Prior Financial Ratio Information and Differential Market Response

Prior Financial Ratio Information and
Differential Market Response to
Regulatory Changes
RAJIV D . BANKER*
SOMNATH DAS**
CHIN S. Q U * * *
This paper presents empirical evidence that previously disclosed financial
ratio information is useful in explaining differential market reactions for
a cross-section offirms facing subsequent common industry-level changes.
We focus our attention on the Airline Deregulation Act of 1978 (Public
Law 95-504), which was aimed at complete deregulation of the U.S. airline industry after 40 years of federal regulation. Our empirical results
indicate that market reaction to the deregulatory changes is positively
associated with operating performance and asset utilization. This suggests
that an airline firm's operating efficiency as measured by these ratios is
a key determinant of how investors expect it to perform in a deregulated
competitive environment.
1. Introduction
This paper presents empirical evidence that financial ratio information is useful
in explaining cross-sectional differences in the market's reaction to subsequent
regulatory changes in the airline industry. Deregulation presents firms with hoth
opportunities and threats that depend on their relative positions within the industry.
Thus two firms facing the same regulatory changes may experience different market
reactions to this event hecause of the market's assessment of their existing relative
competitive positions and their consequences in a changed environment. In this
paper, we demonstrate that previously disclosed firm specific accounting information, such as measures of operating performance, financial leverage, and liquidity
•University of Minnesota
**University of Califomia, Berkeley
***National Chengchi University, Taiwan
This paper has benefitted from the comments and suggestions of seminar participants at National
Chengchi University, National Taipei College of Business, University of Minnesota, the 1994 Annual
Congress of the European Accounting Association, the 1994 Summer Symposium on Accounting Research at the Hong Kong University of Science and Technology, the 1994 JAAF-KPMG Conference
on the Economics of Financial Statements, and especially those of Eli Bartov, Ray Ball, Andrew
Christie, Prem Jain, S. P. Kothari, Holly Johnston, Joshua Livnat, Tom Lys, Joshua Ronen, Eamonn
Walsh, and an anonymous referee.
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JOURNAL OF ACCOUNTING, AUDITING & RNANCE
position, is used by market participants to assess the impact of regulatory changes
on individual firms. Specifically, even though one may not detect any statistically
significant market reaction, on average, to a given regulatory event, predictable
cross-sectional differences may exist. Thus the informational content of previously
disclosed financial ratio information in interpreting subsequent announcement of
regulatory changes is the primary focus of this paper.
Several previous studies (Schipper and Thompson [1983]; Madeo and Pincus
[1985]; Schipper, Thompson, and Weil [1987]; Praegar [1989]) have examined the
impact of regulatory changes on shareholder wealth using the event study methodology. We extend this body of literature by examining whether previously disclosed firm-specific financial ratio information is useful in explaining the revision
in investors' beliefs about firm values subsequent to the announcement of regulatory changes. Much of accounting research has evaluated the information content
and usefulness of accounting disclosures by focusing on a narrow window around
the time of the disclosure. Such an approach may be inconclusive if the information,
although concurrently useless, is useful in assessing the impact of a subsequent
event. This idea of joint informativeness is consistent with Gonedes's (1978) definition of two signals being strict complements if they have information content
jointly but not separately.' Recent research in accounting and finance has also
examined the use of financial statement information in assessing the value implications of subsequent events such as eamings announcements (Antle, Demski, and
Ryan [1994]), provision for loan losses in banks (Liu and Ryan [1995]), stock
distributions (Banker, Das, and Datar [1993]), and dividend changes (John and
Lang [1991]).
Regulatory events provide a particularly attractive economic context in which
to examine the use of financial ratio information by market participants. Regulatory
events typically affect a particular industry and thus provide a relatively homogenous set of firms for the empirical study. Alternative regulatory regimes often alter
the managerial incentives for performance (Abdel-Khalik [1988, 1991]) and the
time series of the earnings generating process (Lee and Chen [1990]). Degree of
competition (Stigler [1963, p. 70]; Lev [1983]) and regulation (Teets [1992]) have
also been found to be associated with earnings behavior. We argue in this paper
that a firm in a stronger competitive position is likely to benefit more from deregulation. The consequences of regulatory changes for a given firm can be better
understood in the context of prior accounting information. For complementary information as defined by Gonedes (1978), regulatory announcements and financial
ratios may not have information content individually, but may have information
content jointly. Consequently, there may be a differential market reaction to deregulation conditional on the financial statement information.
1. It may not be surprising therefore that previous researchers did not find any contemporaneous
information content in replacement cost disclosures (Watts and Zimmerman [1980]). Thus, the value
of replacement cost infonnation, although not useful at the time of its disclosure, may be useful later
in evaluating the impact of events such as takeovers and bankruptcy (Ronen [1979, p. 444]; Bildersee
and Ronen [1987]).
PRIOR FINANCIAL RATIO INFORMATION
701
In this paper, we focus our attention on the Airline Deregulation Act of 1978
(Public Law 95-504), which was aimed at complete deregulation of the U.S. domestic airline industry after 40 years of federal regulation. Our empirical results
indicate that market reaction to deregulation is positively associated with operating
perfonnance. We do not find empirical support for our hypotheses that financial
condition in addition to operating efficiency is also useful in assessing the impact
of regulatory changes. This suggests that a firm's operating efficiency and not
financial health is a key determinant of how investors expect it to perform in a
deregulated competitive environment.
The remainder of this paper is organized as follows. In the next section, we
discuss how financial ratio infonnation is useful to investors in assessing the impact
of airline deregulation. In Section 3, we describe the sample data used to test our
empirical hypotheses. We discuss our empirical methodology and results in Section
4 and conclude the paper in Section 5.
2. Development of Hypotheses
We examine the cross-sectional differences in market reaction to the legislative
deliberations that preceded the enactment of the Airline Deregulation Act of 1978.
The effective date of actual legislative action is an imprecise measure of the event
under examination because events such as congressional hearings that lead up to a
legislative act themselves convey information and in a semi-strong efficient market
such information is rapidly impounded into prices. Some of the congressional hearings are extensively reported in the financial press and involve participation by
industry and consumer representatives. These press reports provide considerable
infonnation regarding the form and substance of the impending legislative act. We
posit that market expectations are revised as information is released at the hearings,
rather than at the actual enactment of the legislation (Brown and Warner [1985]).^
In developing hypotheses relating investor response to airline deregulation and
their use of financial ratio information, we first briefly review key aspects of airline
deregulation. The Airline Deregulation Act of 1978 involved two important elements. First, rate regulation was relaxed to allow price competition among airlines.
Second, route restrictions were relaxed to permit airlines to operate in any market.
Prior to deregulation, the Civil Aeronautics Board (CAB) not only restricted entry
by firms but also restricted the number of existing carriers competing in a given
market. Airlines could only engage in nonprice and nonroute competition, such as
flight frequency and in-fiight services. Relaxation in price and route regulation
signified major changes in the competitive environment of the industry, and
2. Event study methodology is more powerful when the date the expectations change is known
with greater certainty. As an explanation for their finding that the Motor Carrier Act of 1980 met with
little market reaction, Schipper, Thompson, and Weil (1987) note: "While deregulation was significant
to the profitability of established trucking companies, the act itself represented a point in the regulatory
change process that was foreseeable on the basis of prior economic events."
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therefore was expected to affect future cash flows of airline firms and their capitalized values.
Throughout the legislative deliberation process, weaker airlines had continued
to oppose deregulation. They feared that they would be driven out of business once
fare and route protection was withdrawn, as they could not compete with the resources available to some other airlines. Mr. Francesco Lorenzo, the president of
Texas International and a spokesperson for the small carriers, predicted that deregulation would produce a "highly concentrated, less competitive system" (Hearing
of the Senate Subcommittee on Aviation, 1977). Mr. L. B. Maytag, chairman of
National Airlines, was quoted as saying that deregulation would destroy some carriers (Wall Street Journal, March 17, 1977). In contrast. United Airlines, then the
largest firm in the industry, supported deregulation as early as 1975 when almost
all other airlines were opposed to it (Wall Street Journal, October 31, 1975). This
suggests that United believed that it would benefit under a deregulated environment
in which it could set prices and choose profitable routes to operate. Maloney and
McCormick (1982) argue that regulatory events may lead to intraindustry transfers
of wealth such that not all firms may benefit, even when regulation is industry
sponsored. Indeed, as Spiller (1983) notes, individual companies in the airline industry were not homogeneous in their response to deregulation and that regulation
did in fact benefit some of them.^ Furthermore, Spiller (1986) posits the existence
of mobility barriers resulting from costs associated with adjusting to unregulated
route and rate structures in a newly emerging competitive market. Operating characteristics of individual airlines refiect their ability to cope with competition and,
therefore, are likely to be related to the magnitude of these adjustment costs. Adjustment costs are thus likely to vary across individual airlines.
Since deregulation relaxes control over firms' operation and management, firms
in a competitively advantageous position are likely to benefit from a deregulated
environment, whereas firms in a less advantageous position are likely to be adversely affected. As the congressional hearings unfolded, and the likelihood of a
deregulated environment increased, shareholder expectations of airline firms' future
cash flows changed. Financially healthy firms are in a better position to withstand
cut-throat price competition and also avail themselves of opportunities opened up
in a deregulated environment. We hypothesize, therefore, that as the likelihood of
deregulation increases, the change in the value of financially strong firms will be
more positive (or less negative) than of firms that are more financially distressed."
3. A few regulatory event studies have attempted to examine the differential impacts on firms'
values by measuring the association between stock price changes of regulatory or mandatory changes
and explanatory variable such asfirmsize. See, for example, Hughes and Ricks (1984) and Rose (1985).
4. It is, of course, possible that financially healthy firms are typically large, old, and unionized
and these nonaccounting operating factors work in the opposite direction. In this paper, we focus only
on accounting information and are therefore unable to reject this alternative hypothesis. Inrelatedwork,
Beneish (1991) examines nonfinancial operating characteristics, such as stage length, hub structure, and
proportion of first class passengers, and finds significant association of these variables with airline stock
retums around deregulatory events. To the extent that nonfinancial information interacts with the financial information in the formation of the market's expectations, our specification has an omitted
PRIOR FINANCIAL RATIO INFORMATION
703
Deregulatory changes were expected to have several effects on airlines that
were financially weak. First, without the protective umbrella of government regulation to guarantee air fares, these firms were likely to experience even greater
difficulty in improving their operating performance. Even before the deregulation
act in 1978, CAB had begun to loosen its regulatory policy. For example, it had
authorized the expansion of the popular discount fares, which ranged up to 45
percent less than coach, after 15 airlines had requested it (" 'Super-Saver' Fares
Are Cleared for Most of U.S. Air Routes," Wall Street Journal, June 3, 1978, p.
8.) The introduction ofthe cutthroat "Super-Saver" competition was an indication
to market participants of the possible intensity of competition that might be expected in a deregulated environment. In other words, the nature of competition was
to change from nonprice to price, requiring greater emphasis on cost control. Second, financially weak firms were likely to find it difficult (or be required to incur
a substantially higher cost) to attract new capital essential to reposition themselves
for the newly competitive environment with changing route structures.
Several concerns were voiced about the financial risk faced by airline firms
during the period when the deregulation statute was being discussed in the U.S.
Congress. Citing the status of government regulation and bailout requests from Pan
American Airlines, the Wall Street Journal (September 25, 1975) reported, "The
big banks and insurance companies that have financed the air-transport industry are
growing even more doubtful about the wisdom of maintaining the support and
about their ability to do so." An insurance company executive was quoted as
waming, "There isn't a question that there is a growing group within our ranks
that's all in favor of pulling the plug on the next airline that comes along needing
another bailout from us to carry it through a crisis.... We're getting tired of carrying the industry while watching the outlook for our loans growing more and
more uncertain" ("Airline Industry Lenders Grow More Doubtful That They
Should and Can Continue Support," Wall Street Journal, September 25, 1975).
The same article reported Frederick W. Bradley, Jr., vice president of the First
National City Bank of New York, as saying that institutional lenders have not
indicated any interest in extending long-term loans to the industry and many banks
are much less interested in airline loans than in the past. In a deregulated regime,
airlines with increased financial leverage were less likely to be supported by institutional lenders, and consequently, less likely to enjoy an advantageous competitive
position in a deregulated environment.
In this paper, we use publicly available financial statement information to
measure airline firms' financial strength along four dimensions: operating performance, asset utilization efficiency, financial leverage, and liquidity position. These
are four categories of financial ratios suggested by textbooks on financial statement
variable. If, however, nonaccounting variables such as unionization are important omitted variables and
are uncorrelated with the included accounting variables in our specification, the results are unbiased
but also unduly conservative, because the omission of a variable that is uncorrelated with an included
variable results in upwardly biased standard errors (Kmenta [1971, p. 394]).
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analysis (Lev [1974]; Foster [1986]; Gibson [1989]; and Stickney [1990]) to measure different economic aspects of a firm's operations.
2.1 Operating Performance
Operating performance ratios measure a firm's ability to generate financial
resources internally from its operations. Airline firms exhibiting improvements in
operating performance were expected to enhance their advantage when the opportunities for entering new markets became available to them in a deregulated environment. A major structural change such as the move to a deregulated
environment was expected to increase the potential for the firms with worsening
operating performance to be losers in the newly deregulated era (Lee and Chen
[1990]). Improved operating performance also indicated a greater ability to survive
and succeed when airline firms began competing with each other on the price
dimension. We expect, therefore, that improvement in operating perfonmance is
positively correlated with the market response. Specifically, we hypothesize
//,: Market response to the airline deregulatory changes is an increasing function
of the improvement in an airline's operating performance.
2.2 Asset Utilization Efficiency
Under deregulation, route protection for the inefficient airlines would be removed.
The threat of potential entry, therefore, was likely to force firms to become more efficient in their operations. As a result, inefficient airlines that could not offer products
with the desired price and service combinations were likely to be driven out ofthe market or be acquired by other more efficient airlines. Therefore, in the long run, only the
airlines that were improving their efficiency in utilizing their assets were likely to survive in a deregulated environment. Even in the short run, the airlines' asset utilization
efficiency was likely to affect their competitive advantage in adapting to the newly deregulated environment. Therefore, we hypothesize
H^. The market response to the airline deregulatory changes is an increasing function of the improvement in an airline's asset utilization efficiency.
2.3 Financial Leverage
Financial leverage measures the amount of debt a firm carries relative to its
equity. It reflects the likelihood that a firm can maintain its solvency in the future.
High financial leverage increases the risk of a firm being unable to meet interest
and principal payment schedules, and being forced into recontracting its debt, or
entering bankruptcy or involuntary liquidation proceedings. In addition, such a firm
may be forced to give up profitable long-term projects to avoid violating its loan
PRIOR HNANCIAL RATIO INFORMATION
705
agreements.' Increasing financial leverage also enhances the risk for the residual
equity holders, and this risk was expected to be further accentuated because
of the increased uncertainty accompanying a newly competitive environment.*
Specifically,
H^: The market response to the airline deregulatory changes is a decreasing function of an airline's financial leverage.
2.4 Liquidity Position
Liquidity position measures a firm's ability to meet its short-term financial
obligations. Increased price competition witnessed already with the supersaver fares
was expected to intensify immediately on deregulation. As a result, airline firms
were expected to face increased pressure to maintain their liquidity position. Financial analysts like Robert J. Joedicke (airline industry analyst at Kuhn Loeb &
Co.) saw some evidence that "the country's major lenders are developing cold
stares for desperate borrowers. .. . Some airlines are in for rough going, and soon''
("Lenders May Give Some Airlines Rough Time In Wake of New York, W. T.
Grant Problems," Wall Street Journal, October 30, 1975). He was reported as
saying, "It's impossible to predict the outcomes of negotiations in progress....
But there is no doubt that both commercial lenders (mainly banks) and senior
lenders (mainly insurance companies) are taking a much harder line with airline
managements than has been evident in the past.... I think a two-tier market is
going to develop in the stocks.... At one extreme you have Eastern, Pan Am, and
TWA. At the other, the strongest companies are UAL [United] which has $400
million in cash and no new equipment commitments, and Northwest, which is in
somewhat similar position." Lower liquidity was likely to make it more difficult
for a firm to ride out periods of intense price competition. Therefore, we
hypothesize
H^: The market response to the airline deregulatory changes is an increasing function of an airline's liquidity position.
3. Sample Selection and Description of Data
We examined the Wall Street Journal Index for the years 1974—1978 to identify events leading to the enactment of the airline deregulation statute in October
5. For example, Holthausen (1981), after examining bond indenture agreements, found that "the
firm is not permitted to merge, issue new debt, or pay a dividend if leverage (debt ratio) is above the
specified maximum.... If the firm violates the leverage requirement, the firm is in 'technical default.' "
6. It is likely that financial leverage is a choice variable for managers determined by their operating
risk. Thus the lower the operating risk, the higher the leverage. We do not examine this altemative
explanation by controlling for operating risk. Furthermore, our measure of financial leverage is not
adjusted for the presence of operating leases, and to this extent it is measured with error.
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1978. We denote as day 0 the date of publication in the Wall Street Joumai,
assuming it to be the day when the market becomes aware of the regulatory change.
We list in Table 1 the captions for each of the 26 events identified in the Wall
Street Joumai and their dates of publication.
The following conditions were imposed for firms in the U.S. airline industry
that were included in this study: (1) it was a U.S. passenger air carrier, (2) its daily
stock retum data were available in the CRSP Daily Retums File during the deregulatory deliberation period from the beginning of 1974 to the end of 1978, and
(3) its financial data were available in the Quarterly Industrial Compustat Files
during the same time period. These criteria left us with 11 airline firms. National,
Westem, Frontier, Ozark, Republic, Southem, and Seaboard are excluded from our
analysis because they have ceased to exist due to merger, acquisition, or bankruptcy. This may introduce a survivorship bias in our results.' Table 2 lists the
names of sample firms and their total assets at the beginning and at the end of the
deregulatory deliberation period considered by us. As can be seen from Table 2,
all airlines grew considerably in size. For instance. Delta grew about 80 percent
over this five-year period, while Hawaiian grew almost 200 percent. These reflect
the enormous changes taking place in airline operations in anticipation of a transition to a deregulated environment.
Detailed descriptions for some balance sheet and income statement variables
at the beginning and at the end of the deregulatory deliberations period are presented in Table 3. The sample firms grew during this period and their financial
position also improved. The mean profit for the sample firms, for example, increased from $1.05 million loss at the beginning of the period to $8.02 million
profit at the end of the period. We used financial and other information to construct
the following ratios for each firm for each of the 20 quarters during the period
examined in this study.
3.1 Operating Performance
Operating performance is commonly measured using profitability ratios reflecting the ability of a firm to generate revenues in excess of expenses. Foster (1986)
suggests the profit ratio (= net income/sales) as one of the primary measures of
profitability. Altemative measures, such as operating retum (= net income/equity),
are also discussed in Lev (1974, Chap. 2). Most of these other measures are highly
correlated with the profit ratio. We select the profit ratio as our measure of operating
performance because it indicates the extent of profit margin enjoyed by the firm.
Because deregulation would usher in an era of intense price competition, the ability
of airiine firms to compete with each other would be reflected in their profit mar7. Also excluded from our analysis are TWA and Braniff, both of which were then traded in the
Over-the-Counter (OTC) market. We also note that stock retums for Southwest Airlines prior to October
1975 correspond to stock retums for Satum Airlines because Southwest as an airline did not exist prior
to that date.
PRIOR HNANCIAL RATIO INFORMATION
707
TABLE 1
Deregulatory Change Events Leading up to the Enactment of the
Airline Deregulation Act of 1978
Event
Number
Event Date
(Year-Month-Day)
Deregulatory Change Events
1.
74-09-19
Pan Am's request for a new $10.2 million-a-month subsidy
rejected by Transportation Secretary.
2.
75-02-05
Legislative Push to Slash CAB's Powers Over Airlines Is
Expected From Ford.
3.
75-04-09
Fewer but Stronger Airlines, Railroads Is Goal of New
Transportation Secretary.
4.
75-10-31
CAB to Support Airline Financial Health and Limit
Deregulation.
5.
76-01-09
Airline Deregulation Proposal Dropped by CAB;
Piecemeal Approach Is Sought.
6.
76-03-30
Subsidies Asked for Air Service to Small Cities; Ford Bid
to Aid Commuter Lines Likely to Help Bill on Airline
Deregulation.
7.
76-04-09
8.
77-01-10
Ford Widens Proposal to Deregulate Airlines.
9.
77-03-07
Carter Wants Airline-Deregulation Law by Summer, Citing
Need for Competition.
10.
77-06-21
Senate's Bill to Limit CAB Powers Gets Lift From Carter,
Others.
11.
77-08-01
Carter Urges Senate Panel to Strengthen Airline
Deregulation Bill It's Weighing.
12.
77-09-23
Deregulation Bill Aimed at Airlines Clears Big Hurdle;
Significant Easing of Rules on Expansion of Routes
Backed by Senate Panel.
13.
77-09-30
Air Competition Widened in Vote By Senate Panel; Key
Deregulation Provision Lets Carriers Add Routes Without
CAB's Approval.
14.
77-10-28
Bill to Deregulate Airlines Clears Panel But Senate Is
Unlikely to Act This Year.
15.
78-02-03
CAB Studies Policy Changes to Foster Cheaper Airline
Fares, Chairman Says.
16.
78-03-16
House Republicans, Some Democrats Put Obstacles in
Path of Airline Deregulation.
17.
78-04-07
Airline Deregulation Falters Under Conservative's Attack.
,
CAB Urges a Sharp Cut in Own Powers to Control Airline
Services and Prices.
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TABLE 1 (continued)
Event
Number
Event Date
(Year-Month-Day)
18.
78-04-17
Airlines Could Freely Set 50% Rate Cuts Under CAB
Proposal to Lx>osen Its Grip.
19.
78-05-10
Bill to Deregulate Airlines Is Passed by House Panel;
Measure Hailed by Carter as Victory for Passengers
Seeking Low-Cost Fares.
20.
78-05-16
Bill to Deregulate Airlines Partially Clears House Panel.
21.
78-07-10
Cuts in Air Fares Up to 70% Slated Around August 31;
CAB Plan Also Would Trim Some Coach Charges 50%
and Allow Small Boosts.
22.
78-07-13
CAB Plans to Relax Most Restrictions on Charter Airlines.
23.
78-08-18
Most Cuts on Charters Are Scrapped by CAB.
24.
78-09-22
House Vote to Cut Regulation of Airlines by U.S., Giving
Carter Another Victory.
25.
78-10-09
Bill Deregulating Airline Industry Clears Conferences;
Measure Calling for Demise of CAB in 1985 Is Seen
Getting Carter Signature.
26.
78-10-25
Carter Signs Airline Deregulation Law; CAB Will Grant
Routes More Generously.
Deregulatory Change Events
gins. Although altemative measures such as return on equity and return on assets
measure the efficiency of use of capital and assets in place, the key indicator of
the competitive advantage of an airline in sustaining price competition is its profit
ratio.
3.2 Asset Utilization
The objective of this construct is to indicate the extent of operational efficiency
with which assets are utilized. A commonly used measure in financial statement
analysis is the asset tumover ratio (ATR) measured as the ratio of sales to average
total assets (Foster [1986]). Lev (1974, p. 30), however, suggests that for industryspecific analysis, as is the case here, operational characteristics of the industry
should also be incorporated into financial statement analysis. Specifically, for the
case of the airline industry, he suggests the use of load factor (LF), measured as
the percentage of overall capacity that is actually sold and utilized.* We therefore
use load factor as a measure of asset utilization efficiency, based on the premise
8. Specifically, passenger load factor is measured as passenger revenue seat-miles flown divided
by seat-miles offered.
PRIOR FINANCIAL RATIO INFORMATION
709
TABLE 2
List of Airline Firms Included in This Study
Total Assets (millions of dollars)
Name of Firm
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
American Airlines (AMR Corp.)
Alaska Airlines
Continental Airlines
Delta Airlines
Eastern Airlines
Hawaiian Airlines (HAL, Inc.)
Northwest Airlines (NWA, Inc.)
Pan Am World Airways
Southwest Airlines
United Airlines (Allegis Corp.)
USAir Group (Allegheny Corp.)
as of Dec. 31, 1973
as of Dec. 31, 1978
1,680.21
24.45
666.85
907.93
1,432.62
26.80
1,085.63
1,683.72
14.03
2,417.20
269.97
2,767.72
92.43
677.27
1,646.68
1,908.56
79.58
1,392.86
2,048.30
118.71
3,697.22
403.61
that under a deregulated environment, airlines with increasing load factors £ire the
more efficient airlines in using their flight equipment.
3.3 Financia] Leverage
We measure financial leverage by the debt ratio (DR), defined as the ratio of
total debt to total assets. This measure refiects the extent of financial risk of a firm.
It is also highly correlated with altemative measures, such as total debt to stockholders' equity ratio and long-term debt to total assets ratio (Foster [1986]).
3.4 Liquidity Position
Liquidity position refiects a firm's ability to meet its short-term financial obligations. We measure it using the defensive ratio (DEFR) defined as the ratio of
defensive assets to daily cash expenditure, where defensive assets = cash -I- accounts receivable, and cash experiditure = cost of goods sold -I- selling and administrative expanse — depreciation expense + interest expense. Current ratio and
quick ratio are two popularly used ratios to measure liquidity position for manufacturing firms (Lev [1974]). These ratios compare short-term assets to short-term
liabilities comprising largely accounts payable to suppliers. For a firm in the airline
industry, however, current liabilities comprise largely deferred revenues, because
unpaid vendor invoices for supplies are relatively small. Therefore, the defensive
ratio is a better measure of an airline's ability to meet short-term financial obligations as it refiects the availability of funds to cover expenses on a daily basis.
Descriptive statistics for the financial ratios used are presented in Table 4. All
financial ratios, except the asset turnover ratio, exhibit improvement over the fiveyear period of deregulatory deliberation considered by us.
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TABLE 3
Descriptive Statistics for Key Financial Variables (in millions of dollars)
Beginning of
Deregulator]r Change
Deliberations
(4th Quarter of 1973)
Variable Description
End of Deregulatory
Change Deliberations
(4th Quarter of 1978)
N
Mean
Std. Dev.
N
Mean
Std. Dev.
11
11
11
11
11
11
11
928.12
643.95
284.17
198.45
168.86
96.63
180.94
812.38
577.10
258.21
198.32
166.23
100.07
155.80
11
11
11
11
11
11
11
1,348.44
884.30
464.14
377.76
334.88
158.70
337.75
1,202.85
840.20
397.96
416.85
371.15
157.99
326.98
11
11
11
11
206.85
153.79
33.04
19.44
8.27
-1.05
180.30
129.45
31.04
17.38
7.11
14.00
11
366.25
268.90
56.59
27.06
8.99
8.02
327.87
243.00
54.98
22.95
8.83
13.23
Balance Sheet Variables
1.
2.
3.
4.
5.
6.
7.
Total assets
Total liability
Total equity
Current assets
Quick assets
Accounts receivable
Current liability
Income Statement Variables
8.
9.
10.
11.
12.
13.
Sales
Cost of goods sold
Sales and administration expense
Depreciation expense
Interest expense
Net income
11
11
11
11
11
11
11
4. Empirical Methodology and Results
To empirically test our hypotheses, we use a two-stage approach. Iti the first
stage, we estitnate the market response coefficients to assess the extent of revision
in the value of an individual airline firm's stock, consequent to a specific deregulatory decision event. Following Schipper and Thompson (1983) and others, we
specify a multivariate regression model with one equation for each airline firm.
Additionally, the model controls for possible changes in the systematic risk (beta)
of the firm with the change in the political regime from the Republican Ford administration (1974-76) to the Democratic Carter administration (1977-78) (Banker,
Das, and Ou [1995]). We also control for other firm-specific events such as strikes,
mergers, and acquisitions that may confound the effect of the deregulatory deliberation event of interest.' Therefore, we estimate the coefficients of market response
9. Several firm-specific merger and strike activities took place during the deregulatory deliberation
period. Strikes, mergers, and acquisition events have been found to have significant impacts on firms'
stock retums. See Becker and Olson (1986); Davidson, Worrell, and Garrison (1988)- Dodd (1980)and Yen (1987).
PRIOR nNANCIAL RATIO INFORMATION
711
TABLE 4
Descriptive Statistics for Financial Ratios
Beginning of
Deregulatory Change
Deliberations
(4th Quarter of 1973)
Ratio Description
1.
2.
3.
4.
Profit ratio (PR)
Load factor (LF)
Debt ratio (DR)
Defensive ratio (DEFR)
End of Deregulatory
Change Deliberations
(4th Quarter of 1978)
N
Mean
Std. Dev.
11
11
11
11
-0.006
46.82
0.737
291.0
0.057
6.37
0.139
123.4
11
11
11
11
Mean
Std. Dev.
0.030
51.25
0.661
377.2
0.065
5.33
0.112
141.6
PR = net income/sales.
LF = the percentage of overall capacity that is actually sold and utilized.
DR = total debt/total assets.
DEFR = defensive assets/daily cash expenditure, where defensive assets = cash + accounts
receivable, and daily cash expenditure = (cost of goods sold + selling and administrative expense —
depreciation expense + interest expense)/365.
to the deregulatory deliberation events for individual airline firms using the following specification:
= a,
+ e,
Vj,
(1)
where
r,,
a,Pj^
Pic
r^,
F,
=
=
=
=
=
=
Stock retum of firm i at time t
Intercept of firm i
Systematic risk parameter of firm i during Ford administration
Systematic risk parameter of firm i during Carter administration
Equally weighted market index at time t
Dummy variable for Ford administration; 1 during Ford administration and
0 during Carter administration
C, = Dummy variable for Carter administration; 1 during Carter administration
and 0 during Ford administration
5y = Market response coefficient that measures the impact of deregulatory deliberation event j on firm i stock return
DJ, = Dummy variables for deregulatory events; 1 for the period of five calendar
days surrounding deregulatory event y and zero otherwise
(JL,^ = Coefficient that measures the impact of merger event k on firm i stock retum
Miia = Dummy variables for merger events; 1 for the period of five calendar days
surrounding merger event k and zero otherwise
0,;, = Coefficient that measures the impact of strike event h on firm i stock retum
712
JOURNAL OF ACCOUNTING, AUDITING & HNANCE
5,7,, = Dummy variables for strike events; 1 for the period of five calendar days
surrounding strike event h and zero otherwise
e,, = Residuals of firm i stock retum at time /.
The model thus captures the stock market response to the events leading up to the
airline deregulation. The stock price changes refiect the changes in the capitalized
value of firms indicating the extent to which financial markets revised their expectations about firms' future cash flows. We employ the seemingly unrelated regression
(SUR) method to estimate the system of equations in the model of eq. (1).'°
Table 5 summarizes the estimated market response coefficients by for the firststage regression in eq. (1), for individual deregulatory deliberation events summarized across all of the sample firms. The median market reaction coefficient
across all 11 firms was negative for 21 of the 26 deregulatory deliberation events.
However, the upper quartiles for the market response coefficients were positive for
19 of the 26 events. This suggests that all of the deregulatory deliberation events
did not affect the different airline firms uniformly. These results are generally
consistent with previous findings on airline deregulation reported by Spiller (1983)
and Michel and Shaked (1984). Table 5 also identifies 11 of the 26 events that had
a significant price impact. We will first use this subset of events to test our hypotheses and then use the entire sample of events to examine the robustness of our
results to the more general setting that includes the deregulatory events for which
the price impact is not significant across our sample firms.
In the second stage, we assess the association between the market response
coefficients 8,^, obtained from the first stage, and the four financial ratios representing our hypotheses. Use of sequential regressions with multiple explanatory
variables is likely to lead to downward-biased estimates .for the incremental explanatory power of the variables introduced at the second stage (Beaver [1987]).
This is likely to make it more difficult to reject the null of no association.
Our hypotheses predict that an airline's market response is an increasing function of profit ratio (PR), asset turnover ratio (LF), and defensive ratio (DEFR), and
a decreasing function of its financial leverage (DR). We use the following regression model to test our hypotheses:
8,^ = ao + b, PRy + b^ LFy + b, DR.^^ + b, DEFR^ + e^,
(2)
where
8y
= market response coefficient that measures the effects of the deregulatory
change event; on firm Vs stock return, obtained by estimating eq. (1)
10. Because the airline deregulatory deliberations affect all firms in the airline industry at the
same time, the simultaneous effects on all firms are likely to result in contemporaneous cross-sectional
correlations. Zellner's (1962) SUR estimation procedure yields estimators that are asymptotically more
efficient than OLS estimation applied to each individual equation. Note that given our primary interest
in the magnitudes of the coefficient estimates and not their standard errors, we could have used OLS
estimation, which also provides consistent coefficient estimates. However, the use of SUR estimation
allows us to identify which of the selected events had a statistically significant price impact.
PRIOR RNANCIAL RATIO INFORMATION
713
TABLE 5
Market Response Coe£Bcient by Event
Quartiles
Deregulatory
Event Date
N
Mean
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
11
11
U
11
11
11
11
11
11
11
11
11
11
11
11
11
11
11
11
It
11
11
11
11
11
11
-0.00063
-0.01361
0.00926
-0.00085
-0.00161
-0.00417
0.00025
-0.00873
-0.00671
-0.01209
-0.00604
0.00281
-0.01052
-0.00010
-0.00985
0.00202
-0.01070
-0.00508
-0.00473
-0.00686
0.01316
-0.00077
-0.00596
0.00715
-0.00416
0.00479
740919
750205*
750409
751031
760109
760330
760409
770110*
770307*
770621*
770801
770923
770930*
771028
780203*
780316
780407*
780417
780510*
780516*
780710*
780713*
780818
780922
781009
781025
Standard
, Deviation
0.02391
0.01913
0.00741
0.01543
0.01171
0.00875
0.01402
0.00709
0.01870
0.01296
0.00948
0.01034
0.00858
0.01227
0.00899
0.01174
0.00829
0.01256
0.01274
0.00681
0.01452
0.00823
0.01525 .
0.01018
0.00752
0.01825
75%
50%
25%
0.01119
0.00414
0.01657
0.00623
0.00600
-0.00059
0.00902
-0.00492
0.00445
-0.00281
0.00023
0.01165
-0.00346
0.00586
-0.00329
0.01193
-0.00466
0.00417
0.00226
-0.00298
0.02377
0.00388
0.00606
0.01395
0.00220
0.01419
-0.00474
-0.01617
0.01040
-0.00233
-0.00394
-0.00458
-0.00180
-0.00792
-0.00221
-0.01310
-0.00754
0.00132
-0.00935
-0.00308
-0.00728
0.00725
-0.00981
-0.00875
-0.00789
-0.00627
0.01970
-0.00138
-0.00716
0.00540
-0.00316
-0.00199
-0.01250
-0.02324
-0.00143
-0.01354
-0.01143
-0.01167
-0.00873
-0.01000
-0.01815
-0.02117
-0.01123
-0.00478
-0.01624
-0.00536
-0.02014
-0.00862
-0.01624
-0.01120
-0.00953
-0.01426
0.00081
-0.00867
-0.02103
0.00096
-0.01065
-0.00830
*Represents a statistically significant response in the cross section.
PR
LF
DR
DEFR
=
=
=
=
profit ratio
load factor
debt ratio
defensive ratio.
Regression results for eq. (2) using the 11 events which had a significant price
impact are presented in the first column of Table 6." Overall, the results support
the idea that airlines exhibiting a stronger (weaker) financial position are more
(less) likely to benefit from airline deregulation. The joint test of coefficients of all
four explanatory variables (financial ratios) being equal to zero (regression F) is
rejected with an F statistic of 8.64, which is statistically significant at a p value of
11. This and all subsequently reported t statistics in Table 6 are based on White's consistent
covariance matrix estimator.
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JOURNAL OF ACCOUNTING, AUDITING & FINANCE
TABLE 6
Results of Multiple Regressions Explaining Market Response Coefficients
Coefficient Estimates
(White corrected t statistics in parentheses)
Independent
Variables
Intercept
Predicted
Sign
Significant
Events
—
All Events
Nonevents
-0.0334
(-4.20)
-0.0232
(-4.10)
0.0036
(0.43)
Profitability ratio
-1- {b,)
0.0482
(2.90)
0.0199
(2.03)
0.0169
(0.85)
Load factor
H- (fcj)
0.0007
(3.36)
0.0004
(2.84)
-0.0001
(-0.21)
Debt ratio
— (^3)
-0.0132
(-1.55)
-0.0007
(-0.11)
-0.0008
(-0.06)
Defensive ratio
+ (b^)
0.0393E-5*
(0.70)
Adjusted IP
Test: by = b^ = b, = b^ = 0
F statistic
(p value)
X^ statistic'
(p value)
•
0.0350E-5
(0.99)
0.0366E-5
(139)
0.203
0.064
8.64
(0.0001)
22.92
(0.0001)
5.63
(0.0002)
17.71
(0.0014)
0.56
(0.6959)
3.71
(0.4464)
19.72
(0.0001)
17.40
(0.0000)
5.06
(0.0253)
5.09
(0.0241)
1.16
(0.2822)
1.46
(0.2272)
-0.010
Test: bt + b^ - b, + b^ = 0
F statistic
(p value)
X^ statistic
(p value)
Number of Events
Number of Observations
11
121
26
273
20
186
'The chi-square values reported above are based on White's consistent estimator of the covariance
matrix.
*Read as 0.0393 X 10"'
O.OOOI. Similarly, a test that bi + b2 - b^ + b^ is zero (to reflect the expected
signs of these coefficients) is also rejected with an F statistic of 19.72, which is
statistically significant at a/? value of 0.0001. The overall results are thus consistent
with the notion that capital markets utilize previously disclosed financial ratio information to differentiate firms' competitive advantage in adapting to a newly deregulated environment. The second column of Table 6 reports the results from using
all 26 regulatory events. The results are very similar to the results based on only
the 11 events that had a significant price impact. This reinforces our primary hypothesis that investors use previously disclosed financial information to assess the
valuation consequences of subsequently announced regulatory events.
PRIOR FINANCIAL RATIO INFORMATION
715
Specifically, we find that changes in profitability ratio and load factor are
significantly associated with market response coefficients in the expected direction.
We did not find statistically significant evidence to support the use of leverage and
short-term liquidity by market participants. These results are consistent with the
explanation that investors assess competitive strength in a deregulated environment
primarily on the basis of operating perfonnance irrespective of the existing financial
condition.'^
To assess the robustness of our conclusions that investors use previously disclosed accounting infonnation to assess the impact of deregulatory announcements,
we estimate eq, (2) using a sample of nonevent dates. To select the nonevent dates,
we first delete all of the 26 deregulatory events and the strike and merger events
from the period January 1974 through December 1978, including the five calendar
days surrounding each of the events. We then randomly select dates from each
fiscal quarter in each of the five fiscal years yielding a total of 20 nonevent dates.
We estimate the market response coefficient for each of these 20 nonevent dates
using eq. (1), with the exception that no merger or strike dummies are included.
Thejjoefficient estimates for the nonevent dates obtained from eq. (1) are then used
to reestimate eq. (2). The results from this estimation are presented in the third
column of Table 6. It can be seen that there is very little explanatory power in the
regression, and that none of the explanatory variables are statistically significant.
Moreover, these results are not sensitive to the choice of the randomly selected
nonevent dates. Similar results were obtained when we repeated this procedure 25
times, each time randomly generating aitemative nonevent dates.
5. Conclusion
In this paper, we have examined the relation between publicly available financial ratio information and cross-sectional differences in capital market reactions for
firms in the airline industry facing common deregulatory change. We hypothesized
that the market response to the deregulatory deliberation events is an increasing
function of an airline's financial strength as measured by ratios reflecting operating
performance, asset utilization efficiency and liquidity position, and a decreasing
function of financial leverage. Overall, the results support the idea that market
participants use previously disclosed financial ratio information to differentiate between airline firms in assessing the impact of deregulatory changes. The empirical
results confirmed that profit ratio and load factor are related to the market reaction
in the expected direction. We did not find statistically significant relation between
market response coefficients and short-term liquidity, and a financial leverage. The
focus in this paper has been on the use of financial ratios by market participants.
This leaves open the question of how market participants weigh different financial
12. Replacing load factor with asset tumover ratio yielded similar results for PR, ATR,
although the sign of the leverage variable changed to positive. It is possible that asset tumover ratio is
a noisy measure because leased aircrafis were not capitalized on the balance sheet for some airlines.
716
JOURNAL OF ACCOUNTING, AUDITING & HNANCE
and nonfinancial information in evaluating a finn's competitive advantage. This is
an interesting question that remains for further investigation.
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