Analyzing SEC Litigation Releases in Python

   

On this page:

• Litigations by Year and Month
  • Litigations by Year and Settlement Type
  • Litigations by Year And Count of Agreements to Pay Penalties
• Penalty Amount Analysis
  • Penalty Amount by Year
  • Top 10 Penalty Amounts
  • Penalties by Type of Defendant
• Litigations by Category
• Requested Reliefs by Category
• Violated Securities Laws
• Persons & Agencies Conducting the Investigations
• Other Agencies Involved

In this guide, we will analyze all SEC litigation releases from 1995 to 2025 obtained from the litigation database. The dataset contains information about all litigation releases published by the U.S. Securities and Exchange Commission (SEC) from 1995 to 2025 and includes information about the defendants, charges, penalty amounts, and other relevant details.

Our analysis will focus on several key aspects of the dataset, including:

• The number of litigation releases published each year and month
• Litigations by settlement type and agreement to pay penalties
• Penalty amounts by year
• Top 10 penalties by amount
• Categories of requested reliefs, violations, and more

import pandas as pd
import numpy as np
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,
    "text.usetex": False,
    "font.family": "sans-serif",
    "axes.spines.top": False,
    "axes.spines.right": False,
    "grid.color": "grey",
    "axes.grid": True,
    "axes.grid.axis": "y",
    "axes.grid.axis": "x",
    "grid.alpha": 0.5,
    "grid.linestyle": ":",
}

plt.rcParams.update(params)

For the sake of brevity, we downloaded all SEC litigation data from 1995 to 2025 and saved it as a JSONL file. We will load this data into a pandas DataFrame and perform our analysis using Python.

df = pd.read_json(
    # TODO: replace with your path
    "/path/to/sec-litigation-releases.jsonl",
    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"].dt.to_period("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)
df['caseCitation'] = df['caseCitations'].map(lambda x: x[0] if x else None)

print(f"Loaded {len(df):,.0f} SEC litigation releases from 1995 to 2025\n")
print(df.info())

Loaded 11,536 SEC litigation releases from 1995 to 2025

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 11536 entries, 0 to 11535
Data columns (total 26 columns):
 #   Column                    Non-Null Count  Dtype                     
---  ------                    --------------  -----                     
 0   id                        11536 non-null  object                    
 1   releaseNo                 11536 non-null  object                    
 2   releasedAt                11536 non-null  datetime64[ns, US/Eastern]
 3   url                       11536 non-null  object                    
 4   title                     11535 non-null  object                    
 5   subTitle                  11536 non-null  object                    
 6   caseCitations             11536 non-null  object                    
 7   resources                 11536 non-null  object                    
 8   summary                   11524 non-null  object                    
 9   tags                      11524 non-null  object                    
 10  entities                  11524 non-null  object                    
 11  complaints                11524 non-null  object                    
 12  parallelActionsTakenBy    11524 non-null  object                    
 13  hasAgreedToSettlement     11536 non-null  bool                      
 14  hasAgreedToPayPenalty     11536 non-null  bool                      
 15  penaltyAmounts            11524 non-null  object                    
 16  requestedRelief           11524 non-null  object                    
 17  violatedSections          11524 non-null  object                    
 18  investigationConductedBy  11524 non-null  object                    
 19  litigationLedBy           11524 non-null  object                    
 20  otherAgenciesInvolved     11524 non-null  object                    
 21  releasedAtYear            11536 non-null  int32                     
 22  releasedAtMonth           11536 non-null  int32                     
 23  releasedAtYearMonth       11536 non-null  period[M]                 
 24  releasedAtDay             11536 non-null  object                    
 25  caseCitation              11192 non-null  object                    
dtypes: bool(2), datetime64[ns, US/Eastern](1), int32(2), object(20), period[M](1)
memory usage: 2.0+ MB
None

/var/folders/q3/bt7922t52p78qdm75h_8m5yh0000gn/T/ipykernel_39183/1704038985.py:6: UserWarning: Converting to PeriodArray/Index representation will drop timezone information.
  df["releasedAtYearMonth"] = df["releasedAt"].dt.to_period("M")

df.head()

	id	releaseNo	releasedAt	url	title	subTitle	caseCitations	resources	summary	tags	...	requestedRelief	violatedSections	investigationConductedBy	litigationLedBy	otherAgenciesInvolved	releasedAtYear	releasedAtMonth	releasedAtYearMonth	releasedAtDay	caseCitation
0	c3772c013f5a90d0c1c0170bbed8ad6a	LR-26231	2025-01-22 21:17:05-05:00	https://www.sec.gov/enforcement-litigation/lit...	Gabriel Rebeiz	SEC Charges Technical Consultant with Insider ...	[Securities and Exchange Commission v. Gabriel...	[{'label': 'SEC Complaint', 'url': 'https://ww...	The SEC filed settled charges against Gabriel ...	[insider trading]	...	[disgorgement of profits, civil penalties, per...	[Section 10(b) of the Securities Exchange Act ...	[Sara Kalin, John Rymas, Stephen Kam]	[Diana Tani, Joseph Sansone]	[{'name': 'Financial Industry Regulatory Autho...	2025	1	2025-01	Wednesday	Securities and Exchange Commission v. Gabriel ...
1	860c50b5c1bfa963e68568f2b6008ba4	LR-26230	2025-01-21 15:00:18-05:00	https://www.sec.gov/enforcement-litigation/lit...	Old South Trading Co., LLC; Brendan H. Church;...	SEC Charges Father and Son for $25.8 Million U...	[Securities and Exchange Commission v. Old Sou...	[{'label': 'SEC Complaint', 'url': 'https://ww...	The SEC has charged Old South Trading Co., LLC...	[unregistered securities offering, unregistere...	...	[injunctive relief, disgorgement of allegedly ...	[Sections 5(a) and (c) of the Securities Act o...	[Jonathan Shapiro, Andrew Elliott, Margaret Vi...	[Dean Conway]	[]	2025	1	2025-01	Tuesday	Securities and Exchange Commission v. Old Sout...
2	1485373724567b64d7cee1c8ce853fe3	LR-26229	2025-01-18 19:09:46-05:00	https://www.sec.gov/enforcement-litigation/lit...	Nova Labs, Inc.	SEC Charges Nova Labs, Inc. with Fraud and Reg...	[Securities and Exchange Commission v. Nova La...	[{'label': 'SEC Complaint', 'url': 'https://ww...	The SEC has charged Nova Labs, Inc. with fraud...	[fraud, registration violations, crypto]	...	[permanent and conduct-based injunctions, disg...	[Sections 5(a), 5(c), and 17(a)(2) of the Secu...	[Emmy E. Rush, Christopher Colorado, Kim Han, ...	[Emmy E. Rush, Christopher Colorado, Peter Man...	[]	2025	1	2025-01	Saturday	Securities and Exchange Commission v. Nova Lab...
3	35ebfbcaf74cef9401e78f8e92b934e6	LR-26228	2025-01-17 22:20:10-05:00	https://www.sec.gov/enforcement-litigation/lit...	Arete Wealth Management LLC; Arete Wealth Advi...	SEC Charges Arete Wealth Broker-Dealer and Adv...	[Securities and Exchange Commission v. Arete W...	[{'label': 'SEC Complaint', 'url': 'https://ww...	The SEC has charged Arete Wealth Management LL...	[fraud, illegal securities offering, recordkee...	...	[permanent injunctions, civil penalties, condu...	[Sections 206(1) and 206(2) of the Investment ...	[Theresa H. Gue, Austin Thompson, Christopher ...	[Oren Gleich, Preethi Krishnamurthy]	[{'name': 'U.S. Attorney's Office for the East...	2025	1	2025-01	Friday	Securities and Exchange Commission v. Arete We...
4	60cabddfdd7abcf4c1da9bffebc55a1e	LR-26227	2025-01-17 21:56:17-05:00	https://www.sec.gov/enforcement-litigation/lit...	Naufal Sanaullah	SEC Charges Fund Executive with Making False S...	[Securities and Exchange Commission v. Naufal ...	[{'label': 'SEC Complaint', 'url': 'https://ww...	The SEC charged Naufal Sanaullah with making f...	[disclosure fraud, securities fraud]	...	[permanent injunctive relief, disgorgement alo...	[Section 17(a) of the Securities Act of 1933, ...	[Heather Marlow, Kimberly L. Frederick, Nichol...	[Zachary Carlyle, Gregory A. Kasper, Nicholas ...	[{'name': 'Federal Bureau of Investigation', '...	2025	1	2025-01	Friday	Securities and Exchange Commission v. Naufal S...

5 rows × 26 columns

# piv table with years = col, months = index, values = count of cases
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 Litigation Releases by Year and Month")
df_year_month

SEC Litigation Releases by Year and Month

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	23	39	36	25	123	10	0
1996	37	22	30	36	32	40	27	30	73	42	26	34	429	36	33
1997	37	32	39	46	24	20	28	39	53	27	26	29	400	33	30
1998	28	20	35	35	34	31	30	36	53	44	19	25	390	32	32
1999	38	24	31	27	40	33	24	42	52	23	31	21	386	32	31
2000	23	41	38	32	45	40	25	34	68	44	27	34	451	38	36
2001	39	35	28	32	41	36	27	30	55	49	35	41	448	37	36
2002	58	38	60	49	42	55	52	60	49	65	45	44	617	51	50
2003	48	49	49	65	41	45	60	51	64	58	40	49	619	52	49
2004	34	40	50	39	40	44	43	48	53	36	36	32	495	41	40
2005	39	46	59	46	31	50	27	36	57	39	30	38	498	42	39
2006	34	31	53	42	34	38	32	35	39	40	32	26	436	36	34
2007	30	36	41	35	39	39	42	46	51	38	24	40	461	38	39
2008	26	27	42	33	57	26	35	32	63	34	22	22	419	35	32
2009	36	42	63	38	44	48	49	29	40	40	43	42	514	43	42
2010	41	29	44	40	30	45	24	27	43	38	41	37	439	37	39
2011	36	35	44	38	32	39	32	26	31	33	27	41	414	34	34
2012	33	27	40	36	32	24	23	34	36	24	30	27	366	30	31
2013	25	21	34	31	18	24	31	23	38	34	19	20	318	26	24
2014	14	20	24	25	27	24	18	22	25	25	18	24	266	22	24
2015	16	25	15	24	22	22	19	16	44	24	17	26	270	22	22
2016	16	20	33	17	24	35	21	21	39	9	16	18	269	22	20
2017	21	26	34	24	30	22	20	36	28	21	21	25	308	26	24
2018	15	21	31	36	25	27	40	34	47	28	34	17	355	30	30
2019	7	26	21	29	22	40	24	34	46	23	24	22	318	26	24
2020	31	20	32	25	15	21	15	20	59	14	17	32	301	25	20
2021	16	19	24	23	18	26	24	37	47	13	22	23	292	24	23
2022	24	16	16	25	24	30	27	33	53	20	16	21	305	25	24
2023	23	26	33	16	38	28	30	23	57	11	17	14	316	26	24
2024	17	11	15	29	23	25	27	28	53	22	14	24	288	24	24
2025	25	0	0	0	0	0	0	0	0	0	0	0	25	2	0
total	867	825	1058	973	924	977	876	962	1439	957	805	873	11536	957	910
mean	28	27	34	31	30	32	28	31	46	31	26	28	372	31	29
median	28	26	34	32	31	31	27	33	49	33	26	26	386	32	31

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

df_year_month.loc[1995:2024]["total"].plot(
    kind="line",
    ax=ax,
    marker="o",
    markersize=3,
    linewidth=1,
)

ax.set_title("SEC Litigation Releases per Year")
ax.set_xlabel("Year")
ax.set_ylabel("Number of Cases")
plt.tight_layout()
plt.grid(axis="x")
ax.set_axisbelow(True)
plt.show()

# piv table for hasAgreedToSettlement
# col = hasAgreedToSettlement (true, false), index = year, values = count of cases
df_year_settlement = df.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

hasAgreedToSettlement	False	True
releasedAtYear
1995	63	60
1996	215	214
1997	195	205
1998	209	181
1999	196	190
2000	239	212
2001	214	234
2002	337	280
2003	322	297
2004	244	251
2005	241	257
2006	177	259
2007	210	251
2008	188	231
2009	273	241
2010	205	234
2011	194	220
2012	190	176
2013	161	157
2014	135	131
2015	160	110
2016	149	120
2017	174	134
2018	177	178
2019	171	147
2020	161	140
2021	173	119
2022	165	140
2023	165	151
2024	146	142
2025	7	18
total	5856	5680
mean	189	183
median	188	181

# 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 Litigation Releases 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()

# piv table for hasAgreedToPayPenalty
# col = hasAgreedToPayPenalty (true, false), index = year, values = count of cases
df_year_penalty = df.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

hasAgreedToPayPenalty	False	True
releasedAtYear
1995	88	35
1996	296	133
1997	266	134
1998	254	136
1999	222	164
2000	285	166
2001	251	197
2002	375	242
2003	339	280
2004	270	225
2005	243	255
2006	205	231
2007	230	231
2008	224	195
2009	308	206
2010	225	214
2011	219	195
2012	183	183
2013	172	146
2014	144	122
2015	154	116
2016	162	107
2017	173	135
2018	200	155
2019	182	136
2020	171	130
2021	191	101
2022	165	140
2023	192	124
2024	144	144
2025	11	14
total	6544	4992
mean	211	161
median	205	146

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

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

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

all_penalties = []
case_citations = {}

# iterate over all rows, extract penalties and append to all_penalties
for i, row in df.drop_duplicates(subset=["caseCitation"], keep="last").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'],
                        "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=["caseCitation", "imposedOn", "amount"], keep="last"
)

all_penalties_df

	caseCitation	releaseNo	releasedAt	releasedAtYear	releasedAtMonth	amount	imposedOn	imposedOnType	url
0	Securities and Exchange Commission v. Gabriel ...	LR-26231	2025-01-22 21:17:05-05:00	2025	1	360673.00	Gabriel Rebeiz	individual	https://www.sec.gov/enforcement-litigation/lit...
1	Securities and Exchange Commission v. Arete We...	LR-26228	2025-01-17 22:20:10-05:00	2025	1	200000.00	Michael Sealy	individual	https://www.sec.gov/enforcement-litigation/lit...
2	Securities and Exchange Commission v. American...	LR-26225	2025-01-17 17:57:15-05:00	2025	1	1876115.22	Ross C. Miles, American Equities, Inc., and Am...	None	https://www.sec.gov/enforcement-litigation/lit...
3	Securities and Exchange Commission v. American...	LR-26225	2025-01-17 17:57:15-05:00	2025	1	1146307.10	Ross C. Miles, American Equities, Inc., and Am...	None	https://www.sec.gov/enforcement-litigation/lit...
4	Securities and Exchange Commission v. American...	LR-26225	2025-01-17 17:57:15-05:00	2025	1	230464.00	Ross C. Miles, American Equities, Inc., and Am...	None	https://www.sec.gov/enforcement-litigation/lit...
...	...	...	...	...	...	...	...	...	...
9421	SECURITIES AND EXCHANGE COMMISSION v. CROSS FI...	LR-14649	1995-09-21 14:18:04-04:00	1995	9	8600000.00	Douglas S. Cross	individual	https://www.sec.gov/files/litigation/litreleas...
9422	SECURITIES AND EXCHANGE COMMISSION v. CROSS FI...	LR-14649	1995-09-21 14:18:04-04:00	1995	9	2600000.00	Michael J. Colello	individual	https://www.sec.gov/files/litigation/litreleas...
9423	SECURITIES AND EXCHANGE COMMISSION v. ROBERT M...	LR-14644	1995-09-20 14:18:03-04:00	1995	9	922741.00	Stifel, Nicolaus and Company, Incorporated	company	https://www.sec.gov/files/litigation/litreleas...
9424	SECURITIES AND EXCHANGE COMMISSION v. ROBERT M...	LR-14644	1995-09-20 14:18:03-04:00	1995	9	263637.00	Stifel, Nicolaus and Company, Incorporated	company	https://www.sec.gov/files/litigation/litreleas...
9425	SECURITIES AND EXCHANGE COMMISSION v. ROBERT M...	LR-14644	1995-09-20 14:18:03-04:00	1995	9	250000.00	Stifel, Nicolaus and Company, Incorporated	company	https://www.sec.gov/files/litigation/litreleas...

9291 rows × 9 columns

# 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

	amount
releasedAtYear
1995	107.50
1996	187.45
1997	119.11
1998	201.08
1999	384.33
2000	365.14
2001	398.37
2002	1051.64
2003	7185.29
2004	1265.45
2005	2510.20
2006	4326.68
2007	1033.73
2008	2178.02
2009	1787.05
2010	3566.43
2011	1355.98
2012	1681.49
2013	1485.04
2014	1824.30
2015	690.43
2016	3168.99
2017	934.54
2018	411.99
2019	495.31
2020	1043.57
2021	803.07
2022	757.16
2023	539.52
2024	1111.01
2025	44.36

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

penalties_year.loc[1995:2024]["amount"].plot(
    kind="line",
    ax=ax,
    marker="o",
    markersize=3,
    linewidth=1,
)

# format y labels to , notation
ax.get_yaxis().set_major_formatter(plt.FuncFormatter(lambda x, loc: "{:,}".format(int(x))))
ax.set_title("SEC 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()

# 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")
# show all columns in full width
pd.set_option("display.max_colwidth", None)
top_10_penalties[['amount', 'imposedOn', 'releasedAt', 'releaseNo', 'url']]

Top 10 SEC Penalties in Million USD between 1995 and 2024

	amount	imposedOn	releasedAt	releaseNo	url
6846	2,250.0	WorldCom, Inc.	2003-08-07 14:21:04-04:00	LR-18277	https://www.sec.gov/enforcement-litigation/litigation-releases/lr-18277
5887	1,600.0	American International Group, Inc.	2006-02-09 14:22:14-05:00	LR-19560	https://www.sec.gov/enforcement-litigation/litigation-releases/lr-19560
6974	1,510.0	WorldCom Inc.	2003-05-19 14:20:57-04:00	LR-18147	https://www.sec.gov/enforcement-litigation/litigation-releases/lr-18147
2056	957.0	Braskem S.A.	2016-12-21 14:26:01-05:00	LR-23705	https://www.sec.gov/enforcement-litigation/litigation-releases/lr-23705
6731	894.0	ten firms, Grubman and Blodget	2003-10-31 14:21:13-05:00	LR-18438	https://www.sec.gov/enforcement-litigation/litigation-releases/lr-18438
2502	806.2	Marlon Quan and the other defendants	2014-09-25 14:25:28-04:00	LR-23093	https://www.sec.gov/enforcement-litigation/litigation-releases/lr-23093
5888	800.0	American International Group, Inc.	2006-02-09 14:22:14-05:00	LR-19560	https://www.sec.gov/enforcement-litigation/litigation-releases/lr-19560
2058	632.0	Braskem S.A.	2016-12-21 14:26:01-05:00	LR-23705	https://www.sec.gov/enforcement-litigation/litigation-releases/lr-23705
1012	601.0	CR Intrinsic and the relief defendants	2021-02-03 14:27:14-05:00	LR-25022	https://www.sec.gov/enforcement-litigation/litigation-releases/lr-25022
4626	569.0	Siemens Aktiengesellschaft	2008-12-15 14:23:23-05:00	LR-20829	https://www.sec.gov/enforcement-litigation/litigation-releases/lr-20829

# reset display options
pd.reset_option("display.max_colwidth")

# 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

imposedOnType	company	individual
releasedAtYear
1995	32.4	53.1
1996	37.4	58.8
1997	21.2	93.3
1998	36.6	71.4
1999	173.0	186.1
2000	44.2	259.9
2001	122.8	236.4
2002	284.8	680.6
2003	5,197.4	1,043.4
2004	994.0	95.8
2005	856.7	1,648.6
2006	3,749.3	540.9
2007	572.6	451.6
2008	1,893.3	245.2
2009	1,261.2	358.7
2010	2,650.8	451.2
2011	959.1	305.9
2012	964.6	515.1
2013	1,032.5	358.3
2014	225.9	537.7
2015	259.6	374.4
2016	2,760.4	338.6
2017	140.3	732.5
2018	34.3	348.1
2019	230.3	200.8
2020	788.1	104.7
2021	71.2	96.3
2022	85.3	320.9
2023	199.3	239.7
2024	781.9	307.7
2025	5.2	36.0
total	26,465.8	11,291.5
mean	853.7	364.2
median	259.6	307.7

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("SEC 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()

all_tags = []

for i, row in df.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 Litigation Releases")
all_tags.head(10)

Top 10 Tags in SEC Litigation Releases

	tag	count
0	disclosure fraud	5875
1	securities fraud	3389
2	insider trading	1900
3	fraud	1454
4	ponzi scheme	932
5	accounting fraud	676
6	unregistered securities	656
7	misappropriation	474
8	investment fraud	404
9	securities violation	312

# count all unique requestedRelief
all_requested_relief = []

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

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

all_requested_relief.columns = ["requestedRelief", "count"]

print("Top 10 Requested Reliefs in SEC Litigation Releases")
all_requested_relief.head(10)

Top 10 Requested Reliefs in SEC Litigation Releases

	requestedRelief	count
0	permanent injunctions	6522
1	civil penalties	5632
2	disgorgement of profits	3931
3	permanent injunction	1274
4	prejudgment interest	1185
5	disgorgement of ill-gotten gains	979
6	disgorgement	911
7	asset freeze	576
8	civil money penalties	401
9	permanent injunctive relief	390

# count all unique violatedSections
all_violated_sections = []

for i, row in df.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	Rule 10b-5	6185
1	Section 10(b) of the Securities Exchange Act o...	5684
2	Section 17(a) of the Securities Act of 1933	3827
3	Sections 5(a), 5(c) and 17(a) of the Securitie...	690
4	Sections 206(1) and 206(2) of the Investment A...	523
5	Sections 5(a), 5(c), and 17(a) of the Securiti...	469
6	Section 15(a) of the Exchange Act	410
7	Section 17(a) of the Securities Act	402
8	Sections 10(b) and 13(b)(5) of the Securities ...	311
9	Sections 5(a) and 5(c) of the Securities Act o...	309

all_investigation_conducted_by = []

for i, row in df.iterrows():
    investigationConductedBy = row["investigationConductedBy"]
    if isinstance(investigationConductedBy, list):
        all_investigation_conducted_by.extend(investigationConductedBy)

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

all_investigation_conducted_by.columns = ["investigationConductedBy", "count"]

print("Top 10 Investigation Conducted By in SEC Litigation Releases")
all_investigation_conducted_by.head(10)

Top 10 Investigation Conducted By in SEC Litigation Releases

	investigationConductedBy	count
0	Securities and Exchange Commission	437
1	Division of Enforcement	256
2	U.S. Securities and Exchange Commission	161
3	SEC	103
4	John Rymas	83
5	Amy Gwiazda	81
6	New York Regional Office	72
7	Federal Bureau of Investigation	61
8	Boston Regional Office	59
9	Miami Regional Office	55

# count unique otherAgenciesInvolved
all_other_agencies_involved = []

for i, row in df.iterrows():
    otherAgenciesInvolved = row["otherAgenciesInvolved"]
    if isinstance(otherAgenciesInvolved, list):
        all_other_agencies_involved.extend(otherAgenciesInvolved)

all_other_agencies_involved = pd.DataFrame(all_other_agencies_involved)
all_other_agencies_involved = all_other_agencies_involved['name'].value_counts().reset_index()

all_other_agencies_involved.columns = ["otherAgenciesInvolved", "count"]

print("Top 10 Other Agencies Involved in SEC Litigation Releases")
all_other_agencies_involved.head(10)

Top 10 Other Agencies Involved in SEC Litigation Releases

	otherAgenciesInvolved	count
0	Federal Bureau of Investigation	1172
1	Financial Industry Regulatory Authority	552
2	British Columbia Securities Commission	98
3	Ontario Securities Commission	82
4	Options Regulatory Surveillance Authority	78
5	Texas State Securities Board	77
6	New York Stock Exchange	77
7	Internal Revenue Service	71
8	U.S. Attorney's Office for the Southern Distri...	71
9	U.S. Postal Inspection Service	69