame(penalties_year)
penalties_year["penaltyAmount"] = round(penalties_year["penaltyAmount"] / 1_000, 2)
print("Total Penalties in Million USD by Year")
penalties_year.map(lambda x: f"{x:,.1f}")

Total Penalties in Million USD by Year

	penaltyAmount
dateTimeYear
1997	0.0
1999	0.3
2000	26.8
2001	12.4
2002	246.6
2003	8,731.7
2004	1,618.5
2005	2,041.2
2006	1,445.7
2007	707.2
2008	553.8
2009	1,152.3
2010	1,410.6
2011	365.4
2012	251.2
2013	454.1
2014	372.4
2015	351.0
2016	617.0
2017	537.0
2018	1,237.4
2019	686.0
2020	1,095.9
2021	158.4
2022	502.1
2023	388.3
2024	816.0
2025	30.9

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

data_to_plot = penalties_year.loc[2000:2024].copy()
data_to_plot["penaltyAmount"].plot(kind="line", ax=ax)

for year in data_to_plot.index:
    year_y_max = data_to_plot.loc[year, "penaltyAmount"]
    ax.vlines(year, 0, year_y_max, linestyles=":", colors="grey", alpha=0.5, lw=1)

ax.set_xticks(data_to_plot.index[::2])
plt.xticks(rotation=90)
ax.get_yaxis().set_major_formatter(plt.FuncFormatter(lambda x, loc: "{:,}".format(int(x))))
ax.set_title("AAER 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()

all_tags = []

for i, row in aaers.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 AAER Releases from 1997 to 2025")
all_tags.head(10)

Top 10 Tags in AAER Releases from 1997 to 2025

	tag	count
0	disclosure fraud	1648
1	accounting fraud	1092
2	securities fraud	404
3	improper professional conduct	254
4	financial fraud	229
5	accounting violations	161
6	fraud	150
7	insider trading	144
8	reinstatement	127
9	securities violation	125

# count all unique violatedSections
all_violated_sections = []

for i, row in aaers.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 Litigation Releases")
all_violated_sections.head(10)

Top 10 Violated Securities Laws in SEC Litigation Releases

	violatedSections	count
0	Section 17(a) of the Securities Act of 1933	657
1	Rule 10b-5	447
2	Section 13(a) of the Exchange Act	399
3	Section 10(b) of the Securities Exchange Act o...	299
4	Sections 13(a), 13(b)(2)(A) and 13(b)(2)(B) of...	296
5	Sections 10(b) and 13(b)(5) of the Securities ...	268
6	Sections 13(a), 13(b)(2)(A), and 13(b)(2)(B) o...	247
7	Section 10(b) of the Exchange Act	245
8	Section 13(b)(5) of the Exchange Act	243
9	Sections 10(b) and 13(b)(5) of the Exchange Act	222