Analysis of Officer and Director Change Reports

   

On this page:

• Data Loading and Preparation
• Vizualization of Officer and Director Change Disclosures over Time
• Type of Personnel Change
• Appointed Positions
• Compensation
• Departures
• Departures connected to disagreements
• Amendments
• Organizational Changes
• Bonus Plans

We illustrate how to perform an exploratory data analysis on disclosures informing investors about changes of certain officers and directors of publicly traded companies on U.S. stock exchanges. These changes are disclosed in Form 8-K filings with the SEC, specifically under Item 5.02, titled "Departure of Directors or Certain Officers; Election of Directors; Appointment of Certain Officers: Compensatory Arrangements of Certain Officers." These disclosures are presented in text form by companies. Utilizing our Structured Data API, we extract and structure the relevant information from the text, making it available for detailed analysis.

Our analysis will focus on several key areas:

• Number of Item 5.02 disclosures made each year from 2004 to 2024, per quarter, month and at what time of the day (pre-market, regular market, after-market)
• Distribution of disclosures across structured data fields, such as the proportion of disclosures reporting appointments and departures.
• Appointments: number per year to certain position and statistics such as histograms of the age of the appointed person and the annual compensation.
• Departures: number per per year and their reasons and the rate if disagreements in connection with these departures.
• Amendments: distribution of amendments across term duration change, compensation changes and compensation types.
• Changes of the Board of Directors size
• Eligibility of bonus plans

To load and prepare the dataset of over 250,000 structured data objects from Item 5.02 disclosures spanning 2004 to 2024, we utilize the Form 8-K Item 5.02 Structured Data API. The following code handles data loading and preparation by executing multiple download processes in parallel, significantly reducing downloading time. Once downloaded, all structured data objects are saved in JSONL format to ./form-8k-item-5-02-structured-data.jsonl, which will serve as the primary dataset for the analysis. Downloading the entire dataset may take up to 10 minutes.

import os
import json
import random 
import time
import sys
import re
# from multiprocessing import Pool # use in .py files only
from concurrent.futures import ThreadPoolExecutor
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.style as style
import matplotlib.ticker as mtick
import seaborn as sns

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)

!pip install sec-api

from sec_api import Form_8K_Item_X_Api

item_X_api = Form_8K_Item_X_Api("YOUR_API_KEY")

YEARS = range(2024, 2003, -1) # from 2024 to 2004
TEMP_FILE_TEMPLATE = "./temp_file_{}.jsonl"
TARGET_FILE = "./form-8k-item-5-02-structured-data.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"item5_02:* AND filedAt:[{year}-{month:02d}-01 TO {year}-{month:02d}-31]"
            searchRequest = {
                "query": query,
                "from": search_from,
                "size": "50",
                "sort": [{"filedAt": {"order": "desc"}}],
            }

            response = None
            try:
                response = item_X_api.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} Item 5.02 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.

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

structured_data["filedAt"] = pd.to_datetime(structured_data["filedAt"], utc=True)
structured_data["filedAt"] = structured_data["filedAt"].dt.tz_convert("US/Eastern")
structured_data = structured_data.sort_values("filedAt", ascending=True).reset_index(drop=True)
structured_data.drop_duplicates("accessionNo", keep="first", inplace=True)
structured_data["year"] = structured_data["filedAt"].dt.year
structured_data["month"] = structured_data["filedAt"].dt.month
structured_data["dayOfWeek"] = structured_data["filedAt"].dt.day_name()
# filedAtClass: preMarket (4:00AM-9:30AM), regularMarket (9:30AM-4:00PM), afterMarket (4:00PM-8:00PM)
structured_data["filedAtClass"] = structured_data["filedAt"].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 "other"
        )
    )
)
# convert long-form of each item into item id only, e.g. "Item 4.02: ..." => "4.02"
structured_data["items"] = structured_data["items"].apply(
    lambda items: [re.search(r"\d+\.\d+", x).group(0) if x else None for x in items]
)
# explode column "item5_02" into multiple columns
# where each column is a key-value pair of the JSON object
# and drop all structured data columns for items, eg "item4_01"
item_cols = list(
    structured_data.columns[
        structured_data.columns.str.contains(r"item\d+_", case=False)
    ]
)
structured_data = pd.concat(
    [
        structured_data.drop(item_cols, axis=1),
        structured_data["item5_02"].apply(pd.Series),
    ],
    axis=1,
)

unique_years = structured_data["year"].nunique()
unique_companies = structured_data["cik"].nunique()
unique_filings = structured_data["accessionNo"].nunique()
min_year = structured_data["year"].min()
max_year = structured_data["year"].max()
print("Loaded dataframe with structured personnel change data from Form 8-K Item 5.02 filings")
print(f"Number of filings: {unique_filings:,}")
print(f"Number of records: {len(structured_data):,}")
print(f"Number of years: {unique_years:,} ({min_year}-{max_year})")
print(f"Number of unique companies: {unique_companies:,}")

structured_data.head()

Loaded dataframe with structured personnel change data from Form 8-K Item 5.02 filings
Number of filings: 250,956
Number of records: 250,956
Number of years: 21 (2004-2024)
Number of unique companies: 18,056

	id	accessionNo	formType	filedAt	periodOfReport	cik	ticker	companyName	items	year	month	dayOfWeek	filedAtClass	keyComponents	personnelChanges	attachments	organizationChanges	bonusPlans
0	1f87b5f2eb5ebcbe4a33a3b7a62daf64	0001125282-04-004109	8-K	2004-08-23 09:51:12-04:00	2004-08-23	1022671	STLD	STEEL DYNAMICS INC	[5.02]	2004	8	Monday	regularMarket	Tracy L. Shellabarger resigned as CFO to pursu...	[{'type': 'departure', 'departureType': 'resig...	NaN	NaN	NaN
1	34f998c74cf305575e1c99af88687741	0001104659-04-025282	8-K	2004-08-23 11:31:33-04:00	2004-08-23	1038363	NaN	METALS USA INC	[5.02]	2004	8	Monday	regularMarket	Metals USA announced the retirement of E. L. (...	[{'type': 'departure', 'departureType': 'retir...	[Text of Press Release Dated August 16, 2004]	NaN	NaN
2	aaf76daa3dbf9e814ba40d9b220a75c4	0000899715-04-000164	8-K	2004-08-23 12:35:47-04:00	2004-08-23	899715	SKT	TANGER FACTORY OUTLET CENTERS INC	[5.02]	2004	8	Monday	regularMarket	The Board of Directors expanded from five to s...	[{'type': 'appointment', 'effectiveDate': '200...	[Press release announcing expansion of the Com...	{'organ': 'Board of Directors', 'details': 'Ex...	NaN
3	06f7d324ce85534dc7fc2cc0393db153	0001019056-04-001127	8-K	2004-08-23 13:16:24-04:00	2004-08-23	870228	SYBR	SYNERGY BRANDS INC	[5.02]	2004	8	Monday	regularMarket	Michael Ferrone resigned as a director on the ...	[{'type': 'departure', 'departureType': 'resig...	NaN	NaN	NaN
4	05035be548ee5e117923f7b6ca1030d1	0001104659-04-025325	8-K	2004-08-23 16:14:05-04:00	2004-08-17	1003214	SIMG	SILICON IMAGE INC	[1.01, 1.02, 5.02, 9.01]	2004	8	Monday	afterMarket	Dale Brown was appointed as the Chief Accounti...	[{'type': 'appointment', 'effectiveDate': '200...	[Amended and Restated Employment Agreement, da...	NaN	[{'specificIndividuals': True, 'eligibleIndivi...

item_5_02_counts = (
    structured_data.drop_duplicates(subset=["accessionNo"])
    .groupby(["year"])
    .size()
    .to_frame(name="count")
)

print(f"Item 5.02 counts from 2004 to 2024.")
item_5_02_counts.T

Item 5.02 counts from 2004 to 2024.

year	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	...	2015	2016	2017	2018	2019	2020	2021	2022	2023	2024
count	2988	9805	11441	17137	17589	14566	13294	13237	12832	12586	...	12611	11618	11353	11171	10976	10721	11456	11342	11044	10279

1 rows × 21 columns

def plot_timeseries(ts, title):
    fig, ax = plt.subplots(figsize=(4, 2.5))
    ts["count"].plot(ax=ax, legend=False)
    ax.set_title(title)
    ax.set_xlabel("Year")
    ax.set_ylabel("Number of\nItem 5.02 Filings")
    ax.set_xticks(np.arange(2004, 2025, 2))
    ax.yaxis.set_major_formatter(mtick.StrMethodFormatter("{x:,.0f}"))
    ax.set_xlim(2003, 2025)
    ax.grid(axis="x")
    ax.set_axisbelow(True)
    plt.xticks(rotation=45, ha="right")

    for year in range(2004, 2025, 1):
        year_y_max = ts.loc[year, "count"]
        ax.vlines(year, 0, year_y_max, linestyles=":", colors="grey", alpha=0.5, lw=1)

    plt.tight_layout()
    plt.show()


plot_timeseries(
    item_5_02_counts,
    title="Item 5.02 Disclosures per Year (2004-2024)",
)

structured_data["qtr"] = structured_data["month"].apply(lambda x: (x - 1) // 3 + 1)

counts_qtr_yr_piv = (
    structured_data.groupby(["year", "qtr"]).size().unstack().fillna(0)
).astype(int)

print(f"Item 5.02 counts by quarter from 2004 to 2024.")
counts_qtr_yr_piv.T

Item 5.02 counts by quarter from 2004 to 2024.

year	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	...	2015	2016	2017	2018	2019	2020	2021	2022	2023	2024
qtr
1	0	2397	2737	4772	5088	4573	3837	3939	3736	3543	...	3548	3382	3253	3164	3071	2943	3109	3279	3084	2794
2	0	2513	2575	4398	4591	3614	3472	3401	3328	3329	...	3305	3058	3035	2881	2948	2831	2947	2995	2920	2833
3	736	2416	2507	3831	3717	2974	2911	2920	2732	2683	...	2832	2529	2395	2452	2466	2416	2623	2534	2446	2237
4	2252	2479	3622	4136	4193	3405	3074	2977	3036	3031	...	2926	2649	2670	2674	2491	2531	2777	2534	2594	2415

4 rows × 21 columns

plt.figure(figsize=(8, 2))
sns.heatmap(
    counts_qtr_yr_piv.T,
    annot=True,  # Display the cell values
    fmt="d",  # Integer formatting
    cmap="magma",  # Color map
    cbar_kws={"label": "Count"},  # Colorbar label
    mask=counts_qtr_yr_piv.T == 0,  # Mask the cells with value 0
    cbar=False,
    annot_kws={"fontsize": 7},
)
plt.grid(False)
plt.title("Item 5.02 Counts by Quarter (2004-2024)")
plt.xlabel("Year")
plt.ylabel("Quarter")
plt.tight_layout()
plt.show()

counts_qtr_yr = counts_qtr_yr_piv.stack().reset_index(name="count")

fig, ax = plt.subplots(figsize=(6, 2.5))
counts_qtr_yr_piv.plot(kind="bar", ax=ax, legend=True)
ax.legend(title="Quarter", loc="upper right", bbox_to_anchor=(1.15, 1))
ax.set_title("Number of Item 5.02 Disclosures per Quarter\n(2004 - 2024)")
ax.set_xlabel("Year")
ax.set_ylabel("Number of\nItem 5.02 Filings")
ax.yaxis.set_major_formatter(mtick.StrMethodFormatter("{x:,.0f}"))
ax.grid(axis="x")
ax.set_axisbelow(True)
plt.tight_layout()
plt.show()

counts_month_yr_piv = (
    structured_data.groupby(["year", "month"]).size().unstack().fillna(0)
).astype(int)

plt.figure(figsize=(6, 4))
sns.heatmap(
    counts_month_yr_piv,
    annot=True,
    fmt="d",
    cmap="magma",
    cbar_kws={"label": "Count"},
    mask=counts_month_yr_piv == 0,
    cbar=False,
    annot_kws={"size": 7},
)
# convert x-labels to month names: 1 => Jan, 2 => Feb, etc.
plt.xticks(
    ticks=np.arange(0.5, 12.5, 1),
    labels=[pd.to_datetime(str(i), format="%m").strftime("%b") for i in range(1, 13)],
)
plt.grid(False)
plt.title("Item 5.02 Counts by Month (2004-2024)")
plt.xlabel("")
plt.ylabel("Year")
plt.tight_layout()
plt.show()

print(f"Descriptive statistics for Item 5.02 counts by month from 2004 to 2024.")
month_stats = (
    counts_month_yr_piv.loc[2004:]
    .describe(percentiles=[0.025, 0.975])
    .round(0)
    .astype(int)
)
month_stats

Descriptive statistics for Item 5.02 counts by month from 2004 to 2024.

month	1	2	3	4	5	6	7	8	9	10	11	12
count	21	21	21	21	21	21	21	21	21	21	21	21
mean	1030	1125	1167	974	1113	980	860	904	866	900	936	1096
std	320	363	341	291	326	265	253	229	135	168	151	234
min	0	0	0	0	0	0	0	137	599	699	751	782
2.5%	360	408	430	404	423	401	366	458	643	713	760	834
50%	1006	1102	1190	949	1134	1002	839	908	840	872	910	1019
97.5%	1526	1741	1666	1490	1651	1353	1300	1319	1156	1318	1260	1626
max	1540	1853	1695	1545	1689	1357	1301	1433	1211	1343	1334	1748

def plot_box_plot_as_line(
    data: pd.DataFrame,
    x_months=True,
    title="",
    x_label="",
    x_pos_mean_label=2,
    pos_labels=None, 
    pos_high_low=None,
    y_label="",
    y_formatter=lambda x, p: "{:.0f}".format(int(x) / 1000),
    show_high_low_labels=True,
    show_inline_labels=True,
    show_bands=True,
    figsize=(4, 2.5),
    line_source="mean",
):
    fig, ax = plt.subplots(figsize=figsize)

    line_to_plot = data[line_source]
    lower_label = "2.5%"
    upper_label = "97.5%"
    lower = data[lower_label]
    upper = data[upper_label]

    line_to_plot.plot(ax=ax)

    if show_bands:
        ax.fill_between(line_to_plot.index, lower, upper, alpha=0.2)

    if x_months:
        ax.set_xlim(0.5, 12.5)
        ax.set_xticks(range(1, 13))
        ax.set_xticklabels(["J", "F", "M", "A", "M", "J", "J", "A", "S", "O", "N", "D"])

    ax.yaxis.set_major_formatter(mtick.FuncFormatter(y_formatter))
    ax.set_ylabel(y_label)
    ax.set_xlabel(x_label)
    ax.set_title(title)

    ymin, ymax = ax.get_ylim()
    y_scale = ymax - ymin

    max_x = int(line_to_plot.idxmax())
    max_y = line_to_plot.max()
    min_x = int(line_to_plot.idxmin())
    min_y = line_to_plot.min()

    ax.axvline(
        max_x,
        ymin=0,
        ymax=((max_y - ymin) / (ymax - ymin)),
        linestyle="dashed",
        color="tab:blue",
        alpha=0.5,
    )
    ax.scatter(max_x, max_y, color="tab:blue", s=10)
    ax.axvline(
        min_x,
        ymin=0,
        ymax=((min_y - ymin) / (ymax - ymin)),
        linestyle="dashed",
        color="tab:blue",
        alpha=0.5,
    )
    ax.scatter(min_x, min_y, color="tab:blue", s=10)

    x_pos_mean_label_int = int(x_pos_mean_label)
    if show_inline_labels:
        mean_x = x_pos_mean_label
        mean_y = line_to_plot.iloc[x_pos_mean_label_int] * 1.02
        upper_x = x_pos_mean_label
        upper_y = upper.iloc[x_pos_mean_label_int]
        lower_x = x_pos_mean_label
        lower_y = lower.iloc[x_pos_mean_label_int] * 0.95

        if pos_labels:
            mean_x = pos_labels["mean"]["x"]
            mean_y = pos_labels["mean"]["y"]
            upper_x = pos_labels["upper"]["x"]
            upper_y = pos_labels["upper"]["y"]
            lower_x = pos_labels["lower"]["x"]
            lower_y = pos_labels["lower"]["y"]

        ax.text(mean_x, mean_y, "Mean", color="tab:blue", fontsize=8)
        ax.text(upper_x, upper_y, upper_label, color="tab:blue", fontsize=8)
        ax.text(lower_x, lower_y, lower_label, color="tab:blue", fontsize=8)

    if show_high_low_labels:
        high_x_origin = max_x
        high_y_origin = max_y
        high_x_label = high_x_origin + 0.5
        high_y_label = high_y_origin + 0.1 * y_scale
        if pos_high_low:
            high_x_label = pos_high_low["high"]["x"]
            high_y_label = pos_high_low["high"]["y"]
        ax.annotate(
            "High",
            (high_x_origin, high_y_origin),
            xytext=(high_x_label, high_y_label),
            arrowprops=dict(facecolor="black", arrowstyle="->"),
        )

        low_x_origin = min_x * 1.01
        low_y_origin = min_y
        low_x_label = low_x_origin + 1.5
        low_y_label = low_y_origin - 0.1 * y_scale
        if pos_high_low:
            low_x_label = pos_high_low["low"]["x"]
            low_y_label = pos_high_low["low"]["y"]
        ax.annotate(
            "Low",
            (low_x_origin, low_y_origin),
            xytext=(low_x_label, low_y_label),
            arrowprops=dict(facecolor="black", arrowstyle="->"),
        )

    ax.grid(axis="x")
    ax.set_axisbelow(True)

    plt.tight_layout()
    plt.show()


plot_box_plot_as_line(
    data=month_stats.T,
    title="Descriptive Statistics for Item 5.02 Filings by Month\n(2005 - 2024)",
    x_label="Month",
    y_label="Number of\nItem 5.02 Filings",
    y_formatter=lambda x, p: "{:.0f}".format(int(x)),
    x_pos_mean_label=5,
)

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

counts_month_yr_piv.loc[2005:].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("Item 5.02 Filings by Month\n(2005 - 2024)")
ax.set_xlabel("")
ax.set_ylabel("Item 5.02 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_filedAtClass = (
    structured_data.drop_duplicates(subset=["accessionNo"])
    .groupby(["filedAtClass"])
    .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.index = (
    counts_filedAtClass.index.str.replace("preMarket", "Pre-Market (4:00 - 9:30 AM)")
    .str.replace("marketHours", "Market Hours (9:30 AM - 4:00 PM)")
    .str.replace("afterMarket", "After Market (4:00 - 8:00 PM)")
)
counts_filedAtClass = counts_filedAtClass.reindex(counts_filedAtClass.index[::-1])

print(
    f"Item 5.02 counts by pre-market, regular market hours,\nand after-market publication time (2004 - 2024)."
)
counts_filedAtClass

Item 5.02 counts by pre-market, regular market hours,
and after-market publication time (2004 - 2024).

	Count	Pct
Publication Time
other	4,220	2%
Pre-Market (4:00 - 9:30 AM)	31,005	12%
regularMarket	67,424	27%
After Market (4:00 - 8:00 PM)	148,307	59%

counts_dayOfWeek = (
    structured_data.drop_duplicates(subset=["accessionNo"])
    .groupby(["dayOfWeek"])
    .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"Item 5.02 disclosures by day of the week (2004 - 2024).")
counts_dayOfWeek.loc[["Monday", "Tuesday", "Wednesday", "Thursday", "Friday"]]

Item 5.02 disclosures by day of the week (2004 - 2024).

	Count	Pct
Day of the Week
Monday	46,840	19%
Tuesday	50,634	20%
Wednesday	47,351	19%
Thursday	50,749	20%
Friday	55,380	22%

personnel_change_types = (
    structured_data[["year", "accessionNo", "personnelChanges"]]
    .explode(column="personnelChanges")
    .reset_index(drop=True)
)
personnel_change_types = pd.concat(
    [
        personnel_change_types.drop(columns="personnelChanges"),
        pd.json_normalize(personnel_change_types["personnelChanges"]),
    ],
    axis=1,
)
print(f"{len(personnel_change_types):,} personnel changes loaded")
personnel_change_types.head()

474,524 personnel changes loaded

	year	accessionNo	type	departureType	positions	reason	continuedConsultingRole	disagreements	person.name	interim	...	compensationIncreased	compensationDecreased	compensation.onetime	compensation.noCompensation	person.positionsAtOtherCompanies	termEndDate	consultingEndDate	person.familyRelationships	amendmentSummary	oldTermEndDate
0	2004	0001125282-04-004109	departure	resignation	[Chief Financial Officer]	Personal considerations to pursue other busine...	True	False	Tracy L. Shellabarger	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	2004	0001125282-04-004109	appointment	NaN	[Acting Chief Financial Officer]	NaN	NaN	NaN	Theresa E. Wagler	True	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2	2004	0001104659-04-025282	departure	retirement	[Senior Vice President, President, Building Pr...	NaN	NaN	NaN	E. L. (Tom) Thompson	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	2004	0001104659-04-025282	appointment	NaN	[Senior Vice President, President, Building Pr...	NaN	NaN	NaN	Robert C. McPherson III	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	2004	0000899715-04-000164	appointment	NaN	[Board of Directors]	NaN	NaN	NaN	Allan L. Schuman	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

5 rows × 32 columns

changeType = personnel_change_types["type"].value_counts().to_frame()
changeType.index.name = "Personnel Change Type"
changeType.columns = ["Count"]
changeType["Pct."] = changeType["Count"] / changeType["Count"].sum() * 100
changeType["Pct."] = changeType["Pct."].round(1)
changeType["Count"] = changeType["Count"].apply(lambda x: f"{x:,.0f}")

print(f"Types of personnel change if stated in the Item 5.02 filings (2004 - 2024):")
changeType

Types of personnel change if stated in the Item 5.02 filings (2004 - 2024):

	Count	Pct.
Personnel Change Type
appointment	210,156	47.5
departure	178,777	40.4
amendment	41,937	9.5
bonus	8,983	2.0
nomination	1,908	0.4
refusal	219	0.0

change_type_year = personnel_change_types[["type", "year", "accessionNo"]]
changeType_year_pivot = pd.pivot_table(
    change_type_year,
    index="type",
    columns="year",
    values="accessionNo",
    aggfunc="count",
    fill_value=0,
)

changeType_year_pivot["total"] = changeType_year_pivot.sum(axis=1)
changeType_year_pivot = changeType_year_pivot.sort_values(by="total", ascending=False)
# remove artifacts
changeType_year_pivot = changeType_year_pivot[changeType_year_pivot["total"] >= 18]

changeType_year_pivot

year	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	...	2016	2017	2018	2019	2020	2021	2022	2023	2024	total
type
appointment	3132	10089	11466	13076	12841	10552	10678	10718	10365	10075	...	9603	9306	9455	9390	8975	11357	9708	9356	8551	210156
departure	2273	7731	8578	11211	11687	9800	9031	9247	8901	8731	...	8472	8243	8169	8173	7464	7690	8518	8920	8170	178777
amendment	35	142	835	3860	4481	3320	2722	2593	2438	2262	...	1943	1893	1715	1571	2006	1557	1579	1310	1342	41937
bonus	0	0	191	1085	822	637	658	607	548	483	...	489	424	334	249	308	287	295	233	263	8983
nomination	16	105	114	128	173	121	93	66	90	116	...	77	84	97	94	93	90	62	50	52	1908
refusal	5	23	24	16	16	21	2	7	6	11	...	11	8	10	12	5	2	12	3	7	219

6 rows × 22 columns

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

changeType_year_pivot.head(5).drop(columns="total").T.plot(
    kind="bar", stacked=True, ax=ax
)

ax.set_title("Top 5 number of Item 5.02 Filings\nby Personnel Change Type and Year")
ax.set_xlabel("Year")
ax.set_ylabel("Number of Changes")
ax.xaxis.grid(False)
ax.set_axisbelow(True)
ax.yaxis.set_major_formatter(mtick.StrMethodFormatter("{x:,.0f}"))
handles, labels = ax.get_legend_handles_labels()  # reverse order of legend items

ax.legend(
    [h for h in reversed(handles)],
    [l for l in reversed(labels)],
    title="Auditor",
    bbox_to_anchor=(1.05, 1),
    labelspacing=0.3,
    fontsize=8,
)

plt.tight_layout()
plt.show()

appointments = personnel_change_types[
    personnel_change_types["type"] == "appointment"
].copy()

ceo_patterns = ["ceo", "chief executive officer"]
# check if one of these pattersn is in the array in the 'positions' column, set values to NaN if there is an error
appointments.loc[:, "isCEO"] = appointments["positions"].apply(
    lambda x: (
        any([pattern in str(x).lower() for pattern in ceo_patterns])
        if isinstance(x, list)
        else False
    )
)
cfo_patterns = ["cfo", "chief financial officer"]
appointments.loc[:, "isCFO"] = appointments["positions"].apply(
    lambda x: (
        any([pattern in str(x).lower() for pattern in cfo_patterns])
        if isinstance(x, list)
        else False
    )
)
board_patterns = ["board", "director", "chairman"]
appointments.loc[:, "isBoardMember"] = appointments["positions"].apply(
    lambda x: (
        any([pattern in str(x).lower() for pattern in board_patterns])
        if isinstance(x, list)
        else False
    )
)
appointments.loc[:, "isOther"] = ~appointments["isCEO"] & ~appointments["isBoardMember"]
# assign roles based on conditions
appointments.loc[:, "role"] = appointments.apply(
    lambda row: (
        "CEO" if row["isCEO"] else ("CFO" if row["isCFO"] else ("Board Member" if row["isBoardMember"] else "Other"))
    ),
    axis=1,
)

appointments_role_year = appointments.groupby(["role", "year"]).size().unstack().fillna(0)
appointments_role_year.loc["total"] = appointments_role_year.sum()

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

appointments_role_year.T.drop(columns="total").plot(kind="bar", stacked=True, ax=ax)

ax.set_title("Types of Appointments in 5.02 Filings")
ax.set_xlabel("Year")
ax.set_ylabel("Number of Appointments")
ax.xaxis.grid(False)
ax.set_axisbelow(True)
ax.yaxis.set_major_formatter(mtick.StrMethodFormatter("{x:,.0f}"))
handles, labels = ax.get_legend_handles_labels()  # reverse order of legend items

ax.legend(
    [h for h in reversed(handles)],
    [l for l in reversed(labels)],
    title="Appointed as",
    bbox_to_anchor=(1.05, 1),
    labelspacing=0.3,
    fontsize=8,
)

plt.tight_layout()
plt.show()

In this section we analyze the compensation for new appointments including

• one-time payments
• annual cash values
• equity compensation

appointments = personnel_change_types[personnel_change_types["type"] == "appointment"]

fig, ax = plt.subplots(figsize=(4, 2.5))
ax.hist(appointments["person.age"].dropna(), bins=20, edgecolor="darkgrey")
ax.set_title("Histogram of Ages of Persons Appointed")
ax.set_xlabel("Age")
ax.set_ylabel("Number of Persons")
ax.grid(axis="y")
ax.set_axisbelow(True)
ax.yaxis.set_major_formatter(mtick.StrMethodFormatter("{x:,.0f}"))
plt.tight_layout()
plt.show()

# exclude outliers below 18 and above 100
appointments = appointments[
    (appointments["person.age"] >= 18) & (appointments["person.age"] < 100)
]

mean_age = appointments["person.age"].mean()
min_age = appointments["person.age"].min()
max_age = appointments["person.age"].max()
median_age = appointments["person.age"].median()

print(f"Average Age: {mean_age:.2f}")
print(f"Min Age: {min_age:.2f}")
print(f"Max Age: {max_age:.2f}")
print(f"Median Age: {median_age:.2f}")

Average Age: 50.51
Min Age: 20.00
Max Age: 93.00
Median Age: 50.00

fig, ax = plt.subplots(figsize=(4, 2.5))

# convert the amounts to numbers, stripping dollar signs
compensation_annual = pd.to_numeric(
    appointments["compensation.annual"].str.replace(r"[\$,]", "", regex=True),
    errors="coerce",
).dropna()

ax.hist(compensation_annual, bins=20, edgecolor="darkgrey")
ax.set_title("Histogram of Annual Compensation of Persons Appointed")
ax.set_xlabel("Annual Compensation (USD in Millions)")
ax.set_ylabel("Number of Persons")
ax.set_yscale("log")
ax.grid(axis="y")
ax.set_axisbelow(True)
ax.xaxis.set_major_formatter(mtick.FuncFormatter(lambda x, p: f"{x/1_000_000:,.0f}"))
ax.yaxis.set_major_formatter(mtick.StrMethodFormatter("{x:,.0f}"))
plt.tight_layout()
plt.show()

# exclude outliers below 0 and above 1e7
# appointments = appointments[(appointments["compensation.annual"] >= 0) & (appointments["compensation.annual"] < 1e7)]

mean_compensation = compensation_annual.mean()
min_compensation = compensation_annual.min()
max_compensation = compensation_annual.max()
median_compensation = compensation_annual.median()

print(f"Average Compensation: {mean_compensation:,.2f}")
print(f"Min Compensation: {min_compensation:,.2f}")
print(f"Max Compensation: {max_compensation:,.2f}")
print(f"Median Compensation: {median_compensation:,.2f}")

Average Compensation: 383,013.58
Min Compensation: 0.00
Max Compensation: 12,000,000.00
Median Compensation: 330,000.00

# check how many compensations have an equity component
numEquityComponent = appointments["compensation.equity"].notnull().sum()

print(
    f"Appointments with including equity component in the Item 5.02 filings (2004 - 2024):"
)
print(
    f"Number of appointments with an equity component: {numEquityComponent:,} = {numEquityComponent / len(appointments):.1%} of all appointments."
)

Appointments with including equity component in the Item 5.02 filings (2004 - 2024):
Number of appointments with an equity component: 20,845 = 30.1% of all appointments.

departures = personnel_change_types[personnel_change_types["type"] == "departure"]
departureType_year = personnel_change_types[
    ["departureType", "year", "accessionNo", "disagreements"]
].explode("departureType")

departureType_year_pivot = pd.pivot_table(
    departureType_year,
    index="departureType",
    columns="year",
    values="accessionNo",
    aggfunc="count",
    fill_value=0,
)

departureType_year_pivot["total"] = departureType_year_pivot.sum(axis=1)
departureType_year_pivot = departureType_year_pivot.sort_values(
    by="total", ascending=False
)

departureType_year_pivot

year	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	...	2016	2017	2018	2019	2020	2021	2022	2023	2024	total
departureType
resignation	1578	5304	5670	7297	7855	6438	5833	5898	5574	5287	...	4704	4595	4348	4303	3989	3956	4248	4622	4256	106144
other	314	1098	1384	1840	1683	1529	1642	1637	1452	1645	...	1917	1823	1908	1982	1803	1937	2377	2200	2042	35805
retirement	282	996	1087	1381	1363	1075	1011	1132	1236	1260	...	1266	1270	1400	1403	1217	1372	1338	1368	1265	25347
termination	68	208	293	500	611	619	390	404	475	397	...	409	372	364	319	312	229	355	566	428	8052

4 rows × 22 columns

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

departureType_year_pivot.drop(columns="total").T.plot(kind="bar", stacked=True, ax=ax)

ax.set_title("Departures in 5.02 Filings\nby Departure Type and Year")
ax.set_xlabel("Year")
ax.set_ylabel("Number of Departures")
ax.xaxis.grid(False)
ax.set_axisbelow(True)
ax.yaxis.set_major_formatter(mtick.StrMethodFormatter("{x:,.0f}"))
handles, labels = ax.get_legend_handles_labels()  # reverse order of legend items

ax.legend(
    [h for h in reversed(handles)],
    [l for l in reversed(labels)],
    title="Departure Type",
    bbox_to_anchor=(1.05, 1),
    labelspacing=0.3,
    fontsize=8,
)

plt.tight_layout()
plt.show()

percentag_departure_pivot = departureType_year_pivot.copy()
percentag_departure_pivot.loc["total"] = percentag_departure_pivot.sum(axis=0)
percentag_departure_pivot = (
    percentag_departure_pivot.div(percentag_departure_pivot.loc["total"], axis=1) * 100
)

fig, ax = plt.subplots(figsize=(4, 2.5))
percentag_departure_pivot.drop("total").T.plot(kind="line", stacked=False, ax=ax)
ax.legend(title="Departure Type")
ax.set_ylabel("Percentage of Departures")
ax.set_title("Percentage of Departures in 5.02 Filings\nby Departure Type and Year")
plt.xticks(rotation=45, ha="right")
plt.tight_layout()
plt.show()

disagreements_year_pivot = departures.pivot_table(
    # departures,
    index="year",
    values=["disagreements"],
    aggfunc=lambda x: (x == True).sum(),
    fill_value=0,
)

departures_year_pivot = departures.pivot_table(
    index="year",
    values=["accessionNo"],
    aggfunc="count",
    fill_value=0,
)

disagreements_year_pivot = disagreements_year_pivot.T
disagreements_year_pivot["total"] = disagreements_year_pivot.sum(axis=1)
disagreements_year_pivot = disagreements_year_pivot.sort_values(
    by="total", ascending=False
)
disagreements_year_pivot

year	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	...	2016	2017	2018	2019	2020	2021	2022	2023	2024	total
disagreements	25	61	84	87	103	90	81	54	61	48	...	65	57	44	32	45	17	70	46	48	1212

1 rows × 22 columns

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

disagreements_year_pivot.T.drop("total").plot(kind="bar", stacked=False, ax=ax)

ax.set_title(
    "Number of Departures in Item 5.02 Filings\nwith Disclosed Disagreements by Year"
)
ax.set_xlabel("Year")
ax.set_ylabel("Number of Departures")
ax.xaxis.grid(False)
ax.set_axisbelow(True)
plt.tight_layout()
plt.show()

disagreements_year_pivot = departures.pivot_table(
    # departures,
    index="year",
    values=["disagreements"],
    aggfunc=lambda x: (x == True).sum(),
    fill_value=0,
)

departures_year_pivot = departures.pivot_table(
    index="year",
    values=["accessionNo"],
    aggfunc="count",
    fill_value=0,
).rename(columns={"accessionNo": "departures"})

percentage_disagreements = (
    disagreements_year_pivot["disagreements"]
    / departures_year_pivot["departures"]
    * 100
).fillna(0)
percentage_disagreements = percentage_disagreements.apply(lambda x: f"{x:.2f}")

departures_year_pivot["disagreements"] = disagreements_year_pivot["disagreements"]
departures_year_pivot["disagreements_percentage"] = percentage_disagreements
departures_year_pivot

	departures	disagreements	disagreements_percentage
year
2004	2273	25	1.10
2005	7731	61	0.79
2006	8578	84	0.98
2007	11211	87	0.78
2008	11687	103	0.88
2009	9800	90	0.92
2010	9031	81	0.90
2011	9247	54	0.58
2012	8901	61	0.69
2013	8731	48	0.55
2014	8747	53	0.61
2015	9021	41	0.45
2016	8472	65	0.77
2017	8243	57	0.69
2018	8169	44	0.54
2019	8173	32	0.39
2020	7464	45	0.60
2021	7690	17	0.22
2022	8518	70	0.82
2023	8920	46	0.52
2024	8170	48	0.59

This section includes statistics of amendments of contracts. First we will investigate boolean statistics and then analyze which part of the compensation was changed.

amendments = personnel_change_types[personnel_change_types["type"] == "amendment"]
columns_to_check_amendments = [
    "termShortened",
    "termExtended",
    "compensationIncreased",
    "compensationDecreased",
]
amendments_summary = (
    amendments[columns_to_check_amendments]
    .apply(lambda x: x.value_counts())
    .T.fillna(0)
    .astype(int)
)
amendments_summary["False % tot."] = (
    (amendments_summary[False] / len(amendments)) * 100
).map("{:.2f}".format)
amendments_summary["True % tot."] = (
    (amendments_summary[True] / len(amendments)) * 100
).map("{:.2f}".format)
print(f"Summary of the {len(amendments):,} amendments in Item 5.02 filings (2004 - 2024).")
amendments_summary

Summary of the 41,937 amendments in Item 5.02 filings (2004 - 2024).

	False	True	False % tot.	True % tot.
termShortened	893	98	2.13	0.23
termExtended	802	3997	1.91	9.53
compensationIncreased	1010	5100	2.41	12.16
compensationDecreased	968	1049	2.31	2.50

amendments = personnel_change_types[personnel_change_types["type"] == "amendment"]
columns_to_check_amendments = ["compensation.onetime", "compensation.annual", "compensation.equity"]
compensation_summary = amendments[columns_to_check_amendments].count().astype(int)
percentage_summary = (compensation_summary / len(amendments) * 100).round(2)
amendments_summary_df = pd.DataFrame([compensation_summary, percentage_summary], index=["Count", "Percentage"]).T
amendments_summary_df["Count"] = amendments_summary_df["Count"].astype(int)
amendments_summary_df

	Count	Percentage
compensation.onetime	1988	4.74
compensation.annual	18453	44.00
compensation.equity	9251	22.06

In this section we have a look at reported organizational changes.

org_changes = (
    structured_data[["accessionNo", "year", "organizationChanges"]]
    .dropna(subset="organizationChanges")
    .copy()
    .reset_index(drop=True)
)
org_changes = pd.concat(
    [
        org_changes[["accessionNo", "year"]],
        pd.json_normalize(org_changes["organizationChanges"]),
    ],
    axis=1,
)
org_changes

print(
    f"There are {len(org_changes):,} unique Form 8-K filings with a disclosed organization change."
)
org_changes

There are 39,640 unique Form 8-K filings with a disclosed organization change.

	accessionNo	year	organ	details	sizeIncrease	affectedPersonnel	sizeDecrease	created	abolished
0	0000899715-04-000164	2004	Board of Directors	Expansion of the board	True	[Allan L. Schuman]	NaN	NaN	NaN
1	0001193125-04-145794	2004	Board of Directors	Appointment of Marvin S. Hausman, MD to the Board	True	[Marvin S. Hausman, MD]	NaN	NaN	NaN
2	0000950137-04-007089	2004	Board of Directors	Election of Frederick H. Schneider as a Class ...	True	NaN	NaN	NaN	NaN
3	0000066756-04-000085	2004	Board of Directors	Amendments to bylaws including shareholder nom...	False	NaN	False	NaN	NaN
4	0001104659-04-025731	2004	Board of Directors	Reduction in size from ten members to nine	NaN	NaN	True	NaN	NaN
...	...	...	...	...	...	...	...	...	...
39635	0001171843-24-007128	2024	Audit Committee	The Company intends to maintain a two-member A...	False	[W. Thorpe McKenzie, Jan-Paul Waldin]	False	False	False
39636	0001331757-24-000239	2024	Board of Directors	Committee assignments for 2025 were finalized.	False	[Jeff Austin III, J. Mark Riebe, Rufus Cormier...	False	False	False
39637	0001104659-24-132872	2024	Board of Directors	Termination of the International Seaways, Inc....	False	NaN	False	False	False
39638	0001193125-24-287188	2024	Board of Directors	Increased the size of the Board to fifteen dir...	True	[Jacqueline Allard, Somesh Khanna]	False	False	False
39639	0001104659-24-132903	2024	NaN	NaN	False	NaN	False	False	False

39640 rows × 9 columns

org_changes["organ"].value_counts().head()

organ
Board of Directors        31805
Compensation Committee      452
Audit Committee             404
Board of Trustees           253
Equity Incentive Plan       228
Name: count, dtype: int64

board_changes = org_changes[org_changes["organ"] == "Board of Directors"].copy()

# Define classification function
def classify_size_change(row):
    
    if row["sizeIncrease"]:
        return "increase"
    elif row["sizeDecrease"]:
        return "decrease"
    else:
        return "no size change"
    
board_changes["sizeChange"] = board_changes.apply(classify_size_change, axis=1)

print("Size changes of the Board of Directors disclosed in Item 5.02 over the full period (2004 - 2024).")
print(board_changes["sizeChange"].value_counts())

Size changes of the Board of Directors disclosed in Item 5.02 over the full period (2004 - 2024).
sizeChange
increase          25566
no size change     5518
decrease            721
Name: count, dtype: int64

counts_board_size_change_yr_piv = (
    board_changes.drop_duplicates(subset=["accessionNo"])
    .groupby(["year", "sizeChange"])
    .size()
    .unstack()
    .fillna(0)
).astype(int)
print(f"Size changes of the Board of Directors disclosed in Item 5.02 filings by year (2004 - 2024).")
counts_board_size_change_yr_piv.T

Size changes of the Board of Directors disclosed in Item 5.02 filings by year (2004 - 2024).

year	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	...	2015	2016	2017	2018	2019	2020	2021	2022	2023	2024
sizeChange
decrease	4	12	20	35	28	32	14	29	21	32	...	43	29	40	39	40	41	44	51	58	82
increase	296	983	1132	1229	1275	1107	1136	1183	1234	1226	...	1249	1279	1259	1304	1276	1335	1648	1417	1394	1296
no size change	21	100	141	286	352	294	297	281	252	268	...	281	249	280	247	308	309	281	330	329	348

3 rows × 21 columns

counts_bsc_yr = counts_board_size_change_yr_piv.stack().reset_index(name="count")

fig, ax = plt.subplots(figsize=(7, 2.5))
counts_board_size_change_yr_piv.plot(kind="bar", ax=ax, legend=True)
ax.legend(title="Change", loc="upper right", bbox_to_anchor=(1.32, 1))
ax.set_title("Number of Board of Directors changes disclosed in Item 5.02\nby size change and year (2004 - 2024)")
ax.set_xlabel("Year")
ax.set_ylabel("Number of\nItem 5.02 Filings")
ax.yaxis.set_major_formatter(mtick.StrMethodFormatter("{x:,.0f}"))
ax.grid(axis="x")
ax.set_axisbelow(True)
plt.tight_layout()
plt.show()

This section gives a quick overview of the bonus plans reported in the Item 5.02 filings.

For this section, we downloaded all filings including a bonus plan change from the Form 8-K Item 5.02 Structured Data API and prepared a second dataframe. For this, we exploded the list of bonus plans and saved it to disk. This way, we can start with the analysis right away.

bonus_data = (
    structured_data[["accessionNo", "year", "bonusPlans"]]
    .copy()
    .explode(column="bonusPlans")
    .dropna(subset=["bonusPlans"])
    .reset_index(drop=True)
)

bonus_data = pd.concat(
    [
        bonus_data[["accessionNo", "year"]],
        pd.json_normalize(bonus_data["bonusPlans"]),
    ],
    axis=1,
)
print(f"{len(bonus_data):,} bonus plan disclosures loaded.")
bonus_data

50,189 bonus plan disclosures loaded.

	accessionNo	year	specificIndividuals	eligibleIndividuals	compensation.cash	conditional	conditions	compensation.equity	compensation.equityDetails	specificRoles	generalEmployee	eligibleRoles
0	0001104659-04-025325	2004	True	[Dale Brown]	20% of annual salary	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	0000950123-04-010249	2004	True	[Michael P. Huseby]	Annual bonus with a target of 80% of his annua...	True	Performance-based vesting for conjunctive righ...	Annual awards of 10,000 restricted shares of C...	Four-year cliff vesting	NaN	NaN	NaN
2	0000703701-04-000042	2004	True	[Benjamin M. Cutler]	Minimum Annual Bonus of $166,666 for 2004 and ...	True	Achievement of performance targets	Options to purchase 9.9% of the common stock	Vesting over 5 years	NaN	NaN	NaN
3	0001204560-04-000031	2004	True	[Shant Koumriqian]	Targeted annual bonus of 40%	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	0001104659-04-027002	2004	True	[Dr. Hahn, Mr. Howard, Mr. Raymond, Mr. Rice]	$15,000 annual retainer	False	NaN	30,000 stock options	25% vesting immediately with an additional 25%...	False	False	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...
50184	0001493152-24-052707	2024	True	[Pete O'Heeron, Hamid Khoja]	NaN	NaN	NaN	406,339 shares of common stock for Pete O'Heer...	One fourth (1/4th) vest on December 27, 2025, ...	NaN	NaN	NaN
50185	0001193125-24-287183	2024	True	[Matt Reback]	$175,000	True	Subject to the conditions set forth in the Emp...	NaN	NaN	False	False	NaN
50186	0001213900-24-114015	2024	True	[Dr. William J. McGann]	5% of annual base salary	NaN	NaN	NaN	NaN	NaN	NaN	NaN
50187	0001193125-24-287188	2024	True	[Christopher M. Gorman, Clark Khayat, Andrew J...	NaN	True	Achievement of regulatory capital requirements...	Share-settled performance-based equity awards	Vesting in January 2027 based on regulatory ca...	False	False	NaN
50188	0001104659-24-132903	2024	True	[Marc Holliday]	NaN	True	Achievement of specific operational or financi...	Performance-Based Class O LTIP Units with a gr...	Subject to time-based and performance-based ve...	False	False	NaN

50189 rows × 12 columns

bool_variables_to_analyze = [
    "specificRoles",
    "generalEmployee",
    "specificIndividuals",
    "conditional",
]

var_to_label = {
    "specificRoles": "Specific Positions are eligible",
    "generalEmployee": "General Employees are eligible",
    "specificIndividuals": "Specific Individuals are eligible",
    "conditional": "Conditional Bonus",
}


total_samples = len(bonus_data)
# Create a row for the total samples
total_row = pd.DataFrame(
    {
        "Samples": [f"{total_samples:,.0f}"],
        "Pct.": [""],
        "Pct. tot.": [100],
    },
    index=pd.MultiIndex.from_tuples([("Total", "")], names=["Variable", "Value"]),
)


bool_variables_stats = []

for variable in bool_variables_to_analyze:
    variable_stats = (
        bonus_data[variable]
        .value_counts()
        .to_frame()
        .reset_index()
        .rename(columns={variable: "value"})
    )
    variable_stats = variable_stats.sort_values(by="value", ascending=False)
    variable_stats["pct"] = (
        variable_stats["count"] / variable_stats["count"].sum() * 100
    ).round(1)
    variable_stats["pct_tot"] = (variable_stats["count"] / total_samples * 100).round(1)
    variable_stats.index = pd.MultiIndex.from_tuples(
        [(variable, row["value"]) for _, row in variable_stats.iterrows()],
    )
    variable_stats.drop(columns="value", inplace=True)

    bool_variables_stats.append(variable_stats)

bool_variables_stats = pd.concat(bool_variables_stats, axis=0)
bool_variables_stats.index.set_names(["Variable", "Value"], inplace=True)
bool_variables_stats.rename(
    index=var_to_label,
    columns={"count": "Samples", "pct": "Pct.", "pct_tot": "Pct. tot."},
    inplace=True,
)
bool_variables_stats["Samples"] = bool_variables_stats["Samples"].apply(
    lambda x: f"{x:,.0f}"
)


bool_variables_stats = pd.concat([total_row, bool_variables_stats])


print(
    f"Number of Bonus Plans disclosed in Item 5.02\nby their disclosed characteristics (2004 - 2024):"
)
bool_variables_stats

Number of Bonus Plans disclosed in Item 5.02
by their disclosed characteristics (2004 - 2024):

		Samples	Pct.	Pct. tot.
Variable	Value
Total		50,189		100.0
Specific Positions are eligible	True	9,181	27.4	18.3
	False	24,280	72.6	48.4
General Employees are eligible	True	6,632	22.2	13.2
	False	23,295	77.8	46.4
Specific Individuals are eligible	True	32,642	69.6	65.0
	False	14,230	30.4	28.4
Conditional Bonus	True	32,414	77.8	64.6
	False	9,254	22.2	18.4

columns_to_check_bonus = ["compensation.cash", "compensation.equity"]
compensation_summary = bonus_data[columns_to_check_bonus].count().astype(int)
percentage_summary = (compensation_summary / len(bonus_data) * 100).round(2)
bonus_summary_df = pd.DataFrame([compensation_summary, percentage_summary], index=["Count", "Percentage"]).T
bonus_summary_df["Count"] = bonus_summary_df["Count"].astype(int)
bonus_summary_df.rename(columns={"Count": "Total Count", "Percentage": "Share (%)"}, inplace=True)
bonus_summary_df = bonus_summary_df.rename(index={"compensation.cash": "Cash Compensation",
                                                  "compensation.equity": "Equity Compensation"})
print("Number of Bonus Plans disclosed in Item 5.02 filings (2004 - 2024) with compensation details.")
bonus_summary_df

Number of Bonus Plans disclosed in Item 5.02 filings (2004 - 2024) with compensation details.

	Total Count	Share (%)
Cash Compensation	29494	58.77
Equity Compensation	26408	52.62