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THE RELATIONSHIP BETWEEN SHARE
PRICE AND OPERATING CASH FLOW
UNDER THE CASUAL THEME
RESTAURANT SETTING
Ruixue Du
Thesis submitted to the Faculty of the Virginia Polytechnic Institute and State
University partial fulfillment of the requirements for the degree of Master of
Science In Hospitality and Tourism Management
Michael D. Olsen, Ph.D.; Chair
Raman Kumar, Ph.D.
Francis Kwansa, Ph.D.
5/6/2008
Blacksburg, Virginia
Key Words: Share Price, Operating Cash Flow, Multiple Valuation, Earnings, Efficient
Market Hypothesis
THE RELATIONSHIP BETWEEN SHARE PRICE AND
OPERATING CASH FLOW
UNDER THE CASUAL THEME RESTAURANT
SETTING
Ruixue Du
(ABSTRACT)
In spite of the well-accepted belief of the relationship between cash flow and stock
price, there are some controversies about whether cash flow is a good value driver in
terms of explaining the volatility of stock prices, when compared with other value drivers,
such as earnings or dividends.
Most of the previous studies that have focused on the relationship between stock price
and cash flow have used cross-industries data, primarily S&P 500 index. These studies do
not distinguish service industry from manufacturing industry.
However, the service industry is different from manufacturing in many ways. These
differences make cash play different roles in the daily operation between the service
industry and the manufacturing industry.
Given these factors, whether the relationship between stock price and cash flow
indentified in previous studies will hold in the casual theme restaurant industry is the
question this study tries to answer. Therefore, a set of 20 casual theme restaurant
companies are selected through the COMPUSTAT database as the sample of this study.
In this study, the performance of cash flow, earnings and dividends helping to explain
the stock price move will be compared and ranked under the setting of casual theme
restaurants. This result will provide the management of casual theme restaurants a
iii
guideline, which explains how to maintain the stock price increase and minimize the
volatility by monitoring the most important value driver of the industry.
The methodology of this study will follow the traditional multiple valuation model.
The logic of this model is to compare the pricing error of different value drivers and
determine which one is the best.
The results of this study show that operating cash flow outperformed earnings and
dividends in the multiple valuation tests. This is different from the results of previous
studies that earnings has the strongest explanatory power in the variance of share price.
v
ACKNOWLEDGEMENT
I would like to express my deepest and sincere gratitude to my committee members
who provided invaluable guidance, suggestions and support of my study, without which
this thesis would not have been possible.
First of all, I would like to express my sincerest thanks to my advisor, Dr. Olsen, for
his guidance, encouragement, inspiration and patience. He has been providing valuable
help continuously, even during the periods that he was under medical treatments.
I also owe a deep debt to my committee members Dr. Kumar and Dr. Kwansa. Dr.
Kumar provided precious advice and support, especially on the data collecting and
analysis with his expertise on the COMPUSTAT dataset. I also thank Dr. Kwansa for his
advice and help on my thesis, especially on building the connection between the literature
review and my research questions.
Special thanks to my family and my friends who have been constantly supportive
during the entire process.
Thank you.
vi
Table of Contents DEDICATION.................................................................................................................. iv
ACKNOWLEDGEMENT .................................................................................................v
TABLE OF CONTENTS ................................................................................................ vi
CHAPTER ONE ................................................................................................................1
INTRODUCTION..............................................................................................................1
Problem Statement ...................................................................................................................... 1
Purpose of This Study ................................................................................................................... 2
Methodology Used in This Study ................................................................................................. 3
Results .......................................................................................................................................... 3
CHAPTER TWO ...............................................................................................................4
LITERATURE REVIEW .................................................................................................4
Part I Body of Literature .............................................................................................................. 4
Stock Price Determination ........................................................................................................... 5
Case 1 ...........................................................................................................................5
Case 2 ...........................................................................................................................5
Case 3 ...........................................................................................................................6
Stock Price Valuation Models ...................................................................................................... 6
Efficient Market Hypothesis ......................................................................................6
Arguments Concerning the Validity of the EMH ....................................................9
Variance Bounds Tests .............................................................................................10
Debates on Variance Bounds Tests .........................................................................12
Valuation Using Multiples .......................................................................................................... 14
Cash Flow in Stock Price Valuation ............................................................................................ 14
Dividends VS Broader Cash Flow ..........................................................................14
Cash Flow VS Earnings ...........................................................................................15
Summary of Previous Studies ..................................................................................................... 17
Part II Industry Context............................................................................................................... 18
Part III Propositions .................................................................................................................... 21
CHAPTER THREE .........................................................................................................23
vii
METHODOLOGY ..........................................................................................................23
Part I Research Questions and Research Propositions ............................................................... 23
Proposition 1 .............................................................................................................23
Proposition 2 .............................................................................................................24
Proposition 3 .............................................................................................................24
Part II Data Collection ................................................................................................................. 25
Firm Selection ...........................................................................................................25
Variables ....................................................................................................................26
Part III Data Analysis ................................................................................................................... 27
Value Drivers ............................................................................................................27
Multiple Valuation....................................................................................................28
Multiples Construction ............................................................................................28
Analysis Procedure ...................................................................................................30
Summary ...................................................................................................................32
CHAPTER FOUR ............................................................................................................33
RESEARCH RESULTS ..................................................................................................33
Results ........................................................................................................................................ 33
Research Question 1 ................................................................................................33
Table 4-1 Descriptive Statistics of Three Multiples by Groups ...........................34
Table 4-2 One-Way ANOVA Results for Proposition One ..................................35
Table 4-3 Descriptives for Three Groups ...............................................................36
Table 4-4 Test of Homogeneity of Variances .........................................................37
Table 4-5 Multiple Comparisons for Three Groups .............................................38
Research Question 2 ................................................................................................40
Table 4-6 Paired Samples T Test ............................................................................40
Table 4-7 Descriptive Statistics of 320 Firms .........................................................41
Table 4-8 One-Sample Statistics..............................................................................41
Research Question 3 ................................................................................................42
Table 4-9 Multiple Valuation Test Results for All 320 Firm-years Samples ......42
Table 4-10 Results of Liu, Nissim and Thomas (2001) ..........................................43
viii
CHAPTER FIVE .............................................................................................................44
CONCLUSIONS AND DISCUSSION ...........................................................................44
Summary of Results and Discussion ........................................................................................... 44
Research Question 1 ................................................................................................44
Research Question 2 ................................................................................................44
Research Question 3 ................................................................................................44
Implications for Managers .......................................................................................................... 45
Limitations .................................................................................................................................. 46
Suggestions for Further Studies ................................................................................................. 47
REFERENCES .................................................................................................................48
APPENDIX A Sample Firms ..........................................................................................50
APPENDIX B Descriptive Statistics of Three Multiples by Groups ...........................51
APPENDIX C SPSS Out Put for One-Way ANOVA for Proposition One ................52
APPENDIX D SPSS Output for Paired-Samples T Test .............................................55
APPENDIX E Descriptive Statistics of 320 Firms ........................................................56
APPENDIX F SPSS Output for One Sample T Test ....................................................57
1
Chapter One
Introduction
Problem Statement
In finance literature, stock price is believed to be linked to cash flow, and this is
supported by the definition of stock and other stock valuation theories, such as efficient
market hypothesis (Fama 1970).
In spite of the well-accepted belief of the relationship between cash flow and stock
price, there are some controversies about whether cash flow is a good value driver in
terms of explaining the volatility of stock prices, when compared with other value drivers,
such as earnings or dividends. From their research in 2001, Liu, Nissim and Thomas
stated that earnings are better than cash flows in explaining the stock price, using the U.S.
market data. This result also holds in international markets according to a later paper by
Liu, Nissim and Thomas them (Liu, Nissim et al. 2007).
However, most of the previous studies that have focused on the relationship between
stock price and cash flow have used cross-industries data, primarily S&P 500 index, such
as Fama’s famous paper on efficient market hypothesis, Shiller’s variance bound tests in
1981, Ackert and Smith’s paper in 1993, and Liu’s paper in both 2001 and 2007. These
studies do not distinguish service industry from manufacturing industry.
2
It is known that, the service industry is different from manufacturing in many ways,
such as intangibility, simultaneity of production and consumption, customer participation
in the production and delivery of the service, heterogeneity, and perish ability. These
features make the service industry different from the manufacturing industry in the
supply-demand relationship and the inventory management. The manufacturing firms
usually keep a large amount of inventory of raw materials as well as finished goods, and
they do a lot of credit buying and selling. However, casual theme restaurants are
generally viewed as a cash business. They hardly keep finished goods in inventory,
neither a lot of raw materials, like the manufacturing industry usually does. This makes
cash take a dominant role on a balance sheet, as well as in daily operations.
Purpose of This Study
Given these factors, whether the relationship between stock price and cash flow
indentified in previous studies will hold in the casual theme restaurant industry is the
question this study tries to answer. Therefore, a set of 20 casual theme restaurant
companies are selected through the COMPUSTAT database as the sample of this study.
In this study, the performance of cash flow, earnings and dividends helping to explain
the stock price move will be compared and ranked under the setting of casual theme
restaurants. This result will provide the management of casual theme restaurants a
guideline, which explains how to maintain the stock price increase and minimize the
volatility by monitoring the most important value driver of the industry.
3
Methodology Used in This Study
The methodology of this study will follow the traditional multiple valuation model,
which requires that the price of firm i (in the casual theme restaurants sample set) in year
t is directly proportional to the value driver:
tit it itp x (0-1)
Where itx is the value driver for firm i in year t , t is the multiple on the value driver and
t is the pricing error.
The logic of this model is to compare the pricing error of different value drivers and
to determine which one is the best.
Results
The results of this study show that operating cash flow outperformed earnings and
dividends in the multiple valuation tests, different from previous studies that earnings has
the strongest explanatory power in the variance of share price.
4
Chapter Two
Literature Review
In this chapter, the previous research that addressed the relationship between stock
price and cash flow will first be discussed. Then, the differences between the
manufacturing industry and service industry will be compared and contrasted, and the
relationship between stock price and cash flow will be examined within a service industry
other than under a cross-industries setting. After that, this chapter will close with the
propositions of this study.
Part I Body of Literature
The research on the determination of stock price has been an active area for a long
time, ever since the very beginning stage of stock markets. What factors determine the
changes of stock price? The question has been answered variously from the ―animal
spirits‖ of Keynes (Keynes 1936) to the Market Efficiency Hypothesis of Fama (1970).
The value of a stock theoretically can be calculated based on the definition of security
assets, by discounting all the future dividends with an appropriate discount rate. However,
stock price in the empirical world sometimes seems to be too volatile to align with this
calculation (Shiller 1981a).
Among all the stock valuation models, the Efficient Market Hypothesis, the most
famous model, will be discussed and the challenge brought by Shiller and other
economists about this model will also be mentioned. Then, different opinions regarding
5
the relationship between stock price and cash flow are presented. At the end of this
chapter, the performance of cash flow will be compared with other value drivers, such as
earnings and dividends regarding the volatility of stock price.
Stock price determination
An asset value is determined by the present value of its future cash flows. A stock
provides two kinds of cash flows. First, stocks often pay dividends on a regular basis.
Second, the stockholder receives the sale price when selling the stock. Based on the
different growth patterns that a certain stock will follow, there are three types of stock
price valuation models (Ross, Westerfield et al. 2008):
Case 1 (Zero Growth) The value of a stock with a constant dividend is given by
1 2 10 21 (1 )
Div Div DivP
R RR
(1-1)
Here it is assumed that Div1 = Div2 = … = Div. This is just an application of the
perpetuity formula.
Case 2 (Constant Growth) Dividends grow at rate g, and the value of a common
stock with dividends growing at a constant rate is
2 31 1 1 1 1
0 2 3 4
(1 ) (1 ) (1 )
1 (1 ) (1 ) (1 )
Div Div g Div g Div g DivP
R R gR R R
(1-2)
6
Where g is the growth rate. Div1 is the dividend on the stock at the end of the first period.
This is the formula for the present value of a growing perpetuity.
Case 3 (Differential Growth) In this case, an algebraic formula would be too
unwieldy. Stocks growing in this pattern should be valued case by case.
These three types of valuation models are based on the stock price definition. Usually
the parameters in the models are unrealistic to obtain in the real world. Therefore, there
are some alternative valuation methods that derive from the definition, which can be
easier to handle.
Stock price valuation models
Efficient Market Hypothesis
The efficient market hypothesis was first expressed by Louis Bachelier and emerged
as a prominent theoretic position in the mid-1960s. In general form, the hypothesis states
that the price of a security at time t fully reflects all the available information at time t-1.
The early literature on the efficient markets hypothesis was primarily concerned with
whether market participants can make any extranormal profits by taking advantage of the
information embedded in the market. The EMH theory was further developed by Eugene
Fama with his famous efficient capital markets review published in 1970. The majority of
early studies based on return-forecasting regressions provided empirical evidence in
support for the efficient markets model, and the dominance of the efficient markets model
in the literature continued until the late 1970s.
7
Fama’s paper reviewed the theory and empirical work, and refined the theory by
giving definitions for three forms of market efficiency: weak, semi-strong and strong.
Weak
In this form, the information set is just historical prices.
Semi-strong
The concern of semi-strong form tests is whether prices efficiently adjust to other
information that is obviously publicly available (e.g. announcements of annual
earnings, stock splits, etc.) are considered.
Strong
Strong form tests concerned with whether given investors or groups have
monopolistic access to any information relevant for price formation.
Fama concluded from his tests that prices seem to efficiently adjust to obviously
publicly available information in weak and semi-strong form markets. Only limited
evidence against the hypothesis in the strong form tests, i.e., monopolistic access to
information about prices does not seem to be a prevalent phenomenon in the
investment community (Fama 1970).
According to the efficient market hypothesis, stock prices always ―fully reflect‖
available information. However, this definition is too general for any empirically
testable implications (Fama 1970). Fama listed three models developed by previous
studies, which specified the definition in more detail.
8
Expected Return or “Fair Game” Model
The ―Fair Game‖ Efficient Market Model rules out the possibility of excess return,
which is due to information reasons. The expected value of the difference between the
observed stock price and the expected value of the value that was projected at time t
on the basis of the available information set should be zero (Fama 1970).
The Submartingale Model
The Submartingale Model assumes that the expected value of period (t+1)’s price,
as projected on the basis of the information set of time t, is equal to or greater than the
current price. If the expected returns and price changes are zero, then the price
sequence follows a martingale (Fama 1970).
The Random Walk Model
The Random Walk Model assumes that the current price of a security ―fully
reflects‖ available information implies that successive price changes (or more usually,
successive one-period returns) are independent. In addition, successive changes (or
returns) are identically distributed.
Literature regarding the differences among these models was discussed in Fama’s
paper. In spite of different models of market efficiency, all that is required by the EMH is
that investors' reactions be random and follow a normal distribution pattern so that the net
effect on market prices cannot be reliably exploited to make an abnormal profit,
9
especially when considering transaction costs (including commissions and spreads). Thus,
everyone can be wrong about the market, but the market as a whole is always right.
Arguments concerning the validity of the EMH
Before the Efficient Market Hypothesis was introduced in the late 1960s, the
prevailing view was that markets were inefficient. Inefficiency was commonly believed
to exist in the United States and United Kingdom stock markets. Ever since the
introduction of Efficient Market Hypothesis, the arguments never stopped.
Some observers dispute the notion that markets behave consistently with the efficient
market hypothesis, especially in its stronger forms. Some economists, mathematicians
and market practitioners cannot believe that man-made markets are strong-form efficient
when there are prima facie reasons for inefficiency including the slow diffusion of
information, the relatively great power of some market participants (e.g. financial
institutions), and the existence of apparently sophisticated professional investors.
The way that markets react to surprising news is perhaps the most visible flaw in the
efficient market hypothesis. For example, news events such as surprise interest rate
changes from central banks are not instantaneously taken account of in stock prices but
rather cause sustained movement of prices over periods from hours to months.
Skeptics of EMH also argue that there exists a small number of investors who have
outperformed the market over long periods of time, in a way which is difficult to attribute
luck, including Peter Lynch, Warren Buffett, George Soros, and Bill Miller. These
investors' strategies are, to a large extent, based on identifying markets where prices do
10
not accurately reflect the available information, in direct contradiction to the efficient
market hypothesis which explicitly implies that no such opportunities exist.
The efficient market hypothesis also appears to be inconsistent with many events in
stock market history. For example, the stock market crash of 1987 saw the S&P 500 drop
more than 20% in the month of October despite the fact that no major news or events
occurred prior to the Monday of the crash, the decline seeming to have come from
nowhere. This would tend to indicate that rather irrational behavior can sweep stock
markets at random.
The relationship between stock return and market cap is also a challenge to the
efficient market hypothesis. Banz finds that average returns on small cap stocks are too
high, and the average returns on large cap stocks are too low (Banz 1981; Fama and
French 1992).
Variance Bounds Tests
Ever since the hypothesis of market efficiency was brought out, it has been one of the
most controversial areas in modern financial economics. Two influential studies by
Shiller (Shiller 1981a) and LeRoy and Porter (LeRoy and Porter 1981) were two pioneers
in challenging the efficient market hypothesis by the present value model of stock prices.
Both studies have demonstrated that stock prices are too volatile to be consistent with the
present value of rationally expected future dividends discounted by a constant real
interest rate (Yuhn 1996).
11
Shiller used two data sets to compare the movements of the Pt*, which is regressed by
dividends, with the actual stock price Pt.. The two data sets contain annual observations
of the stock price indexes deflated by the Bureau of Labor Statistics wholesale price
index and the associated deflated dividends from both Standard and Poor Series and Dow
Jones Industrial Average Series. The results turned out that the volatility of stock price
appeared to be far too high – five to thirteen times – to be explained by the new
information about future real dividends. Therefore the efficient market hypothesis is not
reliable (Shiller 1981a).
These variance-bound tests compare the variance of actual stock price to an upper
bound, the variance of a function of actual dividends. Shiller's variance-bound inequality
is
*( ) ( )t tP P (1-3)
Where σ(•) is the standard deviation, Pt is actual stock price, and Pt* is the ex post rational
stock price. The ex post rational stock price is the present discounted value of actual
dividends. Under the efficient markets model, actual stock price is the conditional
mathematical expectation of ex post rational price (Ackert and Smith 1993).
The LeRoy and Porter (1981) variance-bound tests are similar in spirit to Shiller's.
The tests differ, however, in that Shiller's test gives a simple point estimate and LeRoy
and Porter are able to attach significance levels. LeRoy and Porter also reject the model
but the rejections are not always statistically significant due to large confidence intervals.
12
Debates on Variance Bounds Tests
The rejection of the PV model by Shiller (1981a) and LeRoy and Porter (1981) has
spawned great debates and controversies in the profession. Several subsequent studies
such as Flavin (1983), Kleidon ((Kleidon 1986) and Marsh and Merton (Marsh and
Merton 1986) have criticized the distributional assumptions maintained in the Shiller test.
Early volatility tests, including Shiller (1981a), LeRoy and Porter (1981) and Blanchard
and Watson ((Blanchard and Watson 1982), were based upon the assumption that stock
prices and dividends are trend-stationary. For example, Shiller (1981a) detrended stock
prices and dividends by dividing the series by the long-term growth rate. However, in
many situations simple detrending of time series does not warrant the stationarity of the
relevant variables. When time series have unit roots, but any allowance for stationarity is
not made, then sample means and variances are not consistent estimates of population
means and variances, therefore, it is invalid to use sample moments as estimates of
population moments. As Marsh and Merton (1986) argue, the Shiller inequality can even
be reversed depending on the time-series properties of the variables in the PV model.
Shiller (Shiller and Grossman 1981b), Kleidon (1986), Campbell and Shiller
(Campbell and Shiller 1987; Campbell and Shiller 1988), Perron ((Perron 1988) and
DeJong and Whiteman (DeJong, Whiteman et al. 1991), among others, have undertaken
formal tests for unit roots, and Kleidon (1986), Campbell and Shiller (1987), West (West
1988), and Mankiw (Mankiw, Romer et al. 1985) have developed second-generation
variance bounds tests which allow for the non-stationarity of stock prices and dividends.
However, even the second-generation variance bounds tests do not appear to have
13
resolved the volatility controversy. They produce conflicting evidence on stock price
volatility. For example, Kleidon (1986) provides no evidence of volatility whereas
Mankiw et al. (1985), West (1988a) and Campbell and Shiller (1987) confirm the
violation of variance inequalities, thus amplifying the confusion surrounding volatility
tests. Another criticism is that the second-generation variance bounds tests are highly
sensitive to the data-generating process for variables.
Flavin argued that in small samples the "volatility" or "variance-bounds" tests tend to
be strongly biased toward rejection of the null hypothesis of no excess volatility. Thus the
apparent violation of the market efficiency hypothesis may be reflecting the sampling
properties of the volatility measures in small samples rather than a failure of the market
efficiency hypothesis.(Flavin 1983)
Ackert and Smith argued that the results of variance-bound tests (Leroy and Porter
1981, Shiller 1981) depend on how cash distributions to shareholders are measured.
They found apparent evidence of excess volatility when a narrow definition of cash flow
(dividends only) is applied. They were unable to reject the hypothesis of market
efficiency when the cash flow measure also included share repurchases and takeover
distributions in addition to ordinary cash dividends (Ackert and Smith 1993). Based on
Ackert and Smith’s study, operation cash flow may be able to smooth the volatility of
stock prices in the present value model.
14
Valuation Using Multiples
Valuation using multiples is a method for determining the current value of a company
(Wikipedia). To value a company, one can examine and compare the financial ratios of
relevant peer groups, multiply a ratio, or value driver, of the company by the
corresponding multiple. The multiples are usually based on the ratio of stock price to that
value driver for a group of comparable companies (Liu, Nissim et al. 2007).
The commonly used ratios include various measures of cash flow, book value,
earnings, and revenues, but earnings and cash flows are by far the most commonly used.
There is a common perception that operating cash flow is better than accounting earnings
in valuations. However, a recent study conducted by Liu, Nissin and Thomas suggested
that earnings dominated operating cash flows and dividends using U.S. market data (Liu,
Nissim et al. 2001; Liu, Nissim et al. 2002). This result also holds in international
markets according to a later paper by them (Liu, Nissim et al. 2007).
Cash Flow In Stock Price Valuation
Dividends VS Broader Cash Flow
According to Ackert and Smith (1993), narrowly defined cash flow in the variance
bounds tests leads to the rejection of efficient market hypothesis, and they proved that it
is unable to reject the hypothesis of market efficiency when using a broader cash flow
definition. Actually, within the finance literature, dividends include all cash distributions
to shareholders. For example, in the seminal work of Miller and Modigliani (Miller and
15
Modigliani 1961), cash distributed through share repurchases has the same economic role
as ordinary cash dividends (Ackert and Smith 1993).
Kleidon (1986) and Marsh and Merton (1986) discuss the problems that arise from
testing present value relations when ordinary dividends do not properly represent cash
flows. In addition, the smoothing of ordinary dividends Lintner (Lintner 1956), Fama and
Babiak (Fama and Babiak 1968) raises concerns because such behavior "implies changes
in a future residual dividend that do not show up in the currently observed dividend
series" (Kleidon (1986). Thus, tests which rely on a narrow definition of dividends may
incorrectly reject the present value relation (Ackert and Smith 1993).
The potential for problems, arising from a narrow definition of dividends, has
become more acute with the rapid growth of share repurchases and cash payments in
mergers and acquisitions. Shoven (1986) and Bagwell and Shoven (1989) show that in
recent years ordinary dividends represent less than half of the total cash distributed to
shareholders. These total cash flows are also more volatile than ordinary cash dividends
(Ackert and Smith 1993).
Cash Flow VS Earnings
Cash flow is an accounting term that refers to the amount of cash being received and
spent by a business during a defined period of time, sometimes tied to a specific project.
Operating cash flow, cash flow provided by operations or cash flow from operating
activities, refers to the amount of cash a company generates from the revenues it brings in,
16
excluding costs associated with long-term investment on capital items or investments in
securities.
Earnings represent the difference between revenues and charges, leading to a change
in net worth during a given period. Since the rationale behind the income statement is not
the same as for a cash flow statement, some cash flows do not appear on the income
statement. Likewise, some revenues and charges are not shown on the cash flow
statement (Vernimmen, Quiry et al. 2005).
Among all the previous studies, earnings have proved to have the greatest
explanatory power (Gallizo and Salvador 2006) to stock price volatility (Liu, Nissim et al.
2002; Liu, Nissim et al. 2007). However, some financial analysts argue that it is difficult
to compare earnings across firms because of the variety of methods used to calculate
accrual items, that managers can manipulate accruals to alter reported earnings, and that
accounting earnings can result in dysfunctional behavior since managers frequently select
projects based on earnings rather than discounted cash flows (Wilson and O'Brien 1986).
However, Liu, Nissim and Thomas (2007) argue that contrary to the common
perception that operating cash flows are better than accounting earnings at explaining
equity valuations, recent studies suggest that valuations derived from industry multiples
based on reported earnings are closer to traded prices than those based on reported
operating cash flows. The question addressed in the article is whether the balance tilts in
favor of cash flows when the following are considered: (1) forecasts rather than reported
numbers, (2) dividends rather than operating cash flows, (3) individual industries rather
17
than all industries combined, and (4) companies in non-U.S. markets. In all cases studied,
earnings dominated operating cash flows and dividends (Liu, Nissim et al. 2007) .
Liu, Nissim and Thomas believe that at a conceptual level, earnings should be the
more representative value driver because earnings reflect value changes regardless of
when the cash flows occur. Still, many practitioners, arguing that accruals involve
discretion and are often used to manipulate earnings, prefer to use cash flow multiples.
They also point out that expenses such as depreciation and amortization deviate
substantially from actual declines in value because they are based on ad hoc estimates
that are, in turn, derived from potentially meaningless historical costs (Liu, Nissim et al.
2007).
Other studies have also been done to compare the information content between
earnings and cash flows. Unfortunately, there is not a general agreement at this time.
Wilson concludes that for a given amount of earnings, the share market reacts more
favorably to cash flows than current accruals (Wilson 1987). However, Bernard and
Stober failed to generalize Wilson’s finding to a larger period (Bernard and Stober 1989).
Summary of previous studies
From the discussion of previous studies, it can be assumed that a relationship between
stock price and cash flow exists. In the efficient market hypothesis model, if the market is
efficient, then the stock price is determined by the future dividends. Any change of stock
price is due to the new information regarding future dividends. Although the efficient
18
market hypothesis was challenged by some economists with the boundary test, which
asserts that the stock price is too volatile to be measured by the discounted present value
of future dividends, studies also show that the rejection of the present value model may
be because of the small sample size or the narrowly defined cash flow.
However, most of the previous studies focused on the S&P 500 or Dow Jones
Industrial Average indexes, which represent the large capitalization firms in
manufacturing industries. Although, in the study of Liu, Nissim and Thomas in 2007,
they conducted the industry-by-industry test to compare the performance of earnings and
operating cash flow. Liu, Nissim and Thomas exclude firms not covered by IBES,
typically with low and medium market capitalization, which casual theme restaurants
usually belong to. It is not focused on the study of casual theme restaurants; therefore, the
relation between the stock price and operating cash flow in casual theme restaurants is
necessary to contribute to the body of literature.
Part II Industry Context
In the following section, the relationship between stock price and cash flow will be
put in a new industry context, the hospitality industry, and the differences between
manufacturing industry and service industry as well as how these differences may affect
the sensitivity of that relationship will be addressed.
First, through the discussion of previous studies that focus on the relationship
between stock price and cash flow, it was found that all of them were conducted under a
19
cross-industries setting. However, the service industry is well-known for its unique
features, such as intangibility, simultaneity of production and consumption, customer
participation in the production and delivery of the service, heterogeneity, and perish
ability, which distinguish the service industry from the manufacturing industry. These
features create the underpinning need to explain the supply and demand relationship and
to help to differentiate them from manufacturing enterprises (Olsen, Tse et al. 2007).
In the manufacturing industry, the consumer products are most durable and demand
is reasonably constant and partially determined by the demographics existing in the
market at the time. These products are likely to be produced in economical quantities to
ensure that there will be sufficient numbers to meet the total demand. Items produced in
excess of current demand are usually inventoried to buffer against unexpected changes in
the demand curve and/or sold later. Seldom are these inventory items considered highly
perishable (Olsen, Tse et al. 2007) .
These features of the demand curve for manufacturing industries are seldom present
in the service industry. There are several reasons for this. First, it is difficult to
aggregate total demand in order to predict how many products will be sold by a service
provider over a specific planning period. The nature of the demand curve in the service
industry is local in nature. Second, finished products cannot be stored for much longer
than 15 minutes before they become unacceptable to the customer. The supply of
individual products produced in each unit must be sold almost immediately. Thus, the
demand curve can be said to be very temporal, fluctuating from hour to hour, day to day,
week to week, and month to month. It is not influenced as much by macro features such
20
as demographics but more by local economic conditions, local competition, and even
weather. As can be seen here, the demand curve for service businesses contains
characteristics not similarly found in the large scale consumer durable goods businesses
(Olsen, Tse et al. 2007).
Another point of difference between manufacturing and service has to do with the
management of inventory. As stated above, the total supply of products produced by
manufacturers can be inventoried if it is not sold immediately. Inventory is also created to
buffer firms from wide swings in the demand curve of the business. If finished products
remain too long in inventory, management can alter their prices in order to reduce
inventory. As was pointed out above, the products and services produced by service firms
such as those in the hospitality industry have little to no inventory life. If a restaurant
does not fill its supply of seats on any given day it can never sell them again (Olsen, Tse
et al. 2007). In other words, this inventory is highly perishable with almost no shelf life,
which determines that the restaurant industry is predominately a cash business, almost
without inventory. Therefore, whether the sensitivity of stock price to cash flow will hold
the same level for both cross-industries setting and service industry context is
questionable.
Second, most of previous research uses S&P 500 index data. However S&P 500
index usually represents the large cap firms in the manufacturing sector with a higher
average book-to-market ratio than the casual theme industry. Consequently, it is hard to
make a direct statement to an industry with small capitalization and low book-to-market
ratio from those studies.
21
Therefore, it is necessary to study the relationship between stock price and operating
cash flow within the casual theme restaurants industry which can provide a better
understanding of operating cash flow for hospitality financial managers. In this thesis, the
relationship between stock price and operating cash flow will be tested using casual
theme restaurants data. A comparison of this relationship between Russell 3000 and
casual theme restaurants will also be conducted to examine the difference that may be
brought by the variation of operating cash flow, market capitalization and book-to-market
ratio.
Part III Propositions
It is obvious that the hospitality industry differs from the manufacturing industry
significantly in the demand curve and inventory management. As a consequence, the
relationship between stock price and cash flow in previous studies, which were conducted
in a cross-industries context, may not hold in hospitality. Here are the propositions in this
study (in the null):
1. We can expect there is no difference in the impact of cash flow on stock price
between large and small casual theme restaurants.
2. We can expect there is no difference in the impact of cash flow, earnings and
dividends on stock price between cross-industries and casual theme restaurant
samples.
22
3. We can expect there is no difference in the impact of cash flow on stock price
between S&P 500 index and casual theme restaurants.
From this study, we expect stock price has a greater sensitivity to cash flow of casual
theme restaurants based on the nature of those companies.
Research questions and propositions will be discussed in detail in chapter three.
23
Chapter Three
Methodology
The preceding chapters reviewed the literature and identified the objectives of this
study. This chapter provides the research propositions and research questions, the
introduction of data collection, and introduction of research models used for data analysis.
Part I Research Questions and Research Propositions
Research Questions:
As discussed in chapter two, almost all of the previous studies focused on the large
capitalization firms. However, most of the casual theme restaurants belong to the small
capitalization category. Then whether market capitalization will affect the impact of cash
flow on stock price in casual theme restaurants? What’s the difference in the impact of
cash flow on share price between large and small casual theme restaurants? Proposition 1
is presented based on these research questions.
Proposition 1: We can expect there is no difference in the impact of cash flow on stock
price between large and small casual theme restaurants.
Research Questions:
Cash flow, earnings and dividends are ranked in terms of explaining the stock price
volatility in previous studies with cross-industries setting. However, due to the
24
differences between manufacturing industry and service industry, whether the result will
hold in a casual theme restaurants setting? These questions bring out proposition 2.
Proposition 2: We can expect there is no difference in the impact of cash flow, earnings
and dividends on stock price between cross-industries and casual theme restaurant
samples.
Research Questions:
Due to the different nature of large capital manufacturing firms and small capital
service firms, whether the impact of cash flow on stock price in a broad market
capitalization index will hold in the casual theme restaurant is questionable. Previous
studies used S&P 500 index as sample set, which includes cross-industry firms with the
largest capitalization. This study tries to make clear the difference in the impact of cash
flow on stock price between S&P 500 index and casual theme restaurant firms. These
questions are followed by proposition 3.
Proposition 3: We can expect there is no difference in the impact of cash flow on stock
price between S&P 500 index and casual theme restaurants.
From the discussion in chapter two, these propositions seem to be plausible to me to
look at.
25
Part II Data Collection
In this part, an introduction of the data collection process will be provided. It includes
the dataset that the entire sample firms’ data came from, the standards used to define the
sample frame in that dataset, and the measurements and calculations of the variables.
Firm Selection
Sample firms used in this study are selected from casual theme restaurants following
three steps:
First, COMPUSTAT North America Industry Annual was chosen as the source
dataset. Two standards are used to select sample firms from the COMPUSTAT North
America Industry Annual database in this step:
North American Industrial Classification System (NAICS)
NAICS is a hierarchical structure and can consist of up to six digits/levels. The first
two digits of the structure designate the NAICS sectors that represent general categories
of economic activity. The third digit designates the subsector, the fourth digit designates
the industry group, the fifth digit designates the NAICS industry, and the sixth digit
designates the national industry.
Casual theme restaurants are under the classification Full Service Restaurants with
the NAICS code 722110.
26
Industry Classification Code
The Industry Classification Code is a four-digit system of classification that identifies
a company's primary operation. SPC assigns these codes by analyzing the sales
breakdown from a company's 10K and annual report. The codes are based on the U.S.
SIC classification. The variable name of this code in COMPUSTAT dataset output is
DNUM.
Casual theme restaurants belong to the Eating Places with the DNUM=5812.
Therefore, the sample firms are selected from the COMPUSTAT North America
dataset with these two standards: NAICS=722110 and DNUM=5812.
Second, firm samples from the first step with annual data available from 1990 to 2005
are kept. Twenty firms are left after this step, which are shown in Appendix A.
Variables
Stock price
Stock price at the end of fiscal year is chosen in this study, which is DATA #1991 in
COMPUSTAT dataset.
Operating Cash Flow (OCF)
Operating cash flow here is defined as:
1 #* is the Data variable number in the COMPUSTAT.
27
OCF = EBITDA (#13) – total of interest expense (#15) – tax expense (#16) – net
change in working capital
Where net change in working capital is change in current assets (#4) – change in cash
and cash equivalents (#1) – change in current liabilities (#5) plus change in debt included
in current liabilities (#34).
Per share cash flow from operations (POCF)
Per share cash flow from operations is defined as OCF deflated by shares outstanding
(#25).
Net Income
Net income is defined as income before extraordinary items—available for common
(#237).
Earnings Per Share (EPS)
Earnings Per Share is the data #58.
Dividends
Dividends for common (#21)
Part III Data Analysis
Value Drivers
28
Three value drivers will be considered and compared in this study: Cash flow from
Operation, Earnings and dividends.
Multiple Valuation
The traditional multiple valuation method is followed, which requires that the price of
firm i (from the comparable group) in year t ( )itp is directly proportional to the value
driver:
tit it itp x (2-1)
Where itx is the value driver for firm i in year t , t is the multiple on the value driver and
t is the pricing error.
To allow comparison of valuation errors for stocks of different values, the pricing
error can be deflated by the stock price (Liu, Nissim et al. 2007):
1 it itt
it it
x
p p
(2-2)
Multiples Construction
An industry multiple is constructed for each value driver of each company, based on
the prices and value drivers for all remaining companies in the sample set. In order to
avoid the target’s valuation being contaminated by its own price, the target company is
deleted from the sample.
Based on the previous studies, harmonic mean of the ratio of price is chosen, which is
calculated by first finding the average value driver to price for the sample set and then
inverting that average (Liu, Nissim et al. 2007). Harmonic mean provides a way to
29
mitigate the effect of low value and reduce the impact on the multiple (Liu, Nissim et al.
2007).
For an illustration, assume there are five companies in the hospitality industry in the
U.S. in 1990, indexed by i =1,2,…5, with earnings per share of $1.50,$3.00, $2.50, $0.50
and $2.00 and share prices of $20, $35, $45, $25 and $30, respectively. Assume that the
multiple that is relevant for company i = 3 is being calculated. If the average ratio of
price to EPS of the remaining four companies is chosen, the multiple will be:
1 20 35 25 30
Average P/E = ( ) 22.54 1.50 3.00 0.50 2.00 (2-3)
However, the harmonic mean P/E would be:
1Harmonic mean P/E = 16.17
(1/ 4)[(1.50 / 20) (3.00 / 35) (0.50 / 25) (2.00 / 30)
(2-4)
As shown in the results, the harmonic mean method reduces the impact of company i
=4, which has a relative high P/E of 50, on the multiple.
Following this method, three multiples will be constructed:
1. P/E
Price to earnings ratio, the multiple of earnings
2. P/D
Price to dividend ratio, the multiple of dividends
3. P/C
Price to operating cash flow ratio, the multiple of operating cash flow
30
Analysis Procedure
Based on the previous description, 16-year data of 20 sample casual theme restaurants
will be processed in two steps: variables calculations and statistic analyses.
Step I Variables Calculations:
1. Dividends per share (#21) and share prices per share (#199) will be changed to
market value by multiplying the shares outstanding (#25).
2. Price to earnings ratio (P/E), price to dividend ratio (P/D) and price to operating
cash flow ratio (P/C) for each firm during each year will be calculated.
3. The industry multiples for earnings, dividend and operating cash flow will be
calculated following the methods stated above.
4. The pricing errors will be calculated following equation (3-2).
Step II Statistic Analyses
1. One-Way ANOVA
The One-Way ANOVA procedure produces a one-way analysis of variance for a
quantitative dependent variable by a single factor (independent) variable. Analysis of
variance is used to test the hypothesis that several means are equal. This technique is
an extension of the two-sample t test.
In addition to determining that differences exist among the means, it is necessary
to know which means differ. There are two types of tests for comparing means: a
31
priori contrasts and post hoc test. Contrasts are tests set up before running the
experiment, and post hoc tests are run after the experiment has been conducted.
For each group, number of cases, mean, standard deviation, standard error of the
mean, minimum, maximum, 95%-confidence interval for the mean, Levene's test for
homogeneity of variance, analysis-of-variance table and robust tests of the equality of
means for each dependent variable, user-specified a priori contrasts, and post hoc
range tests and multiple comparisons will be provided by the output.
One-Way ANOVA is chosen to analyze the differences of variances and means
among different market cap groups. This will be used to test proposition 1. The
sample set will be divided into three categories according to their market value:
Micro-Cap: Market value is equal to or is smaller than 250 million dollars.
Small-Cap: Market value is bigger than 250 million dollars and smaller than or
equal to 2000 million dollars.
Middle-Cap: Market value is bigger than 2000 million dollars.
The differences of variances and means of pricing errors of P/E, P/D and P/C
among these three groups will be compared with One-Way ANOVA.
2. Paired-Samples T Test
The Paired-Samples T Test procedure compares the means of two variables for a
single group. It computes the differences between values of the two variables for each
case and tests whether the average differs from zero.
32
For each variable, mean, sample size, standard deviation, and standard error of the
mean will be provided in the output. For each pair of variables: correlation, average
difference in means, t test, standard deviation and standard error of the mean
difference and confidence interval for mean difference will be provided.
Paired-Sample T Test is chosen to test proposition two to compare whether the
pricing error means computed by P/E, P/D and P/C are different from each other.
3. One-Sample T Test
The One-Sample T Test procedure tests whether the mean of a single variable
differs from a specified constant.
One-Sample T Test is chosen for proposition 2. It will be used to test whether the
pricing errors of P/E, P/D and P/C are equal to zero.
Summary
Through the One-Way ANOVA, Paired-Samples T Test and One-Sample T Test,
three research questions will be answered.
33
Chapter Four
Research Results
The basis for conducting multiple valuation tests within the casual theme restaurants
has been provided in the previous three chapters. In chapter one, the imperative of this
study has been discussed. Previous studies focused on the relationship between share
price and operating cash flow, and the rationale for using multiple valuation model were
all reviewed and discussed in chapter two. Chapter three has provided the detailed
methodologies to test the research questions in this study. In this chapter, the results of
these tests are presented in detail.
Results
Research Question One
As discussed in chapter two, almost all of the previous studies focused on the large
capitalization firms. However, most of the casual theme restaurants belong to a small
capitalization category. Then whether market capitalization will affect the impact of cash
flow on stock price in casual theme restaurants? What’s the difference in the impact of
cash flow on share price between large and small casual theme restaurants? Proposition 1
comes out based on these research questions.
Proposition 1: We can expect there is no difference in the impact of cash flow on stock
price between large and small casual theme restaurants.
In order to answer this question, the 320 firm-years samples are classified into three
categories:
34
1. Micro-Cap: capitalization below $250 million. (Group 1, 194 firm-years)
2. Small-Cap: capitalization between $250 million and $2 billion. (Group 2, 113
firm-years)
3. Middle-Cap: capitalization between $2 billion and $10 billion. (Group 3, 13 firm-
years)
Then, One-Way ANOVA is chosen to test proposition one.
Table 4-1 Descriptive Statistics of Three Multiples by Groups
Group Number Minimum Maximum Mean Std. Deviation
PE2 1 186 -14.010 2.234 -.19511 1.223644
2 113 -4.242 .114 .00726 .405787
3 13 .043 .062 .05087 .005891
PD3 1 53 .000 15.646 .50426 2.261220
2 77 .001 2.025 .00726 .376280
3 6 .098 .931 .52603 .381207
PC4 1 183 -.805 10.718 .63273 1.493107
2 111 .034 1.470 .18450 .209193
3 10 .101 .189 .14481 .033708
Table 4-1 shows the descriptive statistics of three multiples, classified by the market
capitalization into three groups.
2 Price to earnings ratio
3 Price to dividend ratio
4 Price to operating cash flow ratio
35
Table 4-2 One-Way ANOVA Results for Proposition One
Sum of Squares df
Mean Square F Sig.
PRICE_ERROR_PE5 Between Groups 1578.274 2 789.137 .452 .637
Within Groups 539203.216 309 1744.994
Total 540781.490 311
PRICE_ERROR_PD6 Between Groups 79888.968 2 39944.484 7.216 .001
Within Groups 1666128.834 301 5535.312
Total 1746017.802 303
PRICE_ERROR_PC7 Between Groups 168.922 2 84.461 3.918 .021
Within Groups 6488.063 301 21.555
Total 6656.985 303
Table 4-2 shows the SPSS output of the One Way ANVOA. Dependent variables are
pricing errors for P/E (PRICE_ERROR_PE), pricing errors for P/D
(PRICE_ERROR_PD), pricing errors for P/C (PRICE_ERROR_PC), and the group
factor is market value (MARKET_CAP).
According to these results, it is comfortable to conclude that the variances of pricing
error of Price to Earnings ratio among different market cap groups are not significantly
different from each other at a 95% confidence level (.637>.05, it is failed to reject the
null hypothesis).
5 Pricing error of price to earnings ratio
6Pricing error of price to dividend ratio
7 Pricing error of price to operating cash flow ratio
36
However, the variances of pricing error of Price to Dividend and Price to Operating
Cash Flow among different market cap groups are significantly different from one other
at a 95% confidence level (.001<.05 and .021<.05).
These are the analyses regarding variances. The following output presents the
differences of means of different groups.
Table 4-3 Descriptives for Three Groups
N Mean Std.
Deviation Std.
Error 95% Confidence Interval
for Mean Lower
Bound Upper Bound
PRICE_ERROR_PE 1 186 -6.0588 53.60099 3.93022 -13.8126 1.6950
2 113 -1.3888 8.22375 .77363 -2.9216 .1440
3 13 -2.3883 3.04781 .84531 -4.2301 -.5465
Total 312 -4.2145 41.69949 2.36077 -8.8596 .4306
PRICE_ERROR_PD 1 184 1.6349 12.48753 .92059 -.1815 3.4512
2 107 20.9967 99.15919 9.58608 1.9914 40.0020
3 13 74.9484 222.73720 61.77618 -59.6503 209.5472
Total 304 11.5849 75.91070 4.35378 3.0174 20.1523
PRICE_ERROR_PC 1 183 1.4060 5.84597 .43215 .5533 2.2586
2 111 -.0900 1.54464 .14661 -.3805 .2006
3 10 -.3753 .79622 .25179 -.9449 .1943
Total 304 .8012 4.68724 .26883 .2721 1.3302
According to the results in this output, regarding the pricing error of Price to Earnings
ratio, Small-Cap group has the lowest values of both mean (-1.3888) and the standard
error (.77363), followed by Middle-Cap (mean=-2.383, standard error=0.84531) and
Micro-Cap (mean=-6.0588, standard error=3.93022). Therefore, Price to Earnings is the
37
most powerful variable in explaining the variance of share price for Middle-Cap casual
theme restaurant firms.
However, as to the pricing error of Price to Dividend ratio, Micro-Cap has the lowest
values of both mean (1.6349) and standard error (.92059), followed by Small-Cap
(mean=20.9967, standard error=9.58608) and Middle-Cap (mean=74.9484, standard
error=61.77618). Therefore, price to dividend ratio is the most powerful value driver in
explaining the variance of share price for Micro-Cap casual theme restaurant firms.
The results of price to operating cash flow show that Small-Cap group has the lowest
values of both mean (-.0900) and standard error (.14661), followed by Middle-Cap group
(mean=-.3753, standard error=.25179) and Micro-Cap group (mean=1.4060, standard
error=.43215). Therefore, price to operating cash flow is the most powerful value driver
in explaining the variance of share price for Small-Cap causal theme restaurant firms.
Table 4-4 shows whether the equal variance assumption is satisfied for each value
driver. Table 4-5 presents the statistic results of mean difference tests.
Table 4-4 Test of Homogeneity of Variances
Levene Statistic df1 df2 Sig.
PRICE_ERROR_PE 2.466 2 309 .087
PRICE_ERROR_PD 24.659 2 301 .000
PRICE_ERROR_PC 7.514 2 301 .001
38
Table 4-5 Multiple Comparisons for Three Groups
Dependent Variable (I)MARKET_CAP (J)MARKET_CAP Mean Difference (I-J) Std. Error Sig.
PRICE_ERROR_PE LSD 1 2 -4.66999 4.98238 .349 3 -3.67049 11.98382 .760 2 1 4.66999 4.98238 .349 3 .99950 12.23408 .935 3 1 3.67049 11.98382 .760 2 -.99950 12.23408 .935 Tamhane 1 2 -4.66999 4.00563 .570 3 -3.67049 4.02009 .741 2 1 4.66999 4.00563 .570 3 .99950 1.14588 .771 3 1 3.67049 4.02009 .741 2 -.99950 1.14588 .771 PRICE_ERROR_PD LSD 1 2 -19.36182(*) 9.04517 .033 3 -73.31354(*) 21.35126 .001 2 1 19.36182(*) 9.04517 .033 3 -53.95172(*) 21.85235 .014 3 1 73.31354(*) 21.35126 .001 2 53.95172(*) 21.85235 .014 Tamhane 1 2 -19.36182 9.63018 .134 3 -73.31354 61.78304 .592 2 1 19.36182 9.63018 .134 3 -53.95172 62.51552 .789 3 1 73.31354 61.78304 .592 2 53.95172 62.51552 .789 PRICE_ERROR_PC LSD 1 2 1.49591(*) .55855 .008 3 1.78125 1.50774 .238 2 1 -1.49591(*) .55855 .008 3 .28533 1.53287 .852 3 1 -1.78125 1.50774 .238 2 -.28533 1.53287 .852 Tamhane 1 2 1.49591(*) .45634 .004 3 1.78125(*) .50015 .002 2 1 -1.49591(*) .45634 .004 3 .28533 .29136 .715 3 1 -1.78125(*) .50015 .002 2 -.28533 .29136 .715
*The mean difference is significant at the .05 level.
According to the results from multiple comparisons, since the equal variance
assumption is not satisfied at a 95% confidence level (.087>.05), the Tamhane test is
chosen. Based on the p-value, all of the mean differences of price to earnings pricing
error are not significant at the 95% confidence level.
39
As to the price to dividend ratio, the p-value of the Levene Statistic test is .000(<.05),
therefore, the equal variance assumption is satisfied. The LSD test should be chosen. It
can be concluded that the means are significantly different at the 95% confidence level
among three groups (.033<.05, .001<.05, .014<.05, the null hypothesis is rejected),
because group 3 has a significantly higher mean than group 2 and group 1.
Therefore, price to dividend has a different power in explaining the variance of share
price with different market capitalization of casual theme restaurant firms.
However, in the results of price to operating cash flow ratio, the p-value of Levene
Statistic is .001<.05, equal variance assumption is satisfied. The LSD test should be
chosen. Based on the p-value, the mean difference between group 1 and group 2 are
significantly different from each other (.008<.05, null hypothesis is rejected), because
group 1 has a significantly higher mean than group 2. However, the mean difference
between group 1 and group 3 and the mean difference between group 2 and group 3 are
not significantly different from each other (.238<.05, .852<.05, failed to rejected null
hypothesis).
In summary, to answer research question one, the operating cash flow has
significantly different impacts on the share price between Micro-Cap and Small-Cap
casual theme restaurant firms. The impact on the share price of Small-Cap is stronger
than on the share price of Micro-Cap casual theme restaurant firms.
40
Research Question Two
Cash flow, earnings and dividends are ranked in terms of explaining the stock price
volatility in previous studies with cross-industries setting. However, due to the
differences between the manufacturing industry and the service industry, whether the
result will hold in a casual theme restaurants setting? These questions bring out
proposition 2.
Proposition 2: We can expect there is no difference in the impact of cash flow, earnings
and dividends on stock price between cross-industries and casual theme restaurant
samples.
To test this proposition, 320 firm-years samples are analyzed without differentiation
by market capitalization. Paired-Sample T Test and One-Sample T Test are chosen to
test proposition two.
Table 4-6 Paired Samples T Test
Paired Differences
t df Sig. (2-tailed)
Mean Std.
Deviation
Std. Error
Mean
95% Confidence Interval
of the Difference
Lower Upper
Pair 1
PRICE_ERROR_PE - PRICE_ERROR_PD
-15.91361 87.09242 4.99509 -25.74307 -6.08414 -3.186 303 .002
Pair 2
PRICE_ERROR_PE - PRICE_ERROR_PC
-5.08652 43.44719 2.49187 -9.99008 -.18297 -2.041 303 .042
Pair 3
PRICE_ERROR_PD - PRICE_ERROR_PC
11.08204 77.14984 4.48424 2.25688 19.90720 2.471 295 .014
From the results of Paired-Sample T Test, it is obvious that all the p-values are lower
than .05. Therefore, the null hypotheses are rejected. The mean differences between three
41
value drivers are significantly different from each other at the 95% confidence level.
Price to Dividend has a lowest mean difference (-5.08652) of pricing error, followed by
Price to Operating Cash Flow (11.08204) and Price to Earnings (15.91361).
Next, whether the means are different from zero are tested by One-Sample T Test.
Table 4-7 Descriptive Statistics of 320 Firms
N Minimum Maximum Mean Std. Deviation
PE 312 -14.010 2.234 -.11156 .979994
PD 136 .000 15.646 .38744 1.437199
PC 304 -.805 10.718 .45301 1.184934
Table 4-8 One-Sample Statistics
N Mean Std. Deviation Std. Error Mean
PRICE_ERROR_PE 312 -4.2145 41.69949 2.36077
PRICE_ERROR_PD 304 11.5849 75.91070 4.35378
PRICE_ERROR_PC 304 .8012 4.68724 .26883
According to the results, the pricing error of Price to Operating Cash Flow has the
lowest mean (.0812) and standard error mean(.26883), followed by pricing error of Price
to Earnings (mean=-4.2145, standard error mean=2.36077) and pricing error of Price to
Dividend (mean=11.5849, standard error 4.35378).
42
Therefore, to answer research question two, cash flow has the strongest explanatory
power of the variance of share price of casual theme restaurant firms, followed by
earnings and dividend.
Research Question Three
Due to the different nature of large capital manufacturing firms and small capital
service firms, whether the impact of cash flow on stock price in a broad market
capitalization index will hold in casual theme restaurants is questionable. Previous studies
used S&P 500 index as sample set, which includes cross-industry firms with the largest
capitalization. This study tries to make clear the difference in the impact of cash flow on
stock price between S&P 500 index and casual theme restaurant firms. These questions
are followed by proposition 3.
Proposition 3: We can expect there is no difference in the impact of cash flow on stock
price between S&P 500 index and casual theme restaurants.
Table 4-9 Multiple Valuation Test Results for All 320 Firm-years Samples
Mean Median Std. Error Mean
PRICE_ERROR_PE -4.2145 -0.425 2.36077
PRICE_ERROR_PD 11.5849 -1.000 4.35378
PRICE_ERROR_PC .8012 -0.290 .26883
43
Table 4-10 Results of Liu, Nissim and Thomas (2001)
Variables Mean Median Sd.
P/E -0.005 0.015 0.321
P/C -.0.042 0.150 0.989
In the study of Liu, Nissim and Thomas in 2002(Liu, Nissim et al. 2001; Liu, Nissim
et al. 2002), S&P 500 index has been chosen as a sample set. Their results showed that
earnings could be a better indicator than operating cash flow in the multiple valuation.
P/E’s values were all lower than P/C in terms of mean, median and standard deviation, as
shown in Table 4-10.
However, in this study, with 320 firm-years casual theme restaurant samples, the
result came out differently from previous studies. P/C, the multiple of operating cash
flow has the lowest values in all of the three measures: mean, median and standard error.
Therefore, from this study, it can be concluded that operating cash flow has different
impacts on share price between casual theme restaurants and S&P 500 index samples.
Operating cash flow is more powerful in multiple valuation tests with casual theme
restaurant firms, which have relatively smaller market capitalizations.
44
Chapter Five
Conclusions and Discussion
Chapter four has presented the results of the multiple valuation tests conducted under
the casual theme restaurants setting over the period of 1990 to 2005. This chapter
summarizes the results and presents the contribution, limitations and suggestions for
further study.
Summary of Results and Discussion
Research Question One
First, the operating cash flow has significantly different impacts on the share price
between Micro-Cap and Small-Cap casual theme restaurant firms. The impact on the
share price of Small-Cap is stronger than on the share price of Micro-Cap casual theme
restaurant firms.
Research Question Two
Second, the operating cash flow has the strongest explanatory power of the variance
of share price of casual theme restaurant firms, followed by earnings and dividend.
Research Three
Third, according to the results of this study, the operating cash flow has different
impacts on share price between casual theme restaurants and S&P 500 index samples.
45
Operating cash flow is more powerful in multiple valuation tests with casual theme
restaurant firms, which have relatively smaller market capitalizations.
Implications for Managers
Among all the general goals of management, increasing share prices and minimizing
the volatility of stocks could be one the most important task for managers, without
exception for casual theme restaurant managers.
Various studies have been conducted to provide managers with feasible and effective
indicators to explain the move of share prices. According to the results of previous
studies, earning has been proved to have the strongest power in explaining the variance of
share price. However, most of the previous studies that have focused on the relationship
between stock price and cash flow have used cross-industries data, primarily S&P 500
index. These studies do not distinguish service industry from manufacturing industry.
As we all know, the service industry is different from manufacturing in many ways,
such as intangibility, simultaneity of production and consumption, customer participation
in the production and delivery of the service, heterogeneity, and perish ability. These
features make the service industry different from the manufacturing industry in the
supply-demand relationship and the inventory management. The manufacturing firms
usually keep a large amount of inventory of raw materials as well as finished goods. And
they do a lot of credit buying and selling. However, the casual theme restaurant is
generally viewed as a cash business. They hardly keep finished goods in inventory,
46
neither a lot of raw materials, like the manufacturing industry usually does. This makes
cash take a dominant role on balance sheet, as well as in daily operations.
The results of this study provide a more specific guideline to casual theme restaurant
managers that operating cash flow is the best indicator to track share price, followed by
earnings and dividends. Stable share price can be achieved, to some extent, by smoothing
the operating cash flow stream.
Limitations
The fundamental limitation of this study would be the small sample size. In order to
gather enough available data information to carry on the analysis, non-public traded firms
are excluded from this study. Therefore, only 20 public-traded casual theme restaurants
are available with the data during the period of 1990 to 2005. Previous studies conducted
with cross-sectional data can have several hundred sample firms. This limitation might
lead the results of this study questionable. The statistical output of this study may be
different when conducted with a larger sample set.
A second limitation would be the small size (13) of the Middle-Cap category in the
analysis of research question one. Therefore, this group should be taken out from the
analysis in research question one. Actually, none of the results is significant regarding the
comparison between group 3 and the other two groups.
47
Suggestions for Further Studies
As stated above, small sample size is a fundamental limitation of this study. To obtain
a more general result, global sample set can be constructed, other than just limited to the
U.S. market. That would provide a much larger sample size.
48
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50
Appendix A Sample Firms
GVKEY CONAME SMBL NAICS DNUM
1 2163 BENIHANA INC -CL A BNHNA 722110 5812
2 2282 BOB EVANS FARMS BOBE 722110 5812
3 3007 BRINKER INTL INC EAT 722110 5812
4 3424 STEAK N SHAKE CO SNS 722110 5812
5 3570 CBRL GROUP INC CBRL 722110 5812
6 4759 FLANIGANS ENTERPRISES INC BDL 722110 5812
7 4911 FRISCH'S RESTAURANTS INC FRS 722110 5812
8 7132 MAX & ERMAS RESTAURANTS MAXE 722110 5812
9 7566 RUBY TUESDAY INC RT 722110 5812
10 7696 NATHAN'S FAMOUS INC NATH 722110 5812
11 11538 J. ALEXANDER'S CORP JAX 722110 5812
12 11872 ARK RESTAURANTS CORP ARKR 722110 5812
13 13187 EACO CORP 3EACO 722110 5812
14 13188 CALA CORP CCAA 722110 5812
15 15092 CEC ENTERTAINMENT INC CEC 722110 5812
16 16665 APPLEBEES INTL INC APPB 722110 5812
17 19398 DENNYS CORP DENN 722110 5812
18 21153 SIXX HOLDINGS INC SIXX 722110 5812
19 22829 O'CHARLEY'S INC CHUX 722110 5812
20 24186 OSI RESTAURANT PARTNERS INC OSI 722110 5812
51
Appendix B
Descriptive Statistics of Three Multiples by Groups
Group 1
N Minimum Maximum Mean Std. Deviation
PE_RE 186 -14.010 2.234 -.19511 1.223644
PD_RE 53 .000 15.646 .50426 2.261220
PC_RE 183 -.805 10.718 .63273 1.493107
Valid N (listwise) 52
Group 2
N Minimum Maximum Mean Std. Deviation
PE_RE 113 -4.242 .114 .00726 .405787
PD_RE 77 .001 2.025 .29624 .376280
PC_RE 111 .034 1.470 .18450 .209193
Valid N (listwise) 76
Group 3
N Minimum Maximum Mean Std. Deviation
PE_RE 312 -14.010 2.234 -.11156 .979994
PD_RE 136 .000 15.646 .38744 1.437199
PC_RE 304 -.805 10.718 .45301 1.184934
Valid N (listwise) 133
52
Appendix C
SPSS Out Put for One-Way ANOVA for Proposition One
Post Hoc Tests
Descriptives
186 -6.0588 53.60099 3.93022 -13.8126 1.6950 -666.47 75.19
113 -1.3888 8.22375 .77363 -2.9216 .1440 -84.09 12.32
13 -2.3883 3.04781 .84531 -4.2301 -.5465 -10.05 2.16
312 -4.2145 41.69949 2.36077 -8.8596 .4306 -666.47 75.19
184 1.6349 12.48753 .92059 -.1815 3.4512 -1.00 87.74
107 20.9967 99.15919 9.58608 1.9914 40.0020 -1.00 789.13
13 74.9484 222.73720 61.77618 -59.6503 209.5472 -1.00 798.43
304 11.5849 75.91070 4.35378 3.0174 20.1523 -1.00 798.43
183 1.4060 5.84597 .43215 .5533 2.2586 -4.99 55.48
111 -.0900 1.54464 .14661 -.3805 .2006 -.96 11.25
10 -.3753 .79622 .25179 -.9449 .1943 -.96 1.08
304 .8012 4.68724 .26883 .2721 1.3302 -4.99 55.48
1
2
3
Total
1
2
3
Total
1
2
3
Total
PRICE_ERROR_PE
PRICE_ERROR_PD
PRICE_ERROR_PC
N Mean Std. Deviation Std. Error Low er Bound Upper Bound
95% Conf idence Interval for
Mean
Minimum Maximum
Test of Homogeneity of Var iances
2.466 2 309 .087
24.659 2 301 .000
7.514 2 301 .001
PRICE_ERROR_PE
PRICE_ERROR_PD
PRICE_ERROR_PC
Levene
Statistic df1 df2 Sig.
ANOVA
1578.274 2 789.137 .452 .637
539203.2 309 1744.994
540781.5 311
79888.968 2 39944.484 7.216 .001
1666129 301 5535.312
1746018 303
168.922 2 84.461 3.918 .021
6488.063 301 21.555
6656.985 303
Betw een Groups
Within Groups
Total
Betw een Groups
Within Groups
Total
Betw een Groups
Within Groups
Total
PRICE_ERROR_PE
PRICE_ERROR_PD
PRICE_ERROR_PC
Sum of
Squares df Mean Square F Sig.
53
Multiple Com parisons
-4.66999 4.98238 .617 -16.4038 7.0638
-3.67049 11.98382 .950 -31.8932 24.5522
4.66999 4.98238 .617 -7.0638 16.4038
.99950 12.23408 .996 -27.8125 29.8115
3.67049 11.98382 .950 -24.5522 31.8932
-.99950 12.23408 .996 -29.8115 27.8125
-4.66999 4.98238 .349 -14.4737 5.1337
-3.67049 11.98382 .760 -27.2507 19.9097
4.66999 4.98238 .349 -5.1337 14.4737
.99950 12.23408 .935 -23.0731 25.0721
3.67049 11.98382 .760 -19.9097 27.2507
-.99950 12.23408 .935 -25.0721 23.0731
-4.66999 4.00563 .570 -14.3155 4.9755
-3.67049 4.02009 .741 -13.3521 6.0111
4.66999 4.00563 .570 -4.9755 14.3155
.99950 1.14588 .771 -1.8635 3.8625
3.67049 4.02009 .741 -6.0111 13.3521
-.99950 1.14588 .771 -3.8625 1.8635
-19.36182 9.04517 .084 -40.6665 1.9429
-73.31354* 21.35126 .002 -123.6036 -23.0235
19.36182 9.04517 .084 -1.9429 40.6665
-53.95172* 21.85235 .037 -105.4220 -2.4814
73.31354* 21.35126 .002 23.0235 123.6036
53.95172* 21.85235 .037 2.4814 105.4220
-19.36182* 9.04517 .033 -37.1616 -1.5620
-73.31354* 21.35126 .001 -115.3302 -31.2969
19.36182* 9.04517 .033 1.5620 37.1616
-53.95172* 21.85235 .014 -96.9544 -10.9490
73.31354* 21.35126 .001 31.2969 115.3302
53.95172* 21.85235 .014 10.9490 96.9544
-19.36182 9.63018 .134 -42.7183 3.9947
-73.31354 61.78304 .592 -244.4592 97.8321
19.36182 9.63018 .134 -3.9947 42.7183
-53.95172 62.51552 .789 -225.8787 117.9752
73.31354 61.78304 .592 -97.8321 244.4592
53.95172 62.51552 .789 -117.9752 225.8787
1.49591* .55855 .021 .1803 2.8115
1.78125 1.50774 .465 -1.7700 5.3325
-1.49591* .55855 .021 -2.8115 -.1803
.28533 1.53287 .981 -3.3251 3.8958
-1.78125 1.50774 .465 -5.3325 1.7700
-.28533 1.53287 .981 -3.8958 3.3251
1.49591* .55855 .008 .3968 2.5951
1.78125 1.50774 .238 -1.1858 4.7483
-1.49591* .55855 .008 -2.5951 -.3968
.28533 1.53287 .852 -2.7312 3.3018
-1.78125 1.50774 .238 -4.7483 1.1858
-.28533 1.53287 .852 -3.3018 2.7312
1.49591* .45634 .004 .3980 2.5938
1.78125* .50015 .002 .5663 2.9962
-1.49591* .45634 .004 -2.5938 -.3980
.28533 .29136 .715 -.4911 1.0618
-1.78125* .50015 .002 -2.9962 -.5663
-.28533 .29136 .715 -1.0618 .4911
(J) MARKET_CAP
2.00
3.00
1.00
3.00
1.00
2.00
2.00
3.00
1.00
3.00
1.00
2.00
2.00
3.00
1.00
3.00
1.00
2.00
2.00
3.00
1.00
3.00
1.00
2.00
2.00
3.00
1.00
3.00
1.00
2.00
2.00
3.00
1.00
3.00
1.00
2.00
2.00
3.00
1.00
3.00
1.00
2.00
2.00
3.00
1.00
3.00
1.00
2.00
2.00
3.00
1.00
3.00
1.00
2.00
(I) MARKET_CAP
1.00
2.00
3.00
1.00
2.00
3.00
1.00
2.00
3.00
1.00
2.00
3.00
1.00
2.00
3.00
1.00
2.00
3.00
1.00
2.00
3.00
1.00
2.00
3.00
1.00
2.00
3.00
Tukey HSD
LSD
Tamhane
Tukey HSD
LSD
Tamhane
Tukey HSD
LSD
Tamhane
Dependent Variable
PRICE_ERROR_PE
PRICE_ERROR_PD
PRICE_ERROR_PC
Mean
Dif ference
(I-J) Std. Error Sig. Low er Bound Upper Bound
95% Conf idence Interval
The mean dif ference is s ignif icant at the .05 level.*.
54
Homogeneous Subsets
PRICE_ERROR_PE
186 -6.0588
13 -2.3883
113 -1.3888
.893
MARKET_CAP
1
3
2
Sig.
Tukey HSDa,b
N 1
Subset
for alpha
= .05
Means for groups in homogeneous subsets are displayed.
Uses Harmonic Mean Sample Size = 32.913.a.
The group sizes are unequal. The harmonic mean
of the group s izes is used. Type I error levels are
not guaranteed.
b.
PRICE_ERROR_PC
10 -.3753
111 -.0900
183 1.4060
.348
MARKET_CAP
3
2
1
Sig.
Tukey HSDa,b
N 1
Subset
for alpha
= .05
Means for groups in homogeneous subsets are displayed.
Uses Harmonic Mean Sample Size = 26.207.a.
The group sizes are unequal. The harmonic mean
of the group s izes is used. Type I error levels are
not guaranteed.
b.
55
Appendix D
SPSS Output for Paired-Samples T Test
Paired Samples Statis tics
-4.3287 304 42.24033 2.42265
11.5849 304 75.91070 4.35378
-4.2854 304 42.24039 2.42265
.8012 304 4.68724 .26883
11.9180 296 76.90555 4.47004
.8360 296 4.74538 .27582
PRICE_ERROR_PE
PRICE_ERROR_PD
Pair
1
PRICE_ERROR_PE
PRICE_ERROR_PC
Pair
2
PRICE_ERROR_PD
PRICE_ERROR_PC
Pair
3
Mean N Std. Deviation
Std. Error
Mean
Paired Samples Corre lations
304 -.006 .917
304 -.206 .000
296 -.021 .723
PRICE_ERROR_PE &
PRICE_ERROR_PD
Pair
1
PRICE_ERROR_PE &
PRICE_ERROR_PC
Pair
2
PRICE_ERROR_PD &
PRICE_ERROR_PC
Pair
3
N Correlation Sig.
Paired Samples Test
-15.91361 87.09242 4.99509 -25.74307 -6.08414 -3.186 303 .002
-5.08652 43.44719 2.49187 -9.99008 -.18297 -2.041 303 .042
11.08204 77.14984 4.48424 2.25688 19.90720 2.471 295 .014
PRICE_ERROR_PE -
PRICE_ERROR_PD
Pair
1
PRICE_ERROR_PE -
PRICE_ERROR_PC
Pair
2
PRICE_ERROR_PD -
PRICE_ERROR_PC
Pair
3
Mean Std. Deviation
Std. Error
Mean Low er Upper
95% Conf idence
Interval of the
Dif ference
Paired Dif ferences
t df Sig. (2-tailed)
56
Appendix E
Descriptive Statistics of 320 Firms
N Minimum Maximum Mean Std. Deviation
PE 312 -14.010 2.234 -.11156 .979994
PD 136 .000 15.646 .38744 1.437199
PC 304 -.805 10.718 .45301 1.184934
Valid N (listwise) 133
57
Appendix F
SPSS Output for One Sample T Test
One-Sample Statistics
312 -4.2145 41.69949 2.36077
304 11.5849 75.91070 4.35378
304 .8012 4.68724 .26883
PRICE_ERROR_PE
PRICE_ERROR_PD
PRICE_ERROR_PC
N Mean Std. Deviation
Std. Error
Mean
One-Sample Test
-1.785 311 .075 -4.21449 -8.8596 .4306
2.661 303 .008 11.58486 3.0174 20.1523
2.980 303 .003 .80116 .2721 1.3302
PRICE_ERROR_PE
PRICE_ERROR_PD
PRICE_ERROR_PC
t df Sig. (2-tailed)
Mean
Dif ference Low er Upper
95% Conf idence
Interval of the
Dif ference
Test Value = 0
Recommended