Determinants of Profitability in the Airline Industry

ANALYSIS OF THE DETERMINANTS OF PROFITABILITY IN THE AIRLINE INDUSTRY BY: TILAHUN DERIBE TEFFERA Addis Ababa, June 2016 ANALYSIS OF THE DETERMINANTS OF PROFITABILITY IN THE AIRLINE INDUSTRY BY: TILAHUN DERIBE TEFFERA Addis Ababa, June 2016 UNITY UNIVERSITY College of Business, Economics and Social Sciences SCHOOL OF GRADUATE STUDIES ANALYSIS OF THE DETERMINANTS OF PROFITABILITY IN THE AIRLINE INDUSTRY BY TILAHUN DERIBE TEFFERA Thesis submitted to the School of Graduate Studies, College of Business, Economics and Social Sciences, Unity University in partial fulfillment of the requirements for the Degree of Master in Business Administration Addis Ababa, June 2016 UNITY UNIVERSITY College of Business, Economics and Social Sciences SCHOOL OF GRADUATE STUDIES ANALYSIS OF DETERMINANTS OF PROFITABILITY IN THE AIRLINE INDUSTRY BY TILAHUN DERIBE TEFFERA Approval of Board of Examiners Internal Examiner External Examiner Name ___________________ Name _______________________ Signature ___________________ Signature ________________ Date _______________ Date _________________ Advisor Name ________________________ Signature _________________ Date ______________________ Confirmation Chairperson, Department Graduate Committee Name _____________________________ Signature ________________ Date ____________________ ACKNOWLEDGEMENT I am highly grateful to my advisor DR. DEVADOSS SARAVANAN for providing me with guidance and direction throughout the progress of my work. My sincere gratitude also goes to International Civil Aviation Organization (ICAO) for providing me with data for the study. My beloved younger brother, Yared Deribe Teffera was at my side all the time to provide me with insight, assistance and inspiration and sharing his research experience. i TABLE OF CONTENTS Table of contents .............................................................................................................................. i List of tables ...................................................................................................................................iii List of figures ................................................................................................................................. iv Abstract........................................................................................................................................... vi Chapter 1: Introduction…………………………………………………… ..................................1 1.1. Back ground ................................................................................................................. 1 1.2. Statement of the problem ............................................................................................. 3 1.3. Research questions ....................................................................................................... 5 1.4. Objectives .................................................................................................................... 5 1.4.1. General objective ......................................................................................................... 5 1.4.2. Specific objectives: ...................................................................................................... 5 1.5. Significance of the study.............................................................................................. 6 1.6. Scope of the study ....................................................................................................... 6 1.6.1. Limitations .................................................................................................................. 6 1.6.2. Delimitations ................................................................................................................ 6 1.7. Organization of the study ............................................................................................. 7 Chapter 2: Literature review .......................................................................................................... 8 2.1. Review of theoretical literature .................................................................................... 8 2.1.1. Overview of the airline industry .................................................................................. 8 2.1.2. Profitability ................................................................................................................ 12 2.1.3. Measures of profitability in the airline industry ........................................................ 13 2.1.4. Determinants of profitability...................................................................................... 14 2.2. Empirical literature review ........................................................................................ 18 2.2.1. Determinants profitability in other industries ............................................................ 18 2.2.2. Determinants of profitability in the airline industry .................................................. 20 2.2.3. Conceptual model ...................................................................................................... 21 Chapter 3: Methodology .............................................................................................................. 24 3.1. Research design ......................................................................................................... 24 3.2. Sample size ................................................................................................................ 24 3.3. Data collection procedures ......................................................................................... 24 ii 3.4. Variables and measurement ....................................................................................... 25 Chapter 4: Results and discussion ............................................................................................... 27 4.1. Descriptive statistics .................................................................................................. 27 4.2. Association between profitability and its determinants ............................................. 28 4.3. Differences between the profitable and non profitable airlines ................................. 30 4.4. Differences between airlines of different regions ...................................................... 31 4.5. Discussion of results .................................................................................................. 33 Chapter 5: Conclusion and recommendation ............................................................................... 35 5.1. Conclusion ................................................................................................................. 35 5.2. Recommendations ...................................................................................................... 35 References/bibliography ................................................................................................................ 37 Appendices Appendix 1:SPSS out put for segmented linear regression Appendix 2: SPSS output for comparison between profitable and non profitable airlines Appendix 3: SPSS output for comparison between airlines registered in the regions of the world Appendix 4: Industry data used for the study iii LIST OF TABLES Table 3. 1: Variables and Measurement ........................................................................................ 25 Table 4. 1: Descriptive statistics of financial ratios for world Airlines (2010-2014 ) ................... 27 Table 4. 2: Parameter estimates of the segmented linear regression model. ................................. 28 Table 4. 3: Comparison between profitable and non profitable World Airlines ........................... 30 Table 4. 4: Comparison of airlines registered in the different regions of the world. .................... 31 iv LIST OF FIGURES Figure 2 1: Tax revenues & global supply chain jobs supported by the airline industry. ............... 9 Figure 2 2: Return on invested capital & weighted average cost of capital in the airline industry 12 Figure 2. 3: Conceptual model for determinants of profitability ................................................... 23 v ACRONYMS & ABBREVIATIONS A/C Aircraft ASK Available Seat Kilometer ANOVA Analysis of Variance CASK Cost per ASK CI Confidence Interval CoD Cost of Debt EBIT Earnings Before Interest And Tax EASA European Aviation Safety Agency EBITDA Earnings Before Interest Tax, Depreciation and Amortization EBITDAR Earnings Before Interest Tax, Depreciation, amortization and Rent EM Equity Multiplier IATA International Air Transport Association ICAO International Civil Aviation Organization KSE Karachi Stock Exchange LF Load Factor MFI Micro Finance Institutions NETMARGIN Net profit Margin ROA Return On Assets ROE Return On Equity ROIC Return On Invested capital RPK Revenue per Passenger Kilometer SPSS Statistical Package for Social Science TATO Total Asset Turnover Ratio USA United States of America USD United States Dollar UU Unity University WACC Weighted Average Cost of Capital vi ABSTRACT The objective of the study is to investigate the major determinants of Return on Equity as a measure of profitability in the airline industry using empirical financial data of world airlines. The study design is a quantitative study based on Panel secondary data collected by the International Civil Aviation Organization (ICAO). All the available valid data collected by the ICAO was used for the study. Nonlinear regression model and one way ANOVA are employed for data analysis. The results of the study indicate that there is a positive and significant relationship between Return on Equity and Net Profit Margin, efficiency and leverage for the profitable airlines. The relationship between ROE and efficiency becomes negative when the airlines are not profitable. The relationship between ROE and leverage is also negative when the airlines are not profitable. There is a significant difference in ROE, profit margin, and leverage between the profitable and non-profitable airlines while there is no significant difference in efficiency. There exists significant difference between airlines registered in the different regions of the world in terms of efficiency and profit margin. Even though, Airlines should focus on net profit margin to meet their objective of maximizing stockholder equity, airlines with negative profit margin should take action to lower both their efficiency and leverage to minimize their losses. Airlines with positive profit margins should maintain or improve their efficiency and leverage to maximize profitability ensuring that the positive profit margin is maintained or improved. However, leverage should only be increased to the optimum level to avoid excessive risk and interest expense that depletes profit margin. Keywords: Determinants of Profitability; Return on Equity; Net Profit Margin; Leverage; Total Asset Turnover Ratio, Airline Industry. 1 CHAPTER 1: 1.1. INTRODUCTION BACK GROUND The air transportation industry has become an increasingly important part of the global economy. In fact, it has become one of the most important industries. The technical and service achievements make the industry to be one of the greatest contributors to the enhancement of quality of life in the modern world (IATA 2014). The air transport value chain players include Airlines, Airports, aircraft and parts Manufacturers, Lessors, Freight Forwarders, aircraft Maintenance providers, Computer Reservation Systems (CRS), Travel Agents and other service providers. Airlines are the key components of the air transport system that provide the actual transport services (Wensveen 2007). The airline industry contributes greatly to the global and national economy by transporting people and cargo and creating jobs and economic activity (IATA 2014). It also provides worldwide access to time sensitive products from medicines and fresh produce to emergency aid. According to the IATA annual report (2012) nearly three billion people and 47 million metric tons of cargo were transported safely by air in 2012. This activity has supported about 57 million jobs and $2.2 trillion in economic activity which is about 3.5% of global GDP. More than half the world’s tourists travel by air and aviation underpins iconic major global events such as the Olympic Games. Aviation enriches lives by bringing families and friends together, bridging cultures, and spreading ideas (IATA 2012). Similarly, Debt providers to the airline industry are also well rewarded for their capital, usually invested with the security of a very mobile aircraft that can be claimed easily at any time. However, profitability in this vital sector of the industry is at very low levels. According to the IATA, airlines have failed to receive sufficient investor returns before and during the study period. The average annual return on capital of world airlines has been lower than the weighted average cost of capital (IATA 2014). The industry is affected by various factors such as cut throat competition, fuel price volatility, economic recessions, political changes and conflicts, financial pressure from various stakeholders, technological advancements, epidemics, and terrorism. This is mainly due to the global nature of the industry and its international connectivity which makes it vulnerable to local, 2 regional or international events (Oprea 2010; Buhalis 2003; ICAO 2013; Brauer and Dunne 2012). In addition, the industry is a highly regulated industry due to its catastrophic risks and economic importance. On the other hand, increasing competition among airlines and increasing costs of operation necessitate improved efficiency in airline operations to generate profit. Accordingly, airline managers are using various business strategies to stay profitable and continue operations despite the challenges. As a result periodic and continuous monitoring and assessment of determinants of profitability with the ultimate aim of taking proactive and timely measures to maintain profitability and prevent huge financial losses is a mandatory requirement (Schefczyk, 1993). Some of these actions include; implementing stringent cost saving and control mechanisms, outsourcing non-core business activities to enhance efficiency and effectiveness, consolidating and reducing the number of flights on less profitable routes, and acquiring regional airlines to enhance resource utilization and also create economies of scale and synergy (Morrell 2007; Teöke 2010; Matei 2012). Moreover, the airline industry has been undertaking various forms of collaborative actions with the ultimate objective of improving cost efficiency, creating economy of scale, and improving market power (Evripidou 2012). The actions include mergers and acquisitions, various forms of commercial cooperation agreements and establishment of airline alliances (Benjamin 1994; Czipura and Jolly 2007; Rajasekar and Fouts 2009). Despite the aggressive cost cutting, structural and behavioral changes and other efficiency improvement by airlines, profitability is still low in the industry. As a result, Equity owners are not rewarded adequately for risking their capital, except in a few airlines (IATA, 2014). This clearly shows the intensity of competition, and the challenges to doing business in the industry. Even though there is a huge body of research on the determinants of profitability in other industries (Mubin et. al. 2014; Bhutta & Hasan 2013; Dharmendra 2012) , such studies are very limited in the airline industry. The DuPont Analysis clearly states that Return on Equity has three factors i.e. Profit Margin, Total Assets Turnover and Equity Multiplier or leverage (Mubin et. al. 2014). This means that the various determinants of profitability can be explained by these three factors. 3 The few studies done on the airline industry have used return on equity (ROE) as measure of profitability and have analyzed the relationship between ROE and the determinants using a multiple linear regression model (Mantina et. al. 2012; Alahyari 2014 ; Menta 2015). However, almost all of these studies are national or regional in scope despite the global nature of the industry and the results are not in line with the DuPont formula. The use of a linear regression model is also questionable as the relationship between ROE and some of these determinants changes based on the profit or loss of the airline. For example, based on the DuPont formula, leverage can have positive relationship with ROE when the airline is making profits but negative relationship when the airline is incurring losses. The Analysis of the determinants of profitability of airlines at global level using a non linear regression model will reveal facts that can be employed to further improve profitability and formulate policies that enhance the performance of this vital industry and the individual airlines. 1.2. STATEMENT OF THE PROBLEM Even though airlines serve many purposes and benefit the society at large in different ways, Profitability is a key factor for the survival and growth of airlines. (Gitman 2008) as cited by (Mubin et. Al. 2014) stated that maximizing stockholders wealth is the generally accepted goal of financial management. However, profitability in the airline industry has been shown to be at very low levels and equity owners are not generally rewarded adequately for risking their capital. This indicates a critical situation for the sustainable and safe operation of airlines in a very capital intensive and heavily regulated industry. On the other hand, the growing demand for air transport services demands more investment for airlines to expand and continue to provide safe and dependable air transport services. Even though debtors are well rewarded for their capital, the heavy interest expense from high leverage/gearing is depleting the profit margin of airlines and exposing them to higher levels of financial risk and bankruptcy (Stepanayan 2014). Profitability is also a leading indicator as it measures ultimate performance of airlines and is important area reviewed by the regulatory bodies, Airline associations and alliances apart from assessments made by investors and creditors and other stakeholders to ensure its sustainability. Therefore, studies on the determinants of profitability for a more complete and better understanding of the interaction between profitability and its determinants will be very revealing for airline managers , creditors, investors and other concerned bodies to identify appropriate actions that can been taken to deal with this problem (Menta 2015). 4 Unfortunately, such studies are very limited in the airline industry though there is an immense collection of studies carried out on the determinants of profitability in other industries. Additionally, the few studies cited were national or regional in scope despite the global nature of the airline industry. The results of the studies done in the other industries are also conflicting and often times inconclusive. This study was carried out to investigate the determinants of profitability in the airline industry at a global level and fill this identified gap in order to contribute to a better understanding of the problem and gain insight for further action. 5 1.3. RESEARCH QUESTIONS The study tried to answer the following research questions. 1. What is the performance of the sample airlines in terms of return in Equity (ROE), Total Asset Turnover Ratio (TATO), Net profit margin (Netmargin) and leverage during the study period (2010 to 2014)? 2. How and how much do the determinants Total Asset Turnover Ratio (TATO), Net profit margin (Netmargin) and leverage affect profitability (ROE) based on empirical analysis of the financial data of the sample airlines between 2010 and 2014? 3. Is there any significant difference in profitability (ROE) and its determinants (Total Asset Turnover Ratio (TATO), leverage, Net Profit Margin) between the profitable and none profitable airlines? 4. Is there any significant difference in profitability (ROE) and its determinants (Total Asset Turnover Ratio (TATO), leverage, Net Profit Margin) between airlines registered in the different regions of the world? 1.4. OBJECTIVES 1.4.1. GENERAL OBJECTIVE The general objective of the study is to investigate the determinants of profitability in the airline industry. 1.4.2. SPECIFIC OBJECTIVES:  To describe the performance of the sample airlines in terms of return in Equity (ROE), Total Asset Turnover Ratio (TATO), Net profit margin (Netmargin) and leverage during the study  period (2010 to 2014). To investigate how and how much Total Asset Turnover Ratio (TATO), Net profit margin (Netmargin) and leverage affect profitability (ROE) based on empirical analysis of airlines  financial data between 2010 and 2014. To investigate if there is any significant difference in profitability (ROE) and its determinants (Total Asset Turnover Ratio (TATO), leverage, Net Profit Margin) between the 6  profitable and none profitable airlines. To investigate if significant difference exists in profitability (ROE) and its determinants (Total Asset Turnover Ratio (TATO), leverage, Net Profit Margin) between airlines of the different regions (Africa and Middle East, Asia, North America, Latin America and Europe) 1.5. SIGNIFICANCE OF THE STUDY This study contributes to the existing literature by investigating the expected associations between profitability and its determinants to extend the existing knowledge applicable at a global scale for the airline industry. Assessing factors that affect airline’s profitability is very important to equity owners and investors including governments, board of directors, airline managers and debtors to decide on appropriate actions necessary to improve profitability of airlines and maximize return to stockholders. Academicians can also use the results of the study to make further research and extend existing knowledge in the subject area. 1.6. SCOPE OF THE STUDY This study, like many other studies has limited scope. The following are the limitations and delimitations of this study. 1.6.1. LIMITATIONS The study has limitations including lack of data for consecutive years for some of the airlines as airlines do not send reports to ICAO consistently. However, the available data was used as there was sufficient number of airlines with adequate financial data. Additionally, airlines with negative average equity during a financial year have been excluded from study as it is invalid by definition and distorts the nature of the data and nature of relationships. This limits the relevance of the study to airlines with positive average positive equity within a financial year. The analyzed data is also data collected for a short period of time (2010-2014). Using data over a longer time period would have led to more accurate results. The type of data available on the ICAO database has also limited the type and level of analysis that can be carried out on profitability. Resource limitation including the limited time available during for the study is also a major limitation to limit the scope of the study to the research questions identified in the study despite the fact that there was a lot to be explored in the subject area. 7 1.6.2. DELIMITATIONS The study doesn’t include domestic and nonscheduled/charter airlines. It does not also intend to study the relationship between profitability and all its determinants. The study rather focuses on three major determinants which are believed to be the primary determinates of profitability. All the other determinants are considered secondary determinants if their effect can be explained through one or more of the primary determinants. 1.7. ORGANIZATION OF THE STUDY The organization of this study takes the following form: The first chapter is introductory which consists of background of the study and other introductory parts. The second chapter provides summary of literature review on the airline profitability and factors affecting profitability. Chapter three presents data source and methodology; chapter four is devoted to analysis of data and discussion based on data analysis results; finally, chapter five concludes the study and provides relevant recommendation along with insights into future research areas. 8 CHAPTER 2: LITERATURE REVIEW Review of relevant theoretical literature and empirical studies has been presented in two parts. The first part covers theoretical literature review on the nature of the airline business, profitability and measures of profitability and factors that affect profitability of airlines. The second part covers literature review on determinants of profitability based on empirical studies in other industries in general and the airline industry in particular. 2.1. REVIEW OF THEORETICAL LITERATURE 2.1.1. OVERVIEW OF THE AIRLINE INDUSTRY The first fixed wing scheduled air service was started on January 1, 1914 from St. Petersburg, Florida to Tampa, Florida in the US. Since then airlines have been evolving to provide domestic and international air services using small to big airplanes (Wensveen, 2007). Many countries have national airlines that the government owns and operates. Fully private airlines are subject to a great deal of government regulation for economic, political, and safety concerns. For instance, governments often intervene to halt airline labor actions to protect the free flow of people, communications, and goods between different regions without compromising safety. The International Civil Aviation Organization, a United Nations organ, establishes worldwide standards and recommended practices for safety and other vital concerns. It also coordinates International civil aviation activities. National or regional civil aviation authorities such the European Aviation Safety Agency (EASA) or the US Federal Aviation Administration (FAA) regulate the certification and operation of airlines in their respective countries. Most international air traffic is regulated by bilateral agreements between countries or regions, which designate specific carriers to operate on specific routes. Bilateral agreements are based on the "freedoms of the air", a group of generalized traffic rights ranging from the freedom to overfly a country to the freedom to provide domestic flights within a country. The airline industry contributes greatly to the global and national economy by transporting people and cargo and creating jobs and economic activity (IATA, 2014). It also provides worldwide access to time sensitive products from medicines and fresh produce to emergency aid. According to the IATA annual report (2012) nearly three billion people and 47 million metric 9 tons of cargo were transported safely by air in 2012. This activity has supported about 57 million jobs and $2.2 trillion in economic activity which is about 3.5% of global GDP. More than half the world’s tourists travel by air and aviation underpins iconic major global events such as the Olympic Games. Aviation enriches lives by bringing families and friends together, bridging cultures, and spreading ideas. Without airlines, businesses would have much less access to global markets and be less able to achieve greater efficiency in production through globalization. Without airlines, leisure travel would be much less widespread, restricting the economic and development benefits available from a thriving tourism industry. The growth of the global airline industry also involves some wider costs, for example from environmental emissions. However, airlines continue to meet the demands of customers for greater travel while meeting environmental, health, safety and security obligations in a responsible way. Figure 2 1: Tax revenues & global supply chain jobs supported by the airline industry. Source: IATA, 2014 Similarly, Debt providers to the airline industry are also well rewarded for their capital, usually invested with the security of a very mobile aircraft that can be claimed easily at any time. 10 However, profitability in this vital sector of the industry is at very low levels. According to the IATA airlines have failed to receive sufficient investor returns before and during the study period. The average annual return on capital of world airlines has been lower than the weighted average cost of capital (WACC). The industry is exposed to numerous internal and external factors such as cut throat competition, fuel price volatility, economic recessions, political changes and conflicts, substantial commercial and financial pressure from various stakeholders, technological advancements, epidemics, and terrorism. The reason why the industry is exposed to such factors heavily is its international connectivity which makes it vulnerable to various concerns be it local, regional or international (Oprea, 2010; Buhalis, 2003; ICAO, 2013; Brauer and Dunne, 2012). Moreover, the Airline industry is vulnerable to systemic crises and risks which have led to the creation of costly safety regulation which resulted in reduced revenues and excessive capacities. Though airlines have been widely government-owned or supported in many parts of the world, in recent decades the trend has been moving towards independent, commercial public companies by giving more freedom to non-government ownership of airlines. As the result, increasing number of commercial airline companies has put more pressure on their management to continually seek profits. On the other hand, increasing competition among airlines and increasing costs of operation necessitate improved efficiency in airline operations to generate the required profit. Accordingly, airline managers are using various business strategies to stay profitable and continue operations despite the challenges. As a result periodic and continuous monitoring and assessment of determinants of profitability with the ultimate aim of taking proactive and timely measures to maintain profitability and prevent huge financial losses is a mandatory requirement (Schefczyk 1993). Some of these actions include; implementing stringent cost saving and control mechanisms, outsourcing non-core business activities to enhance efficiency and effectiveness, consolidating and reducing the number of flights on less profitable routes, and acquiring regional airlines to enhance resource utilization and also create economies of scale and synergy (Morrell 2007; Teöke 2010; Matei 2012). In addition, airlines also implement various hedging practices to manage risk exposure suck as changing prices of fuel, foreign currency exchange rate 11 fluctuations, interest rate fluctuations and others which affect airline profitability (Abbey 2007; Pwc 2009; Fernando 2006). Moreover, the airline industry has been undertaking various forms of collaborative actions with the ultimate objective of improving cost efficiency, creating economy of scale, and improving market power (Evripidou 2012). The actions include mergers and acquisitions, various forms of commercial cooperation agreements and establishment of airline alliances (Benjamin 1994, Czipura and Jolly , 2007; Rajasekar and Fouts 2009). All these efforts have been carried out to eventually enable airlines to stay in the business, improve profitability and continue to provide the required air transport services. Despite the aggressive cost cutting, structural and behavioral changes, downsizing, mergers and other efficiency improvement by airlines, profitability is still low in the industry. As a result, Equity owners are not rewarded adequately for risking their capital, except in a few airlines (IATA 2014). Investors should expect to earn at least the normal return generated by assets of a similar risk profile, the weighted average cost of capital (WACC). In most industries it’s higher than the cost of capital. As a result, Equity investors are seeing their capital shrink every year. This clearly shows the intensity of competition, and the challenges to doing business in the industry. However, with the sharp decline in the price of fuel, it is showing a positive trend and has brought in a new dawn of hope for the industry. 12 Figure 2 2: Return on invested capital & weighted average cost of capital in the airline industry Source: IATA, 2014 Although many countries continue to operate state-owned or parastatal airlines, many large airlines today are privately owned and are therefore governed by microeconomic principles to maximize shareholder profit. 2.1.2. PROFITABILITY Profitability means ability to make profit from all the business activities of an organization, company, firm, or an enterprise. It shows how efficiently the management can make profit by using all the resources available in the market. According to Howard & Upton, “profitability is the ability of a given investment to earn a return from its use.” 13 2.1.3. MEASURES OF PROFITABILITY IN THE AIRLINE INDUSTRY Various and different measures are employed by airlines to measure profitability. The most common ones have been identified and described below. Operating Ratio The operating ratio is defined as operating revenue expressed as a percentage of operating expenditure; operating margin is an alternative expression that is similar to margin on sales (Morrell, 2007). The operating ratio or margin gives an indication of managerial effectiveness in controlling costs and increasing revenues. However, it can be distorted by changes in depreciation policy, or a switch from ownership of aircraft to operating leases. An alternative formulation of this ratio that avoids the operating lease/owned aircraft distortion is operating profit (after interest charges) expressed as a percentage of operating revenues. Net Profit Margin The net profit margin is after tax profit expressed as a percentage of operating revenue or turnover. This ratio has the advantage over the operating ratio or margin in that it is free of the operating lease distortion. However, the margin for a particular year may be increased or reduced by large asset sales, restructuring costs or asset write-downs. Return on Invested Capital (Capital Employed) Return on invested capital (ROIC) is the pre-tax profit before interest paid as a percentage of average total long-term capital employed. Here the ratio could be calculated before net interest. Some airlines define this ratio as operating profit as a percentage of capital, but it is more logical to include any income from asset sales and investments to show the profit available to provide a return for the two classes of long-term capital providers, debt holders and shareholders. Some investment banks use what is known as NOPAT for the numerator and adjust the denominator to include short-term debt and add back accumulated amortization to goodwill. NOPAT is defined as EBIT plus interest received (income) together with the goodwill amortization that has been added to the denominator. EBIT can also be reduced by the full tax rate. 14 The ratio can be calculated with or without minority interests, but if they are included (as in the example above), they should be included in both numerator and denominator of the ratio. Capitalized interest has been subtracted from interest payable, to reflect interest on lending for current, rather than future operations. Return on Equity Return on equity (ROE) is the net profit after interest and tax expressed as a percentage of average shareholder equity. The numerator is before deducting minority interests and the denominator includes the capital belonging to these interests. This percentage gives an idea of how successful the airline’s management is in using the capital entrusted to it by the owners of the company, or equity shareholders. As the objective of any firm is to maximize the wealth of its owners, return on equity the most holistic and preferable measure to measure profitability (Morrel 2007). 2.1.4. DETERMINANTS OF PROFITABILITY According to the DuPont formula there are three major determinants of profitability which includes Net Profit Margin, Total Assets Turnover and Equity Multiplier or leverage (Mubin et. al. 2014). Although there are a large number of both internal and external factors that affect profitability of a firm, their effect can be explained by one or more of these three variables. The following paragraphs are dedicated to explain these three determinants and the major factors that affect these determinants. Net Profit Margin The net profit margin as a ratio of net profit and operating revenue. Factors that affect profit margin will affect profitability. It is a well-known fact that for a firm to be profitable it has to maximize its revenues and reduce its costs. Net profit margin is determined by unit revenue and unit cost. In the airline industry unit cost is measured in CASK (cost per available seat kilometer) or CATK (cost per available tone kilometer) for cargo while unit revenue is measured by yield (revenue per passenger kilometer or revenue per ton kilometer for cargo). Yield or unit Revenue Yield is the revenue received by a carrier per passenger kilometer or tone kilometer for cargo. However maximizing yield will not always produce higher profits as it will have a negative effect 15 on load factor. Therefore, airlines use a revenue management system to maximize yield while at the same time maintaining load factor at the highest level possible. Unit Cost CASK is the cost of a seat being carried for one kilometer and CATK the cost of a tone of cargo carried for one kilometer. Unit cost is a function of the various costs in the airline fixed or variable, direct and indirect, operating and non-operating costs. Aircraft acquisition cost, aircraft fuel, wages and salaries, a/c maintenance cost, ground handling costs, airport charges and fees are some of the major costs for an airline. Fuel cost has been the major cost of airlines often accounting more than 40% of total costs during the study period. Airlines in high wage nations also bear huge cost in terms of salaries and wages while it is much less for airlines in low wage countries. Average stage length and fleet age also have significant effect on airline costs. System wide average Load factor also affects unit cost by allocating the fixed costs of the airline to a large number of passengers (Wensveen, 2007). Efficiency Efficiency is producing more outputs with less input. In the airline industry the following factors are used to bring about better efficiency. Load factor One of the key measures of airline performance the airline business is load factor. Load factor is ratio between available seat-kilometer (ASK) and revenue passenger kilometers (RPK) or available tone-kilometer (ATK) and revenue tone kilometer (RTK) in case of cargo. Airlines struggle to get higher load factors for all their flights as much as possible. Higher load factors result in more revenue and reduce unit costs. Load factor is also a measure of equipment utilization. Airlines should be able to have load factor above their breakeven load factors to be profitable. According to (Doganis 2002), the profitability of an airline depends on the interplay of three variables, unit costs, unit revenues or yields and load factors. Airline managers must adjust costs, fares and load factors to produce profitable combinations. These is being implemented by using computerized yield management systems to match demand and supply using discounted fares with conditions attached to each fare (Wensveen 2007). 16 Aircraft leasing There are two types of leases, financial and operating lease. A financial lease is a lease that transfers substantially all the risks and rewards incident to ownership of an asset to a lessee. The effect is the same as a loan except that titles to the asset remains with the lessor until all lease payments have been made (Wensveen, 2007). In an operating lease, an airline is provided with the use of an aircraft for an agreed period. The lessor retains ownership of the asset for the duration of the lease and he repossesses the asset at the expiry of the lease term. An operating lease is of a shorter term than a finance lease. The lessee avoids residual ownership risk. This type of lease is more costly compared to the finance lease (because the lessor assumes the residual ownership risk) or the normal long-term debt service obligations. Operating leases are considered to be off-balance sheet financing. Lease payments are treated as an expense in the income statement. An operating lease can be wet or dry. In a dry operating lease, the lessor provides insurance and major overhauls whilst the lessee provides pilots, cabin crew and maintenance activities. In a wet lease, the lessor provides the aircraft, including maintenance, repair and overhaul, pilots and full insurance, whilst the lessee usually provides the cabin crew (Chingosho, 2005). Lease ratio is a measure that can be used to measure the extent of leased equipment used by the airline. Only operating leases (wet or dry) can be considered as capital lease is a form of financing and included as a liability in the balance sheet while operating lease costs are taken as part of the operating cost. An airline can improve its efficiency by leasing- in aircraft if it has the capacity to generate more sales. Conversely, the airline can also improve its efficiency by leasing out idle aircraft at times of overcapacity. Average daily Aircraft utilization Average daily Aircraft utilization is one of the measures of efficiency in the airline industry. It is defined as Aircraft hours flown (block-to-block) divided by the number of days the aircraft is available for service. Aircraft flying longer routes have better average daily aircraft utilization than sort haul flights. For an airline to generate sufficient revenues aircraft must be flown as much as possible with minimum down time (Thoren 2002). 17 Employee productivity Employee productivity is also a key factor for airlines as the industry is also labor intensive. Revenue per employee and Available Seat Kilometer (ASK) per employee are widely used as a measure of employee productivity. However, as there is a significant difference in wage levels between countries, measuring revenue per employee doesn’t show real financial efficiency. Revenue to salary and wage expense ratio will be a better measure of financial efficiency to measure employee productivity in the global airline industry (Thoren 2002). Leverage According to (Kumar 2008) debt positively affects returns to shareholders in good times and adversely affects them in bad times and creates “financial leverage”. A firm that is profitable can use debt to maximize return to owners by using debt as leverage. However, a firm that incurs losses will be affected negatively by using high levels of debt. The determinants of leverage are factors that limit the amount of debt that a firm can get from creditors. The Earning before Tax and Interest (EBIT) of the firm should at least cover its debt expense and debt expense is a function of cost of debt and actual debt balance of the firm. But when a firm increases its debt its will increase the assets of the company. EBIT will increase accordingly based on the new EBIT return on assets of the firm. The firm can cover more debt expense and more debt. However, the firm should build its capacity to use the funds. In the airline business this means developing the market, developing infrastructure, acquiring aircraft and systems and employing and developing people. The capacity to grow will create an internal limit to the amount of debt that the airline can use during a specific financial period. Additionally, creditors’ will evaluate their debt risk and will increase their cost of debt as the leverage increases. When the cost of debt goes beyond the EBIT return on assets the firm cannot anymore cover its debt expense and this will limit the amount of debt the firm can leverage. There are various theories on the determinants of leverage and various factors have been identified as significant determinants of leverage. The factors include profitability and cost of 18 debt , market value of the firm, size, country legal environment, tax laws, age , information asymmetry, experience of management, ownership , governance structure and risk exposure (Kumar 2008). 2.2. EMPIRICAL LITERATURE REVIEW Even though various studies have been carried out on the determinants of profitability in other industries such studies are very limited in the airline industry. As a result it will be relevant to review related literature in other industries to gain insight on the issue 2.2.1. DETERMINANTS PROFITABILITY IN OTHER INDUSTRIES (Dissanayake 2012) studied 11 MFIs in Sri Lanka to identify the significant determinants of Return on Equity in Sri Lankan Microfinance Institutions (MFIs) in the period 2005-2011. The study used operating expense ratio, personal productivity ratio and cost per Borrower ratio as independent variables to measure efficiency and productivity. Financing structure or leverage was also used as an independent variable measured by debt/equity ratio. Profitability as measured by return on equity ratio was the dependent variable. Correlation analysis and multivariate regression was employed for data analysis. The research concluded stating that the Cost per Borrower and Debt/Equity ratios are statistically significant predictor variables in determining return on equity in MFI. Debt to equity ratio showed negative correlation with profitability. (Bhutta and Hasan 2013) examined the impact of firm specific and macroeconomic factors on profitability of food sector in Pakistan. The study explored the impact of firm specific factors on profitability of companies listed in food sector of Karachi stock market by employing multivariate regression analysis for the period of 2002-2006. The firm specific factors include debt to equity, tangibility, growth and size and macroeconomic factor include food inflation. Findings of the study reveal the presence of significant negative relationship between size and profitability. However, tangibility, growth of the firm and food inflation have positive but nonsignificant relationship with profitability. Similarly, a non significant negative relationship is observed between debt to equity ratio of firm and its profitability. Profitability is negatively correlated with debt to equity ratio and tangibility and positively related to size, growth and food 19 inflation. Profitability shows significant moderate relation to size and insignificant weak relation to debt to equity ratio, tangibility, growth and food inflation. Debt to equity is negatively correlated with the tangibility and positively correlated with size, growth and food inflation. (Mubin et. al. 2014) studied which component of the DuPont identity is most consistent or volatile among profit margin, total assets turnover and equity multiplier in Fuel and Energy Sector, Chemicals Sector, Cement Sector, Engineering Sector, textiles Sector and Transport and Communication Sector of Karachi Stock Exchange ( KSE) 100 index by taking data from 2004 to 2012 of 51 companies. The One Way ANOVA shows that it is Assets Turnover which significantly varies from industry to industry whereas Equity Multiplier and Profit Margin are not much volatile among indifferent industries. (Dharmendra 2012) studied the determinants of profitability of Indian Automobiles Industry for a period of five years i.e. 2004-05 to 2008-09. The study analyzed the relationship between profitability and Return on Capital Employed, Size, Liquidity, Inventory Turnover Ratio and Debt-Equity Ratio. Multiple Linear Regression model consisting of the four independent variables has been used to test the effect on dependent variable. The study found that debt to equity ratio, inventory turnover ratio, and SIZE were the most important determinants of profitability which affected the profitability of the companies under the study positively. Only liquidity was found to have negative effect on profitability. Even though literature on determinants of profitability in other industries is immense, almost all of these studies are based on a single country or a single country in a single industry. 20 2.2.2. DETERMINANTS OF PROFITABILITY IN THE AIRLINE INDUSTRY Despite the fact that the studies on determinants of profitability in the airline industry are very limited and also are country level or regional level in scope, the literature review presented below will shade some light on the subject issue. (Mantina, Jen-Hung & Wang 2012) studied the determinants of profitability in the U.S. domestic airline industry by considering operations strategy, productivity, and service measures, while focusing the attention on the effects of the 9/11 attack. It finds that Prior to 9/11, operations strategy, productivity, and service measures are significantly related to profitability. However, after 9/11, none of the service measures are significant. Further analysis suggests that after 9/11 passengers are more forgivable to service glitches or are associating lack of service with the intensified security measures imposed after 9/11. We also find that after 9/11, the profitability of full-service carriers is improving faster than that of focused carriers. (Thoren 2002) examined profits and their determinants based on financial data collected on US, European and Asian airlines. The researcher assessed the impact of revenue components like GDP of a nation, revenue received by a carrier per passenger kilometer (yield) and load factor as well as the impact of cost components like cost paid for fuel, labor, maintenance, landing and other airport use fees, rent paid for aircraft use and the like on profitability of the airline using a multiple regression model. The study concludes that load factor is a major determinant of profitability and unit costs rather than accounting costs are the determinants of the cost function and profit. (Alahyari 2014) studied determinants of profitability for Turkish airlines using the financial data of 13 major airlines in turkey from 1994 to 2013. The study used the variables company size (logarithm of sales), company growth opportunities (growth of sales), leverage ratios, liquidity ratios and tangibility of assets (measured by Ratio between Fixed Assets and Total Assets). Multiple Regression analysis model was also used for analyzing the relationship. Based on the panel data analysis, findings show that tangibility of assets, growth opportunities and liquidity ratios have significant impacts on the profitability of the firms. Tangibility of assets negatively affects the profitability of the firms in the airline industry, while growth opportunities 21 are also inversely associated with the profitability of the airlines in the sample. In addition, liquidity ratio is another factor which showed a negative and statistically significant relationship with the profitability of the firms. (Stepanyan 2014) computed liquidity, profitability and solvency ratios for eight U.S largest airlines over the period 2007-2012 and concluded that profitability in the airline industry has been poor throughout the six-year period and remains so in the face of improvements primarily due to losses incurred during the economic recession, slowing demand for air travel and increasing operating expenses mainly driven by rising fuel expenses and labor costs. The analysis of long-term solvency risk has indicated high financial leverage in the U.S airline industry which puts the leading carriers at higher risk although coverage ratios have showed that on average selected air carriers have been able to cover interest expense and other fixed charges since 2010 when the global economic environment began to gradually better. 2.2.3. CONCEPTUAL MODEL Figure 2.3 below shows the conceptual model developed for the study Based on the literature review above for a better understanding and analysis of the determinants of profitability in the airline industry. The following paragraphs are dedicated to explain the model. In this model, there are only three primary determinants of profitability which are net profit margin, operational efficiency and leverage. The impact of the other determinants identified in profitability literature could be explained with one or more of these determinants. Narrowing down these determinants to the three primary determinants will help to reduce the number of variables and make a meaningful analysis with significant explaining power. As these determinants could be interdependent and cyclical in nature, further systematic analysis could be carried out on the determinants of the primary determinants for a complete understanding of the model. In simple terms Net Profit margin is mainly determined by unit revenue (yield) and unit cost. However various factors can affect both unit revenue and unit cost such as load factor, demand and supply, customer satisfaction, yield management, labor cost and price of fuel. On the other hand, efficiency could also so be affected by the same variables that affect net profit margin and other additional factors. For example load factor is one of the primary determinates of 22 efficiency as it is a significant determinant of unit cost. Average daily aircraft utilization, employee productivity and inventory turnover ratio are also expected to be the factors which significantly influence efficiency. Leverage is also affected by various factors including the existing and past profitability of the firm, the socio political and economic environment, tax laws, size of firm and the cost of debt available for the firm. As higher leverage entails higher debt expense, leverage itself can affect net profit margin. 23 Figure 2. 3: Conceptual Model for Determinants of Profitability Source: Developed by Researcher, 2016 24 CHAPTER 3: METHODOLOGY The preceding chapter presented the review of the existing evidence on factors affecting the profitability of airlines. Accordingly, the results from a review of the literature are used to establish expectations for the relationship of the different determinants. Therefore, the purpose of this chapter is to introduce framework for analyzing determinants of airline profitability, the underlying principles of research methodology and the choice of the appropriate research method for the thesis. 3.1. RESEARCH DESIGN The research design is quantitative study based on panel financial data of airlines in the world from 2010 to 2014. The financial data of “registered commercial airlines are providing scheduled international flight services.” and which voluntarily provided data to the ICAO are included in the study. 3.2. SAMPLE SIZE The minimum sample size required for the study using the rules of thumb for linear regression of three individual predictor variables is 83. However, similar studies (Alahyari 2014; Menta 2015) have used a sample size of about 30. As it is possible to use all the valid data available from ICAO for the study 206 valid observations have been used to improve the power of the study though the actual minimum sample required is much less than 206. All airlines financial data with negative average equity balance were excluded from the study. 3.3. DATA COLLECTION PROCEDURES The data was initially collected by the ICAO from airlines on voluntary basis and availed for use for subscribers. As the data was incomplete and has errors, further data was collected from airline websites and the United States Securities and Exchange Commission (SEC) to complete the data and correct erroneous data as much as possible. 25 3.4. VARIABLES AND MEASUREMENT This study aims to test the determinants of profitability, so the dependent variable is profitability. In addition, in the previous chapter, various determinants of profitability have been identified based on review of the literature. The independent variables identified in the different studies were represented with three major variables based on the DuPont formula. The three variables are net profit margin (NETMARGIN), Total Asset Turnover Ratio (TATO) and Leverage. The table below summarizes the dependent and independent variables and the measurement employed for each variable. Table 3. 1: Variables and Measurement Dependent variable Variable type Measurement /formula (Dependent/Independent ) Return on Equity Dependent Earning _ After _ Tax( EAT ) X 100 Average _ Equity margin Independent Earning _ After _ Tax( EAT ) X 100 Re venue (ROE) Net profit (NETMARGIN) Total Asset Turnover Independent Ratio(TATO) Leverage Independent Re venue Average _ Assets Average _ Total _ Debt X 100 Average _ Assets Source: Developed by researcher, 2016 3.5. DATA ANALYSIS PROCEDURES The Data was coded and entered in SPSS version 20. All data entries with outlier data were carefully identified and corrected when possible or excluded from the study during data analysis. Categorical data (region) were coded based on industry practices. The data was further cross tabulated and described using tables. Airline financial data with negative equity balance within a financial year was also excluded from the study. 26 The relationship between ROE and its determinants was analyzed using segmented linear regression (nonlinear regression), as the data is continuous and the relationship between ROE and asset turnover ratio and leverage is nonlinear (positive when ROE is positive and negative when ROE is negative). Ninety five percent confidence intervals are used to test statistical significance of the association between ROE and its determinants. Additionally one way ANOVA test was used for comparison of groups. IBM SPSS statistics version 20 is used for data analysis. 27 CHAPTER 4: 4.1. RESULTS AND DISCUSSION DESCRIPTIVE STATISTICS Outcomes of the descriptive data analysis of the main variables used in the Regression model are presented on Table 4.1 below. Key figures, including mean, minimum, maximum and standard deviation values are reported to give the overall description of the data. Table 4. 1: Descriptive statistics of financial ratios for world Airlines (2010-2014 ) Observations ROE NETMARGIN TATO LEVERAGE 206 206 206 206 Minimum -161.02 -14.93 .00 9.47 Maximum 131.63 15.06 5.29 96.44 Mean 1.87 1.34 1.48 72.68 Std. Deviation 41.65 5.163 1.069 17.72 Source: Descriptive analysis by researcher using SPSS, 2016 A total of valid 206 observations for 100 airlines were included in the study despite the large size of the initial observations. The reduction in the number of observation was due to the exclusion of observations with negative average Equity during a financial year, observations with outlier data, and observations for which average balance sheet financial data cannot be calculated due to the absence of financial data for consecutive years. The average ROE of the sample airlines during the study period as can be seen on the above table is a mere 1.87%. The average net profit margin is also 1.34% and the average total asset turnover ratio is 1.48. The average leverage is 72.69 % which would be considered a high level of leverage for other industries. It should also be noted that many observations with negative average equity have been excluded from the study as return on equity is undefined when equity is negative. 28 4.2. ASSOCIATION BETWEEN PROFITABILITY AND ITS DETERMINANTS The tables below show the results of the segmented non linear regression based on the model; ROE=a1*TATO+a2*Leverage +a3*NETMARGIN+c1 for ROE<0 and ROE= a4*TATO+a5*leverage +a6*NETMARGIN+c2 for ROE>0. Where; ROE - is Return On Equity a1 to a6 – are coefficients c1 and c2- are constants TATO - represents total asset turnover ratio NETMARGIN - represents net profit margin Leverage - represents leverage and The * sign - represents the multiplication sign. Table 4. 2: Parameter estimates of the segmented linear regression model. Parameter Estimate a1 a2 a3 a4 a5 c1 c2 a6 -17.660 -1.323 7.452 10.548 .664 121.249 -57.632 4.148 Std. Error 2.836 .166 .619 1.425 .093 14.729 8.667 .527 95% Confidence Interval R squared Lower Bound Upper Bound -23.253 -12.068 -1.651 -.995 6.231 8.674 0.811 7.738 13.358 .480 .847 92.203 150.296 -74.724 -40.541 3.109 5.188 Source: SPSS output for Non linear regression by researcher, 2016 The coefficients of both Total Asset Turnover Ratio (a1) and Leverage (a2) are negative (-17.66 and -1.323 respectively) indicating a negative relationship between ROE and the two independent variables when ROE is negative or zero. However, the relationship is positive with net profit margin as indicated by the coefficient of Netmargin (a3= 7.45) 29 On the other hand the relationship of ROE with all the three independent variables is positive when ROE is positive (a4=10.548, a5=.664, a6= 4.148). The standard error values for all the variables are low relative to the actual values which indicate a reliable estimate. The ANOVA test result of the non-linear regression model R squared value is 81.1%. This value indicates the proportion of observation explained by the model which indicates a good model fit to predict the dependent variable from the independent variables (ROE from TATO, Leverage and Netmargin). In summary, as shown on Table 4.2 the results of the segmented nonlinear regression show that: When ROE is negative (less than or equal to zero ) there is o a positive relationship between Net Profit margin and ROE   but negative relationship between ROE and Total Asset Turnover Ratio and Leverage When ROE is positive (ROE>0) there is o a positive relationship between ROE , Total Asset Turnover Ratio and Leverage The R squared parameter of ANOVA test result on table 4.2 also shows that 81.1 % of the relationship has been explained by the model indicating a good model fit. The remaining 18.9 % of the data could be explained with other factors. Detailed results of the segmented linear regression model appendices section. are attached as “APPENDIX 1“in the 30 4.3. DIFFERENCES BETWEEN THE PROFITABLE AND NON PROFITABLE AIRLINES Table 4. 3: Comparison between profitable and non profitable World Airlines N 0-Non- profitable 1-profitable 0-Non - Profitable TATO 1- profitable 0- Non profitable LEVERAGE 1- Profitable 0- Non Profitable NETMARGIN 1- Profitable ROE 64 142 64 142 64 142 64 142 Mean -40.97 21.22 1.44 1.52 78.20 69.58 -4.45 3.97 F Sig. 189.093 .000 .228 .634 9.712 .002 274.254 .000 Source: One way ANOVA test results using SPSS by researcher , 2016 From figure 4.5 above it can be seen that the average ROE of the non-profitable airlines in the sample is -40.97% and 21.22 % for the profitable airlines. The average TATO is 1.44 and 1.52 for the non profitable and profitable airlines respectively. The non profitable airlines have an average leverage of 78.2% and the profitable airlines have an average leverage of 69.58%. The net profit margin for the non profitable airlines was -4.46% and 3.97% for the profitable airlines. The ANOVA test shows that ROE (p= 000), leverage ( p=0.002) and net margin (p=0.000) are significantly different between the profitable and non-profitable airlines. There is no significant difference in efficiency (p=0.634) between profitable and non-profitable airlines. It is also evident that non-profitable airlines have higher debt burden than non-profitable airlines which is also statistically significant. The average leverage of non-profitable airlines is greater than the average of profitable airlines by 8.62% which is an interesting finding. Details of the one way ANOVA results for comparison between profitable and non profitable airlines is attached as “APPENDIX 2” in the appendices section. 31 4.4. DIFFERENCES BETWEEN AIRLINES OF DIFFERENT REGIONS Table 4. 4: Comparison of airlines registered in the different regions of the world. Observations ROE TATO LEVERAGE NETMARGIN 1:Middle East & Africa 2:.Asia pacific 3:Europe 4:.Latin America 5:North America 1:Middle East & Africa 2:.Asia pacific 3:Europe 4:.Latin America 5:North America 1:Middle East & Africa 2:.Asia pacific 3:Europe 4:.Latin America 5:North America 1:Middle East & Africa 2:.Asia pacific 3:Europe 4:.Latin America 5:North America 17 21 113 27 28 17 21 113 27 28 17 21 113 27 28 17 21 113 27 28 Mean -7.69 -.51 1.78 -5.34 17.67 1.08 .91 1.69 2.05 .89 68.61 72.49 73.13 71.55 71.35 -.29 2.35 1.04 .54 3.76 Source: One way ANOVA test results using SPSS by researcher, 2016 F Sig. 1.465 .214 7.922 .000 .430 .787 2.521 .042 32 The results of the one way ANOVA test above shows that there is significant difference in net profit margin and TATO between the regions. South American airlines have the highest efficiency (TATO=2.05) which is significantly different from the other regions, While North American and Asian airlines have a better net profit margin. Airlines in Africa and Middle East have the lowest and negative average profit margin. Even though the difference is not statistically significant, American airlines have the highest average ROE of 17. 5 % mainly due to the high profit margin and good leverage of the airlines during the study period. Details of the one way ANOVA results for comparison between airlines registered in the different regions of the world is attached as “APPENDIX 3” in the appendices section. 33 4.5. DISCUSSION OF RESULTS The results of the linear regression model clearly show negative significant relationship between ROE and asset turnover ratio and leverage when ROE is negative; which is in line with the DuPont formula. However, the relationship is positive and significant when the profit margin is positive which is also in line with the DuPont formula. The results are mixed in other studies as almost all used a linear regression model for both cases. For example, Alahyari (2014) reported a negative relationship between ROE and leverage for Turkish airlines using a multivariate linear regression model. Menta (2015) also reported the same relationship for three airlines in sub-Saharan Africa. In both cases, looking at the descriptive statistics, both profitable and non profitable airlines have been included in the study. However, Dharmendra (2012) has reported a positive relationship between profitability and leverage for the automotive industry, though similar results are not cited for the airline industry. The difference is more probably caused by the lack of segmentation in the studies even though the relationship is not linear. The one way ANOVA analysis for significance difference between the profitable and non profitable airlines shows that both profit margin and leverage are significantly different between the two groups. Asset turnover ratio is not significantly different between the two groups. It means that both the profitable and non profitable airlines have been operating equally efficiently, though this is a huge disadvantage for the non profitable airlines as it has a negative effect on profitability. Additionally, Even though profit margin is the primary determinant for the negative effect on profitability, it is evident that heavy leverage has increased the effect for the non-profiting airlines. The effect of high leverage for the non profitable airlines is twofold. Firstly, the high debt expense depletes their net profit margin. Secondly, the high leverage increases the negative impact of leverage on ROE. In this case high leverage acts as a double edged sword to erode stock holders’ equity. However, high leverage has the opposite effect for the profiting airlines. It helps to maximize return on equity along with efficiency until the debt expense from high leverage and increasing cost of debt starts to shift the profit margin to the negative side. 34 The results of the one way ANOVA test for significant differences between airlines registered in the different regions of the world that there is significant difference in net profit margin and TATO between the regions. Latin American airlines are more efficient than the airlines in the other regions, While North American and Asian airlines have a better net profit margin. Airlines in Africa and Middle East have the lowest and negative average profit margin mainly due to the poor performance of African Airlines. The North American airlines especially those in the US have taken aggressive actions to improve profitability including consolidation in the form of mergers and acquisitions and other structural changes. This has reduced the level of competition and increased load factors which resulted in improved profitability. Aggressive actions to generate ancillary revenue are also the other contributor to the profitability of North American airlines. 35 CHAPTER 5: 5.1. CONCLUSION AND RECOMMENDATION CONCLUSION Studies on determinants of profitably most commonly used multiple linear regressions to investigate the relationship between profitability and its determinants. However, the relationship between ROE and its positive determinants is not linear. Therefore, we have evidenced mixed and conflicting results. The main reason for these conflicting results is the linear model used by the studies while the relationship is nonlinear (positive when the firm is making profits and negative when the firm is making a loss). Additionally, the studies have mixed the primary determinants with the secondary determinants making the effect and explanation power minimal. There is a significant and positive relationship between ROE and net profit margin for both the profitable and non profitable airlines. However, the relationship between ROE and total Asset turnover ratio is positive and significant when the airlines are making profits and negative and significant when the airlines are incurring losses. Similarly, the relationship between ROE and Leverage is positive and significant when the airlines are making profits and negative and significant when the airlines are incurring losses. There is significant difference between profitable and non profitable airlines in terms of net profit margin, ROE and Leverage. Obviously the non profitable airlines have lower ROE and profit margin (negative) than the profitable airlines. Additionally, the non profitable airlines are also leveraged significantly higher than the profitable airlines. There is also significant difference between airline registered in the different regions of the world in terms of efficiency (TATO) and profit margin while there is no significant difference in ROE and leverage. 5.2. RECOMMENDATIONS Even though, profit margin is very low in the airline industry, airlines can create reasonable return on equity if the airline operates efficiently and is leveraged at an optimum level. Leverage should be maintained at optimum level based on the expected EBIT margin, asset turnover ratio and cost of debt of the airline to avoid excessive interest expense that depletes net profit margin. Airlines with expected negative profit margins should take immediate action to improve their 36 profit margin. But if it is unlikely that profit margin will improve to the positive side, it would be imperative to reduce efficiency and leverage to lower their multiplying negative effect on negative profit margin and huge financial loss as both higher efficiency and leverage have negative effect on return on equity ( profitability). Some airlines are observed maintaining higher leverage and Asset turnover ratio entire study period with negative profit margins during the while others have achieved positive margins after one or two years. It is recommended that airlines should take action to avoid high leverage and asset turnover ratio with negative profit margins. They have to lower their leverage and asset turnover ratio to minimize their loss if they can’t improve their profit margin to the positive side. This requires downsizing of the airline’s operations by selling of their idle assets, returning leased aircraft or re-lease them to other operators, and settling part of their debt obviously starting with the debt having the highest cost. The flight frequency on the most non profitable routes should be reduced or the route/s closed. However, action on load factors should be carefully evaluated as reduction on load factor will result in an increase in unit cost exacerbating the already negative profit margin. As the profit margin and return on equity are very low for the airline industry especially for airlines in Africa, governments need to take policy actions and provide support to airlines so that airlines continue to provide the social and economic benefits to the society at large. Additional incentives for investors in the industry should be in place to attract and keep them in the industry including reduction of tax burden on airlines, charges and fees, relaxed regulation with due consideration to safety , liberalization and other government services. Researchers working on determinants of profitability are recommended to consider a segmented regression model rather than a linear model as the relationship of most of the positive variables with profitability will be negative when profits are negative and positive when profits are positive. Further systematic research on the determinants of profit margin, efficiency and leverage is recommended for a complete and better understanding of the determinants of profitability. Aircraft manufacturers should also design more efficient aircraft to reduce operating costs for airlines so that investors continue to invest in airlines. 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Schefczyk(1993): Operational performance of airlines: an extension of traditional measurement paradigms, booz-allen & hamilton inc., dusseldorf, germany, strategic management journal, vol. 14(4) may, 1993, pp. 301-317 published by: john wiley & sons 25. N.T. Bhutta, A.Hasan( 2013) , Impact of Firm Specific Factors on Profitability of Firms In Food Sector, Open Journal of Accounting, 2013, 2, 19-25 39 26. P. S. Morrell( 2007), Airline Finance ,Ashgate Publishing Limited, Hampshire , England, 56-57 27. P. Shaw (2007), Airline Marketing and Management, 6th Edition, Ashgate Publishing Limited, 182-198. 28. Pwc (2014): Airline industry trends, PriceWaterhouseCooper, second edition of Tailwinds, a publication for the airline industry 29. R. Doganis, (2002): Flying off Course – The Economics of International Airlines (2nd edition). New York: Routledge. 30. R. Kumar(2008),Determinants of Firm’s Financial Leverage: A Critical Review, Journal of Contemporary Research in Management, January - March 2008 , 56-86 31. R. Matei (2012): Teaming up for success: Why global airline alliances make sense. Or not? Aarhus School of Business, Aarhus University 32. S. Fernando (2006): Risk Management practices in Airline Industry, project paper in partial fulfillment of requirement of masters in Arts in Business Administration, Simon Fraser University, CANADA 33. Stepanyan A (2006), Traditional Ratio Analysis in the Airline Business: A Case Study of Leading U.S Carriers. International Journal of Advances in Management and Economics, 175-189 40 I, the undersigned, declare that this thesis/dissertation is my work and every material used has been duly acknowledged. Name __Tilahun Deribe ________________ Signature_____________________ Date of Submission _______________ APPENDICES APPENDIX 1: SPSS OUT PUT FOR SEGMENTED LINEAR REGRESSION * NonLinear Regression. MODEL PROGRAM a1=1 a2=1 a3=1 a4=1 a5=1 a6=1 c1=1 c2=1. COMPUTE PRED_=((a1* TATO)+ (a2*Leverage) +(a3*NETMARGIN )+c1) * (ROE0)+((a4* TATO)+(a5*Leverage)+(a6*NETMARGIN)+c2)* (ROE>0). NLR ROE /OUTFILE='C:\Users\admin\AppData\Local\Temp\spss6868\SPSSFNLR.TMP' /PRED PRED_ /CRITERIA SSCONVERGENCE 1E-8 PCON 1E-8. Nonlinear Regression Analysis [DataSet3] C:\Users\admin\Documents\FINALWITHLEVERAGE.sav b Iteration History Iteration a Number Residual Sum of Parameter a1 a2 a3 a4 a5 a6 c1 c2 1.000 1.000 Squares 1.0 1542472.842 1.000 1.000 1.000 1.000 1.000 1.000 1.1 67135.446 -17.660 -1.323 7.452 10.548 .664 4.148 121.249 -57.632 2.0 67135.446 -17.660 -1.323 7.452 10.548 .664 4.148 121.249 -57.632 2.1 67135.446 -17.660 -1.323 7.452 10.548 .664 4.148 121.249 -57.632 Derivatives are calculated numerically. a. Major iteration number is displayed to the left of the decimal, and minor iteration number is to the right of the decimal. b. Run stopped after 4 model evaluations and 2 derivative evaluations because the relative reduction between successive residual sums of squares is at most SSCON = 1.000E-008. Parameter Estimates Parameter Estimate Std. Error 95% Confidence Interval Lower Bound Upper Bound a1 -17.660 2.836 -23.253 -12.068 a2 -1.323 .166 -1.651 -.995 a3 7.452 .619 6.231 8.674 a4 10.548 1.425 7.738 13.358 a5 .664 .093 .480 .847 a6 4.148 .527 3.109 5.188 c1 121.249 14.729 92.203 150.296 c2 -57.632 8.667 -74.724 -40.541 Correlations of Parameter Estimates a1 a2 a3 a4 a5 a6 c1 c2 a1 1.000 .233 -.031 .000 .000 .000 -.490 .000 a2 .233 1.000 .103 .000 .000 .000 -.928 .000 a3 -.031 .103 1.000 .000 .000 .000 .105 .000 a4 .000 .000 .000 1.000 .315 .247 .000 -.543 a5 .000 .000 .000 .315 1.000 .372 .000 -.921 a6 .000 .000 .000 .247 .372 1.000 .000 -.582 c1 -.490 -.928 .105 .000 .000 .000 1.000 .000 c2 .000 .000 .000 -.543 -.921 -.582 .000 1.000 a ANOVA Source Sum of Squares Regression df Mean Squares 289337.862 8 36167.233 67135.446 198 339.068 Uncorrected Total 356473.308 206 Corrected Total 355752.135 205 Residual Dependent variable: ROE a. R squared = 1 - (Residual Sum of Squares) / (Corrected Sum of Squares) = .811. APPENDIX 2: SPSS OUTPUT FOR COMPARISON BETWEEN PROFITABLE AND NON PROFITABLE AIRLINES ONEWAY ROE NETMARGIN TATO LEVERAGE BY PROFITABLE /STATISTICS DESCRIPTIVES /MISSING ANALYSIS. Oneway [DataSet3] C:\Users\admin\Documents\FINALWITHLEVERAGE.sav Descriptives N ROE Std. 95% Confidence Deviation Error Interval for Mean Lower Upper Bound Bound Minimum Maximum 64 -40.9701 42.48751 5.31094 -51.5832 -30.3570 -161.02 -.05 1 142 21.1797 22.53772 1.89132 17.4407 24.9188 .04 131.63 Total 206 1.8711 41.65785 2.90244 -3.8514 7.5935 -161.02 131.63 64 -4.4595 3.77249 .47156 -5.4018 -3.5172 -14.93 -.02 142 3.9544 3.20291 .26878 3.4230 4.4857 .01 15.06 206 1.3404 5.16377 .35978 .6310 2.0497 -14.93 15.06 0 64 1.4448 .84254 .10532 1.2344 1.6553 .02 5.14 1 142 1.4998 1.15935 .09729 1.3074 1.6921 .00 5.29 Total 206 1.4827 1.06924 .07450 1.3358 1.6296 .00 5.29 0 64 78.1997 14.43775 1.80472 74.5933 81.8062 33.53 95.95 1 142 70.2002 18.53343 1.55529 67.1255 73.2749 9.47 96.44 Total 206 72.6855 17.72241 1.23478 70.2510 75.1200 9.47 96.44 NETMARGIN Total LEVERAGE Std. 0 0 TATO Mean ANOVA Sum of Squares ROE 170404.482 1 170404.482 Within Groups 185347.653 204 908.567 Total 355752.135 205 3123.157 1 3123.157 2343.066 204 11.486 5466.224 205 .133 1 .133 Within Groups 234.240 204 1.148 Total 234.373 205 2823.088 1 2823.088 Within Groups 61564.064 204 301.785 Total 64387.152 205 NETMARGIN Within Groups Total Between Groups Between Groups LEVERAGE Mean Square Between Groups Between Groups TATO df F Sig. 187.553 .000 271.919 .000 .116 .734 9.355 .003 APPENDIX 3: SPSS OUTPUT FOR COMPARISON BETWEEN AIRLINES REGISTERED IN DIFFERENT REGIONS OF THE WORLD ONEWAY ROE NETMARGIN TATO LEVERAGE BY REGIONCODE /STATISTICS DESCRIPTIVES /MISSING ANALYSIS. Oneway [DataSet3] C:\Users\admin\Documents\FINALWITHLEVERAGE.sav Descriptives Descriptives N ROE NETMARGIN TATO LEVERAGE Mean Std. Std. 95% Confidence Deviation Error Interval for Mean Lower Upper Bound Bound Minimum Maximum 1.00 17 -7.6922 31.57640 7.65840 -23.9273 8.5429 -98.44 17.85 2.00 21 -.5107 41.08523 8.96553 -19.2125 18.1911 -147.96 81.99 3.00 113 1.5619 43.24636 4.06828 -6.4989 9.6226 -161.02 131.63 4.00 27 -5.3463 49.14825 9.45858 -24.7887 14.0961 -133.93 59.52 5.00 28 17.6711 29.61856 5.59738 6.1862 29.1560 -19.51 122.30 Total 206 1.8711 41.65785 2.90244 -3.8514 7.5935 -161.02 131.63 1.00 17 -.2888 5.21925 1.26585 -2.9723 2.3947 -12.62 5.45 2.00 21 2.3521 4.92193 1.07405 .1117 4.5926 -5.60 12.90 3.00 113 .9894 5.25926 .49475 .0091 1.9697 -14.93 14.03 4.00 27 .5424 4.41671 .85000 -1.2048 2.2896 -9.96 6.04 5.00 28 3.7564 5.01703 .94813 1.8110 5.7018 -6.24 15.06 Total 206 1.3404 5.16377 .35978 .6310 2.0497 -14.93 15.06 1.00 17 1.0793 .57078 .13843 .7858 1.3728 .38 2.16 2.00 21 .9056 .57508 .12549 .6438 1.1674 .35 2.68 3.00 113 1.6606 1.07817 .10143 1.4597 1.8616 .00 5.29 4.00 27 2.0537 1.43053 .27531 1.4878 2.6196 .45 5.14 5.00 28 .8918 .51738 .09778 .6912 1.0924 .02 2.46 Total 206 1.4827 1.06924 .07450 1.3358 1.6296 .00 5.29 1.00 17 68.6061 18.25510 4.42751 59.2202 77.9920 28.23 92.17 2.00 21 72.4869 16.59515 3.62136 64.9328 80.0409 40.42 95.95 3.00 113 73.9398 18.38307 1.72933 70.5133 77.3662 9.47 96.44 4.00 27 71.5458 17.02475 3.27641 64.8110 78.2806 40.26 93.56 5.00 28 71.3485 16.79474 3.17391 64.8362 77.8608 36.43 94.28 Total 206 72.6855 17.72241 1.23478 70.2510 75.1200 9.47 96.44 ANOVA Sum of df Mean Squares Between Groups ROE NETMARGIN 4 2520.271 Within Groups 345671.052 201 1719.756 Total 355752.135 205 261.174 4 65.294 Within Groups 5205.049 201 25.896 Total 5466.224 205 31.918 4 7.979 Within Groups 202.455 201 1.007 Total 234.373 205 Between Groups 546.631 4 136.658 Within Groups 63840.520 201 317.615 Total 64387.152 205 Between Groups TATO LEVERAGE Sig. Square 10081.083 Between Groups F 1.465 .214 2.521 .042 7.922 .000 .430 .787 APPENDIX 4: INDUSTRY DATA USED FOR THE STUDY (Computed by Researcher from Data collected by ICAO) Financial Net Year Region ROE Margin TATO Leverage Airline State Latin Absa Brazil 2013 America -100.83 -5.00 5.14 74.51 Latin 18.43 Absa Brazil 2014 America 1.97 2.95 68.57 North Abx Air United States 2014 America 1.62 1.39 0.72 37.97 Adria Airways Slovenia 2013 Europe -25.95 -1.96 2.01 84.86 Latin 53.53 Aeromexico Mexico 2011 America 5.98 1.63 81.73 Latin Aeromexico Mexico 2012 America -5.25 -0.96 1.20 78.06 Aeromexico Latin Connect Mexico 2011 America 36.53 6.04 3.49 42.33 Aeromexico Latin 41.21 Connect Mexico 2012 America -2.37 -0.41 3.41 Air Arabia Jordan Jordan 2012 Middle East 3.26 4.75 0.45 34.80 Air Arabia Jordan Jordan 2013 Middle East 0.68 1.27 0.38 28.23 Air Asia Malaysia 2011 Asia 15.06 11.51 0.37 71.99 Air Asia Malaysia 2012 Asia 15.13 12.90 0.35 70.25 North Air Canada Canada 2010 America 7.51 1.14 1.01 84.74 Air Europa Spain 2010 Europe 46.11 1.47 2.45 92.21 Air Europa Spain 2011 Europe -41.02 -0.70 2.82 95.17 Air Europa Spain 2013 Europe 131.63 2.91 3.10 93.14 Air Europa Spain 2014 Europe 26.73 0.93 2.93 89.80 Air France France 2010 Europe 30.20 3.97 0.82 89.20 Air France France 2011 Europe -50.43 -3.55 0.92 93.51 Air Madagascar Madagascar 2010 Africa 16.38 1.43 1.39 87.86 Air Madagascar Madagascar 2013 Africa -36.59 -12.62 0.83 71.33 Air Nostrum Spain 2010 Europe 24.66 3.32 1.39 81.35 Air Nostrum Spain 2011 Europe -16.60 -1.85 1.69 81.16 Air Nostrum Spain 2012 Europe -58.56 -6.01 1.59 83.64 Air Nostrum Spain 2013 Europe -123.09 -5.96 1.96 90.53 North 14.07 Air Wisconsin United States 2014 America 0.92 1.02 93.33 North Alaska United States 2014 America 60.80 24.23 11.08 0.86 All Nippon Japan 2010 Asia 5.02 1.93 0.68 73.91 Airline Airways All Nippon Airways All Nippon Airways All Nippon Airways All Nippon Airways State Financial Year Region Japan 2011 Asia 5.59 2.17 0.68 73.64 Japan 2012 Asia 6.77 3.27 0.63 69.40 Japan 2013 Asia -0.07 -0.02 0.92 70.09 Japan 24.65 0.77 2.68 91.59 Allegiant Air United States 20.31 11.63 1.03 41.13 Amaszonas Amerijet International Atlantic Airlines Ltd Atlantic Airlines Ltd Bolivia 2014 Asia North 2014 America Latin 2010 America North 2014 America 14.68 2.76 0.98 81.50 1.65 0.55 0.63 78.72 2011 Europe 0.04 0.01 1.93 76.32 2012 Europe North 2014 America 2010 Europe 2010 Europe 2011 Europe 2010 Europe 2011 Europe 2012 Europe 2013 Europe 2014 Europe 2010 Europe 2011 Europe 45.74 3.64 3.12 75.18 -19.51 -95.23 23.11 0.12 50.67 72.46 26.98 -7.67 -6.14 0.84 -53.64 -4.06 -13.23 2.47 0.01 6.29 9.56 5.78 -1.12 -0.57 0.10 -8.38 1.18 0.34 4.38 5.29 0.64 0.67 0.44 0.55 0.68 1.62 1.17 75.53 95.34 53.12 34.59 92.04 91.21 90.52 92.03 93.71 81.19 81.79 32.82 12.28 -3.82 -6.18 2.39 -60.37 -128.92 1.62 4.36 5.96 9.74 6.16 3.41 -0.97 -1.67 0.57 -12.61 -12.13 1.11 3.19 3.58 6.02 0.76 1.08 1.09 0.79 0.78 1.89 2.07 0.57 0.50 0.54 0.51 85.82 69.87 72.34 78.61 81.24 60.53 80.47 61.24 63.16 67.55 68.63 Atlas Air Aviavilsa Belair Belair Binter Canarias Binter Canarias Binter Canarias Binter Canarias Binter Canarias Blue Air Ams Blue Air Ams United States United Kingdom United Kingdom United States Lithuania Switzerland Switzerland Spain Spain Spain Spain Spain Romania Romania United British Airways Kingdom Cargolux Luxembourg Cargolux Luxembourg Cargolux Luxembourg Cargolux Luxembourg Carpatair Romania Carpatair Romania Cathay Pacific China Cathay Pacific China Cathay Pacific China Compass United States 2011 2010 2011 2012 2013 2010 2011 2012 2013 2014 2014 Europe Europe Europe Europe Europe Europe Europe Asia Asia Asia North ROE Net Margin TATO Leverage Airline State Airlines Usa Croatia Airlines Croatia Czech Czech Airlines Republic Czech Czech Airlines Republic Czech Czech Airlines Republic Czech Czech Airlines Republic Darwin Airline Switzerland Darwin Airline Switzerland Delta Dot Lt Dragonair Easy Jet Switzerland Easy Jet Switzerland Easy Jet Switzerland Easy Jet Switzerland Easyjet Airline Easyjet Airline Easyjet Airline Edelweiss Air Edelweiss Air Edelweiss Air Edelweiss Air Edelweiss Air El Al El Al Emirates Emirates Emirates Financial Year Region America 2010 Europe ROE Net Margin TATO Leverage -66.60 -11.51 0.81 85.94 2010 Europe -9.56 -0.82 1.19 89.73 2011 Europe -43.83 -6.31 1.30 81.30 2012 Europe -30.20 -3.86 1.24 84.15 -62.62 0.77 -161.02 -5.97 0.17 -14.93 1.65 1.31 1.89 84.26 70.20 82.48 United States Lithuania China 2013 Europe 2010 Europe 2011 Europe North 2014 America 2013 Europe 2014 Asia 3.38 13.08 -17.28 1.69 1.06 -3.63 0.80 4.46 1.30 60.07 63.71 72.66 Switzerland 2010 Europe 4.35 6.70 0.50 23.18 Switzerland 2011 Europe 4.88 6.35 0.56 27.80 Switzerland 2012 Europe 16.43 14.03 0.51 56.18 Switzerland United Kingdom United Kingdom United Kingdom Switzerland Switzerland Switzerland Switzerland Switzerland Israel Israel United Arab Emirates United Arab Emirates United Arab Emirates 2013 Europe 26.06 11.92 0.48 78.07 2010 Europe -1.65 -0.42 0.92 76.82 2011 Europe 23.46 6.52 0.95 73.66 12.65 80.55 20.42 16.64 18.77 21.07 -10.14 16.12 3.47 8.57 3.03 3.17 3.82 4.04 -0.97 1.21 0.91 5.01 4.09 3.09 2.83 2.62 1.24 1.37 74.93 46.76 39.20 40.97 42.40 49.83 88.08 89.69 2011 Middle East 4.53 2.63 0.63 63.58 2012 Middle East 7.30 3.38 0.83 61.62 2013 Middle East 10.83 4.23 1.14 55.62 2012 2010 2011 2012 2013 2014 2012 2013 Europe Europe Europe Europe Europe Europe Europe Europe Airline Emirates State United Arab Emirates Endeavor Air United States Estafeta Mexico Estafeta Ethiopian Expressjet Airlines Inc. Farnair Switzerland Farnair Switzerland Farnair Switzerland Farnair Switzerland Farnair Switzerland Flybe.British European Flybe.British European Flybe.British European Mexico Ethiopia Frontier Airlines Georgian Airways Global Supply Systems Global Supply Systems United States Gojet Airlines United States Gol Brazil Gol Brazil Gol Hawaiian Brazil United States Financial Year Region ROE Net Margin TATO Leverage United States 2014 Middle East North 2014 America Latin 2011 America Latin 2012 America 2012 Africa North 2014 America Switzerland 2010 Europe 1.59 0.18 1.13 87.38 Switzerland 2011 Europe 8.62 1.06 1.20 85.24 Switzerland 2012 Europe -11.61 -1.65 1.12 84.11 Switzerland 2013 Europe 12.95 1.79 1.06 85.36 Switzerland United Kingdom United Kingdom United Kingdom 2014 Europe 3.90 0.56 0.97 86.17 2010 Europe 5.94 0.63 1.64 82.45 2011 Europe -6.59 -1.04 1.53 75.81 2012 Europe North 2014 America -60.46 -6.80 1.50 83.07 100.01 8.19 2.03 83.37 2010 Europe 34.10 1.13 3.57 88.23 2010 Europe 49.62 3.10 2.68 83.26 2011 Europe North 2014 America Latin 2010 America Latin 2011 America Latin 2013 America 2014 North 11.92 0.90 2.35 82.19 0.58 0.12 0.73 84.74 11.52 4.19 0.96 65.00 -25.73 -7.72 0.89 73.24 -124.49 25.28 -7.84 3.45 1.02 1.02 93.56 86.05 Georgia United Kingdom United Kingdom 12.22 5.45 0.98 56.14 -0.31 -4.32 0.02 73.31 17.09 2.40 3.89 45.48 27.61 17.85 4.69 4.63 3.52 0.87 40.26 77.35 -13.68 -6.24 0.84 61.93 Airline Airlines Helvetic Airways Helvetic Airways Helvetic Airways State Horizon Air Iberia Iberia Iberia Iberia Express Icelandair Icelandair Icelandair Icelandair Icelandair United States Spain Spain Spain Spain Iceland Iceland Iceland Iceland Iceland United Kingdom United Kingdom United Kingdom Jet2.Com Ltd Jet2.Com Ltd Jet2.Com Ltd ROE Net Margin TATO Leverage Switzerland 2010 Europe 53.60 8.77 3.15 48.55 Switzerland 2012 Europe 40.43 9.08 3.72 16.34 Switzerland 2013 Europe North 2014 America 2010 Europe 2011 Europe 2014 Europe 2013 Europe 2010 Europe 2011 Europe 2012 Europe 2013 Europe 2014 Europe 45.14 8.59 4.76 9.47 5.87 5.76 -4.25 74.65 38.88 58.69 28.14 57.68 40.13 31.34 2.45 1.28 -1.27 7.22 3.07 6.55 2.71 5.21 5.63 5.37 0.46 0.88 0.87 0.93 3.68 1.45 1.55 1.44 1.53 1.56 80.83 80.59 73.85 91.01 70.92 83.86 85.09 87.01 78.49 73.35 2010 Europe 31.00 3.27 1.39 85.37 2011 Europe 18.91 2.01 1.40 85.18 2012 Europe North 2013 America North 2014 America 2011 Africa 2012 Africa 2013 Africa 0.06 2.96 0.00 85.17 9.14 3.08 0.78 73.57 16.97 7.37 -17.89 -8.76 6.52 1.54 -7.95 -3.19 0.80 0.64 0.72 1.06 69.30 86.55 68.07 61.39 16.35 3.98 0.70 83.00 15.89 0.78 4.05 80.13 23.29 5.60 0.79 80.95 1.59 0.83 0.45 76.57 3.33 -34.74 -99.93 0.08 -4.49 -4.50 4.51 1.20 1.13 89.06 84.49 94.93 Jetblue Airways United States Jetblue Airways Kenya Airways Kenya Airways Kenya Airways Financial Year Region America Korean Air United States Kenya Kenya Kenya Republic Of Korea Lan Argentina Argentina Lan Chile Chile Lan Chile Chile Lan Peru Lot Lot Peru Poland Poland 2010 Asia Latin 2010 America Latin 2011 America Latin 2012 America Latin 2014 America 2011 Europe 2012 Europe Airline Lufthansa Lufthansa Lufthansa Lufthansa Lynden Air Cargo Malaysian Airlines Mesa Airlines Monarch Airlines Monarch Airlines Monarch Airlines Olympic Air Pal Pal Pal Precision Air Services . Republic Airlines Royal Jordanian Royal Jordanian Royal Jordanian Royal Jordanian Sas Sas Sata Internacional Sata Internacional Sata Internacional Sata Internacional Financial State Year Region Germany 2010 Europe Germany 2011 Europe Germany 2012 Europe Germany 2013 Europe North United States 2014 America Malaysia United States United Kingdom United Kingdom United Kingdom Greece Philippines Philippines Philippines United Republic Of Tanzania ROE 15.44 -0.16 11.75 4.46 Net Margin TATO Leverage 7.97 0.50 74.33 -0.09 0.52 71.79 5.72 0.59 71.36 2.03 0.55 75.21 11.62 10.96 0.39 63.18 2010 Asia North 2013 America 5.28 0.91 1.19 79.61 29.17 9.03 0.44 86.49 2010 Europe -6.02 -0.61 1.49 84.91 2011 Europe -29.62 -2.78 1.62 84.75 Europe Europe Asia Asia Asia -75.13 -35.10 81.99 -147.96 -50.47 -4.93 -3.24 3.28 -4.71 -4.39 1.73 0.96 1.19 1.27 0.98 88.66 91.10 95.23 95.95 91.45 2012 2012 2010 2011 2012 5.20 0.74 0.55 92.17 United States Jordan Jordan Jordan Jordan Scandinavia Scandinavia 2010 Africa North 2014 America 2010 Middle East 2011 Middle East 2012 Middle East 2013 Middle East 2010 Europe 2011 Europe 23.82 8.66 -66.54 3.17 -98.44 -12.81 2.48 4.44 1.40 -7.74 0.23 -5.10 -5.95 1.27 0.40 1.81 2.03 2.16 1.88 0.89 0.82 92.52 70.72 76.39 84.20 90.25 58.72 57.64 Portugal 2010 Europe -11.60 -2.22 2.89 44.70 Portugal 2011 Europe -2.47 -0.56 2.93 33.53 Portugal 2012 Europe 0.06 0.01 2.71 65.47 Portugal 2013 Europe North 2014 America 2010 Asia 2011 Asia -69.21 -8.15 2.61 69.27 11.77 7.60 3.00 6.79 8.36 3.23 0.38 0.54 0.54 78.36 40.42 41.52 Shuttle America United States Sia Singapore Sia Singapore Airline Sia Skywest Airlines Southwest Spirit Airlines Srilankan Airlines Financial State Year Region Singapore 2012 Asia North United States 2014 America North United States 2014 America North United States 2014 America Sri Lanka Net ROE Margin TATO Leverage -5.67 -5.60 0.59 42.08 3.60 2.46 0.56 61.60 16.11 6.11 0.96 63.72 32.87 15.06 1.39 36.43 -11.35 -0.45 2.08 91.75 -1.27 17.42 9.32 8.42 14.87 16.53 -0.15 6.93 3.41 3.15 4.94 5.15 2.46 1.12 1.24 1.19 1.27 1.24 70.93 55.30 54.48 55.61 57.70 61.50 59.52 1.88 3.90 87.69 36.07 5.26 0.98 85.72 -26.52 -3.34 0.97 87.73 -133.93 -9.96 0.88 93.42 -80.73 -4.53 1.24 93.05 -1.92 -0.22 2.36 72.90 9.99 2.12 2.49 47.30 16.77 5.28 1.59 49.86 8.55 3.28 0.73 72.11 1.10 0.58 0.69 63.64 1.96 0.91 0.73 66.15 Sun Country Swiss Swiss Swiss Swiss Swiss United States Switzerland Switzerland Switzerland Switzerland Switzerland Taca Peru Tam Linhas Aereas Tam Linhas Aereas Tam Linhas Aereas Tam Linhas Aereas Peru Tam Mercosur Paraguay Tam Mercosur Paraguay Tam Mercosur Paraguay Tame Ecuador Tame Ecuador Tame Tap Air Portugal Tap Air Portugal Thai Airways Thomas Cook Ecuador 2010 Asia North 2014 America 2010 Europe 2011 Europe 2012 Europe 2013 Europe 2014 Europe Latin 2010 America Latin 2010 America Latin 2011 America Latin 2012 America Latin 2014 America Latin 2011 America Latin 2012 America Latin 2013 America Latin 2010 America Latin 2011 America Latin 2012 America Portugal 2012 Europe 32.76 0.87 1.34 96.44 Portugal Thailand United 2013 Europe 2010 Asia 2010 Europe 36.26 23.71 13.66 1.37 8.00 1.64 1.41 0.72 1.60 94.68 75.72 80.81 Brazil Brazil Brazil Brazil Airline Airlines Thomas Cook Airlines Thomson Airways Thomson Airways Thomson Airways Thy Thy Titan Airways Titan Airways Titan Airways Ukraine Intl Airlines State Kingdom United Kingdom United Kingdom United Kingdom United Kingdom Turkey Turkey United Kingdom United Kingdom United Kingdom Ukraine United United States United Parcel United States Virgin America United States United Virgin Atlantic Kingdom Financial Year Region ROE Net Margin TATO Leverage 2011 Europe 21.86 2.39 1.80 80.34 2010 Europe -20.77 -3.64 0.72 87.43 2011 Europe 13.05 3.55 0.69 81.13 2012 Europe 2010 Europe 2011 Europe 22.15 8.08 0.43 7.19 3.34 0.15 1.02 0.90 0.85 66.86 62.62 69.32 2010 Europe 36.35 6.79 2.43 54.55 2011 Europe 21.07 4.75 2.18 50.96 2012 Europe 32.96 8.16 2.15 46.87 2010 Europe North 2014 America North 2014 America North 2014 America -80.74 -7.82 2.85 72.41 38.03 2.86 1.03 92.23 -0.05 -0.04 0.80 43.98 122.30 4.03 1.73 94.28 6.56 0.50 1.58 88.02 2010 Europe