The Standard and Poors' Filing Date Database - Research ...

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Professor Joshua Livnat, Ph.D., CPA10-76 K-MEC

New York University44 W. 4th St.

NY NY 10012Tel. (212) 998-0022 Fax (212) 995-4004

[email protected] page: www.stern.nyu.edu/~jlivnat

The Standard & Poors’Filing Dates Database –Research Applications

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Overview

• The need for the database.

• Description of the database.• Three research applications.

3

Data Acknowledgements

• Charter Oak Investment Systems Inc. for providing the preliminary and original Compustat quarterly data.– http://www.charteroaksystems.com/

• S&P’s Filing Dates Database.

• Thomson Financial for providing earnings forecasts available through the Institutional Brokers Estimate System.

4

The Market Need

• The SEC EDGAR database contains a wealth of information on listed companies:– Periodic reports; Form 10-K, Form 10-Q– Special reports; Form 8-K– Proxy statements; DEF14– Registration statements; S-1 through S-4– Insider trading; Forms 3 and 4

• The EDGAR database contains the filing dates of the various forms. Ideal for event studies.

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The Market Need (continued)

• The SEC EDGAR database identifies companies by a unique identification code, CIK.

• The CIK is not linked to the customary research databases such as Compustat, CRSP, or IBES.

• The Compustat Expressfeed and Research Insight databases contain CIK’s, but those are for live companies. Using them in research will cause survivorship bias.

• Commercial EDGAR vendors such as EDGAR Online and 10K Wizard offer linking of CIK’s to ticker symbols or even CUSIP’s. Those are typically good for live companies.

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The Market Need (continued)

• Even with perfect matching between CIK and some other identifiers, there is a need to extract information from SEC filings:– Financial statement period-end dates for

Forms 10-K and 10-Q.

– Reason for filing Form 8-K.

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The S&P Filing Dates Database

• Matching Compustat’s GVKEY for live and inactive companies to CIK’s using the Point-In-Time database.

• Extracting all SEC EDGAR filing dates for each GVKEY.

• Extracting information from key filings to enable research.

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Digression – The PIT Database

• The Compustat quarterly database gets rewritten continuously when firms update the information on previous quarters: – Mergers and acquisitions.– Divestitures.– Restatements.– Disclosure of quarterly results in the 10-K Form.– Compustat captures preliminary information (from

firms’ preliminary earnings announcements) and final information from SEC filings.

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• Charter Oak collected the weekly CD-Rom’s sent by Compustat to its clients. From those, it constructed:

– A preliminary database, which contains information released by companies and captured by Compustat before it became final.

– A PIT database, which contains the information that Compustat users would have had at any given month-end.

• Both databases are now available through WRDS.

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• Research using the PIT database:– Earnings and revenues surprises,

Jegadeesh and Livnat, JAE 2006.– Post earnings announcement drift for

firms covered by IBES, Livnat and Mendenhall, JAR 2006.

– Firms that change earnings between preliminary to SEC filings, Hollie, Livnat and Segal, JOPM 2005.

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The preliminary and PIT databases

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Matching GVKEY-CIK

• Identified all GVKEY’s on the PIT database for firms that had a market value in excess of $1million at quarter-end.

• Matched these firms to CIK’s, and checked each match if the name on EDGAR and Compustat was not identical.

• A few GVKEY’s were not matched. Typically, not listed firms, or Canadian firms.

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Extracting SEC Data

• Using Text-Mining techniques, extracted certain information from filings.

• The report date from periodic reports:– The financial statement period-end date.

• The date for the annual shareholders meeting from proxy statements.

• The date on which the reported event in a special report Form-K occurred.

• Reasons for filing a special report on Form 8-K.

14The S&P Filing Dates Database

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GlobalCompany

Key

Dateof

Filing

Nameof

theCompany

Typeof

FormFiled

ReportDate

4839 20050310 FORD MOTOR CO 10-K 200412314839 20060301 FORD MOTOR CO 10-K 200512314839 20070228 FORD MOTOR CO 10-K 200612314839 20061114 FORD MOTOR CO 10-K/A 200512314839 20050510 FORD MOTOR CO 10-Q 200503314839 20050804 FORD MOTOR CO 10-Q 200506304839 20051107 FORD MOTOR CO 10-Q 200509304839 20060509 FORD MOTOR CO 10-Q 200603314839 20060804 FORD MOTOR CO 10-Q 200606304839 20061114 FORD MOTOR CO 10-Q 200609304839 20070509 FORD MOTOR CO 10-Q 200703314839 20070807 FORD MOTOR CO 10-Q 200706304839 20071108 FORD MOTOR CO 10-Q 200709304839 20061117 FORD MOTOR CO 10-Q/A 200603314839 20061117 FORD MOTOR CO 10-Q/A 20060630

Periodic Reports

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4839 20050406 FORD MOTOR CO DEF 14A 200505124839 20060407 FORD MOTOR CO DEF 14A 200605114839 20070405 FORD MOTOR CO DEF 14A 20070510

Proxy Statements

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Form 8-K

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Ford – All Filings

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Recent Research Applications

• Quarterly accruals or cash flows? Forthcoming in FAJ.– Uses exact filing dates for 10-Q and 10-K to

construct portfolios.

• Tone of MD&A section.• Market reactions to Form 8-K filings.

– Uses filing dates and reasons for filing.

• Proxy filings and annual meeting dates.– Uses the filing and report dates for proxy filings in

the database.

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Joshua LivnatDepartment of Accounting

Stern School of Business AdministrationNew York University

10-76 Kaufman Management Education Center44 W. 4th St.

New York City, NY 10012(212) 998–0022

[email protected]

German Lopez-Espinosa, University of Navarra

Rolling Quarterly Accruals and Cash

Flows: Universe and Industry Analysis

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Overview• Objectives:

– Examine whether quarterly accruals (or rolling four-quarter accruals are superior to net operating cash flow (OCF).

– Examine whether accruals are superior to OCF within an industry.

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Overview (Continued)

• Methodology:– Associate quarterly accruals and OCF, as well as

rolling four-quarter accruals and OCF, with future returns. Returns can be for the immediately subsequent quarter or for a whole year.

• Prior studies typically use annual accruals and annual returns.

– Associate accruals and OCF with future returns within industries to determine which measure is superior within that industry.

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Overview (Continued)

• Results:– OCF is typically a superior measure to accruals. It is inferior

to accruals only in the fourth quarter when the return window is annual and the signal is based on rolling four quarters.

– Accruals are a relevant signal when the return window is annual, or when accruals are based on rolling four-quarters.

– OCF is a value-relevant signal whether the return window is one quarter or annual, and whether based on one quarter or rolling four quarters.

– Within an industry OCF is superior to accruals.

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Data Acknowledgements

• Charter Oak Investment Systems Inc. for providing the Point-In-Time (PIT) Compustat quarterly data.– http://www.charteroaksystems.com/

• S&P’s SEC Filing Dates Database.

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Definition - Accruals

• Accruals = Net Income – Net Operating Cash Flow.– Represents investments in net current assets (such

as inventories and receivables), as well as adjustments for accounting items that are not cash items (such as depreciation and deferred taxes).

• Accruals are typically negative; net income is after depreciation whereas net operating cash flow is not.

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-0.06

-0.04

-0.02

0

0.02

0.04

0.06

Lowest 2 3 4 5 6 7 8 9 Highest

Accrual Decile

1-Year Return

Sloan (1996) – Accrual Anomaly

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Quarterly Accruals

• The accruals anomaly was first documented for annual accruals.

• It was well replicated by many follow-up studies.

• Companies disclose quarterly accruals too.

• Professional investors likely want to use quarterly accruals instead of waiting for annual accruals.

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Institutional Considerations

• Most firms announce preliminary earnings after quarter-end, but do not disclose net operating cash flow in this announcement.

• Firms then file their 10-Q/10-K Forms with the SEC, which include net operating cash flows.– Easton and Zmijewski (1993) and Griffin (2003) show

that most firms file on the last day or two of the allowed period.

QuarterEnd

PreliminaryEarnings

SEC Filing

27 days 17 days

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Quarterly Accruals Research

• Earnings are typically known before accruals. Most firms disclose accruals only in their SEC filings.

• SEC filing dates are not in the Compustat database.

• Most prior studies on accruals did not have access to SEC filing dates, so they could not test for quarterly accruals.

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Accruals and OCF

• Accruals and OCF are negatively correlated, but the correlation is not –100%.

• An investor can construct portfolios based on accruals or OCF (or both).

• Desai, Rajgopal and Venkatachalam (2004) show that OCF dominates accruals and that accruals are not incrementally valuation relevant beyond OCF.

• Cheng and Thomas (2006) and Barone and Magilke (2006) show that accruals are incrementally valuation relevant beyond OCF.

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Open Research Questions

• Are accruals incrementally valuation relevant beyond OCF when using quarterly data?

• Should accruals (and OCF) be calculated using current quarter data or rolling four quarters?

• What is the optimal holding period (one or four quarters)?

• Are accruals (and OCF) more or less effective in certain fiscal quarters?

• Are accruals (or OCF) effective within an industry?

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Sample

• Firms with filing dates that are within 55 (100) days after the balance sheet date.

• Firms that are on the CRSP database.

• Market value and total assets are in excess of $1 million; available data on net income and OCF; available total assets at prior quarter.

• Eliminate firms with the extreme 0.5% of returns.

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Variables

• Accruals are net income minus net operating cash flow.

• Accruals and OCF are scaled by average total assets during the quarter.– Scaled accruals and OCF are winsorized to fall in the

range [-1,+1].

• Excess returns are buy and hold returns (BHR) on the specific firm minus the BHR on a matched Fama and French size and B/M portfolio (6 groups).

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Holding Period

• Buy and Hold returns from two days after the SEC filing date through one day after the subsequent quarter’s preliminary earnings announcement, or 90 days if unavailable.

• For an annual return window, it is from two days after SEC filing date for quarter t through one day after the preliminary earnings announcement for quarter t+4, or 360 days if unavailable.

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Rolling Four-Quarter Data

• Using the Point-In-Time database to ensure these data were what investors knew at the time.

• Rolling four-quarter data in the fourth fiscal quarter are equal to annual accruals, which were used by prior annual studies.

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Analysis• Our analysis is based on estimating regression

equations with future excess returns as the dependent variable and signal ranks as the independent variables.

• We sort accruals or OCF into deciles each quarter, and assign each observation its decile rank (0 through 9). We then divide the rank by 9 and subtract 0.5.

• The intercept in the regression is the mean future excess return. The slope coefficient is the excess return on a hedge portfolio with long positions in the most extreme negative accruals or positive OCF and short positions in the most positive accruals or negative OCF.

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Table 1. Summary Statistics on Rolling Four-

Quarter Accruals and Cash-Flows Industry

N Buy & Hold Return Earnings Accruals

Cash-Flows

Banks, Insurance & Financials 21,771 0.0261 0.0332 -0.0254 0.0585 Cars 2,526 -0.0517 0.0361 -0.0358 0.0719 Chemicals 3,386 -0.0258 0.0211 -0.0459 0.0670 Construction 6,381 0.0082 0.0394 -0.0187 0.0581 Consumer Durables 5,445 -0.0485 0.0066 -0.0493 0.0560 Drugs, Soap, Perfumes & Tobacco 7,328 0.0531 -0.1520 -0.0596 -0.0924 Fabricated Products 1,657 -0.0099 -0.0324 -0.1073 0.0749 Food 5,484 0.0036 0.0407 -0.0386 0.0792 Machinery & Business Equipment 27,394 0.0318 -0.0248 -0.0535 0.0287 Mines 1,435 0.0190 -0.0231 -0.0802 0.0570 Oil 6,646 0.1218 0.0171 -0.1150 0.1322 Other 65,575 0.0139 -0.0787 -0.0804 0.0017 Retail Stores 11,963 0.0081 0.0278 -0.0639 0.0916 Steel 2,823 -0.0041 0.0315 -0.0416 0.0731 Textiles, Apparel & Footwear 3,933 -0.0367 0.0378 -0.0319 0.0698 Transportation 6,278 0.0217 0.0368 -0.0573 0.0940 Utilities 881 0.0563 0.0293 -0.0448 0.0741 All 180,906 0.0181 -0.0260 -0.0610 0.0350

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Table 2. Regressions of Returns on Scaled Accrual

Ranks and Scaled Cash Flows Ranks Panel A: Returns at Quarter t+1

SCALED

ACCRUAL SCALED CASH-

FLOWS Dependent Variable

N

Intercept

p-value Coef. p-value Coef. p-value R2 (%)

BHR -Filing at t Through next

Quarter t+1 186,506 -0.001 0.015 -0.022 0.000 0.08 BHR -Filing at t

Through next Quarter t+1 186,506 -0.002 0.000 0.046 0.000 0.37


Quarter t+1 186,506 -0.002 0.000 0.007 0.000 0.050 0.000 0.37

Panel B: Returns at Quarter t+4

SCALED



N

Intercept



Quarter t+4 186,506 0.014 0.000 -0.068 0.000 0.12 BHR -Filing at t

Through next Quarter t+4 186,506 0.012 0.000 0.121 0.000 0.38


Quarter t+4 186,506 0.012 0.000 0.004 0.509 0.123 0.000 0.38

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Table 3. Fama-MacBeth Regressions of Returns on Scaled

Accrual Ranks and Scaled Cash Flows Ranks Panel A: Returns at Quarter t+1

SCALED



Quarters

Intercept



Quarter t+1 52 -0.001 0.769 -0.010 0.422 1.19 BHR -Filing at t

Through next Quarter t+1 52 0.002 0.619 0.034 0.001 2.55


Quarter t+1 52 -0.001 0.714 0.016 0.398 0.041 0.004 4.34

Panel B: Returns at Quarter t+4

SCALED



Quarters

Intercept



Quarter t+4 52 0.012 0.220 -0.052 0.005 1.26 BHR -Filing at t

Through next Quarter t+4 52 0.014 0.168 0.099 0.000 2.90


Quarter t+4 52 0.010 0.325 0.004 0.909 0.097 0.024 4.98

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Table 4. Regressions of Next Quarter Returns on Scaled

Rolling Four-Quarter Ranks by Fiscal Quarter

SCALED ACCRUAL

SCALED CASH-FLOWS

Panel A: First Fiscal Quarter Dependent Variable

N

Intercept


BHR -Filing at t Through next Quarter t+1 46,125 -0.002 0.051 -0.015 0.000 0.04

BHR -Filing at t Through next Quarter t+1 46,125 -0.003 0.004 0.049 0.000 0.42

BHR -Filing at t Through next Quarter t+1 46,125 -0.003 0.004 0.001 0.791 0.050 0.000 0.42

Panel B: Second Fiscal Quarter Dependent Variable

N

Intercept


BHR -Filing at t Through next Quarter t+1 46,677 -0.010 0.000 -0.032 0.000 0.18


BHR -Filing at t Through next Quarter t+1 46,677 -0.011 0.000 -0.022 0.000 0.029 0.000 0.31

Panel C: Third Fiscal Quarter Dependent Variable

N

Intercept


BHR -Filing at t Through next Quarter t+1 46,083 0.000 0.723 -0.031 0.000 0.13


BHR -Filing at t Through next Quarter t+1 46,083 -0.000 0.953 -0.022 0.000 0.026 0.000 0.22

Panel D: Fourth Fiscal Quarter Dependent Variable

N

Intercept



BHR -Filing at t Through next Quarter t+1 42,345 0.011 0.000 0.026 0.000 0.19

BHR -Filing at t Through next Quarter t+1 42,345 0.011 0.000 -0.008 0.008 0.023 0.000 0.21

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Table 5. Regressions of Returns at Quarter t+4 on Scaled

Rolling Four-Quarter Ranks by Fiscal Quarter

SCALED


FLOWS

Panel A: First Fiscal Quarter Dependent Variable

N

Intercept





Panel B: Second Fiscal Quarter Dependent Variable

N

Intercept





Panel C: Third Fiscal Quarter Dependent Variable

N

Intercept





Panel D: Fourth Fiscal Quarter Dependent Variable

N

Intercept





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Summary

• OCF is uniformly superior to accruals, except for rolling four quarters and annual holding period in the fourth quarter.

• This may shed some light on the inconsistency between Desai et al (2004) and further studies. Accruals seem to be incrementally valuation relevant for rolling four quarter data beyond OCF. They are particularly strong in the fourth fiscal quarter.

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Industry Analysis

• Based on the 17 industries identified by Fama and French.

• It uses 4-digit SIC codes to classify firms into industries.

• Finer industries will have fewer observations for ranking accruals and OCF.

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Table 8. Regressions of Returns at Quarter t+4 on

Scaled Rolling Four-Quarter Ranks by Industry

SCALED ACCRUALS

SCALED CASH-FLOWS

Industry N Intercept p-value Coef. p-value Coef. p-value R2 (%) H0

Banks, Insurance & Financials 434 0.272 0.005 0.094 0.002 0.251 0.000 1.45% 0.000

Cars 51 -0.276 0.001 0.067 0.058 0.330 0.000 9.09% 0.000

Chemicals 67 -0.107 0.104 -0.003 0.927 0.102 0.016 9.28% 0.182

Construction 129 0.029 0.695 0.075 0.101 0.138 0.001 4.01% 0.008

Consumer Durables 110 -0.159 0.006 -0.016 0.699 0.215 0.000 4.78% 0.012

Drugs, Soap, Perfumes & Tob. 146 0.089 0.285 0.122 0.010 0.135 0.083 7.26% 0.002

Fabricated Products 33 0.030 0.475 -0.078 0.107 0.040 0.372 11.27% 0.660

Food 111 0.026 0.732 0.041 0.304 0.173 0.000 3.89% 0.006

Machinery & Business Equip. 536 0.004 0.976 -0.081 0.231 0.379 0.000 7.25% 0.039

Mines 28 -0.115 0.212 0.175 0.000 0.248 0.000 14.05% 0.000

Oil 135 0.264 0.099 0.211 0.000 0.282 0.000 4.25% 0.000

Other 1,260 0.025 0.838 -0.067 0.276 0.440 0.002 3.87% 0.046

Retail Stores 229 -0.014 0.884 -0.054 0.195 0.262 0.000 6.98% 0.013

Steel 55 -0.059 0.465 0.007 0.852 0.077 0.056 7.86% 0.243

Textiles, Apparel & Footwear 78 -0.116 0.115 0.107 0.005 0.317 0.000 5.03% 0.000

Transportation 125 0.113 0.082 0.036 0.356 0.066 0.185 4.52% 0.209

Utilities 17 0.175 0.016 0.102 0.010 0.140 0.001 17.44% 0.001

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Summary - Industry Analysis

• Accruals are not a strong signal for most industries. OCF is a strong signal within all industries except one.

• Accruals are not incrementally valuation relevant beyond OCF within any industry.

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Takeaways – Quarterly Accruals and OCF

• Accruals are an inferior signal in predicting future returns to OCF.

• The best case for accruals seems to be when they are based on rolling four quarters and and when the holding period id one year.

• The only case for accruals to dominate OCF is for annual accruals and one-year holding period.

• Accruals are inferior to OCF for within industry portfolio selection.

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Ronen FeldmanSuresh Govindaraj

Joshua LivnatBenjamin Segal

The Incremental Information Content of Tone and Sentiment in

Management Discussion and Analysis

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Overview

• Can Investors learn from the tone of MD&A incremental information beyond the quantitative information?

• Qualitative vs. quantitative information.

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Overview - Continued• Methodology:

– Use classifications of words into “positive” and “negative”.

– Determine the “tone” of an MD&A by the positive and negative word counts.

– Examine short-window and drift returns associated with “tone” after controlling for earnings surprises and accruals.

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Overview -Continued• Results:

–The “tone” of MD&A has significant association with short-window abnormal returns around the SEC filing dates.

–The “tone” has significant associations with drift returns even after controlling for earnings surprises and accruals.

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Motivation

• Quantitative information such as earnings and accruals is associated with returns and drifts in returns. However, the associations are usually low.

• Can qualitative information be incrementally beneficial for users beyond the quantitative information?.

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Motivation - Continued

• There are several studies of qualitative information in articles written about firms and in preliminary earnings releases.

• There may be self-selection in articles written about firms, because those may be induced by more significant “news”. Also, they do not capture management’s own “tone”.

• Previous studies of earnings announcements did not include sufficiently large strings of releases.

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Motivation - Continued

• We study MD&A disclosures, which are available on a consistent basis four times a year.

• Management is required to provide MD&A, unlike preliminary earnings releases.

• The MD&A is subject to scrutiny by the SEC.• The SEC provided guidelines on what should be

discussed in the MD&A.• The MD&A data are largely interpretive; past

performance. It may include some future expectations.

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Prior Literature – Word Counts

• Tetlock (2007) – “Abreast of the market”.• Tetlock et al (2008) – Articles about the S&P

500 constituents.• Engleberg (2008) – Earnings releases from

Factiva.• Demers and Vega (2007) - Earnings releases.

Diction 5.0.• Abrahamson and Amir (1996) - president’s

letter.

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Sample

• Firms with filing dates that are within 55 (100) days after the balance sheet date.

• Firms that are on the CRSP database.

• Market value and total assets are in excess of $1 million; available data on net income and OCF; available total assets at prior quarter.

• Firms are on NYSE, AMEX or NASDAQ.• Q4/1995-Q2/2006.• Delete extreme 0.5% of returns on either side.• Over 170,000 firm-quarters.

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Measuring Tone• Use the General Inquirer dictionary of “positive’ and

“negative” words.• Extracted the MD&A section. Counted the number of

words (excluding numerical data), as well as the number of positive and negative words.

• Computed the proportion of negative (positive) to total words, as well as

(positive-negative)/(positive+negative). • Subtracted the mean measure in filings made within

the previous 400 days to obtain a measure of tone shift.

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Mean Excess Returns around SEC Filings for Various Signals

Signal Bottom

20% Middle

60% Top

20% (Positive-Negative) Signal -0.003 -0.001 0.001 Positive Word Signal -0.002 -0.001 -0.001 Negative Word Signal 0.001 -0.001 -0.004 Accruals -0.003 -0.001 0.001 Net Operating Cash Flow -0.006 -0.001 0.002 SUE -0.012 -0.001 0.008 N 33395 102060 34001

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Scaled Ranks

• Sorted signals into quintiles.

• Assigned the rank (0,4).

• Divided by 4.

• Subtracted -.5.

• Slope regression coefficient can be interpreted as the hedge portfolio return of holding long (short) positions in the top (bottom) quintile.

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Correlations among Regression Variables

BHR-Drift

BHR - Filing Accrual OCF SUE

(POS-NEG) POS

BHR - Filing 0.007 Accrual -0.019 0.017 OCF 0.018 0.031 -0.550 SUE 0.045 0.078 0.106 0.086 (POS-NEG) 0.013 0.014 0.007 0.026 0.070 POS 0.008 0.000 -0.045 0.026 0.012 0.540 NEG -0.010 -0.020 -0.051 -0.009 -0.083 -0.670 -0.029

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Regression of Short Window Returns on Various Signals

Model 1 2 3 4 5 6 7 8 9 Intercept -0.0010 -0.0010 -0.0010 -0.0010 -0.0010 -0.0010 -0.0010 -0.0010 -0.0010

0.0765 0.0765 0.0765 0.0765 0.0765 0.0765 0.0776 0.0776 0.0776 Accruals 0.0039 0.0040 0.0037 0.0021 0.0021 0.0020

0.0001 0.0001 0.0001 0.0064 0.0060 0.0084 OCF 0.0069 0.0070 0.0069

0.0001 0.0001 0.0001 SUE 0.0177 0.0178 0.0176

0.0001 0.0001 0.0001 (POS-NEG) 0.0031 0.0030 0.0019

0.0001 0.0001 0.0037 POS 0.0002 -0.0001 -0.0001

0.6830 0.7852 0.8451 NEG -0.0043 -0.0044 -0.0029

0.0001 0.0001 0.0001 Average R-

Square 0.0014 0.0010 0.0015 0.0031 0.0028 0.0033 0.0096 0.0094 0.0096

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Mean Excess Drift Returns for Various Signals

Signal Bottom

20% Middle

60% Top

20% (Positive-Negative) Signal 0.005 0.011 0.019 Positive Word Signal 0.008 0.010 0.020 Negative Word Signal 0.017 0.012 0.004 Accruals 0.003 0.008 0.004 OCF 0.010 0.006 0.031 SUE -0.006 0.003 0.055 N 33395 102060 34001

63

Regression of Drift Excess Returns on Various Signals

Model 1 2 3 4 5 6 7 8 9 Intercept 0.0114 0.0114 0.0114 0.0114 0.0114 0.0114 0.0114 0.0114 0.0114

0.0716 0.0716 0.0716 0.0717 0.0717 0.0717 0.0714 0.0714 0.0714 Accruals -0.0239 -0.0234 -0.0241 -0.0300 -0.0296 -0.0302

0.0010 0.0012 0.0009 0.0001 0.0001 0.0001 OCF 0.0239 0.0241 0.0239

0.1167 0.1120 0.1169 SUE 0.0578 0.0586 0.0579

0.0001 0.0001 0.0001 (POS-NEG) 0.0167 0.0159 0.0123

0.0001 0.0002 0.0007 POS 0.0094 0.0089 0.0083

0.0123 0.0171 0.0241 NEG -0.0130 -0.0125 -0.0081

0.0006 0.0016 0.0143 Average R-

Square 0.0022 0.0021 0.0022 0.0088 0.0087 0.0088 0.0079 0.0080 0.0079

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Mean Hedge Portfolio Returns on Various Signals

Portfolio Mean t-statistic Significance N Diff vs. Port Mean t-statistic Significance

(POS-NEG) Signal 1 0.013 3.6 0.0009 1580

POS 2 0.012 2.9 0.006 1580

NEG 3 0.012 3.0 0.0049 1580

Accruals 4 0.024 3.5 0.0013 1580

OCF 5 0.022 1.4 0.1791 1580

SUE 6 0.060 10.3 0.0001 1580

(POS-NEG) Signal+SUE 7 0.072 7.1 0.0001 368 6 0.012 1.36 0.1802

(POS-NEG) Signal+Accruals 8 0.045 2.8 0.0075 316 4 0.021 1.95 0.0580

(POS-NEG) Signal+OCF 9 0.040 3.0 0.0041 334 5 0.018 2.55 0.0146

SUE+Accruals 10 0.097 6.6 0.0001 301

(POS-NEG) Signal+Accruals+SUE 11 0.130 4.3 0.0001 71 10 0.032 1.85 0.0716

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Alina Lerman

Joshua Livnat

The New Form 8-K Disclosures

66

Objective

• New disclosure rules in 2004 about Form 8-K.

• Some new items. Some expanded disclosure.

• Shorter time to disclose (4 days).

• How did it affect market reactions?

• Are firms filing on time?

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Methodology

• Examine immediate market reactions to the different categories of filing.

• Examine abnormal volume and return volatility around filings.

• Examine return drifts for the various categories.

• Examine timeliness and its market reactions.

68

Sample in 2005-2006Frequency Percent

1.01 Entry into a Material Definitive Agreement 34,997 27.9%1.02 Termination of a Material Definitive Agreement 2,637 2.1%1.03 Bankruptcy or Receivership 18 0.0%2.01 Completion of Acquisition or Disposition of Assets 2,508 2.0%2.02 Results of Operations and Financial Condition 37,704 30.1%2.03 Creation of a Direct/Off-Balance Sheet Financial Obligation 4,747 3.8%2.04 Increase/Acceleration of a Direct/Off-Balance Sheet Financial Obligation 361 0.3%2.05 Cost Associated with Exit or Disposal Activities 907 0.7%2.06 Material Impairments 532 0.4%3.01 Notice of Delisting … Transfer of Listing 2,137 1.7%3.02 Unregistered Sales of Equity Securities 2,518 2.0%3.03 Material Modifications to Rights of Security Holders 967 0.8%4.01 Changes in Registrant's Certifying Accountant 1,148 0.9%4.02 Non-Reliance on Previously Issued Financial Statements 1,124 0.9%5.01 Changes in Control of Registrant 152 0.1%5.02 Departure / Election of Directors or Principal Officers 15,056 12.0%5.03 Amendments to Articles of Incorporation or Bylaws 2,720 2.2%5.04 Suspension of Trading Under Registrant's Employee Benefit Plans 279 0.2%5.05 Amendments to the Registrant's Code of Ethics 246 0.2%7.01 Regulation FD Disclosure 19,332 15.4%8.01 Other Events 28,467 22.7%9.01 Financial Statements and Exhibits 94,153 75.0%

ITEM

69

Abnormal Returns [-1,+1]Expected

New Sign 3 Day ED 3 Day FD ED to FD1.01 Entry into a Material Definitive Agreement Yes ? Or + 0.52% *** 0.41% *** 0.74% ***

1.02 Termination of a Material Definitive Agreement Yes ? Or - 0.11% -0.24% -0.16%

1.03 Bankruptcy or Receivership No - 5.77% -11.90% * -8.86%

2.01Completion of Acquisition or Disposition of Assets

No ? 0.91% *** 0.44% *** 1.13% ***

2.02 Results of Operations and Financial Condition No (voluntary) ? -0.15% *** -0.24% *** -0.12%

2.03Creation of a Direct/Off-Balance Sheet Financial Obligation

Yes ? 0.25% *** 0.19% ** 0.47% ***

2.04Increase/Acceleration of a Direct/Off-Balance Sheet Financial Obligation

Yes - -0.04% -1.05% -2.00% **

2.05 Cost Associated with Exit or Disposal Activities Yes ? -0.58% ** -1.08% *** -1.42% ***

2.06 Material Impairments Yes - -1.13% *** -1.65% *** -2.31% ***3.01 Notice of Delisting … Transfer of Listing Yes - -1.13% *** -2.90% *** -3.65% ***3.02 Unregistered Sales of Equity Securities Yes (from periodic) + 0.97% *** 0.19% 1.23% ***

3.03Material Modifications to Rights of Security Holders

Yes (from periodic) ? 2.84% *** 2.17% *** 3.73% ***

4.01 Changes in Registrant's Certifying Accountant No - -0.14% -0.59% *** -0.84% ***

4.02Non-Reliance on Previously Issued Financial Statements

Yes - -1.04% *** -1.61% *** -2.34% ***

5.01 Changes in Control of Registrant No ? 1.62% ** 0.40% 0.94%

5.02Departure / Election of Directors or Principal Officers

No (expanded) ? -0.04% -0.11% ** -0.08%

5.03Amendments to Articles of Incorporation or Bylaws

No (expanded) ? 0.14% 0.18% 0.29% *

5.04Suspension of Trading Under Registrant's Employee Benefit Plans

No - 0.47% ** 0.05% -0.04%

5.05 Amendments to the Registrant's Code of Ethics No ? 0.34% -0.27% -0.08%

7.01 Regulation FD Disclosure Yes (voluntary) ? 0.22% *** 0.16% *** 0.29% ***8.01 Other Events No (voluntary) ? 0.26% *** 0.06% 0.40% ***9.01 Financial Statements and Exhibits No ? 0.13% *** 0.02% 0.18% ***

Abnormal ReturnsItem

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