Stock Price Reactions to Item 4.02 Disclosures in SEC Form 8-K Filings

On this page:

• Key Results
• Data & Method
  • Data
  • Event Study Methodology
    • Event Study Example
• Results
• Key Findings
  • Stock Price Impact
  • General Trends
  • Disclosed Information
  • Auditor Analysis

When a company issues a non-reliance disclosure under Item 4.02 in SEC Form 8-K filings, it signals to the market that previously reported financial statements can no longer be relied on. Such disclosures often indicate errors, restatements, or significant issues within a company’s financials and internal controls, raising questions for investors and stakeholders.

We examine the impact of these disclosures on stock prices, finding a negative market reaction that compounds over time. By analyzing over 8,000 Item 4.02 disclosures from 2004 to 2023, we provide insights into cumulative abnormal returns (CARs) following these announcements. Additionally, we look at information content, auditor involvement, and disclosure timing to better understand the implications of these events.

• 8,143 Item 4.02 disclosures were filed between 2004 and 2023, with 864 filings in 2021, the third highest number in the period.
• From 2007 to 2023 (excluding 2021), Item 4.02 disclosures are associated with an average cumulative abnormal return (CAR) of -1.1% one day post-disclosure, extending to -2% over a 20-day event window. Median CAR values register at -0.5% one day post-disclosure and -2.6% at 20 days, with all results statistically significant at the 0.1% level.
• The volume of disclosures peaked in 2006 with 1,004 filings, followed by a consistent decline to 96 filings in 2020. However, the COVID-19 pandemic led to an 800% surge in 2021, with filings reaching 864; filings in 2022 and 2023 are gradually reverting to pre-pandemic levels.
• In 97% of cases, a restatement of previously reported financial statements is deemed necessary, with the majority of non-reliance disclosures indicating that the identified issues are material—75% of filings explicitly state this.
• Big 4 auditors are involved in 24% of Item 4.02 cases, with PwC as the most frequently cited auditor, appearing in 9.7% of filings.
• A notable 64% of these disclosures are published after hours, between 4:00 PM and 8:00 PM, peaking at 5:00 PM. No significant pattern is observed regarding the day of the week for these disclosures.

The event study methodology is applied to assess the impact of Item 4.02 disclosures on stock prices over a 1 to 20 business day event window. Cumulative abnormal returns (CARs) are calculated using the market model, with the S&P 500 SPY ETF serving as the market proxy.

The study period for analyzing Item 4.02 content spans from 2004 to 2023, while the event study analysis is conducted from 2007 to 2023, reflecting the availability of adjusted pricing data starting in 2007.

Structured data on Item 4.02 disclosures is retrieved through the Form 8-K Structured Data API, and pricing data is sourced from AlgoSeek.

The following filing metadata and content data points are used in the analysis.

Metadata of Form 8-K filings containing Item 4.02 disclosures:

• Filing information:
  • accessionNo (str) - The unique accession number of the 8-K filing.
  • filedAt (str) - Date and time the filing was accepted by the EDGAR system for publication. Example: "2022-03-01T16:00:00-05:00".
  • filedAtClass (str) - filedAt converted to a disclosure time category: preMarket (6:00am - 9:30am), marketHours (9:30am to 4:00pm) and afterMarket (4:00pm to 8:00pm).
  • dayOfWeek (str) - filedAt converted to day of week, Monday to Friday.
  • month (str) - filedAt converted to month, January to December.
  • year (str) - filedAt converted to year, 2004 to 2024.
  • qtr (str) - filedAt converter to quarter, Q1 to Q4.
• Filer information:
  • cik (str) - Central Index Key of the filing company.
  • ticker (str) - Ticker symbol of the filing company, if available.
  • companyName (str) - Name of the filing company.

Content specific information:

• items (list of strings) - Item IDs of triggering events, for example ["4.02", "9.01"] in case an 8-K filing includes Item 4.02 and Item 9.01.
• reportedWithOtherItems (bool) - true if the disclosure includes other items in addition to Item 4.02, false otherwise. Derived from items.
• reportedWithEarnings (bool) - true if items includes Item 2.02 or Item 9.01, false otherwise. Derived from items.
• keyComponents (list of strings) - Key components of the disclosure, such as the identification of material errors in revenue recognition
• identifiedIssues (list of strings) - Issues identified in the disclosure, such as "Revenue recognition errors"
• affectedReportingPeriods (list of strings) - Reporting periods affected by the identified issues, such as "Q1 2022"
• numberPeriodsAffected (int) - Number of periods affected by the identified issues. Derived from affectedReportingPeriods.
• numberQuartersAffected (int) - Number of quarters affected by the identified issues. Derived from affectedReportingPeriods.
• numberYearsAffected (int) - Number of years affected by the identified issues. Derived from affectedReportingPeriods.
• identifiedBy (list of stirngs) - Who identified the issues, such as "Company", "Auditor", "SEC".
• identifiedByAuditor (bool) - true if the issues were identified by the auditor, false otherwise. Derived from identifiedBy.
• identifiedByCompany (bool) - true if the issues were identified by the company, false otherwise. Derived from identifiedBy.
• identifiedBySec (bool) - true if the issues were identified by the SEC, false otherwise. Derived from identifiedBy.
• impactOfError (str) - The impact of the identified issues, such as "Net income was overstated by $10 million"
• reasonsForRestatement (list of strings) - Reasons for the restatement, such as "Revenue recognition errors"
• restatementIsNecessary (bool) - Whether the disclosure indicates that a restatement of previously filed financial statements is necessary.
• impactYetToBeDetermined (bool) - true if the company explicitly states that the impact of the error is yet to be determined, false otherwise.
• impactIsMaterial (bool) - true if the company explicitly states that the impact of the error is material, false otherwise.
• materialWeaknessIdentified (bool) - Whether a material weakness in internal controls was identified.
• auditors (list of strings) - Names of auditors involved in the disclosure, i.e. the company's auditor/s.
• hasBig4Auditor (bool) - true if the company's auditor is one of the Big 4 accounting firms (Deloitte, EY, KPMG, PwC), false otherwise. Derived from auditors.
• affectedLineItems (list of strings) - Financial statement line items affected by the identified issues, such as "Revenue", "Net income".
• netIncomeDecreased (bool) - Whether net income decreased as a result of the identified issues.
• netIncomeIncreased (bool) - Whether net income increased as a result of the identified issues.
• netIncomeAdjustment (str) - If explicitly disclosed, the magnitude of the net income adjustment, e.g. "$348 million".
• netIncomeAdjustmentContainsWordMillion (bool) - true if the net income adjustment contains the word "million", false otherwise. Derived from netIncomeAdjustment.
• revenueDecreased (bool) - true if the company explicitly states that revenue decreased as a result of the restatement, false otherwise.
• revenueIncreased (bool) - true if the company explicitly states that revenue increased as a result of the restatement, false otherwise. Note, this field is not mutually exclusive with revenueDecreased as both can be true if the restatement affected different periods.
• revenueAdjustment (str) - If explicitly disclosed, the magnitude of the revenue adjustment, e.g. "$19 million".
• revenueAdjustmentContainsWordMillion (bool) - true if the revenue adjustment contains the word "million", false otherwise. Derived from revenueAdjustment.

This study examines the impact of SEC Form 8-K, Item 4.02 disclosures on stock prices using an event study approach. Cumulative abnormal returns (CAR) are measured for each filing over event windows ranging from 1 to 20 business days post-filing to quantify the disclosure’s effect on the stock price. CAR is calculated relative to the broader market, represented by the S&P 500 index (ticker: SPY).

Method for CAR Calculation:

For each disclosure event, CAR is calculated over a specified period from day $t_0$ (the filing date) to day $t_0+x$ days as follows:

1. Data Points Required:

   • Adjusted closing price of the filer’s stock at $t_0$
   • Adjusted closing price of the filer’s stock at $t_0+x$
   • Adjusted closing price of SPY at $t_0$
   • Adjusted closing price of SPY at $t_0+x$

2. Calculation Steps:

   • Calculate the percentage change in SPY’s adjusted closing price over the period $(t_0,t_0+x)$, denoted as $A$.
   • Calculate the percentage change in the filer’s adjusted closing price over the same period, denoted as $B$.

3. Cumulative Abnormal Return (CAR):

   • The CAR for the filer’s stock is then computed as:$\text{CAR}=B-A$

This approach isolates the filer’s excess return by comparing it to the SPY’s performance over the event window, thereby removing the impact of general market movements.

Suppose Company XYZ filed a Form 8-K, Item 4.02 disclosure on January 1, which is designated as day $t_0$. We aim to calculate the cumulative abnormal return (CAR) over a 10-business day event window, ending on January 12 (day $t_0+10$).

1. Data Points:

Data Point	Date	Value
Adjusted closing price of XYZ at $t_0$	Jan 1	$50.00
Adjusted closing price of XYZ at $t_0+10$	Jan 12	$47.00
Adjusted closing price of SPY at $t_0$	Jan 1	$400.00
Adjusted closing price of SPY at $t_0+10$	Jan 12	$410.00

2. Calculating Percentage Changes:

   • SPY Percentage Change (A):$A=\frac{410-400}{400}\times 100=2.5\mathrm{\%}$

   • XYZ Percentage Change (B):$B=\frac{47-50}{50}\times 100=-6.0\mathrm{\%}$

3. Cumulative Abnormal Return (CAR):

   Using the formula $\text{CAR}=B-A$:$\text{CAR}=-6.0\mathrm{\%}-2.5\mathrm{\%}=-8.5\mathrm{\%}$

Interpretation:

The CAR of -8.5% over the 10-day event window indicates that Company XYZ’s stock underperformed the market by 8.5% following the disclosure. This negative abnormal return suggests that the market responded unfavorably to the filing, leading to a decrease in the stock price relative to the S&P 500 index over this period.

print(f"Loaded {len(all_data):,} structured data records from 2004 to 2023.")

Loaded 8,143 structured data records from 2004 to 2023.

print(f"Item 4.02 disclosures by day of the week (2004 - 2023).")
counts_dayOfWeek.loc[['Mon', 'Tue', 'Wed', 'Thu', 'Fri']]

Item 4.02 disclosures by day of the week (2004 - 2023).

	Count	Pct
Day of the Week
Mon	1,584	20%
Tue	1,655	21%
Wed	1,490	19%
Thu	1,519	19%
Fri	1,758	22%

print(
    f"Item 4.02 counts by pre-market, regular market hours,\n"
    + "and after-market publication time (2004 - 2023)."
)
counts_filedAtClass

Item 4.02 counts by pre-market, regular market hours,
and after-market publication time (2004 - 2023).

	Count	Pct
Publication Time
Pre-Market (4:00 AM - 9:30 AM)	899	11%
Market Hours (9:30 AM - 4:00 PM)	2,022	25%
After Market (4:00 PM - 8:00 PM)	5,085	64%

print("Characteristics of Non-Reliance Disclosures (2004-2023)")
bool_variables_stats

Characteristics of Non-Reliance Disclosures (2004-2023)

		Samples	Pct.
Variable	Value
Impact is Material	True	6,064	74.5
	False	2,079	25.5
Restatement Necessary	True	7,935	97.4
	False	208	2.6
Impact yet to be determined	True	1,987	24.4
	False	6,156	75.6
Material Weakness Identified	True	2,122	26.1
	False	6,021	73.9
Reported with Other Items	True	3,572	43.9
	False	4,571	56.1
Reported with Earnings	True	3,155	38.7
	False	4,988	61.3
Net Income Decreased	True	2,053	25.2
	False	6,090	74.8
Net Income Increased	True	749	9.2
	False	7,394	90.8
Revenue Decreased	True	615	7.6
	False	7,528	92.4
Revenue Increased	True	196	2.4
	False	7,947	97.6
Has Big 4 Auditor	True	1,990	24.4
	False	6,153	75.6
Revenue Adj. contains 'million'	True	186	2.3
	False	7,957	97.7
Net Income Adj. contains 'million'	True	444	5.5
	False	7,699	94.5
Identified by Company	True	6,792	83.4
	False	1,351	16.6
Identified by Auditor	True	2,129	26.1
	False	6,014	73.9
Identified by SEC	True	1,262	15.5
	False	6,881	84.5

print("Top 10 auditors involved in \nnon-reliance disclosures from 2004 to 2023:")
auditors.head(10)

Top 10 auditors involved in 
non-reliance disclosures from 2004 to 2023:

	auditors	count	pct
0	PwC	634	9.73
1	EY	570	8.75
2	Marcum	459	7.05
3	Deloitte	430	6.60
4	KPMG	425	6.53
5	Unknown	323	4.96
6	WithumSmith+Brown	322	4.94
7	BDO	317	4.87
8	Grant Thornton	163	2.50
9	MaloneBailey	99	1.52

print("Top 10 auditors involved in \nnon-reliance disclosures from 2004 to 2023 (excluding 2021):")
all_auditors_ex_2021.head(10)

Top 10 auditors involved in 
non-reliance disclosures from 2004 to 2023 (excluding 2021):

	auditors	count	pct
0	PwC	625	10.96
1	EY	546	9.57
2	Deloitte	424	7.43
3	KPMG	396	6.94
4	BDO	299	5.24
5	Unknown	287	5.03
6	Grant Thornton	153	2.68
7	Marcum	119	2.09
8	MaloneBailey	97	1.70
9	WithumSmith+Brown	85	1.49

print("Statistics of all Cumulative Abnormal Returns (2007-2023)")
excess_return_stats

Statistics of all Cumulative Abnormal Returns (2007-2023)

CAR:	t0+1 Day	t0+5	t0+10	t0+20
Statistics:
Samples	2,398	2,396	2,378	2,341
Mean CAR	-0.87%	-1.15%	-1.17%	-1.54%
Median CAR	-0.26%	-0.46%	-0.71%	-1.31%
Std. Dev.	10.93	14.06	17.34	23.89
Min	-81.45%	-93.13%	-94.19%	-68.31%
Max	320.62%	341.79%	363.04%	631.01%

print("Statistics of all Cumulative Abnormal Returns (2007-2023, excluding 2021)")
excess_return_stats_ex_2021

Statistics of all Cumulative Abnormal Returns (2007-2023, excluding 2021)

CAR:	t0+1 Day	t0+5	t0+10	t0+20
Statistics:
Samples	1,712	1,712	1,694	1,664
Mean CAR	-1.10%	-1.54%	-1.58%	-2.00%
Median CAR	-0.50%	-1.07%	-1.40%	-2.64%
Std. Dev.	12.82	16.23	19.69	27.36
Min	-81.45%	-93.13%	-94.19%	-68.31%
Max	320.62%	341.79%	363.04%	631.01%

• From 2007 to 2023, Item 4.02 disclosures resulted in an average cumulative abnormal return (CAR) of -0.9% one day after the filing, extending to -1.5% over a 20-day event window, indicating a negative market reaction.
• The median CAR over the same period was -0.3% one day after filing, expanding to -1.3% over 20 days.
• Excluding 2021, the impact was more pronounced, with an average CAR of -1.1% one day post-filing and -2.0% over a 20-day event window. Median CARs were -0.5% and -2.6% over these respective periods.
• All results are statistically significant at the 0.1% level.

• The number of Item 4.02 disclosures peaked in 2006, reaching 1,004 filings following the Sarbanes-Oxley Act’s 2002 enactment.
• Since 2006, disclosures have declined yearly, reaching a low of 96 filings in 2020. However, in 2021, the COVID-19 pandemic spurred an 800% increase, with filings surging to 864. Filings in 2022 and 2023 then decreased to 304 and 262, respectively, with 2023 still up by 172% compared to 2020.
• The 2021 peak was largely driven by filings in Q2 and Q4.
• Disclosures are evenly distributed across weekdays: Wednesday and Thursday account for 19% each, while Friday is the highest at 22%. Monday and Tuesday each represent 20% and 21%, respectively.
• A majority (64%) of non-reliance disclosures are published after market close, between 4:00 PM and 10:00 PM EST. During market hours (9:30 AM to 4:00 PM EST), 25% of filings are released, while 11% are published pre-market (6:00 AM to 9:30 AM EST).

• In 97% of cases, a restatement of previously reported financial statements is required.
• Most non-reliance disclosures (75%) indicate that the identified issues are material.
• Approximately 24% of filings explicitly state that the financial impact of the error is uncertain at the time of disclosure.
• A material weakness in internal controls is reported in 26% of filings.
• Companies disclose Item 4.02 alongside other triggering items, such as Item 9.01 or Item 2.02, in 44% of cases.
• Earnings-related items (e.g., Item 2.02 or Item 9.01) accompany 39% of filings.
• Net income adjustments are required in 25% of filings (downward) and 9.2% (upward).
• Revenue adjustments are specified in 7.6% of filings (downward) and 2.4% (upward).
• Large adjustments exceeding $1 million are required in 2.3% of revenue adjustments and 5.5% of net income adjustments.
• Big 4 auditors are involved in 24% of filings.
• In 83% of disclosures, the company identified the issues leading to the non-reliance disclosure, while auditors were responsible in 26% of cases and the SEC in 15%.

• A total of 856 unique auditors were involved in non-reliance disclosures between 2004 and 2023.
• Big 4 auditors participated in 31% of filings, with PwC being the most frequently involved (9.7%), followed by EY (8.8%), Deloitte (6.6%), and KPMG (6.5%).
• Marcum was the most common non-Big 4 auditor, involved in 7% of filings, primarily driven by an increase during 2021. Excluding 2021, Marcum’s involvement drops to 1.7%. Similarly, WithumSmith is present in 4.9% of all filings (2004 - 2023), but only 1.5% when excluding 2021.
• BDO maintained consistent involvement, appearing in 4.9% of all cases and 5.2% when excluding 2021.