the impact of the ceiling test write-off cost oil and …
TRANSCRIPT
THE IMPACT OF THE CEILING TEST WRITE-OFF
ON THE SECURITY RETURNS OF FULL
COST OIL AND GAS FIRMS
DISSERTATION
Presented to the Graduate Council of the
University of North Texas in Partial
Fulfillment of the Requirements
For the Degree of
DOCTOR OF PHILOSOPHY
By
Taisier F. AlDiab, B.C.A., M.S.
Denton, Texas
May, 1992
AlDiab, Taisier F., The Impact of the Ceiling Test Write-off on The Security
Returns of Full Cost Oil and Gas Firms. Doctor of Philosophy (Accounting), May,
1992, 124 pp., 18 tables, bibliography, 99 titles.
This study examined the impact of the ceiling test write-off on the stock prices
of affected full cost (FC) oil and gas firms.
To examine the research question, separate hypotheses were developed and
tested (1) for FC firms that announced their ceiling test write-offs first in the Wall
Street Journal (WSJ), and (2) research hypotheses for FC firms that did not announce
their ceiling test write-offs in the WSJ prior to disclosure of the amount of write-offs
in their quarterly financial statements.
To test the research hypotheses, three expectation models were developed: an
expectation model for the ceiling test write-off, an expectation model for earnings,
and a stock prices expectation model. The results of the sign test are weakly
consistent with the hypothesis that the sign of cumulative abnormal residual is
negatively associated with the sign of the unexpected amount of the ceiling test write-
off, but interpretation is difficult given the evidence of bias in the results. In general,
the results of the magnitude tests indicate that the unexpected amount of the ceiling
test write-off negatively affects abnormal returns. The results suggest that, on
average, security price changes in the test period appeared to be significantly higher
than the estimation period. The results of testing the cross-sectional variation in the
abnormal returns suggest that the size of the firm, risk of the firm, debt to equity
ratio, type of debt, unexpected earnings, and unexpected ceiling test write-off
included in the cross-sectional model, appeared to explain a significant proportion of
the variation in the abnormal returns.
For FC firms that did not announce their ceiling test write-offs in the WSJ, a
cross-sectional regression model was constructed and tested. The results suggest that
the unexpected ceiling test write-off expense and the other type of expense jointly do
possess incremental information content beyond earnings.
THE IMPACT OF THE CEILING TEST WRITE-OFF
ON THE SECURITY RETURNS OF FULL
COST OIL AND GAS FIRMS
DISSERTATION
Presented to the Graduate Council of the
University of North Texas in Partial
Fulfillment of the Requirements
For the Degree of
DOCTOR OF PHILOSOPHY
By
Taisier F. AlDiab, B.C.A., M.S.
Denton, Texas
May, 1992
TABLE OF CONTENTS
Page
LIST OF TABLES
Chapter
I. INTRODUCTION 1
Motivation of the Study Background Ceiling Test Why is the Write-off a Problem For FC Firms? Purpose of the Study
II. REVIEW OF THE LITERATURE 12
Prior Studies that Examine the Reliability, Integrity, and Bias of the Components of the Ceiling Test Studies That Examine the Impact of Some of the Components of the Ceiling Test on Security Returns Lending Agreements and the Ceiling Test Write-off
III. RESEARCH HYPOTHESES AND RESEARCH DESIGN 22 Research Hypotheses Research Hypotheses for FC Firms that Announced their Ceiling Test Write-off Research Hypotheses for FC Firms that Disclosed their Ceiling Test Write-off on Their Financial Statements Research Design
m
Page
Chapter
Timing of the Ceiling Test Write-off Announcement Test Procedures Testing Hypotheses HI and H2 Expectation Models for the Amount of the Ceiling Test Write-off Expectation Models for Earnings Expectation Models for FC Firms Reporting the Ceiling Test Write-off in the Financial Statements Expectation of Stock Prices Testing Hypothesis H3 Testing Hypotheses H4, H5, H6, H7, and H8 Testing Hypothesis H9 Estimation Period Test Period Sample Selection
IV. ANALYSIS AND INTERPRETATION OF THE RESULTS 46
Data Collection Results of the Multi-Factor Market Model Unexpected Amount of Write-off Scaling Unexpected Amount of Write-off Results of Testing Hypothesis HI Results of Testing Hypothesis H2 Results of Testing Hypothesis H3 Results of Testing Hypothesis H4, H5, H6, H7, and H8 Independence Among Explanatory Variables Primary Regression Results Results of Testing Hypothesis H9
IV
Page
Chapter
V. CONCLUSIONS, LIMITATIONS, AND FUTURE RESEARCH 78 Summary and Conclusions Future Research Limitations
APPENDIX 85
REFERENCE 117
LIST OF TABLES
Table Page
1. LIST OF THE COMPANIES IN THE SAMPLE 86
2. TYPE OF ANNOUNCEMENTS AND THE DATES OF WRITE-OFF AND THEIR FREQUENCIES 89
3. AVERAGE SIMPLE PEARSON CORRELATION COEFFICIENTS BETWEEN RMT, INX, AND POr 91
4. SUMMARY STATISTICS RELATING TO THE MULTI-FACTOR MARKET MODEL: RIT = A + B^Mt + B2INx -I- B3POt FOR THE ESTIMATION PERIOD 92
5. THE RELATION BETWEEN THE UNEXPECTED AMOUNT OF WRITE-OFF AND CAR FOR COMPANIES WHICH ANNOUNCED THE AMOUNT OF THE CEILING TEST WRITE-OFF ALONE 93
6. CORRELATION BETWEEN CAR, UNEXPECTED WRITE-OFF, AND UNEXPECTED EARNINGS FOR COMPANIES WHICH ANNOUNCED THE AMOUNT OF CEILING TEST CONCURRENT WITH EARNINGS (64 OBSERVATIONS) 94
7. THE RELATION BETWEEN UNEXPECTED CAR, UNEXPECTED WRITE-OFF, AND UNEXPECTED EARNINGS (POSITIVE VS. NEGATIVE UNEXPECTED WRITE-OFF) 95
VI
Table Page
8. CHI-SQUARE TEST OF SIGNIFICANCE BETWEEN THE SIGNS OF CAR, UNEXPECTED WRITE-OFF, AND UNEXPECTED EARNINGS 96
RESULTS OF THE REGRESSIONS FOR COMPANIES WHICH ANNOUNCE THE AMOUNT OF THE CEILING TEST WRITE-OFF ALONE (MAGNITUDE TEST) 98
10. RESULTS OF THE REGRESSIONS FOR COMPANIES WHICH ANNOUNCE THE AMOUNT OF THE CEILING TEST WRITE-OFF CONCURRENT WITH EARNINGS (MAGNITUDE TEST) 64 OBSERVATIONS 99
11. F-RATIO: COMPARISON: FULL MODEL VS. REDUCED MODEL 64 OBSERVATIONS 104
12. RESULTS OF THE REGRESSIONS FOR COMPANIES WHICH ANNOUNCE THE AMOUNT OF THE CEILING TEST WRITE-OFF CONCURRENT WITH EARNINGS (MAGNITUDE TEST) WITHOUT OUTLIERS 60 OBSERVATIONS 105
13. F-RATIO: COMPARISON: FULL MODEL VS. REDUCED MODEL 64 OBSERVATIONS 110
14. THE Uc,t RATIO FOR FIRMS WHICH ANNOUNCED THE AMOUNT OF CEILING TEST WRITE-OFF ALONE I l l
vn
Table Page
15. SIMPLE CORRELATION COEFFICIENT MATRIX BETWEEN THE INDEPENDENT VARIABLES FOR COMPANIES THAT ANNOUNCED THE AMOUNT OF THE CEILING TEST WRITE-OFF ALONE BASED ON 5 OBSERVATIONS 112
16. SIMPLE CORRELATION COEFFICIENT MATRIX BETWEEN THE INDEPENDENT VARIABLES FOR COMPANIES WHICH ANNOUNCED THE AMOUNT OF THE CEILING TEST WRITE-OFF CONCURRENT WITH EARNINGS BASED ON 64 OBSERVATIONS 113
17. CROSS-SECTIONAL REGRESSION ESTIMATES CAR = a + BXPPDEBT + B2SD + B3TDEBT + B4TAMKTE + BjDE + B6UN02 + ByUWROS BASED ON 64 OBSERVATIONS 114
18. RESULTS OF CROSS-SECTIONAL VALUATION MODELS 116
vm
CHAPTER I
INTRODUCTION
In December 1978, the Securities and Exchange Commission (SEC) issued
Accounting Series Release (ASR) No. 258 which required the capitalized assets1 of
oil and gas (OG) firms using the full cost (FC) method to be limited to a "ceiling"
amount specified by a cost center ceiling related to the market prices of OG on the
date of the financial statements. According to the SEC's rules, any capitalized
amount in excess of the ceiling was to be expensed in the current period. Any
amount written off in one period could not be reinstated in a subsequent period.
During 1979, 1980, and 1981, OG market prices were at historic highest. The long
decline in OG prices that begin in 1982, soon made the ceiling test a concern to FC
firms.
The drastic decline in OG prices between December 31, 1985 and March 31,
1986, led to a corresponding drastic reduction in the applicable FC ceiling amounts
for December 31, 1985 and March 31, 1986 dates when many firms needed to issue
quarterly or annual financial statements. In April 1986, the SEC accountants
recommended to the Commission that the ceiling test rules be suspended to allow FC
'In this study, the terms "capitalized assets," "unamortized costs," and "unamortized capitalized costs," are used interchangeably.
2
companies flexibility in determining "market prices" on March 31, 1986 for financial
statements purposes. In May 1986, the SEC rejected the staff proposal and required
FC firms to implement the ceiling test without modification for March 31, 1986
financial statements. As a result, many FC OG firms reported lower earnings and
lower net worth relative to what they would have reported if they were not subject to
the ceiling test rule.
Did the ceiling test write-off have an adverse impact on the stock prices of
affected FC OG firms? Many FC firms claimed that mandatory application of the
ceiling test would substantially depress reported earnings and net worth and that they
would incur additional administrative costs associated with renegotiating credit
agreements. Consequently stock prices of many FC firms would be negatively
impacted. Whether the ceiling test write-off had a significant adverse effect on stock
prices of affected FC firms is an empirical question. Two studies have considered
this issue — the SEC [1986] and Frost and Bernard [1989]. Each examined the
impact of the SEC's decision in May 1986 mandating the application of the ceiling
test.
Although both studies found significant positive abnormal returns around the
SEC' decision in May 1986, their results are questionable for several reasons. First,
they did not control for the effects of a major confounding event: the Senate Finance
Committee's approval of the 1986 Tax Reform Act. Secondly, they omitted several
relevant variables, such as the percentage change in OG prices and the industry factor
from the market model. Finally, they did not use a representative sample.
3
The purpose of this research is to examine the impact of the actual ceiling test
write-off on security returns of affected FC OG firms.
Motivation for the Study
The ceiling test write-off is chosen because:
1) The results of prior research [Frost and Bernard, 1989; and the SEC, 1986]
suggest that the ceiling test write-off had a positive impact on the stock prices of
affected FC OG firms. However, as will be shown in Chapter II these studies
suffered from methodological problems.
2) If the impact of the ceiling test write-off on the security returns of FC firms
is at issue, the impact of the announcement of the actual amount of the ceiling test
write-off should also be of major concern.
3) The ceiling test write-off had a material negative impact on the financial
statements of many FC OG firms.
Thus, there are good reasons to address the impact of the ceiling test write-off
on the security returns of affected FC OG firms.
Background
The debate on accounting standards for the OG industry has been going on for
years. Since companies may drill many dry holes in the pursuit of oil reserves, there
is no necessary relationship between expenditures made in the search for OG and the
value of any reserves found. Consequently, it is difficult to match expenses with
4
revenues for OG companies to assess the periodic economic performance.
OG producing companies use two different approaches to match exploration
and development costs with revenues: the FC method and the successful-efforts (SE)
method. The FC method capitalizes all costs of acquisition, exploration and
development activities incurred in searching for OG reserves on a cost center basis,
regardless of the success or failure of the particular venture. In contrast, the SE
method capitalizes all costs incurred in searching for, acquiring, and developing OG
properties only when they result directly in reserves.
Many versions of the two methods were developed through industry practice
before the Financial Accounting Standards Board (FASB) issued Statement No. 19,
"Financial Accounting and Reporting by Oil and Gas Producing Companies" (SFAS
No. 19) in December 1977. SFAS No. 19 eliminated the FC method in favor of the
SE method. The U.S. Securities and Exchange Commission (SEC) received a number
of letters protesting FASB No. 19 and decided to hold public hearings on the
statement.
In August 1978, the SEC issued ASR No. 253 which introduced new financial
accounting and reporting rules for OG producing activities. The release required OG
producing companies to disclose estimated future net revenues from the production of
OG reserves based on certain assumptions. This new method was incorporated as
part of "Reserve Recognition Accounting" (RRA). In December 1978, the SEC
issued ASR No. 258 which allowed both the FC and SE methods to be used but
eliminated many alternative applications of the two methods. The FASB responded to
5
the SEC's actions by issuing FASB No. 25, "Suspension of Certain Accounting
Requirements for OG Producing Companies" in February 1979 which suspended
indefinitely the Statement No. 19 requirement that only the SE method be used. In
February 1981, the SEC issued ASR No. 289 and effectively rescinded RRA. The
SEC release cited the substantial degree of uncertainty implicit in OG reserve
estimates as the rationale for this decision. Subsequently, the SEC directed the FASB
to develop a comprehensive disclosure package for OG producing companies. Late in
1982, the FASB issued FASB No. 69, "Disclosure About Oil and Gas Producing
Activities".
Ceiling Test
Potentially, under the FC method, the capitalized costs of acquisition,
exploration, and development activities may exceed the underlying value of OG assets
in a particular cost center. To preclude this, the SEC established a ceiling test rule.
ASR No. 258 establishes the following rule for computing the ceiling test for publicly
held companies using the FC accounting method:
For each cost center, capitalized costs less accumulated amortization and related deferred income taxes, shall not exceed an amount equal to the sum of the cost center ceiling.
In accounting, valuation of long-lived assets has been anchored in the notion of
historical cost. One of the assumptions underlying the use of historical cost is that the
present value of expected future cash flows to be derived from using those assets will
be at least equal to the unamortized capitalized cost. There are, however, other
6
attributes that may be used to measure the value of different assets. For example, net
realizable value may be used to measure accounts receivable and lower of cost or
market to measure inventory. There is no single valuation method for all assets.
Sterling [1967] asserts that these variations in valuation methods were developed in
order to enhance conservatism in asset valuation. He argues that these "conservative"
variations show that the cost rule is not a "fundamental" pillar of accounting; rather,
it is a "derivative" of the conservatism doctrine.
Under certain economic conditions, the net present value of future cash flows
derived from using an asset is likely to be greater than the unamortized cost of the
asset. Since the latter value is more conservative, long-lived assets are valued at cost
in the balance sheet. It is generally believed that the circumstances under which the
present value of future cash flows are less than the unamortized capitalized costs are
rare. Therefore, historical cost will typically be less than future cash flows and
therefore provide a more conservative measurement.
Some researchers have argued that the present value of future cash flows
suffers from such a high degree of subjectivity that the information is of limited value
[e.g., Connor 1979; Porter, 1980; McCarty, 1983], while historical cost is more
objective. Historical cost, based upon verifiable evidence, is reliable, while present
value of future cash flows lacks objectivity since it is based on expectations that can
not be verified.
However, historical cost is objective and verifiable only at the moment of
acquisition. After that moment, all fixed assets are valued at less than their cost,
7
because they are subject to depreciation. Thomas [1974] argues that depreciation is
arbitrary and incorrigible. The result is that fixed assets are valued at something
other than their objective cost. They are valued subjectively. Since both historical
cost and the present value of future cash flows are subjective, which method should
be used to value long-lived assets in the balance sheet? Either the historical cost or
the net present value can be used as a basis for valuing assets in the balance sheet as
long as the chosen one yields a conservative value. Because the conservative value
minimizes the degree of uncertainty about the recoverability of long-lived assets, the
users of financial statements will be put in the best possible position to form their own
opinion about the expected benefits to be derived from the use of long-lived assets.
Accountants agree that "potential benefit" is an essential characteristic of an
asset [e.g., Canning, 1929; Paton and Littelton, 1940; Vatter, 1947; and Sprouse and
Moonitz; 1962; AICPA Trueblood Report, 1973; SFAS Concepts No. 6, 1986].
However, there is less agreement on whether or not the permanent decrease in
potential benefits of fixed assets should be recognized in the period in which the
decrease occurs. There is also disagreement on the way the decrease should be
treated. For example, Devine [1966] proposes that if the decline in the present value
of future cash flows below the unamortized cost of long-lived assets is permanent,
then the unamortized cost should be reduced to reflect the decrease. Similarly, APB's
statement No. 4 and Schuetze [1987] conclude that if the potential benefits of long
lived assets are permanently impaired, a write down may be appropriate.
8
The ceiling test rule for FC firms is unique in that it requires the application
of lower of cost (unamortized cost) or market value (ceiling) for property, plant, and
equipment. The different nature of the OG industry may dictate the departure from
historical cost and require the application of the lower of cost or market value
method. The SEC requires the application of the lower of cost or market value for
FC firms. The SEC, however, did not require the application of the ceiling test rule
for SE firms. The FASB has debated the issue of whether to write-down the carrying
amount of long-lived assets to an amount expected to be recoverable for SE firms.
The FASB reached an agreement on only one point: to recognize permanent rather
than temporary decline in the value of the expected future benefits derived from long-
lived assets [Schuetze, 1987].
According to ASR No. 258, the cost center ceiling equals to: (1) the present
value of future net revenues from estimated production of proved reserves"
(discounted future net cash flows), plus (2) the costs of assets that are not being
amortized, plus (3) the lower of cost or market value of unproved properties included
in the cost center being amortized, less (4) income tax effects related to differences
between the book value and the tax basis of the properties involved. Any amount in
excess of the cost center ceiling must be expensed and disclosed. Any amount written
off in one period may not be reinstated in a future period.
ASR No. 253 specifies how to calculate discounted future net cash flows (the
first item in the cost ceiling). To calculate the discounted future net cash flows firms
must estimate (1) proved reserve quantities at the end of each quarter, (2) future
9
production from each field in all future periods until reserves are exhausted, (3) future
cash flows by assuming that current quarter end prices and costs will prevail in the
future. Net cash flows for a quarter are obtained by subtracting the estimated
production cost from the estimated revenue for that quarter, (4) finally, discounted
future net cash flows are obtained by applying a 10% discount rate to the future net
cash flows obtained in step (3) [SFAS No. 69]. Discounted future net cash flows is
usually the largest component in the cost center ceiling.
Until the decline in OG prices in 1982 and 1983, the ceiling test was not of
concern to FC firms because the cost center ceiling was likely to be greater than the
total unamortized capitalized costs. The decline in OG prices in 1982-1986 created
problems for many FC companies. Ceteris paribus, a decline in the prices of OG
reduces the amount of total discounted future net cash flows. Consequently, the
amount of the cost center ceiling will be reduced and may lead to the total
unamortized capitalized costs being greater than the ceiling amount. The SEC rule
requires an immediate write-off of the excess of the total unamortized costs over the
cost center ceiling amount. This write-off reduces net current period income and
equity. For example, Frost and Bernard [1989] found that ceiling test write-offs for
their sample of 18 FC firms ranged up to 132 percent of net worth and 32 percent of
total assets in the first quarter of 1986.
10
Why is the Write-off A Problem for FC Firms?
The previous discussion suggests that when prices of OG decline, application of
the ceiling test will cause FC firms to report lower earnings and lower net worth
compared to what would have been reported if these firms had not been subject to the
ceiling test. Many FC firms reacted negatively to the required application of the
ceiling test rule in 1986 when OG prices declined dramatically. These firms claimed
that mandatory application of the ceiling test would substantially depress reported
earnings and net worth and that they would incur additional administrative costs
associated with renegotiating credit arrangements. They also noted they would incur
increased costs for engineering studies to update reserves on a quarterly rather than
yearly basis.2
A survey conducted by Arthur Andersen & Co. in 1986 corroborated the fact
that the ceiling test had a depressive effect on earnings and net worth. The survey
found that 74% of the 146 FC firms examined were subject to a ceiling test write-
downs. These write-downs slashed the net worth of 18 companies by 50% or more.
The SEC's staff accountants recommended suspending the ceiling test rules and
allowing the companies to use market prices higher than those on March 31, 1986
when calculating discounted future net cash flows. In May 1986, the SEC, however,
2This point of view was expressed by numerous officials of FC firms. For a more detailed explanation of these view points see Wall Street Journal 4/21/1986, 5/7/1986, 10/30/1986, 10/31/1986; Deloitte, Haskins, and Sells Review dated 5/12/1986; and Price Waterhouse Petroleum Industry Group Update dated May 1986.
11
rejected staff proposals and required FC firms to implement the ceiling test without
modification.
Purpose of the Study
The purpose of this study is to examine the actual impact of the ceiling test write-
off on the security returns of affected FC firms. It is possible that investors use the
ceiling test write-off to formulate their expectations regarding future cash flows.
Evidence of a stock price reaction would indicate that the ceiling test write-off itself
has information for the capital market. The evidence also may help policy-makers
assess whether the ceiling test write-off has economic consequences.
CHAPTER II
REVIEW OF THE LITERATURE
This chapter reviews the literature which examines the reliability, integrity, and bias
of the components of the ceiling test, the impact of some of the components of the ceiling
test on security returns, and lending agreements and how the ceiling test may affect such
agreements.
Prior Studies That Examine the Reliability, Integrity, and Bias of the Components of the Ceiling Test
Previous studies question the integrity of the estimates required by the SEC rules in
computing discounted future net cash flows from OG production. For example, Connor
[1979] concluded that discounted future net cash flows were unacceptable and imprecise.
Porter [1980] came to the same conclusion after observing significant discrepancies in
estimates made by different parties at the same point in time. McCarty [1983] also showed
that reserve information lacks reliability because of the special nature of OG reserve
estimation and the uncertainty regarding the oil under the ground.
Cooper et al. [1979] noted that the SEC's requirement for the use of a uniform 10%
discount rate was arbitrary. They suggested that the proper discount rate must reflect both
the general interest rate structure and risk. They contend that the estimates of the amount
12
13
and value of reserves are uncertain, since they depend on many unknown factors such as the
quality of reserves, concentration of reserves, accessibility, and recoverability. Khan et al.
[1983] examined the reliability and bias of reserve quantity forecasts. Their results also
indicate that the reserve estimates were unreliable.
Bell [1983] concluded that estimates of OG reserves are imprecise because the
estimation process attempts to use current and past information about the characteristics of a
petroleum reservoir to predict how the reservoir will perform in the future. Persky [1984]
cited the objections of the critics of the ceiling test rule who contended that the ceiling test
should not be based on the present value of future net cash inflows or at least that the net
future cash inflows should not be discounted for the purpose of the ceiling test computation.
In addition, they recommended the inclusion of other categories of reserves such as probable
reserves in addition to the proved reserves used in calculating future net cash inflows.
In contrast, other researchers suggest that the estimates required by the SEC rule may
be useful. For example, Walther and Evans [1982] found the estimates of OG reserves to be
reliable. Fraser [1979] stated that the discounted future net cash flows should receive
acceptance from financial analysts, since the balance sheet of an OG firm would reflect a
closer measure of the value of the assets rather than costs based on past management
decisions.
Prior studies that examined the usefulness of discounted future net cash flows for
financial analysts have had mixed results. For example, in a questionnaire survey, Deakin
and Deitrick [1982] found that the majority of the respondents (more than 90%) supported
the disclosure of companies' estimates of reserve value. The results of their survey also
14
indicated that 90.5% of the financial analysts, who responded to the survey, believed that the
reserve value data were useful for their investment decisions. Avard [1982] found strong
support from financial analysts for the discounted future cash flows approach. However,
financial analysts viewed the use of current costs, current prices, and the 10% discount rate
as inappropriate.
Studies That Examine the Impact of Some of the Components of the Ceiling Test on Security Returns
The controversy over the value of the discounted future cash flow touches on the
usefulness of the ceiling test write-off. Several researchers analyzed some of the components
of the ceiling test in different contexts. For example, Bell [1983] studied the impact of RRA
information on the security returns of the OG companies and found that the stock market
reacted significantly to the 1979 RRA disclosure. His study, however, did not use any
expectation model for RRA disclosures; therefore, the observed market reaction may not
signify information content [Dharan, 1984]. Since Bell's study used all RRA disclosures, it
is not possible to attribute the stock market reaction to any specific disclosure item (i.e.,
discounted future net cash flows).
Dharan [1984] examined the incremental information content of the RRA disclosure
by examining whether the disclosure reserve values are obtainable from a transformation of
other concurrently available non-RRA data. The results of his study indicated that the RRA
data have low incremental information content.
Harris and Ohlson [1987] examined whether the book value of OG properties,
discounted future net cash flows, undiscounted future net cash flows, and direct profit margin
15
explain the implied market value of OG properties. Their results suggest that the book value
is highly significant in explaining the imputed market value of OG properties. Discounted
future net cash flows was found to be statistically significant, however, book value dominated
this variable. In other words, book value contributed more than discounted future net cash
flow in explaining the implied market value of OG properties. The dominance of book value
over discounted future net cash flows could be because investors give more credit to a
conservative value than an objective value as indicated by Harris and Ohlson in determining
the market value of OG properties. If this is the case, we would expect to find the
discounted value of future net cash flows more significant than book value when the former
is lower than the latter in explaining the imputed market value of OG properties. Therefore,
the generalizability of Harris and Ohlson's assertion is limited to instances where the book
value is less than the discounted future net cash flows.
Frost and Bernard [1989] studied the impact of the SEC's May 1986 decision
mandating the application of the ceiling test rule on the loan agreements and stock prices of
FC OG firms. Their results indicate that there were few violations of debt covenants and no
negative abnormal returns surrounding the SEC's decision for the affected FC firms. They,
however, found significant positive abnormal returns associated with the SEC's decision.
The Office of the Chief Economist of the SEC [1986] investigated the impact of the
SEC's decision on the stock prices of FC firms. The results of the SEC study are similar to
those obtained by Frost and Bernard in that significant positive abnormal returns were
observed during the week of the SEC's decision.
16
The results of Frost and Bernard [1989] and the SEC [1986] have several limitations.
First, the observed significant positive abnormal returns could be attributable to the Senate
Finance Committee's approval of the 1986 Tax Reform Act. The OG industry viewed this
bill as good news [Frost and Bernard, 1989]. Possibly, the impact of the Senate Finance
Committee's approval (good news) was greater than the impact of the SEC's decision (bad
news) on the stock prices of FC firms. Hence, positive abnormal returns were observed.
Since the two events occurred concurrently, it is impossible to control for the effect of the
Senate Finance Committee's approval on the stock prices of FC firms. Thus, the conclusions
of these two studies are questionable.
Second, the prices of OG increased about 11% during the test period (the week
surrounding the SEC's decision) in the two studies. The increase in the prices of OG may
also have caused positive abnormal returns. No attempts were made to control for the price
of OG in the previous two studies.
Third, since all the firms in the samples of the prior two studies came from the same
industry and the event times are the same for all the firms, the residuals obtained from the
single-factor market model are likely to be correlated across equations (firms). The return
predictions will be biased and consequently will lead to biased prediction errors.
Fourth, the previous two studies utilized a single-factor market model. Farrell [1974]
and Livingston [1977] provide evidence that the security returns of OG firms move together
in a way not captured by a market factor alone. The omission of the industry factor from the
market model will result in biased prediction errors.
17
Finally, Frost and Bernard excluded many FC firms that were affected by the ceiling
test rule, while the SEC study included many FC firms that were not affected by the ceiling
test rule. This decreases the likelihood that the samples are representative of FC firms that
were actually affected by the ceiling test rule. The SEC decision may have a strong market
reaction for the excluded FC firms in the Frost and Bernard sample.
Given the existence of a major confounding event, the omission of relevant variables
such as the percentage change in OG prices and the industry factor from the market model,
and the lack of representative samples, it is difficult to asses the validity of the results of the
two studies.
Lending Agreements and the Ceiling Test Write-off
Implementation of the ceiling test rule may alter the accounting numbers and the
terms of a company's contracts may have to be renegotiated. Lending agreements provide
examples of such contracts. Agency theorists posit that firms with debt agreements that
utilize accounting numbers may be negatively impacted by mandatory accounting changes
and/or mandatory implementation of existing rules.
Prior studies covering other industries have shown that debt agreements are often
written in terms of GAAP [Leftwich, 1981; Smith and Warner, 1979]. The mandatory
change from FC to SE methods has been intensively studied by many researchers [e.g.,
Collins and Dent, 1979; Collins et al. 1981; Larcker and Revsine, 1983; and Lys, 1984].
These studies assume that debt covenants of many FC firms are tied to key accounting
numbers such as net income and net worth. However, Deakin [1979, 1980] provided
18
evidence that debt agreements of many FC firms did not use accounting numbers. The
change from FC to SE essentially alters key accounting numbers. The change from FC to
SE or vice versa would not affect the amount and the value of reserves, has no tax
consequences and no other effects on a company's worth.
Previous studies, which examine the impact of the change from FC to SE on the
capital markets, have implicitly assumed that debt agreements are written in terms of
accounting numbers and not the amount and/or value of proved reserves. If the debt
agreements are written in terms of the amount and/or the value of proved reserves alone, we
would not expect to find debt effects associated with the change from FC to SE since the
change would not result in violations of debt covenants. The value and the amount of proved
reserves would not be affected by a change of accounting method.
Both Collins's et al. [1981] and Lys's [1984] studies provide evidence of the existence
of a debt effect associated with the mandatory change from FC to SE method. The results
suggest that debt agreements of FC firms may have included provisions based on accounting
numbers. Nichols [1988] found that debt covenants of many OG firms are tied to both
proved reserves and the financial ratios.
Deakin [1989] studied the issue of why some FC firms lobbied for the retention of FC
method while others did not. One of the variables he investigated was debt covenant costs.
The results of his study indicate that the debt covenant variable was significant at the 5%
level for two of the three events studied in predicting the firm's decision to lobby. Two of
the three factors used by Deakin to measure debt covenant costs variable are based on
accounting numbers. Therefore, it seems reasonable to conclude that debt agreements are
19
written in terms of accounting numbers. Even if existing debt agreements are not expressed
in terms of accounting numbers such as net income and/or equity, mandatory application of
the ceiling test may result in increased cost of borrowing. The debt/equity ratio and other
ratios might be negatively impacted by the ceiling test write-off of FC firms relative to other
firms within the OG industry. Pogue and Soldofsky [1969] found that many institutional
investors define a relevant range of potential investments by screening device such as
coverage ratios, leverage ratios, and earnings ratios. The adverse impact of the ceiling test
write-off on these ratios may force FC firms to seek out new capital suppliers at higher cost.
Many FC firms expressed concern about the adverse impact of the ceiling test write-
off on their debt covenants. For example, Russell Pennoyer, the general counsel of
American Exploration Company stated that:
[The OG industry] is going to report enormous losses in the [first quarter of 1986] because of write-downs....that would have caused a technical default under companies lending agreements. [The Wall Street Journal, May 7, 1986].
A number of producers are concerned not only with the adverse impact on first quarter earnings but also with the additional administration costs associated with renegotiating credit arrangements for violations of net worth and other covenants and updating reserve engineering studies on a quarterly basis. [Deloitte, Haskins, and Sells, Energy Executive Briefs, May 1986].
SEC officials did recognize that the ceiling test write-off might cause some FC firms
to be in technical default according to their debt covenants. For example, John Albert, an
SEC accountant, stated that "It could cause some companies to be in default of loan
covenants....Loans could be called." [The Wall Street Journal, April 21, 1986].
John Shad, SEC chairman, acknowledged that "[The ceiling test write-off] may
trigger defaults on bank loans." [Wall Street Journal, May 7,1986].
20
Debt covenants of FC firms may include (i) dividend restrictions, (ii) maintenance of
working capital, (iii) investment restrictions, (iv) restrictions of assets dispositions, (v)
additional debt restrictions [Smith and Warner, 1979; Leftwich, 1980; Collins and Dent,
1979; Collins et al., 1981; Lys, 1984; and Nichols, 1988], and/or restrictions on proved
reserves [Nichols, 1988].
The mandatory application of the ceiling test rule may have a significant impact on
accounting numbers used in debt covenants that could result in redistribution of wealth
between debtholders and stockholders. FC firms forced to apply the ceiling test are expected
to experience reduced reported earnings, reduced retained earnings, and reduced reported
asset values.
The ceiling test write-off will increase the likelihood of violation of loan covenants for
FC firms with debt covenants written in terms of reported earnings, retained earnings, or
asset value. FC firms with debt covenants that use reported earnings, retained earnings,
asset value, and the value of proved reserves, any debt covenant violation may be due to
both the ceiling test write-off and the decrease in the value of proved reserves, since a
decline in OG prices will affect both the ceiling test and the value of proved reserve.
FC firms faced with an increased probability of a costly debt covenant violation, may
be forced to (1) adjust financing and investment policies by selling less debt relative to the
levels that would have chosen in the absence of the ceiling test write-off or issue additional
equity or (2) attempt to renegotiate the covenants with the lenders. Renegotiation may
impose renegotiation costs and interest rate concessions, to achieve lenders' approval [Watts
and Zimmerman, 1986].
21
Not all FC firms with lending contracts would be equally affected by the ceiling test
write off. Leftwich [1980, 1981], and Dhaliwal [1980] found that highly leveraged firms are
the ones most likely to be impacted by a mandatory change (or implementation) of an
accounting method such as the ceiling test. Collins et al. [1981] Leftwich [1981] and Deakin
[1989] argued that companies with publicly rather than privately held debt will face a greater
potential impact because public debt is more costly to renegotiate. Prior research indicates
that FC firms tend to be more highly leveraged than their SE counterparts. Deakin [1979]
looked at the operating characteristics of 28 SE companies and 25 FC companies. He found
that the only systematic difference between the two groups was that the FC companies were
more highly leveraged. Dhaliwal [1980] and Deakin [1980] found similar results.
The previous discussion implies that present and future cash flows may be indirectly
affected by the ceiling test write-off through debt covenants violations. Thus, stock prices
may be affected.
CHAPTER III
RESEARCH HYPOTHESES AND RESEARCH DESIGN
Research Hypotheses
This section discusses two groups of research hypotheses: first, research
hypotheses for FC firms that announced the amount of the ceiling test write-off in the
Wall Street Journal and second, research hypotheses for FC firms that disclosed the
write-off in their quarterly financial statements.
Research Hypotheses for FC Firms That Announced Their Ceiling Test Write-off
The stock market reactions of FC firms taking the ceiling test write-off was
investigated to test the following hypotheses (stated in alternate form):
HI: The abnormal rates of returns are positive (negative) around the announcement of the ceiling test write-off, if the actual amount of write-off is less (more) than the expected amount of write-off.
Hypothesis HI investigates the relation between the ceiling test write-off and
the sign of the abnormal rate of returns. I expected a negative (positive) abnormal
rate of return around the ceiling test write-off announcement if the actual amount of
the ceiling test write-off (discussed in research design section) was more (less) than
the expected amount of the ceiling test write-off. Such a relationship was expected,
since the ceiling test write-off may affect cash flows.
22
23
H2: The larger the unexpected amount of the ceiling test write-off, the larger the negative abnormal rates of return.
Hypothesis H2 examines the relation between the magnitude of the unexpected
ceiling test write-off and the magnitude of the abnormal rates of return. I expected
that the larger the unexpected amount of the write-off, the larger the negative
abnormal rates of return.
H3: The variance of abnormal rates of return is larger on days surrounding the announcement of the ceiling test write off.
Since prior work suggests that the ceiling test write-off could affect cash
flows, investors' estimates of the probability distributions of the firm's cash flows will
change. Hence, the firm's stock price will change. To test hypothesis H3, a
comparison between the variance of abnormal return on days surrounding the
announcement of the ceiling test write-off with the variance of abnormal return
outside the announcement period was conducted. I expected a larger variance of
abnormal return around the announcement period than the nonannouncement period.
H4: Of these FC firms taking the write-off, firms with loan covenants defined in terms of accounting numbers and/or the value of proved reserves will experience greater negative abnormal return than firms without such covenants.
Hypothesis H4 compared the abnormal return of FC firms with loan covenants
defined in terms of accounting numbers and/or the value of proved reserves with the
abnormal return of FC firms without such covenants. I expected a larger negative
abnormal return for FC firms whose loan covenants were defined in terms of
accounting numbers and/or value of proved reserves than those firms who loan
covenants did not include such covenants.
24
To test hypotheses H4, loan covenants were identified for each FC firms by
reviewing annual reports and/or 10-Ks. To test hypothesis H4, debt agreements were
examined to determine whether they were based on accounting numbers, value of
proved reserves, both accounting numbers and the value of proved reserves, or other
numbers.
H5: Of these FC firms taking the write-off, firms with public debt will experience greater negative abnormal returns than firms with only private debt.
Hypothesis H5 compared the abnormal return of FC firms that had public debt
with the abnormal return of FC firms that had only private debt. Only FC firms with
debt covenants defined in terms of accounting numbers were studied. To test
hypotheses H5, private and public debt agreements were identified for each FC firms
by reviewing annual reports and/or 10-Ks.
H6: The higher the debt/equity ratio, the higher the negative abnormal returns. FC firms with high debt default's risk may be more affected by the ceiling test write-off.
The debt/equity ratio has been used by Collins et al. [1981] and Lys [1984] as
a proxy for the debt default's risk.
H7: FC firms with higher "total risk" will experience greater negative abnormal returns than those firms with lower total risk.
Lys [1984] found that the debt equity ratio was negatively correlated with the
"total risk" (defined below) of the firm. He concluded that the omission of one of
these two variables can result in an omitted variable problem, which could lead to an
insignificant coefficient for the variable that was included in the cross-sectional
regression. Therefore, this study included the total risk of the firm as one of the
25
explanatory variables in the cross-sectional regression. Lys [1984] defined total risk
as "the weighted average of the standard deviations of debt and equity returns" (p. 62).
This study utilized Lys' procedures to estimate the total risk of the firm av as:
oy = <rD [D/(D+E)] + <rs [E/(D+E)]
Where: D: book value of debt. E: market value of equity.
av: standard deviation of the return on the firm. crD: standard deviation of the return on the debt. <rs: standard deviation of the return on the equity.
If one assumes that crD is relatively small when compared to <rs and thus can be
ignored, then the above equation becomes
Oy = crs (E/D+E)
Lys [1984] estimated the standard deviation of the return on the firm (crv)
which was used as a proxy for the total risk of the firm.
H8: The larger the unexpected earnings (actual earnings - expected earnings), the higher the positive abnormal returns.
Earnings announcements are potential confounding events for FC firms that
announce their ceiling test write-off amounts concurrently with earnings
announcements. To control for the effect of earnings announcements on stock prices,
unexpected earnings (see research design section for the methods that will be used to
calculate unexpected earnings) were included as one of the explanatory variables in
the cross-sectional regression.
Atiase [1985, 22] asserts that "the amount of private predisclosure information
production and dissemination is an increasing function of firm size." This implies
26
that stock market participants would find small firms' ceiling test write-off
announcements more informative than larger companies' announcements. Therefore,
size of the firm must be controlled to avoid bias in interpretation of the results. Size
of the firm was included as one of the explanatory variables in the cross-sectional
regression. Total assets were used as a proxy of the size of the firm.
Research Hypothesis for FC Films That Disclosed Their Ceiling Test Write-off on Their Financial Statements
The ceiling test write-off is similar to other types of expenses in the sense that
it reduces reported earnings of the current period. Investors may perceive the
reduction in reported earnings caused by the ceiling test write-off to be meaningless.
That is, investors may ignore the amount of write-off and may only consider earnings
before deduction of the ceiling test write-off in forming their expectations of future
cash flows. This raises the question of whether the reduction in earnings caused by a
dollar of the ceiling test write-off should be viewed as equivalent to one caused by
other expenses.
Previous research studied the stock market reactions to components of earnings
[Patell and Kaplan, 1977; Bowen, 1981; Wilson, 1986; Lipe, 1986; Stober, 1986;
Wilson, 1986, 1987; Bowen, Burgstahler, and Daley, 1987; and Bernard and Stober,
1989]. The results of the prior research indicate that some decompositions of
earnings have information content. For example, Lipe [1986] tested the relations
between six components of earnings- gross profit, general and administrative expense,
depreciation expense, interest expense, income tax, and other expense - and stock
27
market returns. He found that these six components of earnings explain more of the
variation in stock returns than do earnings. Bowen [1981] investigated the impact of
two components of earnings- allowance for funds used during construction (AFC) and
operating earnings- on the valuation of stock prices. The results of his study indicate
that the cross-sectional valuation model with the two components of earnings
explained more of the variation in stock prices than do earnings alone.
This study decomposed unexpected earnings into two components, unexpected
earnings before deducting the unexpected ceiling test write-off and the unexpected
ceiling test write-off, in order to test the following hypothesis:
H9: The two components of unexpected earnings (unexpected earnings before deducting the unexpected ceiling test write-off and the unexpected ceiling test write-off amount) explain more of the variation of the abnormal returns than is explained by unexpected earnings alone.
If earnings have been announced in the WSJ prior to their appearance in the
quarterly reports, unexpected earnings will be zero at the quarterly report and/or 10-Q
release date. A test of hypothesis H9 then becomes a test of only one of the two
components (either one), since each component must be equal in amount and opposite
in sign to sum to zero.
Research Design
Timing of the Ceiling Test Write-off Announcement
Some of the FC firms announced the amount of the ceiling test write-off in the
Wall Street Journal, others disclosed the write-off in their quarterly financial
statements. If the firm discloses the ceiling test write-off in the quarterly statements,
28
it may appear as an expense item in the income statement or as part of depreciation,
depletion and amortization expense of that quarter with footnote disclosure about the
amount of the write-off. This results in two possible event dates: (1) the
announcement date of the ceiling test write-off in the Wall Street Journal (WSJ) if
there was such a date, and, otherwise (2) the date of the release of the financial
statements.
A total of 86 quarters/firms announced the write-off in the WSJ.1 Seven
quarters/firms announced the amount of the write-off alone forming the first
subsample. 79 quarters/firms announced the amount of the ceiling test write-off
concurrent with earnings announcement forming a second subsample. In no case for
this sample did firms announce earnings alone in the WSJ prior to announcing the
amount of write-off alone in the WSJ. The second event date, the date of the release
of the financial statements, was used for 113 quarters/firms that did not announce the
amount of the ceiling test write-off prior to its appearance in the financial statements.
In all cases for this subsample, firms did announce earnings in the WSJ prior to the
financial statement release date.
'Note that a firm may have announced the ceiling test write-off for one quarter in the WSJ and did not announce for another quarter, but rather waited and disclosed the amount of write-off in the quarterly financial statements.
29
Test Procedures
Testing Hypotheses HI And H2
Testing hypotheses HI and H2 requires expectation models for both the
amount of the ceiling test write-off and for the stock returns of FC firms. Following
the prior research literature, random-walk with drift models were developed for both
required expectations [see Beaver, Clarke, and Wright, 1979; Foster, 1977; and
Foster, Olsen, and Shevlin, 1984].
Expectation Models for the Amount of the Ceiling Test Write-off
The stock price reaction expected from the ceiling test write-off depends on
the extent to which the write-off is anticipated by investors.
In a semi-strong efficient stock market, the market's response to the write-off
amount reflects only the unexpected part of that write-off, i.e., the deviation of the
actual amount of write-off from the expected one. Consequently, the direction of the
abnormal returns observed (positive or negative) at the announcement date of the
ceiling test write-off will depend on the direction of the difference between the
expected and the actual write-off. If the actual amount is greater than the expected
amount of write-off, then negative abnormal returns would be observed at the date of
the announcement. If the actual amount is equal to the expected one, then no
abnormal return would be observed at the announcement date. If the actual amount is
less than the expected, then positive abnormal returns would be observed at the
announcement date.
30
The expectation model for the ceiling test write-off is based on a yearly data.
FC OG firms do not disclose the components of the ceiling test write-off in the
quarterly financial statement. There are two roles of the ceiling test write-off
expectation model. The first role is to classify firms into those with negative
unexpected amounts of write-off and those with positive amounts of write-off. The
second role is to calculate the amount of the unexpected write-off. Therefore, the
following random-walk with drift model was used to predict the amount of ceiling test
write-off:
(1) E(AMCto) = AMCy.j + [(AMCy.j - AMC1979) -r T] * [T + qt]
Where: E(AMCqty) = the expected unamortized capitalized costs for quarter (qt) in year y. AMCy4 = unamortized capitalized costs reported at the end of year y-1. AMC1979 = unamortized capitalized costs in year 1979. (AMCy.j - AMCy) T T = average quarterly drift estimated from 1979 to y-1.
y = the year of the ceiling test write-off. qt = the quarter of ceiling test write-off announcement (1, 2, 3, or 4). T = number of quarters from the beginning 1979 to ending of year y-1.
(2) E(CAqty) = [{PVy., + {(PVy.! - PVy)+T}* {T + qt} * {P0qt)y+P0y4}] + + [CANW1 + {(CAN^-C AN1979) -r T} * {T + qt}] + [LCM M + {(LCMT4-LCM1979) -r T} * {T + qt}]
Where: E(CAqty) = the expected ceiling amount of quarter (qj in year y.
PV = the "standardized measure" (FASB) reported by the firm at y-1 (the present value of future net revenue from estimated production of proved reserves discounted at 10 percent.
POqty = OG prices at the end of quarter qt of year y. POy.j = OG prices at the end of year y-1. CANy.i = the cost of assets that are not being amortized at the end of year y-1. LCMy.! = the lower of cost or market value of unproved properties at the end of
year y-1.
(3) E(WROqt,y) = E(AMCqty) - E(CAqt>y) Where: E(WROqtiy) = the expected amount of write-off for quarter qt in year y.
31
(4) UWROqt>y = AWRO^ - E(WROqty) Where: AWROqt)y = the actual amount of the ceiling test write-off.
UWROqt y = the unexpected amount of the ceiling test write-off.
The above model involves two problems which may induce bias, but it is
nonetheless consistent with the data available to financial analysts. First, if the
expected unamortized capitalized costs E(AMCqt>y) are less than expected ceiling
amount E(CAqty), then the expected amount of write-off E(WROqty) would be
negative. In other words, no write-off would be expected and the expectation of "no
write-off be stronger the more negative E(WROqty) was. Since it was desired to
scale the unexpected ceiling test write-off by the expected ceiling test write-off as one
of the measures, 15 observations with negative expected write-off were dropped from
the sample. The restriction of the sample to firms that both expected a write-off and
were expected to experience a write-off under this model may induce sample selection
bias.
Second, the expectation model may be biased itself. The method for
calculating the "standardized measure" reported in accordance with FASB 69 differs
in the handling of tax estimates from the method use for the ceiling amount and
results in the standardized measure being lower than it would be if calculated under
the ceiling method. Using the standardized measure in the expectation model results
in a lower expected ceiling amount E(CAqty) and a higher write-off E(WROqty). The
ceiling test also considers the difference, if any, in the book value and tax basis of
properties not being amortized and unproved properties. Failure to include this
32
adjustment increases the expected ceiling amount E(CAqt y) and lowers the expected
write-off. On balance, the expected write-off is probably biased upward. However,
it is based on the only data available to financial analysts. If the expected amount of
write-off is biased upward, then the actual amount of write-off will, on average, be
smaller and the unexpected amount of write-off, on average, would be falsely biased
to be negative. A negative unexpected amount of write-off would be good news (the
actual write-off was smaller the expected one). Bias in the model may result in good
news being estimated when bad news is the case.
The expectation model of the amount of the ceiling test may be biased in fact,
but it is consistent with the presumption that financial analysts use supplementary
historical data available in the annual reports and 10-Ks to determine future expected
amounts of the ceiling test write-off.
Expectation Models for Earnings
If a FC firm announces its ceiling test write-off concurrently with its earnings
then unexpected earnings will be a confounding event. To control for unexpected
earnings and to remove the information effects of earnings releases on stock prices,
random walk with drift earnings expectation models were used.2
Prior research relied upon either time series models [e.g., Beaver, Clarke, and
Wright, 1979; Foster, 1977] or upon a financial analysts' forecast [e.g., Brown,
2Earnings expectations are not the central issue, but are necessary to control for the effect of unexpected earnings on security returns of FC firms released their earnings simultaneous with the ceiling test write-offs.
33
Griffin, Hagerman, and Zimijewski, 1987; Brien, 1988; Foster, Olsen, and Shevlin,
1984] to determine unexpected earnings. Prior research also attempted to determine
whether predictions from time series models or financial analysts' forecasts provided
a better proxy for market expectations. The results of this research are mixed. For
example, Brown, Griffin, and Hagerman [1987], and Brien [1988] found the analysts'
forecast to be more accurate than the time series models. Foster [1977] and Foster,
Olsen, and Shevlin [1984] found that time-series models are good predictors of the
market expectations of quarterly earnings.
Since these studies used either time series models or analysts' forecasts to
determine expected earnings for firms belonging to many industries, their results can
not be generalized to the OG industry. Therefore, this study will employ the
following random walk with drift models to determine expected earnings.
E(E,t,y) = Eqt y.! + D - E(WROqty)
Where: E(Eqt y) = expected earnings of quarter qt in year y after write-off. Eqt,y-i = actual earnings of quarter qt in year y-1. ECWROqt y) = expected ceiling test write-off in quarter qt in year y.
D = annual drift term, the average of earnings over the available history.
While this model may appear unsophisticated, Foster, Olsen, and Shevlin
[1984] find that their conclusions regarding the standardized unexpected earnings
effect are the same with this model as with the more accurate Foster [1977] first-order
autoregressive model in seasonal differences. Actual earnings were compared with
expected earnings to determine unexpected earnings as follows:
Unexpected earnings (UNO) = Actual earnings - Expected earnings
34
If the ceiling expectation model is biased such that expected write-off is
overestimated, expected earnings will be biased downward, unexpected earnings are
biased upwards (on average estimated falsely as positive), and unexpected write-off is
biased downward (on average estimated falsely as negative) by an equal amount.
Expectation Models for FC Firms Reporting the Ceiling Test Write-off in the Financial Statements
To test whether the ceiling test write-off has information content, unexpected
earnings were decomposed into two components: unexpected earnings before
deducting the amount of unexpected ceiling test write-off and the unexpected ceiling
test write-off amount itself.
To compute unexpected earnings, the unexpected earnings before deducting
unexpected ceiling test write-off, and the unexpected ceiling test write-off, the
following procedures will be used:
(1) Expected earnings and expected amount of ceiling test write-off were calculated using the time-series expectation models introduced earlier.
(2) Expected earnings before deducting ceiling test write-off equals expected earnings plus expected ceiling test write-off.
(3) Actual earnings before deducting ceiling test write-off equals actual earnings plus actual ceiling test write-off.
(4) Unexpected earnings equals actual earnings minus expected earnings.
(5) Unexpected ceiling test write-off equals actual ceiling test write-off minus expected ceiling test write-off.
35
(6) Unexpected earnings before deducting ceiling test write-off equals unexpected earnings plus unexpected ceiling test write-off.
Those firms that announced their ceiling test write-off only in their quarterly
financial statements had zero total unexpected earnings at the time of announcing the
write-off. The two components of unexpected earnings (unexpected earnings before
deducting the ceiling test write-off and unexpected ceiling test write-off) in this case
must be equal in amount and opposite in sign.
Expectations of Stock Prices
Many researchers have attempted to test both the Capital Asset Pricing Model
(CAPM) and the one-factor Arbitrage Pricing Theory (APT) empirically. The early
tests by Black et al. [1972], and Fama and MacBeth [1973] supported the single
factor security market model. However, later research has raised serious questions
about the one factor model. There is some evidence that a multi-factor model is
better than one factor model. Research by Farrell [1974], Livingston [1977], Roll
and Ross [1980], Collins, Rozeff, and Dhaliwal [1981], Bell [1983], and Conover
[1989] suggest that several factors are important in explaining security returns.
Farrell [1974] and Livingston [1977] provided evidence that the security
returns of OG firms move together in a way not captured by a market factor alone.
The omission of the industry factor from the market model may result in biased
prediction errors. They recommended the inclusion of the industry index as one of
the explanatory variables in the market model.
36
Bell [1983] argues that the use of the market factor as the only explanatory
variable is inappropriate when assessing the impact of an intraindustry event, such as
the ceiling test write off. He believes that the one factor model is appropriate only
when examining the effect of an interindustry event since industry effects would be
mitigated through diversification. Beaver [1987] contends that the one factor market
model procedure suffers from some misspecification problems. He recommended
better model specification in the market model, i.e., more explanatory variables may
be added to the market factor, to estimate the abnormal return more accurately.
Ricks [1982] stated that an omitted variable leads to a systematic bias in the
interpretation of the results. He suggested that any factor differentially distributed
across test firms and known to affect security prices should be controlled to avoid a
biased analysis. Conover [1989, 1-2] argues that "any method that can be developed
to remove systematic error would allow a higher power test of the abnormal return."
Her results showed that the multi-factor market model performs better than the one
factor market model.
OG prices declined during the period 1982 through 1987. This decline
affected the cash flows of OG firms. Hence, stock prices of these OG firms would be
affected. Adding the percentage change in OG prices to the market model as an
explanatory variable would prevent a biased interpretation of the results. That is, the
observed abnormal return would be due to the ceiling test write-off rather than the
general change in the price of OG.
37
To control for industry events and to remove the effects of these events on the
security returns of FC firms, an industry factor was added as an explanatory variable
in this study. To measure the impact of the ceiling test write off on the security
prices of FC firms, the following model was developed:
Rit = a + BjRMt + B2POt + B3INt + Uit
where: Rjt: is the expected return on security i in time t. a : is the risk free rate.
RMt: is the expected return on the market portfolio. POt: is the percentage change in OG prices calculated as follows: POt = (Price of OG in day t minus prices of oil and gas in day t-1) / prices of OG
in day t-1.
IN : industry factor.
In this model a, Bl5 B2, and B3 were calculated and obtained from the
estimation period. Rit, RMt, POt, and INt were for the test period. The industry
factor (IN,) was measured as the equally weighted portfolio return of the combined
FC and SE firms as follows: INt (= ERit /n) is the weighted average return on all OG
firms stocks. IN was calculated from the estimation period. The market factor
(RMJ, industry factor (IN,), and the percentage changes in OG prices (POJ might be
highly correlated.
Judge et al. [1982] state that if the purpose of regression is prediction, then
multicollinearity is not a problem "....as long as the value of the explanatory
variables for which predictions are made obey the same near-exact linear
dependencies as the original matrix X" [Judge et al. 1982, 619]. That is, if in the
estimated multi-factor market model it was found that IN = 3 PO, for example, then
for the test period observations used to forecast the returns, IN should also be
38
approximately equal to 3PO, a condition difficult to meet. This could cause the
prediction security returns to become uncertain. It is possible that the industry index
(IN,) and the percentage change in OG prices (PO,) may be irrelevant variables.
There is no theory to support the use of these two variables as explanatory variables
in the market model. Kennedy [1979] stated that "if irrelevant variables are included
in the model", and are not orthogonal to the other independent variables, then "the
ordinary least square estimates are not as efficient" [Kennedy 1979, 58].
In fact, the correlation coefficients among the multi-factor model explanatory
variables were low. Had they been found to be too high, then the industry factor
(INt) would have been first regressed against the market factor (RM,) and the
percentage change in OG prices (PO,) for the estimation period as follows:
INt = a + BtRM, 4- B2POt + residual (INN,).
The residual (INN,) would have been a new industry factor taking out the effects of
the market return and the percentage change in OG prices. The residual (INN,) would
have been used in the multi-factor model instead of using the average industry return
(IN,), to insure that the market factor and the percentage change in OG prices were
orthogonal to the industry factor. The multi-factor model then would have been:
Uit = RI, - [a + BiRM, + B^O, + B3INNJ
This reinforcement was not found to be necessary.
39
Testing Hypothesis H3
To test hypothesis H3, the variance of the abnormal return in the test period
(the period around the ceiling test write-off announcements) was compared with the
variance of abnormal return in the non-test period. Beaver [1968] was the first to use
the variance of the abnormal return as a measure of the information content of annual
earnings announcements. Because the variance test eliminates the need to specify ana
expectation model, it has been used frequently in other types of information
disclosures. Beaver's methodology was utilized in this study. He calculated the
following ratio:
U c > t ®c,/^(c,t)
Where: ec t : is the square of the prediction error from the multi-factor model for the ceiling
test write-off announcement in day t (test period). a(c t): is the residual variance from the estimated multi-factor model for the ceiling
test write-off announcement (estimation period).
If there is no information content in the ceiling test write-off announcement,
then the abnormal return variance should not change when the ceiling test write-off is
announced, and the ratio for the announcement period (Uc>t) should be approximately
1. If there is information content in the ceiling test write-off announcement then the
ratio should be greater than 1 [Beaver, 1968].
Testing Hypotheses H4, H5, H6, H7, and H8
The residual (Uit) of the test period obtained from the multi-factor model was
accumulated for each firm to obtain the cumulative abnormal residuals (CAR). The
40
CAR measure represents the sum of the difference between the actual and expected
returns given a security's previous relationship with the market return (RM), industry
return (IN), and the percentage change in OG prices (PO). In this study, CAR
represents the sum of the abnormal returns across the test period for each FC firms.
CAR was measured over 23 trading days, starting 11 days prior to the event date, for
FC firms whose stocks are traded over the OTC, and 13 trading days, starting 6 days
prior to the event date, for FC firms whose stocks are traded on an AMEX and the
NYSE.
To test the relationship stated by hypotheses H4, H5, H6, H7, and H8, the
following cross-sectional model was examined.3
CAR = a + BjTDEBT + B2PPDEBT + B3DE + B4SD + B5UN02
+ B6TAMKTE + B7UWR03
Where: (1) CARit: Cumulative abnormal residuals for each FC firm across the days of the test
period.
(2) TDEBT: This variable will be coded "0" if a FC firm's debt (Public or private) covenants are tied to accounting numbers and/or "1" otherwise. This variable was used to test hypothesis H4. A positive relation between TDEBT and CAR was expected
(3) PPDEBT: Book value of public debt divided by book value of total debt. A negative relationship between PPDEBT and CAR was expected. This variable was used to test H5.
(4) DE: Book value of total debt divided by book value of equity. A negative relationship between DE and CAR was expected. This variable was used to test H6.
3The variable UN02 was included only if a FC firm announced its ceiling test write-off concurrent with earnings.
41
(5) SD: Standard deviation of return data on the firm. The relationship between SD and CAR was expected to be negative. This variable was used to test H7.
(6) UN02: Unexpected earnings divided by the market value of equity. UN02 was expected to be positively related to CAR. This variable was used to test H8.
(7) TAMKTE: Total assets divided by the market value of equity. TAMKTE and CAR were expected to be positively related. This variable was used to control for size of the firm since stock market participants may find small firms' ceiling test write-off announcements more informative than larger firms' announcements.
(8) UWR03: Amount of unexpected ceiling test write-off divided by the market value of common equity. The relationship between UWR03 and CAR was expected to be negative.
Testing Hypothesis H9
To test Hypothesis H9, the following cross-sectional model was developed:
CAR; = a + BiUEBWRO + B2UWRO + eit (1)
Where: CAR;: is the cumulative abnormal return for firm i during the test period. UEBWRO: Unexpected earnings before deducting the ceiling test write-off for firm i
at time t. UWRO: is Unexpected ceiling test write-off for firm i during the test period. eit: disturbances term.
The coefficient Bt on unexpected earnings before the write-off is expected to
be positive, and the coefficient B2 is expected to be negative.
However, unexpected earnings before deducting the ceiling test write-off
(UEBWRO) equals unexpected earnings (UN) plus unexpected ceiling test write-off
(UWRO), thus
UEBWRO = UN + UWRO (2)
42
Substituting equation (2) for UEBWRO in equation (1), yields:
CAR; = a + BJUN + UWRO] + B2UWRO + eit (3)
Since earnings have already been announced in the WSJ prior to their
appearance in the financial statements and assumed to have been impounded in stock
prices, unexpected earnings (UN) at the financial statement release date must be zero
(UN=0). Under these conditions, equation (3) becomes
CARi = a + (Bj + BO UWRO + eit (4)
let (B2 + Bj) = B3, then equation (4) becomes
CAR; = a + B3UWRO + eit (5)
The coefficient B2 is expected to be negative. The coefficient B3 will be zero
if the value of B2 equals -B,. If B3 is insignificantly different from zero, then the
market is not distinguishing between the ceiling test write-off and any other expense.
IF B3 is positive and significant, then the market is ignoring part of the ceiling test
write-off. If the coefficient of B3 is negative and significant, then the market is
reacting more negatively to the ceiling test write-off than to other expenses.
Estimation Period
Determining the length of the estimation period represents a problem since
stationarity of the coefficients of the multi-factor model must be assumed.
Selecting a long estimation period allows time for the individual constituents of
the market factor to average out, thereby allowing the average security responsiveness
of the market factor to be estimated. The stationarity of the multi-factor market
43
model parameters over the estimation period must be assumed. However, OG firms
may have structurally changed over the period between 1979 through 1982. Some of
these OG firms may have added or dropped some of the lines of business. All such
changes can violate the assumption of stationarity of the multi-factor model
relationship. Selecting a short estimation period may avoid the violation of the
assumption of stationarity of the multi-factor market model over an extended time
period.
Prior research has not provided a definite answer to the question of what is the
appropriate length of the estimation period. Prior studies have used estimation period
of different lengths. For example, Leftwich [1981] estimated the market model over
500 trading days, while Collins et al. [1981] used 70 weeks (420 days) as an
estimation period. Atiase [1985] estimated the market model over 104 weeks (624
days). This research used 250 trading days as a length of the estimation period which
is within the range of prior research.
Test Period
Having decided on the test event(s) to be studied and the length of the
estimation period, the next logical step is to determine the length of the test period for
measuring the abnormal return for each event. Prior studies have selected different
length test periods.4
4Leftwich [1981] calculated the CAR for 11 day for each event studied. Foster and Vickery [1978] measured the abnormal return over 5 weeks. Beaver's [1968] test period was 17 weeks. Beaver, Clarke, and Wright [1979] computed the monthly
44
One possible way is to select a very long test period, however, this approach
is not recommended since this increases the problem of confounding events. Selecting
a very short test period on the other hand might solve the problem of confounding
events but there is still a possibility that the exact event date might not be captured.
The test periods in this study were 13 days for firms whose stocks were traded on the
major exchanges and 23 days for others. These periods were deemed long enough to
capture the effect of the ceiling test write off announcement. Profile analysis, as
recommended by Foster [1980], was conducted for each firm during the test period to
determine whether there were confounding events.
Lease and Lewellen [1982] provide evidence that the reaction of the stock
markets to new information is dependent on the exchange over which a company's
stock is traded. Brown [1988] concluded that the American Exchange Stocks
(AMEX) and Over the Counter (OTC) were less efficient than the New York
Exchange Stock (NYSE), i.e., it took from four to five weeks for AMEX and OTC to
show the behavior exhibited by the NYSE in the first week after the earnings
announcement. Grant [1980] and Atiase [1987] provided evidence that the stock price
behavior for OTC firms differs from those for NYSE and AMEX firms. Therefore,
the test period of this study will be 23 trading days, starting 11 days prior to the event
date for FC firms whose stocks are traded on the OTC and 13 trading days starting 6
days prior to the event date for FC firms whose stocks are traded on an AMEX and
the NYSE.
unsystematic return for 12 months. Atiase [1985] used one week as a test period.
45
Sample Selection
To be included in the sample, an OG firm had to meet all the following
criteria:
(1) Each firm used the FC method to handle exploration costs during the ceiling
test write-off announcement period and the estimation period of this study. This
criterion insured that no SE firm was included in the sample. ASR No. 258 did not
require or prohibit application of the ceiling test rule by the SE firms. Consequently,
some SE firms may have applied the ceiling test rule and took the write-off. In a
questionnaire survey, Gallun and Bruno [1988] found that 71% (30 firms) of the 42
SE firms responding to their survey applied the ceiling test rule. This criteria was
used to restrict the sample to firms where the application of the ceiling test rule is
mandatory.
(2) Each firm was publicly held. Since the purpose of this study is to examine the
effect of the ceiling test write-off on the security returns of affected FC firms,
privately held FC firms were eliminated from the sample.
(3) Firms that announced other news such as stock splits, dividend announcements,
etc., during the test period surrounding the ceiling test write-off announcement were
eliminated. However, this study did allow for the announcement of the ceiling test
write-off concurrent with earnings announcements. This criterion increased the
likelihood that the observed abnormal return is the result of the ceiling test write-off
announcement and not due to some confounding event.
CHAPTER IV
ANALYSIS AND INTERPRETATION OF THE RESULTS
Data Collection
A list of FC firms was obtained from Arthur Anderson's Survey of 1986 and
the Directories of Companies Required to File Annual Reports with the Securities and
Exchange Commission under the Securities Exchange Act of 1934 (SEC, September
30, 1984-1988).
The annual reports and/or 10-Ks of all FC firms were examined for the period
1982 through 1988 to determine which companies were subject to the ceiling test
write-off and the year(s) of the write-offs. The SEC's ceiling rule which was based
on the FASB No. 69 began in 1982. 1988 was the last year for which ceiling test
write-off data were available.
For those years in which the ceiling test write-off was taken, quarterly reports
and/or 10-Qs were examined to determine the quarter(s) of write-off. The result was
a sample of 95 firms. The 95 firms had a total of 199 quarters of write-offs. A list
of firms and the quarter(s) of write-offs are presented in Table 1.
The Wall Street Journal Index (Corporate News), and the quarterly financial
statements were then examined to determine whether the sampled companies
announced the amount of the ceiling test write-off alone, announced it concurrently
46
47
with earnings, or did not announce the amount of write-off prior to its appearance in
the quarterly financial statements. The date of the release of the financial statements
for the quarters of the write-off was collected by an independent vendor, Securities
Documents Service Inc., from the SEC office in Washington, D.C.
To determine the event date, defined as the date when the actual amount of
write-off became public, the date of the release of the quarterly financial statements
was compared with the announcement date in the Wall Street Journal. The earlier of
the two dates was considered to be the event date.
This led to the construction of three subsamples. The first subsample
consisted of FC firms that announced the amount of the ceiling test write-off alone,
the second subsample contained FC firms that announced the amount of the ceiling
test write-off concurrent with earnings, and the third subsample was comprised of FC
firms that did not announce the amount of the ceiling test write-off but disclosed the
write-off in their quarterly financial statements. The number of the write-offs in each
subsample and the dates of write-off for all sampled firms and their respective
frequencies are presented in Table 2.
Approximately 57 percent of the write-offs were not announced but were
disclosed in the quarterly financial statements.1 Only seven firms/quarters (3.5%)
announced the amount of the ceiling test write-off alone. About 40 percent of the
!Note that a firm may have announced the ceiling test write-off for one quarter in the WSJ and did not announce for another quarter, but rather waited and disclosed the amount of write-off in the quarterly financial statements. In this case, the announcement in the WSJ was studied as part of the first subsample, but the disclosure in the financial statements was studied as part of the third subsample.
48
sampled firms/quarters announced the amount of the ceiling test write-off concurrent
with earnings.
Daily stock prices were obtained from CRSP tapes. Data necessary to
calculate debt/equity ratio, public debt/total debt, private debt, book value of equity,
total assets, expected and actual amount of the ceiling test write-off, and expected and
actual earnings were obtained from the annual reports, 10-K's, and 10-Q's and the
COMPUSTAT PC.
Results of the Multi-Factor Market Model
The market model introduced in Chapter III was used to extract the effect of
the ceiling test write-off on the security returns.
Two different measures of the industry index (IN,) have been calculated in this
study.2 The first industry index was calculated by taking the weighted average
returns of all OG firms whose stocks were traded on the OTC during the estimation
and test periods of the OTC sample firms. This index was used in the multi-factor
market model to calculate the expected security returns (RI,) for the OTC sampled
firms. The second industry index was calculated by taking the weighted average
returns of all OG firms whose stocks were traded on the NYSE & AMEX during the
estimation and test periods of the NYSE & AMEX sampled firms. The second index
2Due to differences in the sources of stock returns data for OTC FC firms and NYSE & AMEX FC firms, Grant [1980] and Atiase [1987] provided evidence that the behavior of security returns for OTC firms differs from those for NYSE and AMEX firms.
49
was used in the multi-factor market model to calculate the expected security returns
(RIJ for NYSE & AMEX sampled firms.
As indicated in Chapter III, the market factor (RMJ, industry index (IN,), and
the percentage changes in OG prices (POO might be highly correlated. In order to
test whether RMt, INt, and POt were highly correlated, it was necessary to calculate
the simple correlation coefficients among RMt, INt, and POt for the estimation periods
preceding the 199 event dates.
Table 3 presents the average simple correlation coefficients among RMt, INt,
and POt. The average correlation coefficients between RMt and INt are 0.218 and
0.219 for OTC firms and NYSE & AMEX firms, respectively. INt is positively
correlated with POt. Thus, an increase in OG prices would have a positive impact on
the stock prices of FC companies. However, the variables RMt and POt are
negatively correlated. The average correlation coefficients are -0.029 for OTC firms
and -0.078 for NYSE & AMEX firms. This implies that any increases in OG prices
would have had a negative impact on the return on the market (RMJ.
The low correlation coefficients among the independent variables RMt, INt,
and POt, suggest that there was not a multicollinearity problem among those
variables. Therefore, contrary to the procedures suggested in Chapter III, RIj was
regressed against RMt, INt and POt, without regressing INt against the market factor
(RMJ and the percentage change in OG prices (PO,).
In the multi-factor market model, the coefficients a, B1; B2, and B3 were
calculated for the estimation period, a 250-day period which ended 11 (6) days before
50
the event date for FC firms whose stocks were traded on the OTC (NYSE & AMEX).
The multi-factor market model parameters were estimated 199 times, one for each
event date. RIt, RMt, INt, and POt were obtained from the test period: 23-days for
OTC forms starting 11 days prior the event date, and 13-days for NYSE & AMEX
starting 6 days prior to the event date.
Some summary statistics relating to the regressions appear in Table 4. The
average value of R2 for the OTC sampled firms (0.028) is less than the average value
of R2 (0.074) for the NYSE & AMEX sampled firms. Consequently, the average F-
Value for OTC firms (2.481) is lower than average F-Value for NYSE & AMEX
firms (6.672). The R2 for OTC sampled firms ranged between 0.002 (lowest) and
0.199 (highest), while R2 for NYSE & AMEX sampled firms ranged between 0.005
(lowest) and 0.269 (highest). This suggests that the multi-factor market model with
the three explanatory factors explains more of the variation in the security returns
(RIt) of NYSE & AMEX sampled firms than in the security returns of OTC sampled
firms.
An inspection of the statistical significance of the estimated coefficients for
each independent variable reveals that none of the coefficients are significant at any
reasonable level in 33 (9) regressions out of 86 (113) regressions for OTC (NYSE &
AMEX) sampled firms. The coefficient for the independent variable, RMt, is found
to be significant at 10% or better in 56 regressions out of 113 regressions of NYSE &
AMEX sampled firms. The coefficient for INt is found to be significant at 10% or
better in 86 regressions out of 113 regressions of NYSE & AMEX sampled firms.
51
The coefficient for POt is found to be significant at 10% or better in 33 regressions
out of 113 regressions of NYSE sample firms. These results suggest that the multi-
factor market model, with the three independent variables, is better than the one
factor market model in explaining variation in security returns of NYSE & AMEX
FC firms.
The Durbin-Watson statistic (D-W = 2.118 for OTC firms and 2.176 for
NYSE & AMEX firms) indicated a negative autocorrelation of residuals, but was not
significant at any conventional level of significance. Hence, the return prediction may
not be biased and, consequently, the estimated prediction errors (abnormal returns) in
this study may be more accurate than the one calculated by Frost and Bernard [1989]
and the SEC [1986] studies.
In this study, CAR was measured over 23 trading days, starting 11 days prior
to the event date for OTC FC firms, and 13 trading days, starting 6 days prior to the
event date for NYSE & AMEX FC firms.
Unexpected Amount of Write-off
In order to calculate the expected amount of the ceiling test write-off, the
expectation models of the ceiling test write-off, developed in Chapter III, were used.
To prevent scaling problems, 15 firms with negative expected write-off were
eliminated from the sample in any test involving scaling since expected write-off is
one of the scaling factors used. The expectation models were estimated 199 times,
one for each quarter of a write-off. If a firm took the ceiling test write-off in two
52
quarters in a year, for example, the first and the third quarters of 1986, then the
amount of the ceiling test write-off in the first quarter of 1986 was deducted from the
expected unamortized capitalized cost of the third quarter of 1986 for that particular
company.
There are two roles of the ceiling test write-off expectation models. The first
role is to classify FC firms into those with negative unexpected amounts of write-off
and those with positive unexpected amounts of write-off. The second role is to
calculate the amount of the unexpected write-off.
The expectation model of the amount of the ceiling test introduced in Chapter
III is consistent with the presumption that financial analysts use supplementary
historical data available in the annual reports and/or 10-Ks to determine future
expected amounts of the ceiling test write-off. Furthermore, the use of time series
models that utilize the properties of the components of the ceiling test write-off is not
possible since there are only, at best, eight years of data available for every
component of the ceiling test. Finally, there are no prior studies to support the use of
any expectation model of the ceiling test write-off.
Scaling Unexpected Amount of Write-off
The unexpected amount of the ceiling test write-off was deflated by four
different variables. The first deflating variable is the actual amount of write-off. The
choice to deflate by actual amount of write-off is consistent with Brown, Foster, and
Noreen (1984) who use the average absolute change as a deflator. Biddle and Lindahl
53
(1982) consider two deflators: the beginning of the period market value of equity and
the last period's value of the series. Their results were not sensitive to the choice of
deflators.
The second, third, and fourth deflating variables are the end of the prior
period total asset, the end of the prior period market value of common equity, and the
expected amount of write-off.3 The second, third, and fourth deflators were used to
assess the sensitivity of the results of testing hypotheses HI through H9 to the choice
of the deflator. Deflating by actual amounts of the ceiling write-off, total assets,
market value of common equity, or expected amount of write-off may serve as an
adjustment of heteroscedasticity; as the unexpected amount of the ceiling test write-off
increases, the variances of the unexpected amount of write-off also increases. To
prevent scaling problems, 15 firms with negative expected write-off were eliminated
from the sample in all tests involving scaling.
Results of Testing Hypothesis HI
Hypothesis HI predicts a negative (positive) abnormal rate of return around
the announcement of the ceiling test write-off if the actual amount of write-off is more
(less) than the expected amount of the ceiling test write-off. If the amount of the
ceiling test write-off is related to stock prices, negative abnormal rates of return are
expected for announcements with a positive unexpected amount of ceiling test write-
3The expected amounts of write-off are positive numbers since observations with negative amounts were eliminated.
54
off and positive abnormal rates of return for announcements with a negative
unexpected amount of ceiling test write-off. If there is no relation, abnormal rates of
return are expected to be zero.
If the ceiling expectation model is biased downward (a possibility discussed in
Chapter III), the expected will be biased upward. If the expected write-off is biased
upward, then the actual write-off will on average be smaller and the unexpected write-
off on average falsely biased to be negative. Normally, a negative unexpected write-
off would be good news (the actual write-off was smaller than the expected one).
Bias in the model may result in good news being estimated when bad news is the
case. Although the expectation model may be biased in fact, but it is consistent with
the presumption that financial analysts use supplementary historical data available in
the annual reports and 10-Ks to determine future expected amounts of the ceiling test
write-off.
If the ceiling expectation model is biased such that expected write-off is
overestimated, expected earnings will be biased downward, unexpected earnings are
biased upward (on average estimated falsely as positive), and unexpected write-off is
biased downward (on average estimated falsely as negative) by an equal amount.
To test hypothesis HI, the sample of companies which announced the amount
of ceiling test write-off was decomposed into two subsamples: first, a subsample of
companies that announced the amount of ceiling test write-off alone and, the second,
a subsample of companies which announced the amount of the ceiling test write-off
concurrent with earnings.
55
The relation between CAR and the unexpected write-off is presented in Table
5. The behavior of the abnormal rates of return is generally as predicted for
companies which announced the amount of ceiling test write-off alone. For each of
the two cases of a positive unexpected amount of write-off, CAR is negative. CAR
was found to be positive for 2 of the 3 companies which have negative unexpected
amounts of write-off. The correlation coefficient between CAR and the four
measures of unexpected percentage write-off was also calculated. The correlation
coefficient was found to be negative for the four measures of unexpected write-off
(r= -0.54, -0.97, -0.88, and -0.71 respectively).
The second subsample consisted of companies which announced the amount of
the ceiling test write-off concurrently with earnings. The correlation coefficients
between CAR on one hand and the four measures of unexpected write-off and the two
measures of unexpected earnings on the other hand for companies which announced
the amount of the ceiling test write-off concurrently with earnings are presented in
Table 6 (Panel A). The relation between the second measure (UWR02) of
unexpected write-off and CAR appeared to be negative, as was hypothesized. This
suggested that if the actual amount of the ceiling test write-off was less than the
expected amount, then a positive CAR was observed at the announcement date. In
addition when the actual amount of write-off was more than the actual amount, CAR
appeared to be negative. However, the hypothesized relation between the first
(UWROl), third (UWR03), and fourth (UWR04) measures on one hand and CAR on
the other hand did not hold. A high positive correlation appears to exist between
56
unexpected write-off and unexpected earnings in Table 6 (Panel A) when each is
scaled by the same deflator. A simple bias to underestimate unexpected write-off
would produce a bias to overestimates unexpected earnings. That would not change
the predicted negative relation between the variables. A more complex bias suggested
that may cause joint underestimation. Table 6 (Panel B) shows less positive
correlation between unexpected write-off and unexpected earnings, suggesting that
much of the possible expectation model biases concerns outliers.
The results of the correlation between the two measures (UNOl and UN02) of
unexpected earnings and CAR, were found to be positive, which were consistent with
prior research [Ball and Brown, 1968; Foster, 1977; Collins and Kothari, 1989; and
Shores, 1990]. In other words, if the actual earnings were greater than the expected
earnings, CAR is positive and vice versa.
The subsample of companies which announced the amount of the ceiling test
write-off concurrently with earnings was subdivided according to the sign of the
unexpected write-off and the sign of unexpected earnings to determine whether
investors respond differently to the positive unexpected amount of write-off than the
negative unexpected amount of write-off. According to hypothesis HI, CAR is
predicted to be positive for the subsample with negative unexpected write-off, and
negative for the subsample with positive unexpected write-off.
The average CAR for the two subsamples is presented in Table 7. For the
largest subsample (N=45) with negative unexpected amount of write-off (good news)
and negative unexpected earnings (bad news), the average CAR was found to be
57
negative suggesting that the negative unexpected earnings dominated or that the
expectation models were biased to underestimate unexpected write-off (good news)
falsely, to underestimate unexpected earnings (bad news) falsely, or both. The bias
can not be in the unexpected write-off only. If that bias is present, it would bias
unexpected earnings upward meaning that the negative unexpected earnings were in
fact more negative than estimated. For the subsample (N=5) with a negative
unexpected amount of write-off (good news) and positive unexpected earnings (bad
news), the average CAR was found to be negative suggesting that the ceiling
expectation model was biased to estimate both negative unexpected write-offs (good
news) and positive unexpected earnings (good news) falsely. That combination could
result from a simple bias to underestimate unexpected write-off. For the subsample
(N=8) with a positive unexpected write-off (bad news) and negative unexpected
earnings (bad news), the average CAR appeared to be positive which is not as
predicted and which can not result from a bias to underestimate unexpected earnings.
It might result from a separate bias to underestimate earnings is strong enough. In
other words, the write-off was bad, but the rest was very good news. For the
subsample (N=6) with positive unexpected write-off (bad news) and unexpected
earnings (good news), the average CAR appeared to be negative. A bias for good
news would suggest that the unexpected write-off in fact was greater and the
unexpected earnings were less than estimated, which would be consistent with a
negative CAR.
58
Results of the test of significance between the sign of CAR on one hand and
the unexpected ceiling test write-off (four measures) and the unexpected earnings on
the other hand are provided in Table 8. The results of the Chi-Square (X2) test
suggest that the sign of CAR and the sign of the unexpected ceiling test write-off are
weakly dependent or, after the Yates correction, not dependent for the four measures
of unexpected amount of ceiling test write-off (Chi-Square = 3.291 before the Yates
correction and 2.286 after). The results of the Chi-Square (X2) test suggest that the
sign of CAR and the sign of the unexpected earnings are not dependent (Chi-Square=
0.987 before the Yates correction and 0.439 after). The results of the Chi-Square
(X2) test indicate that the sign of the unexpected earnings and the sign of unexpected
write-off are dependent (Chi-Square = 8.296 before the Yates correction and 6.148
after). The CARs are equally negative and positive consistent with a maintained
efficient market hypothesis. Given a maintained efficient market hypothesis, the
strength of association between CAR and the unexpected amount of the ceiling test
write-off is dependent on how accurately expectation models of the ceiling test write-
off captures the market's expectation of the amount of the ceiling test write-off. An
inspection of the positive and negative unexpected write-off frequency in Table 8
reveals that about 78% of the unexpected write-offs are negative. This implies that
the expectation models used to predict the expected amount of write-off tend to
overestimate the expected amount of write-off and consequently produce more
negative than positive unexpected write-off.
59
Such a bias to underestimate unexpected write-off would also mean a bias to
overestimate unexpected earnings. However, 82% of the unexpected earnings are
negative. The random-walk model for expected earnings before write-off [ E ^ + D]
has apparently seriously overestimate expected earnings and underestimated
unexpected earnings by an amount that more than offsets the overestimate of
unexpected earnings resulting from any ceiling expectation model bias. A bias to
underestimate unexpected write-off and a separate bias to underestimate unexpected
earnings (in excess of any offset from the first bias) produces a joint bias to
underestimate both which is seen in the first cell (N=45 out of 65) of the third Chi-
Square Table. Table 6 shows a high positive correlation between unexpected write-
off and unexpected earnings when each is scaled by the same deflator.
In general, the results of the sign test are weakly consistent with the hypothesis
that the sign of CAR is negatively associated with the sign of the unexpected amount
of the ceiling test write-off, but interpretation is difficult given the evidence of bias in
the results.
Results of Testing Hypothesis H2
Hypothesis H2 predicts that the larger the unexpected amount of write-off, the
larger the negative abnormal rates of return. To test this hypothesis for companies
which announced the amount of the ceiling test write-off alone (no earnings
announcement) the following regression model was estimated.
CAR = a + b, UWRO
60
Where: UWRO: is the percentage unexpected write-off. CAR: is the cumulative abnormal residual for each announcement during the test
period. a: is the intercept. bx: is the regression coefficient.
The results of regressions are presented in Table 9. The results suggest that
the second (UWR02) and third (UWR03) measures of unexpected amount of write-
off are significant in explaining the variation of the cumulative abnormal return.
About 94 (73) percent of the variation in CAR can be explained by the variation in
the second (third) measures of unexpected amount of write-off, as measured by the
coefficient of determination (R2). The overall models (for second and third measures)
are significant at the 0.10 level of significance. The average percentage change in
CAR to the percentage change in the second and third measures of unexpected
amount of the ceiling test write-off are -0.631 and -0.033, respectively.
When the first (UWROl) and fourth (UWR04) measures of the unexpected
ceiling test write-off were used as explanatory variables in the previous regression
model, the overall model became insignificant at any reasonable level of significance.
Thus, the results suggest that the use of the total assets or the market value of
common equity may be more relevant than the actual or expected amount of write-off
as deflators for the unexpected ceiling test write-off.
To test H2 for firms which announced the amount of the ceiling test write-off
concurrently with earnings the following three regression models were estimated.
Model I: CAR = a + B, UWRO + B2 UNO
61
Model II: CAR = a + Bj UWRO
Model III: CAR = a + B2 UNO
Where: UNO: is the unexpected earnings. UWRO: is the unexpected ceiling test write-off.
Unexpected earnings is not the central issue in the above model, but it is
necessary to control for the effect of unexpected earnings on security returns of FC
firms which announced their earnings simultaneously with the ceiling test write-off.
The results of the regression equation for the overall sample for the four
measures of the unexpected amount of the ceiling test write-off are presented in Table
10. For the first and second measures of the unexpected write-off, there is a negative
relation between the unexpected ceiling test write-off and CAR and a positive relation
between unexpected earnings and CAR. The average percentage changes in CAR to
the average percentage changes in the unexpected write-off for the first and second
measures are -0.025 and -0.070, respectively. The results suggest, however, that
neither the unexpected write-off (all measures) nor the unexpected earnings (first
measure) explained the variation in CAR. However, when unexpected earnings was
deflated by market value of common equity (the second measure, UN02), and the
model was reestimated the unexpected earnings (second measure) became significant
at reasonable level of significance.
To investigate the effects of the type of deflator used to deflate unexpected
earnings variable on the results of the regression, the regression equation was
reestimated eight times.
62
The results of the regression with eight combinations between the four
measures (UWROl, UWR02, UWR03, and UWR04) of unexpected write-off and
the two measures (UNOl and UN02) of unexpected earnings are reported in Table
10. In Table 10, it can be seen that the second (UWR02) and third (UWR03)
measures of the unexpected ceiling test write-off and the second measure (UN02) of
unexpected earnings in the model provided greater explanatory power than the models
with the other measures of unexpected write-off (UWROl, UWR04) and the first
measure of unexpected earnings (UNOl). Thus the major findings to note are:
1. The R2 for the models which included the second measure (UN02) of
unexpected earnings (R2 = 0.086, 0.067, 0.065, and 0.063) exceed the R2 for the
models which included the first measure (UNOl) of unexpected earnings (R2 =
0.048, 0.029, 0.056, and 0.031). Thus, the use of the market value of common
equity may have increased the explanatory power of the unexpected earnings two
variables. It is likely that the market value of common equity is a more appropriate
variable to deflate earnings than total assets, consistent with Christie [1987].
2. The sign on the coefficients of UWROl and UWR02 are negative as
predicted, those on UWR03 and UWR04 are not. The actual write-off and total
assets may be more useful for scaling unexpected write-off than the market value of
common equity and the expected write-off.
3. The t-statistics for the first (UWROl) measure of unexpected ceiling test write-
off exceeded the t-statistics for the second (UWR02) measure of unexpected ceiling
test write-off. The F-statistic for each equation using UWROl exceeds that using
63
UWR02. Both set of results suggests that actual write-off may be more appropriate
for scaling unexpected write-off than total assets.
4. The average percentage changes in CAR to the first measure of the unexpected
amount of the ceiling test write-off are -0.025 with UNOl and -0.027 with UN02.
When the second, third, and fourth measures of the unexpected ceiling test write-off
were used as explanatory variables instead of the first measure, the regression
coefficients changed when UNOl was replaced with UN02. Apparently the stability
of the coefficients depend on the joint choice of deflators for unexpected write-off and
unexpected earnings. The use of actual write-off to scale unexpected earnings appears
the most stable.
5. UN02 alone had a superior adjusted R2 and F-statistic than any equation with
both unexpected earnings and unexpected write-off or containing only one.
To test whether the increase in the explanatory power of the two components
model (UWRO and UNO)) is significant and hence, the two components model
(Model I) provide significant incremental explanatory power relative to the models
with one component (Models II and III), the F-statistics were calculated.
Based on the F-statistics which are reported in the last four lines of Table 11
(Panel A), the two components model (UWRO and UNO) does not provide significant
additional explanatory power over the one component model, Model III (UNO), when
unexpected earnings are scaled by the market value of assets. When unexpected
earnings were deflated by total assets (only), unexpected write-off deflated by the
market value of common equity appeared even marginally to provide additional
64
explanatory power. The result is weak and may reflect the problem of scaling
unexpected earnings by total assets rather than the market value of equity. Nothing in
the regression equations suggested that the market value of equity was the correct
deflator for unexpected write-off. Further, unexpected write-off scaled by market
value of equity appears to perform as a surrogate variable for unexpected earnings
scaled by the market value of equity. This is reflected by the high correlation shown
between these variables in Table 6. This is probably a result of the separate bias in
the expectation models to underestimate unexpected write-off and underestimate
unexpected earnings making each negative in 45 out of 64 cases (see the discussion of
Table 8).
The F-statistics reported in the first four lines of Table 11 (Panel B) indicate
that the two components model (UWRO, UNO) does not provide significant additional
explanatory power over the one component (UWRO) model, Model II, when
unexpected earnings were deflated by the total assets. However, the remaining lines
of Table 11 (Panel B) indicate that when unexpected earnings were scaled by the
market value of equity and the unexpected write-off was deflated by either actual
write-off (UWROl), total assets (UWR02), or expected write-off(UWR04), the two
components model (UWRO, UNO) does provide significant additional explanatory
power over one component model (UNO). No improvement occurred when
unexpected write-off was scaled by market value of equity.
There are many possible reasons why unexpected write-off failed to add
explanatory power to unexpected earnings and unexpected earnings scaled by the
65
market value of equity did add explanatory power. First, security prices may have
already reflected the ceiling test write-off because of the prior availability of
alternative data to that provided by FASB No. 69. Second, the expectation models
may have been poorly specified, hence the empirical results understated the
association between the unexpected amount of the ceiling test write-off and security
returns. The discussion of Tables 6, 7, and 8 have suggested that the expectation
models have a bias to underestimate unexpected write-off and a separate bias to
underestimate unexpected earnings in excess of the overestimation of unexpected
earnings that would be induced by the first bias. Third, the presence of outlier
observations may have understated the relation between the unexpected amount of the
ceiling test and the unexpected earnings on one hand and CAR on the other hand.
To investigate the effects of outlier observations on the results of regression
equation, the regression equation was reestimated without the outliers. An outlier, for
the purpose of testing this hypothesis, is an observation whose value exceeds the value
of other observations in the sample by two standard deviations from the mean value of
all observations. This resulted in eliminating four observations.
The results of the regression without outliers are reported in Table 12. In
Table 12, it can be seen that the unexpected ceiling test write-off (all measures) and
unexpected earnings (both measures) provided greater explanatory than the model
with outlier observations. Thus the major findings to note are:
1. The R2 for the models without outlier observations (Table 12) exceed the R2
for the models with outlier observations (Table 10). Thus, the presence of outlier
66
observations may have decreased the explanatory power of the two variables.
2. The t-statistics for UWROl, UWR02, UWR03, UWR04, UNOl, and UN02
without outlier observations exceeded the t-statistics for those variables with outliers
observations. Thus, we can conclude that the magnitude of the four measures
(UWROl, UWR02, UWR03, UWR04) of unexpected ceiling test write-off and the
two measures (UNOl, UN02) of unexpected earnings may be affected by the
presence of outliers.
To test whether the increase in the explanatory power of the two components
model without outliers (UWRO and UNO)) is significant and hence, the two
components model (Model I) provide significant incremental explanatory power
relative to the models with one component without outliers (Models II and III), the F-
statistics were calculated.
Based on the F-statistics which are reported in Table 13, the two components
model (UWRO and UNO) provide significant additional explanatory power than the
one component model, Model III (UNO) (see Panel A). Thus, the unexpected ceiling
test write-off (UWRO) and unexpected earnings (UNO) jointly do posses incremental
information content beyond unexpected earnings (UNO) alone. UN02 is superior to
UNOl for each form of UWRO. UWROl is superior to all other forms of UWRO.
The F-statistics reported in Table 13 (Panel B) indicate that the two
components model (UWRO, UNO) provide significant additional explanatory power
than the one component (UWRO) model , Model II, when unexpected earnings were
deflated by the market value of common equity. However, when unexpected ceiling
67
test write-off and the unexpected earnings were deflated by the market value of
common equity (UWR03) and total assets (UNOl), respectively, or reverse (UWR02
and UNOl), the two components models did not provide significant (P=0.36 and
P=0.39) additional explanatory power than the one component model, Model II. A
similar rather relating to these combinations was also evident in Table 11 (Panel B).
As may be seen in Table 6 (Panel B), removing outliers reduces the correlations
coefficient induced by expectation model biases.
In general, the results of the magnitude tests are consistent with the results of
the test that use the signs of unexpected ceiling test write-off and CAR. Both sets of
results are consistent with the hypothesis that the unexpected amount of the ceiling
test write-off conveys information to the capital market.
Results of Testing Hypothesis H3
Hypothesis H3 predicts that the variance of abnormal rates of return is larger
during the test period of the ceiling test write-off than during the non-test period. To
test this hypothesis, the variance of abnormal return in the test period was compared
with the variance of abnormal rates of return in the estimation period.
The ratio Uc t introduced in Chapter III was computed 87 times, once for each
announcement. Table 14 presents the results of UC)t for companies which announced
the amount of the ceiling test write-off by itself. Two companies excluded from
Table 5 because of negative expected write-off are included were: Chapman Energy
and Mitchell Energy. Five of the seven announcements have Uc t greater than 1. The
68
average Uc>t ratio for the seven announcements is 1.88. This suggests that, on
average, the magnitude of the security price changes in the test period is 88 percent
higher than the average security price change during the estimation period.
The ratio, Uc t, was also computed across the days of the test period for
companies which announced the amount of the ceiling test write-off concurrently with
earnings. Forty-three out of 79 values of Uc>t have values of one or greater. The
results suggest that, on average, the security price changes during the test period are
59 percent higher than the average security price changes during the estimation
period. The higher price changes during the test periods for firms that announced the
amount of the ceiling test write-off concurrently with earnings could be caused by
either the ceiling test write-off, earnings, or both the ceiling test write-off and
earnings.
Results of Testing Hypotheses H4, H5, H6, H7, and H8
As stated earlier, there are two different types of announcement dates; one is
the announcement of the amount of the ceiling test write-off alone, the second is the
announcement of the amount of the ceiling test write-off concurrent with earnings.
Hypotheses H4, H5, H6, H7, and H8 were tested for both types of announcements.
In the subsequent analysis of hypotheses H4, H5, H6, H7, and H8, the results for the
subsample of companies which announced the amount of the ceiling test write-off
alone will be reported separately from the results of the subsample of companies
which announced the amount of the ceiling test write-off concurrently with earnings.
69
Independence Among Explanatory Variables
To test the degree of multicollinearity among the independent variables
TDEBT, PPDEBT, DE, SD, TAMKTE, UN02, and UWR034 included in the cross-
sectional regression model, simple correlations were computed. The results of the
simple correlation among those variables for companies which announced the amount
of the ceiling test write-off alone are reported in Table 15. Significant positive
association is found between TAMKTE and DE, consistent with the fact, by definition
D/E = TA/E - 1.
For companies that announced the amount of the ceiling test write-off
concurrently with earnings, the simple correlation, which is reported in Table 16,
indicated that UWR03 is significantly negatively related to DE and significantly
positively related to UN02 and TAMKTE. Significant negative correlation was found
between TAMKTE and SD. TAMKTE is significantly negatively correlated to
TDEBT. In general, the collinearity among the set of explanatory variables does not
appear to be severe, with the most serious collinearity existing between UN02 AND
UWR03.
The interrelationships among firms' type of debt, public debt to total debt,
debt to equity, risk of the firms, size of the firms, unexpected earnings, and
unexpected write-off may be of special interest in their own right; however, the more
immediate concern is how these dependencies may affect the cross-sectional
4For definition of variables see Chapter III (Testing Hypotheses H4, H5, H6, H7, and H8).
70
regression results.
In the subsequent analysis, results for the complete model with all explanatory
variables will be reported first. Variables which are highly correlated with one
another will then be omitted to help in understanding which variable(s) are more
important in explaining CAR, and to determine how sensitive the results are to the
multicollinearity among the independent variables.
Primary Regression Results
The estimates of the cross-sectional model which hypothesized the relationship
between CAR and the independent variables, using the sample of companies which
announced the amount of the ceiling test write-off concurrently with earning, are
reported in Table 17.
The results of the cross-sectional regression provided in Table 17 suggest that
the explanatory power of the model which contains all seven variables (Model 1) is
significant at about 6% level of significance (F-Value = 2.030). All the variables,
except UWR03 and DE, have the hypothesized signs; however, UN02 and DE are
the only significant variables.
As indicated earlier, UWR03 has a significant positive correlation with UN02
and TAMKTE while UWR03 is negatively associated with DE. TAMKTE has a
significant negative correlation with TDEBT and SD. To determine how sensitive the
estimated coefficients and the explanatory power of the model as a whole as measured
by R2 are to the multicollinearity among the independent variables, the model was
71
reestimated; the highly correlated variables being omitted.
Models 2, 3, 4, 5, and 6 in Table 17 show the results of the cross-sectional
regressions where TDEBT, UWR03, UN02, TAMKTE, and SD have been omitted,
respectively. To determine the effect of the multicollinearity on the signs and the
significance of the independent variables, the model was reestimated several times
omitting those highly correlated variables.
Model 2 in Table 17 shows the results of the cross sectional model where
TDEBT was omitted. The coefficients of PPDEBT, SD, and UN02 variables
included in model 2 have the hypothesized signs. The overall explanatory power of
Model 2 is slightly higher than for Model 1 and is significant at the 4.6% level of
probability. UN02 and DE have significant coefficients.
However, when unexpected ceiling test write-off (UWR03) was excluded from
the independent variables set and the cross-sectional regression model was rerun
(Model 3), UN02 was the only explanatory variables whose coefficient was
significant with its expected sign. The overall explanatory power of Model 3 is the
same as the one for Model 2. UN02 and DE have significant coefficients. Hence,
the unexpected ceiling test write-off (UWR03) variable did not contribute
significantly to explaining the cross-sectional variation of CAR,.
In model 4, UN02 was eliminated. In this model, UWR03 and DE, contrary
to the hypotheses, enter with positive signs. DE and UWR03 are significant
variables in explaining the cross-sectional variations in CAR. The unadjusted R2 is
slightly lower than ones for models 1,2, and 3. Hence, the unexpected earnings
72
(UN02) variable did contribute significantly to explaining the cross-sectional variation
of CAR.
In the cross-sectional model (Model 6) that excluded SD from the independent
variables set, the overall explanatory power is the highest compared with the other
models (Models 1, 2, 3, 4, and 5). UN02 and DE have significant coefficients. The
coefficients of PPDEBT and UN02 variables have the hypothesized signs.
In all the cross-sectional models, the following conclusions were drawn: (1)
the explanatory power (unadjusted R2) of the models and their probability levels of
significance were relatively stable across the six models, (2) the estimated coefficients
of the debt to equity (DE) and the unexpected earnings (UN02) appeared to be
significant at the 15 % level of probability, (3) the coefficient of the unexpected
earnings (UN02) was significant and had its hypothesized sign, (4) the coefficient of
the unexpected ceiling test write-off (UWR03) was not significant in 5 of the 6
models and contrary to the hypothesis entered with positive sign.
The previous analysis suggests that the seven variables which were
hypothesized as contributing to an explanation of the cross-sectional variation
associated with the ceiling test write-off announcements were found to explain a
significant proportion of the cross-sectional variation in the abnormal returns of the
sampled firms in the test period surrounding the announcements of the ceiling test
write-off.
Furthermore, the most significant explanatory variables appeared to be the
unexpected earnings (UN02) and debt to equity ratio (DE). However, unexpected
73
ceiling test write-off (UWR03), risk of the firm (SD), size of the firm (TAMKTE),
and type of debt covenants (TDEBT) exhibited the lowest explanatory power.
The insignificance of the individual independent variables can be caused by:
(1) Weak measurements of the independent variables, (2) other significant variables
were not included in the cross-section-regression [Lys, 1984; Atiase, 1984], and (3)
the measurement of the included independent variables may be correct and these
variables are indeed insignificant, and (4) presence of outliers [Cohen and Cohen,
1983].
To investigate the effects of outliers on the results of the cross-sectional
model, the models were reestimated without the outliers. An outlier, for the purpose
of testing these hypotheses, is an observation whose value exceeds the value of other
observations in the sample by two standard deviations from the mean value of all the
observations. Outliers reduce the sum of square variation explained by the
independent variables (R2).
In order to determine if the results reported in Table 17 were dominated by
relatively few observations, the residuals of the cross-sectional models reported in
Table 17 were examined for outliers. Two observations were identified with residuals
in excess of two sample standard deviations above the sample mean of the residuals.
In order to determine if the outliers might have caused the insignificant
individual independent variables, the outliers were deleted from the sample, and the
cross-sectional regression model was rerun using the sample of 62 observations. The
results of the regression models indicate that the overall significance of the models, as
74
measured by R2, and the signs and significance of the individual variables were not
affected by the exclusion of the outliers.
Results of Testing Hypothesis H9
Hypothesis H9 predicts that the two components of unexpected earnings
(unexpected earnings before deducting the unexpected ceiling test write-off and the
unexpected ceiling test write-off) explain more of the variation of the abnormal
returns than is explained by unexpected earnings alone. Hypothesis H9 was examined
for FC firms which disclosed the amount of the ceiling test write-off in their financial
statements.
Since the main issue of this research is the ceiling test write-off, earnings were
decomposed into two components: earnings before deducting the ceiling test write-off
and unexpected ceiling test write-off.
The actual income statements of FC OG firms may not comprise the two
components as specified in this research. Ceiling test write-off may appear as an item
in the income statement or as part of depreciation, depletion and amortization expense
with footnote disclosure about the amount of write-off. Therefore, a test of the
information contained in the unexpected ceiling test write-off and the unexpected
earnings before deducting the ceiling test write-off is an examination of the
information contained in the disclosure footnotes of the FC OG firms.
To test hypothesis H9, expectation models that were developed in Chapter III
were used to compute the unexpected ceiling test write-off. The following cross-
75
sectional regression model developed in Chapter III (see Testing Hypothesis H9) was
estimated:
CARj = a + B3UWRO + eit
Since earnings have already been announced in the WSJ prior to their
appearance in the financial statements and assumed to have been impounded in stock
prices, unexpected earnings at the financial statement release date must be zero.
If the coefficient B3 is insignificantly different from zero, then the market is
not distinguishing between the ceiling test write-off and any other expense. If B3 is
positive and significant, then the market is ignoring part of the ceiling test write-off.
If the coefficient of B3 is negative and significant, then the market is reacting more
negatively to the ceiling test write-off than to other expenses.
The unexpected ceiling test write-off was deflated by four different variables:
actual value of the series, the end of the prior period total assets, the end of the prior
period market value of common equity, and the expected value of the series. The
four deflators were used to assess the sensitivity of the results of testing hypothesis
H9 to the choice of the deflator. Deflating by actual amounts of the ceiling write-off,
total asset, market value of common equity, or expected amount of write-off may also
serve as an adjustment of heteroscedasticity; as the unexpected amount increases, the
variances of the unexpected amount also increases.
Results of ordinary least squares estimations of the model using actual amounts
as deflators are reported in Table 18 (Panel A). The R-square of the model is
0.0047. However, the R-square of the model is very low when it is compared to
76
those obtained in the prior cross-sectional valuation studies [Lipe, 1986; Landsman,
1986; Stober, 1986; Bowen et al, 1987; and Bernard and Stober, 1989].
When the unexpected ceiling test write-off variable was deflated by the total
assets (UWR02) and the model was reestimated (Panel B), the explanatory power of
the model (R2=0.00007) is lower than the one for model that deflated by actual
amount of write-off (Model 1).
Results of ordinary least squares estimations of model 4, using expected
amounts as deflators (Panel D), indicate that the R-square of the model (R2=0.00112)
is higher than the one for the model 2 which used total assets as deflator
(R2=0.00007). However, the R-square of model 4 is lower than the one
(R2=0.0047) obtained in model 1 (Panel A).
When the unexpected ceiling test write-off variable was deflated by the market
value of common equity and the model was reestimated (Panel C), the explanatory
power of the (R2=0.0928) is higher than the ones for the other three models and
significant at the 0.0071 probability level.
The results, based on Model 3, suggest that the unexpected ceiling test write-
off expense and the other type of expense jointly do posses incremental information
content beyond earnings. The significant coefficient of the unexpected ceiling test
write-off variable (B3=-0.04489) indicates that the stock market reacted more
negatively to the ceiling test write-off expense than the other type of expenses (see
Panel C, Model 3).
77
The result is Subject to the limitations that will be discussed in Chapter V, the
results of this hypothesis have the following implication for accounting research:
information is lost when the two components, unexpected ceiling test write-off and
unexpected earnings before deducting the ceiling test write-off, are aggregated into
earnings. The results, based on Model 3, of this hypothesis are consistent with the
prior studies [Lipe; 1986; Bowen 1981; Bowen et al., 1987; and Bernard and Stober,
1989] which showed that the decomposition of earnings provides a statistically
significant amount of information that would be lost if only earnings were reported.
This result is subject to three interpretations. The first is that expectation
model of the ceiling test write-off were poorly specified and hence underestimate the
relation between the two components (see Models 1, 2, and 4), second, the quarterly
reports or 10-Q reports contain considerable information besides historical cost
earnings and the ceiling test write-off, third, the use of other deflators other than the
market value of common equity such as expected value of series (Model 1), total
assets (Model 2), or actual value of the series (Model 4) may be irrelevant.
CHAPTER V
CONCLUSIONS, LIMITATIONS, AND FUTURE RESEARCH
Summary and Conclusions
The purpose of this research was to examine the actual impact of the ceiling
test write-off on the security returns of affected FC firms.
Two prior studies, the SEC [1986] and Frost and Bernard [1989] examined the
impact on stock prices of the SEC's decision in May 1986 mandating the application
of the ceiling test. This study reexamines the impact of the ceiling test write-off on
security returns using an approach that differs from the SEC [1986] and Frost and
Bernard [1989] studies in three major ways. First, the announcement date of the
actual amount of write-off for each company was determined, and consequently the
event times are not the same for all the firms. When different event dates rather than
one event date for all firms is used, the likelihood that the residuals are correlated
across equations (firms) is reduced. The return predictions are likely to be unbiased
leading to unbiased prediction errors (abnormal returns).
Second, utilizing the multi-factor market model which included the percentage
changes in prices of OG and the industry factor as explanatory variables controlled for
the general change in the price of OG and industry events. Consequently, the
observed abnormal returns would more likely be due to the ceiling test write-off
78
79
rather than some other event.
Third, restricting the sample to FC firms that actually took the ceiling test
write-off produced a representative sample of FC firms that were actually affected by
the ceiling test rule.
In this study, three subsamples were constructed: the first subsample consisted
of FC firms that announced the amount of the ceiling test write-off alone, the second
subsample contained FC firms that announced the amount of the ceiling test write-off
concurrently with earnings in the WSJ, and the third subsample was comprised of FC
firms that disclosed the amount of the ceiling test write-off in their quarterly financial
statements. This study utilized the multi-factor market returns (RM,), Industry index
(IN,), and the percentage changes in prices of OG (PQ) as explanatory variables, to
extract the effect of the ceiling test write-off on security returns of affected FC firms.
The multi-factor market model was estimated 199 times, one for each firm/quarter.
This study used two expectation models to predict the unexpected amount of
the ceiling test write-off and unexpected earnings: an expectation model for the
ceiling test write-off and an earnings expectation model.
Two groups of research hypotheses were developed and tested: research
hypotheses for FC firms that announced the amount of the ceiling test write-off in the
WSJ and research hypothesis for FC firms that disclosed their ceiling test write-off in
their financial statements. The behavior of abnormal rates of return is as predicted:
CAR is negative for positive (actual amount is greater than the expected) unexpected
amount of write-off and vice versa.
80
To test the relationship between the magnitude of the ceiling test write-off and
the magnitude of abnormal rates of return, two regression models were constructed
and tested. The results of the first regression model for companies that announced
the amount of the ceiling test write-off in the WSJ suggest that the average percentage
change in CAR relative to the percentage in the unexpected amount of the ceiling test
write-off for measure two and three are -0.63 and -0.033, respectively and statistically
significant. The results of the second regression model for companies that announced
the amount of the ceiling test write-off concurrently with earnings indicated that the
first measure (UWROl) of the unexpected ceiling test write-off and the two measures
(UNOl, UN02) of the unexpected earnings explained the variation in CAR.
However, when outlier observations were eliminated, the R2 increased significantly.
The results of testing hypothesis H3 suggest that, on average, security price
changes in the test period were 88 percent higher than the average security price
change during the estimation period (variance test).
The results of tests of hypotheses H4, H5, H6, H7, and H8, suggest that the
seven variables (TDEBT, PPDEBT, SD, DE, UN02, UWR03, and TAMKTE)
which were hypothesized to explain the cross-sectional variation in CAR appears to
explain a significant proportion of the cross-sectional variation in abnormal rates of
return of the sampled firms in the period surrounding the announcement of the ceiling
test write-off. The most significant explanatory variables were the debt to equity ratio
of the firm (DE) and unexpected earnings (UN02).
81
The results of testing hypothesis H9 suggest that the unexpected ceiling test
write-off and unexpected earnings before deducting the unexpected ceiling test write-
off jointly do posses incremental information content beyond earnings. Subject to the
limitations that will be discussed in the following section, the results of this
hypothesis have the following implication for accounting research: Information is lost
when the two components, unexpected ceiling test write-off and unexpected earnings
before deducting the ceiling test write-off, are aggregated into earnings.
Future Research
This study used two expectation models to predict the unexpected amount of the
ceiling test write-off and unexpected earnings: an expectation model for the ceiling
test write-off and an earnings expectation model.
The result of this study might be sensitive to the use of expectation models other
than the one utilized in this study such as cross-sectional time-series expectation
models. Future studies may look at alternative expectation models to see whether the
results are sensitive to the choice of the expectation models and to see if other
model(s) perform better than the one utilized in this study.
Future studies may utilize cross-sectional time-series models to predict the
unexpected ceiling test write-off. That is, the ceiling test write-off component
information of all the FC OG firms that took the ceiling test write-off could be used
to predict the unexpected ceiling test write-off for given FC firms. However, one
requirement of using such models is that the information needs to be available for
82
every firm in the cross-section for the period of the study.
Future studies may use a matched pair design by comparing the abnormal
returns of FC firms that took the ceiling test write-off with the abnormal returns of
FC companies that switched from FC to SE accounting methods to avoid taking the
ceiling test write-off.
Limitations
This study has several limitations. First, the cumulative abnormal returns
technique may capture not only the impact of the ceiling test write-off, but also the
effect of any other information released during the test period. Although this study
attempted to eliminate firms that have announced other information in the test period,
the possibility of release of other information still exists. Hence, the impact of the
ceiling test write-off on security returns may be confounded by other news.
A disadvantage of the expectation models that were used to calculate the
unexpected ceiling test write-off and unexpected earnings is that they ignore
information about other FC firms that may be useful in forming expectation about
another firm. Thus, the expectation models used will be deficient to the extent that
information about such other firms is useful in forming expectation. There is no
empirical evidence or a theory to guide the choice of the ceiling test write-off
expectation model.
The expectation models that were used to calculate the unexpected ceiling test
write-off involve two problems which may induce bias, but they are nonetheless
83
consistent with the data available to financial analysts. First, if the expected
unamortized capitalized costs are less than expected ceiling amount, then the expected
amount of write-off would be negative. In other words, no write-off would be
expected and the expectation of "no write-off' be stronger the more negative was.
The restriction of the sample to firms that both expected a write-off and were
expected to experience a write-off under this model may induce sample selection bias.
Second, the expectation model may be biased itself. The method for
calculating the "standardized measure" reported in accordance with FASB 69 differs
in the handling of tax estimates from the method use for the ceiling amount and
results in the standardized measure being lower than it would be if calculated under
the ceiling method. Using the standardized measure in the expectation model results
in a lower expected ceiling amount and a higher write-off. The ceiling test also
considers the difference, if any, in the book value and tax basis of properties not
being amortized and unproved properties. Failure to include this adjustment increases
the expected ceiling amount and lowers the expected write-off. On balance, the
expected write-off is probably biased upward. However, it is based on the only data
available to financial analysts. If the expected amount of write-off is biased upward,
then the actual amount of write-off will, on average, be smaller and the unexpected
amount of write-off, on average, would be falsely biased to be negative. Abnormally,
a negative unexpected amount of write-off would be good news (the actual write-off
was smaller the expected one). Bias in the model may result in good news being
estimated when bad news is the case.
84
Another disadvantage of the expectation models that were used to calculate the
unexpected ceiling test write-off and unexpected earnings is that they produce more
negative unexpected amounts than positive ones. Thus, the expectation models
produce bias results. There is, however, no empirical evidence or a theory to guide
the choice of the ceiling test write-off expectation model.
Two contracting explanatory variables (DE and UN02) in the cross-sectional
regression are found to be insignificant. There are two possible explanations for such
results. First, the ceiling test write-off may affect the insignificant debt covenant
variables, but the measurements of these variables are weak and/or there are some
other significant variables that were not included in the cross-sectional regression.
Second, the measurement of the included variables may be correct and these variables
are indeed insignificant.
Subject to these limitations, evidence of abnormal returns would indicate that
the ceiling test write-off has information content for stock market participants. The
evidence may help the SEC to evaluate the validity of the claim that the ceiling test
write-off has economic consequences.
Subject to the above limitations, the results of this study have the following
implication for accounting research: no information is lost when the two components,
unexpected ceiling test write-off and unexpected earnings before deducting the ceiling
test write-off, are aggregated into earnings.
APPENDIX
TABLES
85
86
TABLE 1 LIST OF THE COMPANIES IN THE SAMPLE
NO. NAME OF THE COMPANY QUARTER(S) OF THE WRITE-OFFA
1 Adobe Resources 4-85 2-86 3-86 2 Alta Energy 2-88 3 Amber Resources Corp. 4-84 4-85 4 American National Petroleum 4-85 5 Apache Corporation 4-85 4-86 4-87 6 Apache Petroleum 4-85 4-86 4-87 7 Arapaho Petroleum 4-84 4-85 8 Barret Resources 3-86 3-87 9 Basic Earth Science Systems 1-85 1-86 1-87 10 Callon Petroleum 4-85 1-86 11 Calvin Exploration 3-84 3-85 12 Chaparral Resources 4-86 4-87 13 Chapman Energy 1-86 4-86 14 Chieftain Development 4-86 15 Cibola Energy 4-85 16 Columbine Exploration 4-85 17 Convest Energy 4-85 2-86 18 Credo petroleum 4-86 19 Damson Oil 3-85 1-86 20 Diversified Energies 4-85 4-86 21 Dome Petroleum 4-86 4-87 22 Energen 1-86 23 Energy Ventures 4-85 24 Ensource 4-85 1-86 2-86 25 Evergreen Resources 1-87 26 Federated Natural Resources 4-85 27 First Mississippi 1-86 2-86 28 Forest Oil Corp. 1-88 2-88 29 Freeport-McMoran 1-86 30 Galaxy Oil Corp. 4-85 31 GeoResources, Inc. 4-86 32 Global Marine 1-85 2-85 3-85 2-86 4-86 33 Hadson Corp. 4-85 34 Harken Oil and Gas 4-84
87
TABLE 1 - CONTINUED LIST OF THE COMPANIES IN THE SAMPLE
NO. NAME OF THE COMPANY QUARTER(S) OF THE WRITE-OFF 35 Hershey Oil 4-85 1-86 4-86 36 Houston Oil Fields 4-84 37 Howell Corp.. 1-86 2-86 38 Inexco Oil 4-85 1-86 39 J.M. Resources 2-84 40 Kaneb Energy Partners 3-86 4-86 41 Kencope Energy 1-86 2-86 2-87 42 Kimbark Oil & Gas 4-85 1-86 2-86 43 Lear Petroleum 2-85 3-85 4-85 1-86 2-86 3-86 44 May Energy Partners 1-86 3-86 45 May Petroleum 4-83 4-84 4-85 1-86 2-86 46 MCO Holdings 4-85 1-86 2-86 47 MCO Resources 4-85 2-86 48 Mitchell Energy & Development 4-84 49 Moore McCormick Resources 2-86 4-86 50 Nahama & Weagan 4-86 4-87 51 NP Energy Corp. 1-87 52 Nugget Oil 4-85 53 Oxoco Inc. 1-85 2-85 3-85 54 Pacific Lighting 1-86 55 Parallel Petroleum 1-86 2-86 4-86 4-87 56 Partners Oil 4-85 1-86 57 Patrick Petroleum 4-85 1-86 2-86 58 Petroleum Investment 4-84 4-85 4-86 4-87 59 Petromark Resources 4-85 4-86 60 Petrotech, Inc. 4-85 61 Pogo Producing 4-85 1-86 2-86 4-86 62 Premier Resources 3-86 63 Prima Energy 2-85 2-86 64 Questar Corp. 1-86 3-87 3-88 65 Ranger Oil Limited 4-84 66 Reading & Bates 4-85 2-86 4-86 4-87 67 Reserve Exploration 1-86 68 Ridgeway Exco, Inc. 4-85
88
TABLE 1 - CONTINUED LIST OF THE COMPANIES IN THE SAMPLE
NO. NAME OF THE COMPANY QUARTER(S) OF THE WRITE-OFF 69 Roberts Oil & Gas 4-86 4-87 5-31 70 Royal Gold Inc. 4-85 1-86 2-86 4-86 71 Samson Energy LP 1-86 72 Seagull Energy 4-86 73 Snyder Oil Partners 1-86 2-86 74 Sonat Corp. 4-85 4-86 75 Southdown Corp. 2-86 76 Southland Energy 4-86 77 Striker Petroleum 1-86 4-86 4-87 78 Summit Energy 1-85 4-86 79 Sunlite Corp. 4-85 1-86 2-86 80 Tesoro Petroleum 3-85 3-86 81 Texas American Energy 4-85 82 Thor Energy Resources 2-85 4-86 4-87 83 Tipperary Corp. 3-84 3-85 3-86 2-84 84 Tosco Corp. 4-84 4-85 1-86 85 Transco Exploration Partners 4-86 86 Triton Energy 4-86 4-87 87 Unit Corp. 4-86 88 Universal Resources 4-86 89 Valex Petroleum 4-85 90 Wainoco Oil 4-85 1-86 2-86 4-86 91 Walker Energy LP 1-86 2-86 4-86 92 Whiting Petroleum Corp. 4-85 93 Wicor Corp. 4-84 4-85 94 Woodbine Petroleum 3-84 95 Zapata Corp. 2-85 1-86 3-86 4-87 4-88
89
TABLE 2 TYPE OF ANNOUNCEMENTS AND THE DATES OF WRITE-OFF
AND THEIR FREQUENCIES TYPE OF
ANNOUNCEMENTS NUMBER OF
QUARTERS OF WRITE-OFF
PERCENT
BY 7 3.5 WSJW 79 39.7 QFS 113 56.8
TOTAL 199 100.0 NOTES:
BY: Firms which announced the amount of the ceiling test write-off alone. WSJW: Firms which announced the amount of the ceiling test write-off concurrent with
earnings. QFS: Firms which did not announce the amount of the ceiling test write-off but disclosed their
write-offs in the quarterly financial statements.
DATE OF WRITE-OFF
NUMBER OF ANNOUNCEMENTS
PERCENT
12/31/83 7 .5 6/30/84 1 .5 9/10/84 1 .5 9/30/84 3 1.5 12/31/84 9 4.5 1/31/85 1 .5 3/31/85 3 1.5 6/30/85 5 2.5 7/31/85 2 1.0 9/30/85 6 3.0 12/31/85 42 21.1 1/31/86 1 .5 3/31/86 34 17.1 4/30/86 2 1.0 5/31/86 2 1.0 6/30/86 24 12.1 9/30/86 9 4.5 10/31/86 2 1.0 12/31/86 23 11.6 1/31/87 3 1.5 3/31/87 3 1.5 5/31/87 2 1.0
90
TABLE 2 - CONTINUED TYPE OF ANNOUNCEMENTS AND THE DATES OF WRITE-OFF
AND THEIR FREQUENCIES
DATE OF WRITE-OFF
NUMBER OF ANNOUNCEMENTS
PERCENT
6/30/87 1 .5 9/30/87 3 1.5 12/31/87 7 3.5 1/31/88 1 .5 3/31/88 1 .5 6/30/88 2 1.0 9/30/88 2 1.0
TOTAL 199 100.0 NOTE: * THE MOST FREQUENT Q (UARTERS O OF WRITE-OFF.
91
TABLE 3 AVERAGE SIMPLE PEARSON CORRELATION COEFFICIENTS
BETWEEN RMT, IN,, AND PO,
CO RRELATION COEFFICIENTS (OTC FIRMS)
RMT INT POT
RMT 1 INT 0.218 1 POT -0.029 0.088 1
CO ( RRELATION NYSE AND .
COEFFICIE A.MEX FIRM
;NTS S)
RMT INT POT
RMT 1 INT 0.219 1 POT -0.078 0.182 1
TABLE 4 SUMMARY STATISTICS RELATING TO THE MULTI-FACTOR
MARKET MODEL: RIt = A + BxRMt + B^Nt + B3POt
FOR THE ESTIMATION PERIOD
92
ADJUSTE D R2
R2 F-VALUE PROBABILIT Y
D-W
AVERAGE OTC 0.016 0.028 2.481 0.275 2.118 AVERAGE NYSE* 0.062 0.074 6.762 0.080 2.176 LOWEST R2 OTC -0.011 0.002 0.024 0.990 1.937 LOWEST R2 NYSE* -0.006 0.005 0.438 0.720 2.470 HIGHEST R2 OTC 0.190 0.199 20.787 0.000 2.183 HIGHEST R2 NYSE* 0.260 0.269 30.597 0.000 1.935 LOWEST D-W OTC 1.439 LOWEST D-W NYSE* 1.555 HIGHEST D-W OTC 2.918 HIGHEST D-W NYSE* 2.889
NOTE: * NYSE and AMEX.
NUMBER OF REGRESSION EQUATIONS
SIGNIFICANT VARIABLES
OTC NYSE & AMEX
ALL= 3 13 INt,RMt= 2 28 POt,INt= 6 13 POt,RMt= 2 5 RMt= 12 10 INt= 19 32 POt= 9 3 NONE 33 9
TOTAL 86 113
93
TABLE 5 THE RELATION BETWEEN THE UNEXPECTED AMOUNT OF WRITE-OFF AND CAR
FOR COMPANIES WHICH ANNOUNCED THE AMOUNT OF THE CEILING TEST WRITE-OFF ALONE
DOWR CAR UWROl UWR02 UWR03 UWR04
Damson Oil 860331 0.070 -0.801 -0.160 -1.358 -0.445
May Petroleum 831231 -0.046 0.335 0.018 0.042 0.504
Pacific Light. 860331 0.023 -3.738 -0.099 -0.144 -0.789
Sonat Inc. 851231 -0.050 -0.274 -0.013 -0.041 -0.215
Tipperary Corp. 840910 -0.044 0.221 0.033 0.129 0.284
AVERAGE -0.009 -0.851 -0.044 -0.274 -0.132
CORRELATION COEFFICIENTS 5 OBSERVATIONS
CAR UWROl UWR02 UWR03 UWR04
CAR 1.000
UWROl -0.538 1.000
UWR02 -0.970 0.590 1.000
UWR03 -0.885 0.124 0.872 1.000
UWR04 -0.709 0.857 0.810 0.465 1.000
NOTES: UWROl = (UNEXPECTED WRITE-OFF/ACTUAL WRITE-OFF) UWR02 = (UNEXPECTED WRITE-OFF/TOTAL ASSET) UWR03 = (UNEXPECTED WRITE-OFF/MARKET VALUE OF COMMON EQUITY) UWR04 = (UNEXPECTED WRITE-OFF/EXPECTED AMOUNT OF WRITE-OFF) DOWR : DATE OF THE CEILING TEST WRITE-OFF
94
TABLE 6 CORRELATION BETWEEN CAR UNEXPECTED WRITE-OFF, AND UNEXPECTED
EARNINGS FOR COMPANIES WHICH ANNOUNCED THE AMOUNT OF CEILING TEST CONCURRENT WITH EARNINGS
PANEL A: (64 OBSERVATIONS)
CAR UWROl UWR02 UWR03 UWR04 UNOl UN02
CAR 1.000
UWROl 0.004 1.000
UWR02 -0.075 0.265 1.000
UWR03 0.014 0.393* 0.486* 1.000
UWR04 0.084 0.265 0.328* 0.432* 1.000
UNOl 0.061 0.162 © vo
H-k
K> *
0.449* 0.278 1.000
UN02 0.248 0.223 0.369* O
<1
OO
h-* • 0.288 0.505* 1.000
PANEL B: (60 OBSERVATIONS) - OUTLIERS ARE EXCLUDED
CAR UWROl UWR02 UWR03 UWR04 UNOl UN02
CAR 1.000
UWROl -0.215 1.000
UWR02 0.024 0.534* 1.000
UWR03 0.214 0.453* 0.711* 1.000
UWR04 0.118 0.615* 0.411* 0.361* 1.000
UNOl 0.229 0.127 0.496* 0.436* 0.310* 1.000
UN02 0.332 0.161 0.505* 0.780* 0.235 0.705* 1.000
NOTES: UNOl = UNEXPECTED EARNINGS/TOTAL ASSET UN02 = UNEXPECTED EARNINGS/MARKET VALUE OF COMMON EQUITY UWROl = (UNEXPECTED WRITE-OFF/ACTUAL WRITE-OFF) UWR02 = (UNEXPECTED WRITE-OFF/TOTAL ASSET) UWR03 = (UNEXPECTED WRITE-OFF/MARKET VALUE OF COMMON EQUITY) UWR04 = (UNEXPECTED WRITE-OFF/EXPECTED AMOUNT OF WRITE-OFF) * Significant at the 0.01 probability level.
95
TABLE 7 THE RELATION BETWEEN UNEXPECTED CAR, UNEXPECTED WRITE-OFF, AND
UNEXPECTED EARNINGS (NEGATIVE VS.POSITIVE UNEXPECTED WRITE-OFF)
UWRO UN CAR UWROl UWR02 UWR03 UWRO 4
NEGATIVE NEGATIVE -0.012 -6.474 -0.307 -0.737 -0.641
NEGATIVE POSITIVE -0.033 -0.670 -0.047 -0.141 -0.283
POSITIVE NEGATIVE 0.004 0.341 0.045 0.192 1.020
POSITIVE POSITIVE -0.039 0.461 0.144 0.848 1.243
UWRO UN CAR UNOl UN02 SAMPLE SIZE
NEGATIVE NEGATIVE -0.012 -0.353 -0.905 45
NEGATIVE POSITIVE -0.033 0.060 0.217 5
POSITIVE NEGATIVE 0.004 -0.084 -0.338 8
POSITIVE POSITIVE -0.039 0.162 0.848 6
NOTES: UNOl = UN02 = UWROl UWR02 UWR03 UWR04
UNEXPECTED EARNINGS/TOTAL ASSET UNEXPECTED EARNINGS/MARKET VALUE OF COMMON EQUITY
= (UNEXPECTED WRITE-OFF/ACTUAL WRITE-OFF) = (UNEXPECTED WRITE-OFF/TOTAL ASSET) = (UNEXPECTED WRITE-OFF/MARKET VALUE OF COMMON EQUITY) = (UNEXPECTED WRITE-OFF/EXPECTED AMOUNT OF WRITE-OFF)
96
TABLE 8 CHI-SQUARE TEST OF SIGNIFICANCE BETWEEN THE SIGNS OF CAR, UNEXPECTED
WRITE-OFF, AND UNEXPECTED EARNINGS
Crosstabulation: CAR By UWRO
CAR
PANEL A
UWRO Count Negative Positive Row
Total Negative 22 10 32
50.0% Positive 28 4 32
50.0% Column Total
50 78.1%
14 21.9 %
64 100%
Chi-Square D.F. Significance Min E.F. Cells with E.F. <5 2.28571 3.29143
0.1306 0.0696
7.000 None (Before Yates Correction)
Crosstabulation: CAR By UN
CAR
PANEL B
UN Count Negative Positive Row
Total Negative 25 7 32
50% Positive 28 4 32
50% Column 53 11 64 Total 82.8% 17.2
% 100%
Chi-Square D.F. Significance Min E.F. Cells with E.F. < 5 0.43911 0.98799
0.5076 0.3202
5.5300 None (Before Yates Correction)
97
TABLE 8 - CONTINUED CHI-SQUARE TEST OF SIGNIFICANCE BETWEEN THE SIGNS OF CAR, UNEXPECTED
WRITE-OFF, AND UNEXPECTED EARNINGS
PANEL C Crosstabulation: UWRO
By UN
UWRO UN
Count Negative Positive Row Total
Negative 45 5 50 78.1%
Positive 8 6 14 21.9%
Column 53 11 64 Total 82.8% 17.2
% 100%
Chi-Square D.F. Significance Min E.F. Cells with E.F. <5 6.14814 8.29601
0.0132 0.0040
2.406 1 of 4 (25.0%) (Before Yates Correction)
98
TABLE 9 RESULTS OF THE REGRESSIONS FOR COMPANIES WHICH ANNOUNCE THE
AMOUNT OF THE CEILING TEST WRITE-OFF ALONE (MAGNITUDE TEST)
UNEXPECTED WRITE-OFF MEASURES R2
ADJUSTED R2 F-VALUE CONSTANT
REGRESSION COEFFICIENT
UWROl 0.289 0.052 1.220 -0.024 -0.017 UWR02 0.940 0.921 47.785* -0.037 -0.631* UWR03 0.732 0.642 8.201* -0.030 -0.033* UWR04 0.503 0.337 3.039 -0.018 -0.072
NOTES: UWROl = (UNEXPECTED WRITE-OFF/ACTUAL WRITE-OFF) UWR02 = (UNEXPECTED WRITE-OFF/TOTAL ASSET) UWR03 = (UNEXPECTED WRITE-OFF/MARKET VALUE OF COMMON EQUITY) UWR04 = (UNEXPECTED WRITE-OFF/EXPECTED AMOUNT OF WRITE-OFF)
* Significant at the 0.10 probability level.
99
TABLE 10 RESULTS OF THE REGRESSIONS FOR COMPANIES WHICH ANNOUNCE THE
AMOUNT OF THE CEILING TEST WRITE-OFF CONCURRENT WITH EARNINGS (MAGNITUDE TEST) 64 OBSERVATIONS
RESULTS OF THE REGRESSION USING THE FIRST MEASURE OF UNEXPECTED WRITE-OFF (UWROl) AND THE FIRST MEASURE OF UNEXPECTED EARNINGS (UNOl)
COEFFICIENTS* R2 ADJUSTED
R2
F STATISTICS
PROB. OF
F CONSTANT UWROl UNOl R2 ADJUSTED
R2
F STATISTICS
PROB. OF
F -0.0059 -0.025 0.133 0.048 0.017 1.553 0.219
(-0.174) (-1.157) (1.460)
RESULTS OF THE REGRESSION USING THE SECOND MEASURE OF UNEXPECTED WRITE-OFF (UWR02) AND THE FIRST MEASURE OF UNEXPECTED EARNINGS (UNOl)
COEFFICIENTS* R2 ADJUSTED
R2
F STATISTICS
PROB. OF F CONSTANT UWR02 UNOl R2 ADJUSTED
R2
F STATISTICS
PROB. OF F
0.0082 -0.070 0.137 0.029 -.002 0.919 0.404
(0.305) (-0.326) (1.308)
RESULTS OF THE REGRESSION USING THE THIRD MEASURE OF UNEXPECTED WRITE-OFF (UWR03) AND THE FIRST MEASURE OF UNEXPECTED EARNINGS (UNOl)
COEFFICIENTS* R2 ADJUSTED
R2
F STATISTICS
PROB. OF F CONSTANT UWR03 UNOl R2 ADJUSTED
R2
F STATISTICS
PROB. OF F
0.002 0.050 0.061 0.056 0.025 1.836 0.168
(0.106) (1.375) (0.615)
NOTE: t-statistics in parentheses.
100
TABLE 10 - CONTINUED
RESULTS OF THE REGRESSION USING THE FOURTH MEASURE OF UNEXPECTED WRITE-OFF (UWR04) AND THE FIRST MEASURE OF UNEXPECTED EARNINGS (UNOl)
COEFFICIENTS* R2 ADJUSTED
R2
F STATISTICS
PROB. OF F CONSTANT UWR04 UNOl R2 ADJUSTED
R2
F STATISTICS
PROB. OF F
0.006 0.011 0.106 0.031 -0.0005 0.9818 0.380
(0.229) (0.477) (1.104)
RESULTS OF THE REGRESSION USING THE FIRST MEASURE OF UNEXPECTED WRITE-OFF (UWROl) AND THE SECOND MEASURE OF UNEXPECTED EARNINGS (UN02)
COEFFICIENTS* R2 ADJUSTED
R2
F STATISTICS
PROB. OF F
CONSTANT UWROl UN02 R2 ADJUSTED
R2
F STATISTICS
PROB. OF F
-0.007 -0.027 0.041A 0.086 0.056 2.901 0.062
(-0.303 (-1.307) (2.188)
RESULTS OF THE REGRESSION USING THE SECOND MEASURE OF UNEXPECTED WRITE-OFF (UWR02) AND THE SECOND MEASURE OF UNEXPECTED EARNINGS (UN02)
COEFFICIENTS* R2 ADJUSTED
R2
F STATISTICS
PROB. OF F
CONSTANT UWR02 UN02 R2 ADJUSTED
R2
F STATISTICS
PROB. OF F
0.005 -0.128 0.043A 0.067 0.036 2.195 0.120
(0.255) (-0.618) (2.063)
NOTE: t-statistics in parentheses.
101
TABLE 10 - CONTINUED
RESULTS OF THE REGRESSION USING THE THIRD MEASURE OF UNEXPECTED WRITE-OFF (UWR03) AND THE SECOND MEASURE OF UNEXPECTED EARNINGS (UN02)
COEFFICIENTS* R2 ADJUSTED
R2
F STATISTICS
PROB. OF F
CONSTANT UWR03 UN02 R2 ADJUSTED
R2
F STATISTICS
PROB. OF F
0.004 0.025 0.027 0.065 0.034 2.133 0.127
(0.177) (0.517) (0.971)
RESULTS OF THE REGRESSION USING THE FOURTH MEASURE OF UNEXPECTED WRITE-OFF (UWR04) AND THE SECOND MEASURE OF UNEXPECTED EARNINGS (UN02)
COEFFICIENTS* R2 ADJUSTED
R2
F STATISTIC S
PROB. OF F
CONSTANT UWR04 UN02 R2 ADJUSTED
R2
F STATISTIC S
PROB. OF F
0.007 0.009 0.0358 0.063 0.033 2.085 0.133
(0.303) (0.419) (1.844)
NOTES: * : t-statistics in parentheses. A : Significant at the 0.05 probability level. B : Significant at the 0.10 probability LEVEL.
102
TABLE 10 - CONTINUED
RESULTS OF THE REGRESSION USING THE FIRST MEASURE OF UNEXPECTED EARNINGS (UNOl) COEFFICIENTS*
R2 ADJUSTED R2
F STATISTICS
PROB. OF F
CONSTANT UNOl R2 ADJUSTED
R2
F STATISTICS
PROB. OF F
0.008 0.120 0.027 0.012 1.758 0.189
(0.310) (1.326)
RESULTS OF THE REGRESSION USING THE SECOND MEASURE OF UNEXPECTED EARNINGS (UN02) COEFFICIENTS*
R2 ADJUSTED R2
F STATISTICS
PROB. OF F
CONSTANT UN02 R2 ADJUSTED
R2
F STATISTICS
PROB. OF F
0.007 0.037A 0.061 0.046 4.048 0.048
(0.331) (2.012)
RESULTS OF THE REGRESSION USING THE FIRST MEASURE OF UNEXPECTED WRITE-OFF (UWROl)
COEFFICIENTS* R2 ADJUSTED
R2
F STATISTICS
PROB. OF
CONSTANT UWROl R2 ADJUSTED
R2
F STATISTICS X
-0.027 -0.021 0.015 -0.0007 0.956 0.331
(-1.100) (-0.978) NOTE: t-statistics in parentheses.
103
TABLE 10 - CONTINUED
RESULTS OF THE REGRESSION USING THE SECOND MEASURE OF UNEXPECTED WRITE-OFF (UWR02) COEFFICIENTS*
R2 ADJUSTED R2
F STATISTICS
PROB. OF F CONSTANT UWR02
R2 ADJUSTED R2
F STATISTICS
PROB. OF F
-0.014 0.067 0.002 -0.014 0.126 0.723
(-0.486) (0.356)
RESULTS OF THE REGRESSION USING THE THIRD MEASURE OF UNEXPECTED WRITE-OFF (UWR03) COEFFICIENTS"
R2 ADJUSTED R2
F STATISTICS
PROB. OF F CONSTANT UWR03
R2 ADJUSTED R2
F STATISTICS
PROB. OF F
-0.007 0.061 0.051 0.035 3.327 0.072
(-0.361) (1.824)
RESULTS OF THE REGRESSION USING THE FOURTH MEASURE OF UNEXPECTED WRITE-OFF (UWR04) COEFFICIENTS*
R2 ADJUSTED R2
F STATISTICS
PROB. OF F CONSTANT UWR04
R2 ADJUSTED R2
F STATISTICS
PROB. OF F
-0.013 0.019 0.011 -0.004 0.772 0.392
(-0.621) (0.862)
NOTE: t-statistics in parentheses.
TABLE 11 F-RATIO: COMPARISON:
FULL MODEL VS. REDUCED MODEL 64 OBSERVATIONS
104
PANEL A:
VARIABLES IN FULL MODEL
VARIABLES IN REDUCED MODEL
F-RATIO PROBABILITY OF F-RATIO
UWROl, UNOl UNOl 2.344 0.13
UWR02, UNOl UNOl 1.085 0.30
UWR03, UNOl UNOl 2.889 0.09
UWR04, UNOl UNOl 1.215 0.27
UWROl, UN02 UN02 2.713 0.11
UWR02, UN02 UN02 1.395 0.24
UWR03, UN02 UN02 1.259 0.26
UWR03, UN03 UN02 1.124 0.29
PANEL B:
VARIABLES IN FULL MODEL
VARIABLES IN REDUCED
MODEL
F-RATIO PROBABILITY OF F-RATIO
UWROl, UNOl UWROl 2.149 0.15
UWR02, UNOl UWR02 1.724 0.20
UWR03, UNOl UWR03 0.328 0.57
UWR04, UNOl UWR04 1.228 0.27
UWROl, UN02 UWROl 4.816 0.03
UWR02, UN02 UWR02 4.319 0.04
UWR03, UN02 UWR03 0.928 0.34
UWR04, UN02 UWR04 3.388 0.07
105
TABLE 12 RESULTS OF THE REGRESSION FOR COMPANIES WHICH ANNOUNCED THE
AMOUNT OF THE CEILING TEST WRITE-OFF CONCURRENT WITH EARNINGS (MAGNITUDE TEST) WITHOUT OUTLIERS
60 OBSERVATIONS
RESULTS OF THE REGRESSION USING THE FIRST MEASURE OF UNEXPECTED WRITE-OFF (UWROl) AND THE FIRST MEASURE OF UNEXPECTED EARNINGS (UNOl)
COEFFICIENTS* R2 ADJUSTED
R2
F STATISTICS
PROB. OF
F CONSTANT UWROl UNOl R2 ADJUSTED
R2
F STATISTICS
PROB. OF
F -0.018 -0.036A 0.166A 0.113 0.082 3.650 0.032
(-0.733) (-1.981) (2.075)
RESULTS OF THE REGRESSION USING THE SECOND MEASURE OF UNEXPECTED WRITE-OFF (UWR02) AND THE FIRST MEASURE OF UNEXPECTED EARNINGS (UNOl)
COEFFICIENTS* R2 ADJUSTED
R2
F STATISTIC S
PROB. OF F CONSTANT UWR02 UNOl R2 ADJUSTED
R2
F STATISTIC S
PROB. OF F
0.0013 -0.150 0.183A 0.062 0.030 1.916 0.156
(0.056) (-0.798) (1.948)
RESULTS OF THE REGRESSION USING THE THIRD MEASURE OF UNEXPECTED WRITE-OFF (UWR03) AND THE FIRST MEASURE OF UNEXPECTED EARNINGS (UNOl)
COEFFICIENTS* R2 ADJUSTED
R2
F STATISTICS
PROB. OF F CONSTANT UWR03 UNOl R2 ADJUSTED
R2
F STATISTICS
PROB. OF F
-0.002 0.050 0.106 0.068 0.035 2.099 0.131
(0.108) (0.993) (1.180)
* : t-statistics in parentheses. A : Significant at the 0.10 probability level.
106
TABLE 12 - CONTINUED
RESULTS OF THE REGRESSION USING THE FOURTH MEASURE OF UNEXPECTED WRITE-OFF (UWR04) AND THE FIRST MEASURE OF UNEXPECTED EARNINGS (UNOl)
COEFFICIENTS* R2 ADJUSTED
R2
F STATISTICS
PROB. OF F CONSTANT UWR04 UNOl
R2 ADJUSTED R2
F STATISTICS
PROB. OF F
-0.0001 0.008 0.135 0.055 0.021 1.659 0.199
(-0.005) (0.390) (1.571)
RESULTS OF THE REGRESSION USING THE FIRST MEASURE OF UNEXPECTED WRITE-OFF (UWROl) AND THE SECOND MEASURE OF UNEXPECTED EARNINGS (UN02)
COEFFICIENTS* R2 ADJUSTED
R2
F STATISTICS
PROB. OF F
CONSTANT UWROl UN02 R2 ADJUSTED
R2
F STATISTICS
PROB. OF F
-0.024 -0.040A 0.055A 0.185 0.156 6.486 0.002
(-1.113) (-2.284) (3.116)
RESULTS OF THE REGRESSION USING THE SECOND MEASURE OF UNEXPECTED WRITE-OFF (UWR02) AND THE SECOND MEASURE OF UNEXPECTED EARNINGS (UN02)
COEFFICIENTS* R2 ADJUSTED
R2
F STATISTICS
PROB. OF F
CONSTANT UWR02 UN02 R2 ADJUSTED
R2
F STATISTICS
PROB. OF F
-0.005 -0.246 0.062A 0.138 0.108 4.580 0.014
(-0.274) (-1.352) (3.020)
NOTES: * : t-statistics in parentheses. A : Significant at the 0.10 probability level.
107
TABLE 12 - CONTINUED
RESULTS OF THE REGRESSION USING THE THIRD MEASURE OF UNEXPECTED WRITE-OFF (UWR03) AND THE SECOND MEASURE OF UNEXPECTED EARNINGS (UN02)
COEFFICIENTS* R2 ADJUSTED
R2
F STATISTICS
PROB. OF F
CONSTANT UWR03 UN02 R2 ADJUSTED R2
F STATISTICS
PROB. OF F
0.002 -0.027 0.061A 0.116 0.085 3.746 0.029
(0.123) (-0.588) (2.129)
RESULTS OF THE REGRESSION USING THE FOURTH MEASURE OF UNEXPECTED WRITE-OFF (UWR04) AND THE SECOND MEASURE OF UNEXPECTED EARNINGS (UN02)
COEFFICIENTS* R2 ADJUSTED
R2
F STATISTICS
PROB. OF F
CONSTANT UWR04 UN02 R2 ADJUSTED
R2
F STATISTICS
PROB. OF F
-0.001 0.006 0.047A 0.112 0.081 3.615 0.033
(-0.087) (0.335) (2.514)
RESULTS OF THE REGRESSION USING THE FIRST MEASURE OF UNEXPECTED EARNINGS (UNOl) COEFFICIENTS*
R2 ADJUSTED R2
F STATISTICS
PROB. OF
CONSTANT UNOl R2 ADJUSTED
R2
F STATISTICS r
0.002 0.111 0.026 0.009 1.559 0.216
(0.070) (1.249) NOTES: * : t-statistics in parentheses. A : Significant at the 0.10 Probability level.
108
TABLE 12 - CONTINUED
RESULTS OF THE REGRESSION USING THE SECOND MEASURE OF UNEXPECTED EARNINGS (UN02) COEFFICIENTS*
R2 ADJUSTED R2
F STATISTICS
PROB. OF F
CONSTANT UN02 R2 ADJUSTED
R2
F STATISTICS
PROB. OF F
-0.0021 0.034A 0.059 0.043 3.746 0.057
(-0.096) (1.936)
RESULTS OF THE REGRESSION USING THE FIRST MEASURE OF UNEXPECTED WRITE-OFF (UWROl)
COEFFICIENTS* R2 ADJUSTED
R2
F STATISTICS
PROB. OF F CONSTANT UWROl
R2 ADJUSTED R2
F STATISTICS
PROB. OF F
-0.020 -0.025 0.036 0.020 2.218 0.141
(-1.013) (-1.490)
RESULTS OF THE REGRESSION USING THE SECOND MEASURE OF UNEXPECTED WRITE-OFF (UWR02)
COEFFICIENTS R2 ADJUSTED
R2
F STATISTICS
PROB. OF F
CONSTANT UWR02 R2 ADJUSTED
R2
F STATISTICS
PROB. OF F
-0.022 -0.473A 0.042 0.035 3.0123 0.093
(-1.309) (-2.903)
NOTES: * : t-statistics in parentheses. A : Significant at the 0.10 Probability level.
109
TABLE 12 - CONTINUED
RESULTS OF THE REGRESSION USING THE THIRD MEASURE OF UNEXPECTED WRITE-OFF (UWR03) COEFFICIENTS*
R2 ADJUSTED R2
F STATISTICS
PROB. OF F CONSTANT UWR03
R2 ADJUSTED R2
F STATISTICS
PROB. OF F
-0.007 -0.060* 0.054 0.037 3.321 0.073
(-0.456) (-1.822)
RESULTS OF THE REGRESSION USING THE FOURTH MEASURE OF UNEXPECTED WRITE-OFF (UWR04) COEFFICIENTS*
R2 ADJUSTED R2
F STATISTICS
PROB. OF F CONSTANT UWR04
R2 ADJUSTED R2
F STATISTICS
PROB. OF F
-0.003 0.011 0.006 -0.011 0.366 0.547
(-0.182) (0.606)
* : t-statistics in parentheses. A : Significant at the 0.10 Probability level.
TABLE 13 F-RAT10: COMPARISON:
FULL MODEL VS. REDUCED MODEL WITHOUT OUTLIERS
60 OBSERVATIONS
110
PANEL A
VARIABLES IN FULL MODEL
VARIABLES IN REDUCED MODEL
F-RATIO PROBABLITY OF F- RATIO
UWROl, UNOl UNOl 5.688 0.02
UWR02, UNOl UNOl 2.226 0.14
UWR03, UNOl UNOl 2.613 0.11
UWR04, UNOl UNOl 1.779 0.19
UWROl, UN02 UN02 8.966 0.04
UWR02, UN02 UN02 5.315 0.02
UWR03, UN02 UN02 3.739 0.06
UWR04, UN02 UN02 3.461 0.07
PANELB
VARIABLES IN FULL MODEL
VARIABLES IN REDUCED
MODEL
F-RATIO PROBABILITY OF F-RATIO
UWROl, UNOl UWROl 5.035 0.02
UWR02, UNOl UWR02 3.957 0.06
UWR03, UNOl UWR03 0.865 0.36
UWR04, UNOl UWR04 3.007 0.09
UWROl, UN02 UWROl 10.607 0.01
UWR02, UN02 UWR02 0.807 0.39
UWR03, UN02 UWR03 4.061 0.05
UWR04, UN02 UWR04 6.923 0.01
I l l
TABLE 14 THE Uc,t RATIO FOR FIRMS WHICH ANNOUNCED THE AMOUNT OF CEILING TEST WRITE-OFF ALONE
NAME OF THE COMPANY EVENT DATE RATIO Uc,t 1. Chapman Energy 01/06/87 2.605* 2. Damson Oil Corporation 05/22/86 1.430* 3. May Petroleum 01/25/84 1.766* 4. Mitchell Energy & Devel. 02/21/85 0.914 5. Pacific Lighting 04/14/86 1.701* 6. Sonat, Inc. 12/13/85 0.878 7. Tipperary Corporation 09/10/84 3.872*
AVERAGE 1.881
NOTE: the ratio Uc,t is equal t or greater than one
112
TABLE 15 SIMPLE CORRELATION COEFFICIENT MATRIX BETWEEN THE INDEPENDENT
VARIABLES FOR COMPANIES WHICH ANNOUNCED THE AMOUNT OF THE CEILING TEST WRITE-OFF CONCURRENT WITH EARNINGS
BASED ON 5 OBSERVATIONSA
TDEBT PPDEBT DE SD TAMKTE UWR03
TDEBT 1.00
PPDEBT -0.29 1.00
DE -0.48 0.70 1.00
SD 0.03 0.72 0.13 1.00
TAMKTE -0.53 0.80 0.97B 0.19 1.00
UWR03 0.27 -0.50 -0.93 0.15 -0.88 1.00
A See Chapter HI for definition of variables. B Significant at the 0.01 probability level.
113
TABLE 16 SIMPLE CORRELATION COEFFICIENT MATRIX BETWEEN THE INDEPENDENT
VARIABLES FOR COMPANIES WHICH ANNOUNCED THE AMOUNT OF THE CEILING TEST WRITE-OFF CONCURRENT WITH EARNINGS
BASED ON 64 OBSERVATIONSA
TDEBT PPDEBT DE SD UN02 TAMKTE UWR03
TDEBT 1.00
PPDEBT -0.19 1.00
DE -0.17 -0.13 1.00
SD 0.15 -0.22 -0.10 1.00
UN02 -0.11 0.16 -0.21 -0.04 1.00
TAMKTE -0.31° 0.22 -0.04 -0.36c 0.21 1.00
UWR03 -0.15 0.21 -0.37c -0.04 0.78B 0.32c 1.00
NOTES: A See Chapter III for definition of variables. B Significant at the 0.001 probability level. C Significant at the 0.01 probability level.
114
TABLE 17 CROSS-SECTIONAL REGRESSION ESTIMATES
CAR = a + BiPPDEBT + B2SD + B3TDEBT + B4TAMKTE + B5DE -I- B6UN02 + B7UWR03
BASED ON 64 OBSERVATIONS
MODEL 1 2 3 4
VARIABLES
REGR. COEFF.* REGR. COEFF. REGR. COEF. REGR. COEFF
VARIABLES B T COEFF.
VALUE
B T COEFF. VALUE
B T COEFF.
VALUE
B T COEFF.
VALUE CONSTANT 0.003 0.06 0.007 0.89 0.006 0.11 -0.013 -0.25 PPDEBT -0.163 -1.15 -0.173 -1.28 -0.154 -1.13 -0.154 -1.11 SD -0.484 -0.13 -0.047 -0.12 -0.344 -0.09 -0.567 -0.15 TDEBT 0.038 0.66 0.034 0.60 0.041 0.70
TAMKTE 0.001 0.98 -0.005 -0.12 0.008 0.17 -0.003 -0.64 UWR03 0.037 0.67 0.033 0.61 0.097 2.63A
UN02 0.044 1.41C 0.045 1.44C 0.061 2.95A
DE 0.019 2.52A 0.181 2.42A O
O
O
>
0.021 2.71A
ADJUSTED R2 0.109 0.118 0.118 0.092 UNADJUSTED R2
0.214 0.208 0.207 0.184
F-VALUE 2.030 2.319 2.317 1.997 PROBABILITY OF F-VALUE
0.068 0.046 0.046 0.082
* Regression coefficients. A Significant at 0.01 Probability level. B Significant at 0.10 Probability level. C Significant at 0.15 Probability level.
115
TABLE 17 - CONTINUED CROSS-SECTIONAL REGRESSION ESTIMATES
CAR = a + BiPPDEBT + B2SD + B3TDEBT + B4TAMKTE + B5DE + B6UN02 + B7UWR03
BASED ON 60 OBSERVATIONS
MODEL 5 6
VARIABLES
REGR. COEFF.* REGR. COEFF.
VARIABLES B T COEFF.
VALUE
B T COEFF.
VALUE CONSTANT 0.004 0.09 0.001 0.03 PPDEBT -0.159 -1.17 -0.161 -1.20 SD -0.514 -0.15 TDEBT 0.038 0.68 0.033 0.58
TAMKTE -0.007 -0.18 UWR03 0.037 0.70 0.040 0.72 UN02 0.044 1.43c 0.051 1.64B
DE 0.019 2.52A 0.019 2.59A
ADJUSTED R2 0.125 0.159 UNADJUSTED R2
0.214 0.241
F-VALUE 2.413 2.921 PROBABILITY OF F-VALUE
0.038 0.015
NOTES: * Regression coefficients A Significant at the 0.01 Probability level. B Significant at the 0.10 Probability level. C Significant at the 0.15 Probability level.
116
TABLE 18 RESULTS OF CROSS-SECTIONAL VALUATION MODELS
MODEL 1: CAR = a + B3UWR01 MODEL 2: CAR = a + B3UWR02 MODEL 3: CAR = a + B3UWRO3 MODEL 4: CAR = a + B3UWR04
| PANEL A PANELC MODEL 1 MODEL 3
CONSTANT 0.03128 CONSTANT 0.02011 UWROl 0.0028 UWR03 -0.04489 R-SQUARE 0.0047 R-SQUARE 0.0928 SUM OF SQUARES 0.02647 SUM OF SQUARES 0.51945 F-VALUE 0.35638 F-VALUE 7.67166 PROBABILITY OF F 0.5523 PROBABILITY OF F 0.0071
PANEL B PANELD MODEL 2 MODEL 4
CONSTANT 0.0225 CONSTANT 0.02206 UWR02 -0.0058 UWR04 0.00062 R-SQUARE 0.00007 R-SQUARE 0.00112 SUM OF SQUARES 0.00037 SUM OF SQUARES 0.00626 F-VALUE 0.00492 F-VALUE 0.08396 PROBABILITY OF F 0.9443 PROBABILITY OF F 0.7728
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