Valuation of airline companies: a function of earnings or cash?
It could be said that no other industry conjures visions of glamour, excitement, wonder and technological awe as the airline industry. Conquering flight is one of man's achievements. It only takes the noise of an airplane, big or small, flying overhead to draw attention upwards! The advances made in flight over the past century have facilitated quick and safe travel to all four corners of the globe. Yet, like any other industry, apart from visionaries and professionals from a variety of disciplines, airlines need and have attracted investors to finance these awesome technological feats which are nowadays taken for granted (IATA, 2013).
Over the years, the airline industry has morphed into a unique and complicated business model. This model necessitates an increase in valuation to sustain growth, create jobs while at the same time, continuing to attract more investors. With this in mind, the valuation of airline companies emerges from the background and into the spotlight (Vasigh, Fleming, & Tacker, 2013).
This study sets out to answer the question: what affects the value of airline companies? While there are several variables that affect company valuation, this paper posits that operating income and cash flows from operations play a statistically significant role in determining company valuation. Additionally, this study posits that company size too is statistically significant in determining company value.
The rest of this paper is organized as follows: an overview of the current state of the airline industry, a review of past related literature, a brief discussion of the research methodology and hypotheses, followed by a discussion of results. Limitations of this study as well as conclusions and recommendations for future research in this area are then discussed to end the paper.
Recent Development in the Airline Industry
Since the Wright brothers made their historic flight in 1903 in Kitty Hawk, North Carolina, the airline industry has grown exponentially. Fuelled by two world wars and a cold war, advances in flight have made aviation the safest mode of transportation and created thousands of jobs. Throughout the past 110 years, it has not just been the technological aspect of aviation which has progressed but also the economic aspect. Recessions, economic downturns and oil crises have had an impact on the industry. The consolidation of aircraft manufacturers not only changed the manufacturing landscape, but created two veritable giants--Boeing and Airbus.
While the literature is replete with the historical progression of the industry, this paper picks up the recent developments of the industry.
Mergers and acquisitions in the US airline industry have led to consolidation in the industry, as the big players in the market merge and concentration in the market reduces (Vasigh, Fleming, & Tacker, 2013). Following Delta Airlines' acquisition of Northwest Airlines in 2007, United Airlines acquired Continental in 2010, followed by Southwest Airlines merging with AirTran in 2011. With the recent ongoing merger talks between American Airlines and US Airways, major airlines in the US with international operations will reduce to just three, with Southwest Airlines being a fourth major domestic player.
This spate of mergers coupled with increasing demand for air travel by both passengers and cargo freight has led to a very positive outlook of the industry from investors' point of view. Although the International Air Transport Association (IATA) has forecasted modest improvements in profit for 2013, about a five percent increase from the previous year, it also stated in its latest industry outlook that there is still quite a long way to go before airline returns for investors are adequate (IATA, 2013). Proponents of more consolidation in the industry believe that, with fewer airlines, there would be less price competition which was historically detrimental to the industry. They argue that not only will airlines be able to charge reasonably higher prices and create value for investors, but also benefit consumers by allowing airlines to build more efficient networks with greater economies of scale and scope (Vasigh, Fleming, & Tacker, 2013). This seems to be a win-win situation, although this is something that most consumers will beg to differ with, since routes which have only one or two airlines tend to have higher ticket prices than routes served by multiple airlines as evidenced by Origin and Destination data on historic average ticket prices. Average prices on routes tended to increase when competitors flying a particular route merge.
Airline stocks have usually traded very low compared to other industries. For example, the average stock price of US airlines was about $27 as of April, 2013 and this figure drops to an average price of less than $14 if Alaska Air and Allegiant are not included, according to data obtained from Yahoo Finance and MSN Money. Such low prices do not paint a very good picture about the US airline industry as a whole. However, there is much optimism that the worse days of this industry are behind, as bankruptcy protections and mergers have positioned the airlines to be much more adept and efficient at managing resources.
In a quite rare move in the airline industry, Delta Airlines announced in May 2013, that they would start paying quarterly dividends and also implement a stock buy-back program to create value for customers (Jacobs, 2013). This seems to affirm the general buzz and optimism about the airline industry in this new decade and lends some credence to the fact that the airlines may just be beginning to give value to their investors.
The airline industry has not always looked rosy and there are genuine fears, the current positive outlook may be short-lived. Of all the airlines that have been in operation since the pre-deregulation era, only Southwest Airlines has neither filed for bankruptcy protection nor liquidated (Schlangenstein, 2011). To some investors, airline stocks were to be avoided like the plague due to the high risk involved. Most airlines consistently operated in the red and engaged in price wars in order to capture market share (Vasigh, Fleming, & Tacker, 2013). Airlines have also historically suffered cyclical hiccups and are subject to fuel price risks, terrorism and economic recessions among others.
Market Capitalization and Firm Value
Valuing any business can be a tricky adventure and this is even truer for airlines which have operated at a loss for the best part of their lifetime. What determines value and what are some of the indicators that can help us to effectively value an airline company? Many approaches have been used to value companies--balance sheet-based methods, income statement-based methods, cash flow discounting--based methods as well as the goodwill-based --approach among others (Fernandez, 2013). This paper does not calculate firm value but investigates the effect that income and cash flow as well as total size, measured by total assets has on the market value of a firm. As a result, emphasis is placed on Operating Income and Cash Flow without any due consideration for other valuation methods.
The market capitalization of a firm, also known as its firm value is a common measure of firm value which considers the stock price at which a firm trades. It is simply calculated by multiplying the stock price by the number of outstanding shares of stock the company has issued (The World Bank, 2013). Since the current market price is used, this measure gives a good picture of the current market value of the firm.
It is argued that actual market values are set by two parties acting in their own self-interests and as such the prices at which stocks trade are practical expressions of value (Strischek, 1983). Stock prices, readily available on online trading websites such as Yahoo Finance and MSN Money and can be used to estimate the value of an airline rather than performing a discounted cash flow analysis or other complicated methods of valuation which could prove cumbersome.
The Income Statement is one of the financial statements that companies are required to prepare according to the Generally Accepted Accounting Principles (GAAP). This statement shows how much a company has incurred in order to sell its product or service, and shows whether a company is making a profit or a loss.
Operating income is the income or profit of a firm which it realizes primarily from its core business operations; and is calculated as the difference between the revenues of a firm and operating expenses. This income does not include other extraordinary expenses and thus gives a fair indication of its profitability as opposed to say, gross income or net income. It is also referred to as earnings before interest and taxes (EBIT) or the Operating Margin (Business Dictionary, 2013). In most cases deductions of depreciations are also taken from the gross margin, but in cases where they are not, this metric is referred to as earnings before interests, taxes, depreciation and amortization (EBITDA) and is a very useful metric for comparing companies (Jacobs, 2013).
This paper considers the fact that operating income metric should be a good predictor of airline firms' value. A study of the literature however shows that, though quite a lot of work has been done in finding out the relation between earnings or net income and firm value, not much has been done in comparing operating income to firm value. This can be understood since operating income and all other income are highly related, that is almost all other types of income come out of the operating income. Some of the past work which considers the relationship between income and firm value are discussed in the following paragraphs.
Bradshaw (2004) found that analysts recommend stocks based on their earnings forecast. There is a direct link between the income of a firm and its value, as seen even in the discounted cash flow valuation approach, which is one of three income approaches for firm valuation (Wilhoitte & Lynn, 2011). The Discounted Cash Flow approach is a multiple period valuation model that converts a future series of benefit streams into present value by discounting them into a present value using an appropriate discount rate (Trugman, 2013). The same document also lists pre-tax income and cash flow among seven metrics which could be used as a starting point for doing valuations via the income approach.
McCann and Olson (1994) provide further support for the relationship between a firm's earnings and dividends by establishing that earnings have a positive impact on a firm's decision to pay dividends, giving value to shareholders. Liu, Nissim, and Thomas (2007) investigate if operating cash flows or accounting earnings are better at explaining equity valuations. In all cases in their study, earnings forecasts were a better summary measure of value, concluding that earnings and cash flows can give a good indication of firm value.
Cash Flows from Operations
The Statement of Cash Flows is another financial statement required under GAAP. This statement reports the amount of cash generated and used by a reporting entity. Inflows and outflows of cash are reported in three sections: cash flow from operations (or operating cash flows), cash flows from investing and cash flows from financing. A company which consistently has negative operating cash flows is headed for danger as this simply means it is not generating money in its core business operations. The other two activities' cash flows can be negative without any repercussions; actually it could be a good sign that a company is expanding (Wright, 2013).
People invest in companies with the view to receiving reasonable returns on their investment. One of the most common ways by which companies give back returns to their investments is in the form of cash dividends, though there are other ways of paying dividends (Jiang & Koller, 2011), and if a company is not generating enough cash from its operations, but rather burning up cash, it will be unable to pay dividends and this has been the bane of the airline industry for a long time as stated earlier. It is expected then that, operating cash flows should have a positive effect on the value of an airline company. Markets normally respond to news about expected future earnings and this in turn forms the basis for firm valuations.
Operating cash flows assist in predicting a firm's ability to generate future cash flows. Research shows that cash flows exhibit superiority over earnings as an estimator of future cash flows (Subramanyam & Venkatachalam, 2007). In an earlier paper by the same authors, they concluded that earnings are not a better predictor of firm value compared to operating cash flows, but neither did they assert that cash flows better predict future cash flows than do earnings. This according to them contradicts an earlier position by the Financial Accounting Standards Board (FASB), that earnings better predict future streams of cash (Bowen, Burgstahler, & Daley, 1986).
Another school of thought states that each of either earnings or operating cash flows may not accurately predict or assess firm value. Charitou and Ketz (1991) also found out that cash flow alone is not sufficient for firm valuation and other components of earnings have incremental valuation. They also discovered that operating cash flows and accruals provide the same information to the market about future expected cash flows (Charitou & Ketz, 1991). Consler, Lepak and Havanek (2011) found out that cash flow per share better predicts dividends, related to firm value than earnings per share, while Bailey et al. (2008) found out that the residual income model provides a better estimate of company value than the discounted cash flow method and dividend discount model.
Not all authors necessarily agree that cash flows and income are accurate predictors of firm value. Magni and Velez-Pareja (2009) posit that potential dividends that are not distributed to investors but invested in liquid assets should be ignored in firm valuation, since it is only distributed cash flows that add value to shareholders.
However, as stated earlier, this paper focuses on whether operating income and cash flows do have an effect--whether positive or negative on the value of airline companies. It does not attempt to create a model to value the companies, just to investigate if there is any relationship between these metrics and airline value and what the nature of the relationship is.
Firm size is another important measure in determining firm value (Hall, 1987). Larger firms are expected to be more highly valued than relatively smaller ones. In this paper, the total assets metric is used as a proxy for firm size, and this is consistent with the work of some earlier authors who also used assets or some other variants as a measure of firm size. For example, Fama and French (2002) used the natural logarithm of total book assets as their proxy for firm size.
Past research also offers some interesting insight into the asset-based valuation models. An example of the asset based-model, known as the book value model (BVM) was found to be a more accurate valuation method than income only approaches at low levels of firm profitability (Jenkins and Kane, 2006). The same authors also discovered that a hybrid approach, known as the excess earnings method (EEM), which combines both the income and asset approaches, is generally a better predictor of firm value than either the income or asset approaches used alone (Jenkins and Kane, 2006). Again their findings show that for higher profitability and increasing intangible assets, the asset based approach is less accurate than the income based approach in valuing companies.
These studies show that the assets of a firm play a large role in how much the company is valued. This makes accounting sense since for every business the goal is to make considerable profit by using the assets that the company possesses. Also, the total equity of a company is the residual claim that the shareholders of a firm have on its assets, while the debt or liability part is the claim that bondholders have on the assets and in case, the company went bankrupt, the bondholders will be able to sell off some of the company's assets to defray the debt.
The objective of this study is to examine whether a relationship exists between operating income, cash flow from operations, firm size and firm value with a view to finding associations and statistical significance and making predictions. Consequently, this paper poses the following research question: Is there a relationship between firm value, operating income, firm size and cash flow from operations? This question will be answered by testing the following hypotheses:
[H.sub.1]: There is a positive and significant relationship between airline companies' operating income and airline company firm value.
[H.sub.2]: There is a positive and significant relationship between airline companies ' operating cash flows and airline company firm value.
[H.sub.3]: There is a positive and significant relationship between airline companies' size and airline company firm value.
Sample and Data Collection
Airline companies' data was extracted from the Mergent database using the SEC code 4512 to track the number of active airlines. Data on cash flows from operations, operating income and total assets for years 2008 to 2013 were extracted from the form 10-K filings of the various US airlines available on the SEC website. A total of 15 companies were obtained with data for years 2008 to 2013, leading to a sample size of 75 which was used to run the regression in SPSS.
Since the main purpose of this study is to examine whether a relationship exists between operating income, operating cash flows, size and the valuation of airline companies, associational inferential statistics were used to analyze the data. This entailed creating a model and running a regression analysis using the ordinary least squares (OLS) method to test the hypotheses. As explained above, databases were used in this study in order for the regression to yield results based on all the available valid data for each observation. The results are explained in the next section. The model can be expressed as follows:
MKTCAP = [alpha] + OPTINC ([[beta]].sub.1]) + CFOPTS ([[beta].sub.2]) + SIZE ([[beta].sub.3]) + e (1)
where MKTCAP = the value of an airline company measured in terms of market capitalization, OPTINC = operating income, CFOPTS = cash flow from operating activities, and SIZE = firm size.
In this study, each coefficient is observed and tested, [H.sub.0]: [[beta].sub.1-k] = 0, using the t- test, T = [beta]/[S.sub.[beta]] where [beta] is the coefficient and [S.sub.[beta]] is the estimated standard error of [beta]. The overall regression fit is also tested, [H.sub.0]: [[beta].sub.1] = [[beta].sub.2] = [[beta].sub.k] = 0, using the F statistic, F = MRS/MSE (mean square regression divided by mean square error). The coefficient of determination, [R.sup.2], is also examined to determine the explanatory power of the regression model.
Table 1 shows the means and standard deviations of the variables selected in the model.
Table 2 shows that the variables in the equation explain a significant proportion of the variation in earning power with an [R.sup.2] (coefficient of determination) of 36.9 percent. Furthermore, the results also indicate that there is no evidence of first-order serial correlation (autocorrelation) in the error term of the model. This is evidenced by the Durbin-Watson statistic that yielded 1.240. Additionally, an inspection of the residuals plotted against fitted values indicates no heteroskedasticity, in other words, the variance of the error terms is constant for all observations.
Table 2 also presents the Standard Error of the Estimate (SEE), indicating the margin of error for the regression equation. Thus, using this model to make forecasts, 68.2 percent of the data will fall within one standard error of the predicted value, while just over 95 percent will fall within two standard errors of the estimate.
Table 3 demonstrates that the regression model is highly statistically significant with an F-value of 21.066 [F (2, 72) =21.066, p < 0.05] with the hypothesis that all coefficients are jointly zero rejected at the five percent significant level. The F-value is used to test the hypothesis that all the independent variables taken together explain a significant proportion of the variation in the dependent variable (voluntary disclosures). Thus, the F-value tests the null hypothesis [H.sub.0]: All [[beta].sub.i] = 0 against the alternative hypothesis Ha: At least one [[beta].sub.i] [not equal to] 0. Stated otherwise, this is testing whether at least one of the explanatory variables contributes information for the prediction of voluntary disclosures. Since the F-value is significant (.000< .05) at the five percent significance level, the null hypothesis can be rejected and the conclusion can be made that the independent variables taken together are useful in explaining voluntary disclosures. Using the unstandardized coefficients presented in Table 4 below, the model can be represented as follows:
MKTCAP = [alpha]-1.008(OPTINC) + 2.891(CFOPTS) (2)
Table 4 illustrates that all the independent variables selected in this model appear to be appropriate in that they are not highly dependent on each other. This appropriateness is indicated by the relatively high tolerance of each independent variable signifying that there is no multicollinearity among the independent variables.
In testing the null hypothesis that there is no relationship between airline company firm value (MKTCAP) as measured by each of the independent variables at the .05 significance level, the null hypothesis can be rejected for both operating income (OPTINC) and cash flow from operations (CFOPTS). In other words, these independent variables (operating income and cash flow from operations) significantly contribute to the model.
The Ordinary Least Squares (OLS), which assumes a linear relationship between the dependent and independent variables, was used to perform a regression analysis. Autocorrelation was accounted for by the Durbin-Watson statistic, while variance inflation factors accounted for multicollinearity. Tolerances for individual variables were used to check heteroskedasticity. Because operating income (OPTINC) and cash flow from operations (CFOPTS) have a significant relationship with airline company firm value, it can be assumed that they do play a significant role effect on airline company firm value.
Table 5 shows the regression results when size was included as an independent variable.
Table 6 shows that the variables in the equation explain a significantly higher proportion of the variation in earning power with an [R.sup.2] (coefficient of determination) of 70.04 percent. The results also indicate that there is no evidence of first-order serial correlation (autocorrelation) in the error term of the model as is evidenced by the Durbin-Watson statistic of 0.740.
Table 7 shows that when adding size as an independent variable, the results yielded a highly statistically significant model with an F-value of 56.155 [F (3, 71) = 56.155, p < 0.05] with the hypothesis that all coefficients are jointly zero rejected at the five percent significant level. The resulting model is shown as follows.
MKTCAP = [alpha] + 0.699(OPTINC) - 0.625(CFOPTS) + 0.209 (SIZE) (3)
Table 8 illustrates that all three independent variables selected in this model, namely operating income, cash flow from operations, and size appear to be appropriate in that they are not highly dependent on each other. This appropriateness is indicated by the high tolerance of each independent variable signifying that there is no multicollinearity among the independent variables.
Thus in testing the null hypothesis that there is no relationship between airline firm value as measured by each of the independent variables at the .05 significance level, the null hypothesis can be rejected for operating income (OPTINC) firm size (MKTCAP). In other words, these independent variables (operating income and firm size) significantly contribute to the model.
The Ordinary Least Squares (OLS), which assumes a linear relationship between the dependent and independent variables, was used to perform a regression analysis. Autocorrelation was accounted for by the Durbin-Watson statistic, while variance inflation factors accounted for multicollinearity. Tolerances for individual variables were used to check heteroskedasticity. Because disclosure strictness (WEIG), individualism (INDV), and firm size (SIZE) have a significant relationship with voluntary disclosures, it can be assumed that they do play a significant role in voluntary disclosures. On the other hand, because the relationship between power distance (PODI) and opacity (OPAC) is not statistically significant, it can be assumed that power distance and opacity do not have an effect on voluntary disclosures.
Perhaps operating income and cash flow from operations are not just the only two variables that affect firm value. It could be envisaged that other variables that affect firm value could be included. Such variables include both quantitative and qualitative ones. For instance, it is a known fact that a strong top management team increases firm value (or plays a crucial role in increasing firm value) (Mackey, 2013). As has been suggested in this paper, the incidence of airline companies embarking on a policy of paying dividends might effectively make investing in airline companies more attractive thus resulting in an increase in firm value. Another limitation lies in the fact that the data were collected from secondary sources. To this effect, several databases were utilized.
This study included only U.S. airline companies. It is quite possible that different results could be obtained from the inclusion of non U.S. airline companies given the growth that the airline industry has experienced outside the U.S.
It would have been ideal if information on several other airline companies was readily available. Although several airline companies are captured under the SIC 4512, information is not available for all of them, either because they have sought bankruptcy protection, filed for bankruptcy or are simply not active anymore.
Additionally, in this study, no distinction was made between privately-owned, publicly-owned or government-owned airline companies.
RECOMMENDATIONS AND FUTURE RESEARCH
This study could be used as a stepping stone for future research by including data from non-U.S. airline companies. This could very well enable a study from an international perspective. Additionally, data could be expanded to cover the past ten years. Moreover, further research could also investigate whether there exist other quantitative or even qualitative variables that could be used in determining the value of an airline. Perhaps the positive turn in the economy has opened the door for airline companies to start paying dividends. This new path, hitherto closed to most airline companies, might necessitate the inclusion of such a variable in the model.
This study set out to examine whether a relationship exists between operating income, cash flow from operations, firm size, and the valuation of airline companies. Results of this study show that operating income and cash flow from operating activities are statistically significant in determining the valuation of an airline company. When the size of an airline company was added as a control variable, the results show that operating income and size are statistically significant with respect to airline company valuation. Regardless of the sometimes negative news surrounding the profitability of airline companies that is prevalent in the media, airline companies do generate operating cash flows and operating income that not only keep them in business but create firm value.
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Embry-Riddle Aeronautical University
Bert J. Zarb is a professor of accounting in the College of Business at Embry-Riddle Aeronautical University in Daytona Beach, Florida, USA, where he has been teaching financial, managerial, and international accounting at the undergraduate and graduate level since 1998. He is a licensed CPA in Florida and Georgia, a Chartered Global Management Accountant (CGMA), and is a member of the AICPA, FICPA, and IMA. Dr. Zarb is a past president of the FICPA Volusia County Chapter and served on several committees of the FICPA.
Table 1 Descriptive Statistics Mean (In Millions) Std. Deviation N (In millions) MKTCAP 2142.57 2937.95 75 OPTINC 13.63 1287.14 75 CFOPTS 384.20 818.03 75 Table 2 Regression Results--Model Summary [R.sup.2] ADJ STD. ERROR DURBIN- [R.sup.2] OF THE WATSON ESTIMATION 0.369 0.352 2365.68 1.240 Table 2 Regression Results--Model Summary [R.sup.2] ADJ STD. ERROR DURBIN- [R.sup.2] OF THE WATSON ESTIMATION 0.369 0.352 2365.68 1.240 Table 3 Regression Results--ANOVA Model Sum of df Mean Square F Sig. Square 1 Regression 2.358E8 2 1.179E8 21.066 0.000 (a) Residual 4.029E8 72 5596435.08 Total 6.387E8 74 Table 4 Regression Results--Coefficients COEFFICIENT STD ERR t-VALUE SIGNIFICANCE TOLERANCE (Constant) 1045.695 322.086 3.247 0.002 OPTINC -1.008 0.287 -0.442 0.001 0.556 CFOPTS 2.891 0.451 6.411 0.000 0.556 Table 5 Descriptive Statistics Mean (In Millions) Std. Deviation N (In Millions) MKTCAP 2142.58 2937.95 75 OPTINC 13.63 1287.14 75 CFOPTS 384.20 818.03 75 SIZE 9522.81 13147.16 75 Table 6 Regression Results--Model Summary [R.sup.2] ADJ STD. ERROR OF DURBIN-WATSON [R.sup.2] THE ESTIMATION 0.704 .691 1633.21 0.740 Table 7 Regression Results--ANOVA Model Sum of df Mean Square F Sig. Square 1 Regression 4.494E8 3 1.498E8 56.155 0.000 (a) Residual 1.894E8 71 2667358.688 Total 6.387E8 74 Table 8 Regression Results--Coefficients COEFFICIENT STD ERR t-VALUE SIG TOLERANCE (Constant) 384.311 234.323 1.640 0.105 OPTINC 0.699 0.272 2.455 0.017 0.293 CFOPTS -0.625 0.501 -1.248 0.216 0.214 SIZE 0.209 0.023 8.948 0.000 0.383
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|Publication:||International Journal of Business, Accounting and Finance (IJBAF)|
|Date:||Sep 22, 2014|
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