Mergers and acquisitions in the airline industry
a study on the longer term stock-price effectFaculty of Economics and Business
BSc Economics and Business, Organization and Finance track Bachelor thesis
Sander Montanus , 6049648 June 2015
Abstract
This thesis focuses on the long-term effects of mergers and acquisitions in the airline industry, measured by abnormal returns on stock-prices in the longer run. Not much
research has been done so far on the longer-term stock price effects for the airline industry and for the shorter term only older papers are available.
17 mergers that occurred between 2000 and 2010 on different continents were analyzed, calculating the post-merger Cumulative Abnormal Return for 5 years following the merger. On average a positive CAAR was found for the complete 5 year period, while for all separate years a positive or insignificantly different from zero CAAR was found. Compared to former research on mergers and acquisitions in general, which states that companies are not expected to generate a positive CAAR in the long run, it seems the airline industry outperforms.
Table of Contents Abstract ... 2 Table of Contents ... 3 1. Introduction ... 4 2. Literature review ... 5 2.1 Background on mergers ... 5
2.2 Theoretical framework on long-term vs short-term gains ... 6
2.3 The airline industry ... 8
2.4 Earlier studies on short term stock-price effects ... 9
2.5 Earlier studies on longer term stock-price effects ... 11
2.6 Factors that influence announcement effects ... 12
3. Methodology and data ... 13
3.1 Hypotheses ... 13 3.2 Methodology ... 13 3.3 Data ... 15 4. Analysis ... 16 4.1 Results ... 16 4.2 Cross-sectional ... 19
5. Discussion and Conclusion ... 21
5.1 Abnormal returns over 5 years ... 21
5.2 Abnormal return over the first year... 21
5.3 Changes in abnormal returns ... 22
5.4 Conclusion ... 22
References ... 24
Appendix ... 27
Table 4a: CAAR Calculations ... 28
Table 4b: CAAR Calculations continued ... 29
1. Introduction
Mergers and acquisitions are common phenomena in the economic world. To illustrate: 38.4% of companies in the US were acquired by other companies in the period between 1964 and 1984, and 42% of the companies in the UK were acquired by other companies in the period between 1950 and 1977 (Dickerson et al., 1997). Mergers and acquisitions have been widely discussed in the economic science. However, a lot of papers focus on the short term effects of mergers, which is understandable, because if markets are efficient the announcement effect would cover the expectations of investors and result in zero abnormal returns. The airline industry is an inefficient merger market and besides that, it is quite unpredictable (Knapp, 1990). Not much research has been done so far on the longer-term stock price effects for the airline industry and for the shorter term only older papers are available, so it can be interesting to create a broader view on longer term effects, because lots of effects turn out to be integrated over a longer period of time.
Most papers find insignificant or negative effects on shareholder returns in the longer-term after a merger (e.g. Agrawal et al. (1992), Tuch and O’Sullivan (2007)), while on the short run in the more recent studies it is insignificant or slightly positive (e.g. Heron and Lie
(2002), Martynova and Renneboog (2008)). However, specifically for the airline industry the results are more positive on the short run (Knapp, 1990), but on the long-run almost no research have been done. This thesis will give a view on mergers of different magnitude in the airline industry from the last two decades in the European, American and Asian markets, answering the question: Do mergers in the airline industry create positive effects on longer-term shareholder value? And also: How do these effects hold in the longer run? For all of the mergers used in the dataset it holds that at least the acquiring company is publicly listed. In the analysis section some notes on relative firm size difference and other factors are made.
These questions are answered by conducting empirical research into the abnormal returns, using the method also used by Agrawal et al. (1992) and Dimson and Marsh (1986) to calculate the Cumulative Average Abnormal Returns (CAAR) over a 5 year period after 15 different mergers in the airline industry between 2000 and 2010 on different continents.
This paper is structured as follows:
First, in section 2, this paper covers some of the background literature and research that has been conducted on this subject before, focusing on the background of mergers and some findings on the effects of mergers in general, as well as in the airline industry. Then the methodology of the research is presented in section 3, including the hypotheses and data. In section 4 the results are given and analyzed. In the last section the results are discussed and concluded.
2. Literature review
2.1 Background on mergers
During the late 1990s a huge wave of mergers in acquisitions started in the US and around Europe. It was recognized as the fifth big merger wave in history. During this wave most mergers occurred between companies active in the same industry. This is remarkable, as most of the merger waves before this wave mainly consisted of hostile takeovers from financing companies or firms active in different industries. This wave of mergers and acquisitions was explained as a response to deregulation, as well as several different industry shocks (Schleiffer and Vishny, 2003).
Another way to look at these merger waves, as Gorton et al. (2005) did, is as a company’s management’s fear of being acquired, so instead they acquire another company
themselves. Because when management prefers staying in control, defensive mergers occur, which are not always profitable. This explains that not all motives for mergers and acquisitions will result in positive returns for the company.
Of course there are a lot of positive motives that drive mergers. These positive motives can vary from more efficiency, economies of scale, synergies and better management of assets to higher market power, especially if the merging companies have a substantial amount of market overlap (Focarelli and Panetta, 2003). For all markets it holds that if effects that occur in the short run, such as market power effects are larger than the effects that occur in the longer run, such as efficiency effects, on the short term more profit is made for the company, while the effects such as improved efficiency will be noticeable on the middle-long term, as the company has to reorganize after the merger.
But why do firms choose to invest in acquisition, instead of internal project-based investment, which can also lead to substantial positive returns and growth?
Growth of firms is incidental to profit maximization. This is mainly equated with growth maximization by investment, by investing in all projects with a positive net asset value (Dickerson et al., 1997). There are two different types of growth that can be exploited, internal or external growth, thus investing in projects or mergers and acquisitions. If internal growth goes too fast, growth maximization, and thus profit maximization gets less
manageable, which means it gets harder to achieve substantial growth levels. In that case, using growth by acquisition, higher growth rates can be achieved, as this managerial constraint does not hold for external growth, because companies are acquired that have their own management levels. Dickerson et al. (1997) state that there are more benefits to mergers and acquisitions compared to internal growth; there is very little delay between the acquisition and the financial effects, they materialize quickly. You not only acquire a regular type of investment, but it is ready-made and includes the personnel required to operate it . Besides that, when acquiring another company, in effect you remove a competitor from the market, which can lead to higher market power.
2.2 Theoretical framework on long-term vs short-term gains
The question on why mergers occur has been studied in lots of different papers. Most of them agree on the fact that the main reasons that drive mergers are either efficiency (managerial or other synergy) related or market power related, but other reasons could be to take advantage of opportunities for diversification, market discipline or self-serving attempts to over-expand (Andrade et al., 2001).
Former research clearly shows that mergers often increase the combined equity value of the two merging companies and that the positive effect on these stock prices can be derived from these higher efficiency-, market power-, but as well from tax related reasons (Devos et al., 2009). Higher efficiency gains, because of higher income or lower costs, are economically beneficial. Market power or tax benefits are wealth transfers, which generate returns on the expense of governments or other stakeholders, such as customers or suppliers. Devos et al. (2009) only found evidence for a substantial wealth gain because of operational synergies, and for a very small amount of tax benefits. They did not find evidence for merger gains contributed by wealth transfers due to increase of market power. This could be due to
antitrust laws that prevent mergers from happening if there are substantial anti-competitive reasons.
In line with finance theory, companies tend to undertake new capital investments, if the investments have a positive expected effect on firm value. This is also why a net positive share price effect is noticed when a firm releases plans to extend capital expenditures (Jarrel and Poulsen, 1989). The noticeable effects are dependent on the target company’s firm size relative to the acquirer’s firm size. If the target is a relatively small company compared to the acquirer, the abnormal returns will be lower, or less significant than if the target is a relatively large company. There is a large amount of mergers and acquisitions, that do not generate positive abnormal returns. The effects of relative firm size were also, more recently, confirmed by Moeller et al. (2004) using a sample of 12,023 acquisitions of public firms between 1980 and 2001. They found that smaller firms that announce an acquisition generate a larger significant abnormal return, where larger firms that announce an
acquisition even experience shareholder wealth losses.
Jarrel and Poulsen (1989), investigating 770 tender offers of between 1963 and 1986, found that there are two more effects besides the relative firm size that influence this
phenomenon. First there is the theory that it can be influenced by competition in the specific merger market. If there are multiple firms bidding for one firm, the final abnormal return generated is not significantly different from zero or even slightly negative, because if a bidder is the only one trying to acquire a company it can offer a price just high enough to buy the shares he wants to buy. If there are more bidders in the market, the prices will rise and less profit and return is generated for the acquirer, hence more for the seller. Jarrel and Poulsen (1989) state that managers also have a lot of different incentives to stimulate growth in their companies. Overbearing confidence can result in over payment for a specific company, or they prefer to lead a larger company, as control by shareholders gets harder the larger the company gets.
It is clear that there are lots of different factors that can influence the final success of a merger or acquisition, both occurring in the short-run and in the longer run.
2.3 The airline industry
The Airline industry used to be a highly regulated industry, but over the years it was deregulated more and more. In the time after deregulation it also became a lot more competitive and global (Bowen, 2002). Besides that, a big part of the airline industry is under governmental control, which goes a lot further than just control towers and runways. Since the mid-1980s the international airline industry has been liberalized by privatization and deregulation, but still it is the most politicized transport industry. This makes this industry highly competitive, but also on a local level highly influenced, creating possible advantages for specific (local) carriers. In a merger market this could be one of the reasons inefficiency exists, because advantages for specific firms acquiring other firms differ
between firms. For the airline industry this inefficient merger market does exist (Knapp, 1990).
In this industry, if two merging airlines have a lot of routes in common, there can be spoken of the substantial market overlap and thus higher expected market power gains, if possible within the extent of antitrust regulations. However, as Morrison (1996) states, in the airline industry it can take somewhat longer for final effects on market power to be noticed, as the industry is a competitive one, but it takes longer for airlines to adapt to the new
competition environment. There are even cases known where airlines were still adapting to the new environment after 20 years. Bernile et al. (2012) state that evidence was found for multiple sectors, including the airline industry, that horizontal mergers attract possible entrants. If a new larger airline gains more market power after a merger, this could create opportunities to invest the routes involved by the merger, so the gap the merged airline leaves could be filled in a couple of years by another airline operating the same route. That also tells something about the unpredictability of the airline markets. This means the effects of a merger or acquisition have to be measured for multiple years after the event, before the final effects of it can be analyzed.
Kim and Singal (1993), as well as Brueckner and Pels (2005) found statistical evidence for the exercise of market power after airline mergers. Kim and Singal (1993), using a sample of routes from 14 airline mergers between 1985 and 1988, found that in many of the mergers, especially when the merger was not initiated because of financial distress of the acquired firm, fares on routes went up quite a lot. The expectation was that if the efficiency gains
would dominate the mergers’ effects the fares would drop, following the lower operating expenses, but if the market power gain dominates the fares rise. Unfortunately the paper by Kim and Singal (1993) only focused on the short term effects of the fares compared to the industry level, but they gave enough reasons to believe mergers can lead to a substantial rise in market power in the airline industry.
Brueckner and Pels (2005) did a study inspired on European airline mergers, inspired by the Air France – KLM merger, where they found that the airlines report higher profit against higher fares to consumers, which represents market power effects. They also report that often with this kind of mergers the improved efficiency related effects do not reverse the anti-competitive (improved market power) effects.
2.4 Earlier studies on short term stock-price effects
As stated before, most papers on the subject of effects of mergers and acquisitions focus on the short term after the merger. A lot of researchers found that very often the returns for the acquiring company turn out to be negative. Which in effect means the shareholders would have been better off without the merger (Lang et al., 1989). Lang et al. found that there is a strong connection by the level at which a firm is managed and the effects on short term. Well managed firms taking over poorly managed firms do gain a positive abnormal return of up to 10%, where poorly managed firms taking over well managed firms will definitely end up with negative abnormal returns. These effects can be explained by the fact that well managed firms have greater possibilities to create extra value from a merger, where the poorly managed are unable to do so.
Other, more recent papers that focus on the short-term performance after mergers of firms in general find different results. Heron and Lie (2002) investigated the effect of the way of payment when acquiring a company on the operational performance using a sample of 12,023 acquisitions of public firms between 1980 and 2001. They did not find significant evidence for a difference between stock and cash payment, but they did find that for stock-bought companies there is a significant negative return after the merger, where for cash-bought companies there is a return of zero for the period after the merger.
Analyzing the returns after mergers and acquisitions of 2,419 mergers and acquisitions in Europe, Martynova and Renneboog (2006) found that for the acquiring companies in the
very short run where slightly positive, but afterwards would become negative as well. Over a 10 year window around the event day a small positive CAAR of 0.8% is reported, but after a 3 month period it went negative to -3%.
Tuch and O’Sullivan (2007) found that at best the results in the short run are not
significantly different from zero. They also state that mergers and acquisitions in the 1950s and 1960s were a lot more profitable than they are in later periods, which makes it possible that earlier papers did find positive returns if they included mergers and acquisitions from this period in their research.
Unfortunately, specifically for the stock-price effects of mergers and acquisitions in the airline industry only not very recent papers are available.
Singal (1996), investigating Airline mergers between 1985 and 1988, found that, due to the exhibit of market power and more efficiency, the operational profits can rise, which on the short run should be anticipated for by higher stock prices and returns. However all the returns found for the acquiring companies turn out to be insignificant.
Knapp (1990), did find significant abnormal returns for mergers in the airline industry, using a slightly different approach. He found that the abnormal returns for all of the 19 mergers in 1986 he investigated were consistently positive in the window around the merger
announcement. Excess returns between 6% and 12% were reported, depending on the window. Knapp (1990) stated that this shows that the Airline merger market is less than competitive, as otherwise rival airlines would have outbid the acquiring airline to gain these returns. Probably there is no other airline combination that can gain the same synergies as the merging airlines themselves. Jarrel and Poulsen (1989) also found that in general on single-bidder acquisitions the acquirer is able to generate a positive abnormal return, but in a multiple-bidder acquisition the abnormal returns for the acquirer are insignificantly different from zero.
Another thing Knapp (1990) did differently is that he also studied the effect of mergers on direct rivals in the same industry. They also turned out to gain positive abnormal returns in the event window, of about 3% to 6%. This could be a result of higher market power in the market, because all airlines, including the merged one, but as well the rivals have less competition in their market.
Most results found in papers on short term stock price effects of mergers and acquisitions report insignificant or even slightly negative returns for mergers and acquisitions in general. For the airline industry the results are not that one-sided. Some papers find results that are in line with those of the regular M&A results, but others find that because of the unique character of the airline industry not everything has to hold like it does for mergers and acquisitions in general.
2.5 Earlier studies on longer term stock-price effects
Not a lot of research has been done on the long-term stock price effects in the airline
industry. This makes it harder to say something about the longer-term stock-price effects for both the airline industry and mergers and acquisitions in general. Besides that, it is harder to connect longer-term effects to the specific event, as more bias occurs.
Despite of that, several studies have been done on the longer-term effects on stock-prices after mergers, just not for the airline industry. For example Langetieg (1978) and
Magenheim and Mueller (1988), which both served as a basis for the research conducted by Agrawal et al. (1992). Constantly finding negative abnormal returns would mean that in general the value of mergers and acquisitions are overvalued.
All of these researchers find an insignificant or negative CAAR over the first 3 to 5 years after the merger. The results differ however. Langetieg (1978), investigating 149 mergers
between NYSE firms, does not find a significant difference between the industry and the merged companies in this time, which makes him conclude that mergers in general do not contribute to stockholder welfare. Magenheim and Mueller (1988), using a sample of 78 mergers between 1976 and 1981, do find a significant difference in abnormal returns compared to the benchmark, which is negative, but using the same set of data with a new methodology Bradley and Jarrell (1988) do not find any significant underperformance (or outperformance) compared to the benchmark.
Agrawal et al. (1992), investigating 937 mergers and 227 tender offers between 1955 and 1987, do find significant underperformance compared to the market returns. Over the 5 years following the merger the return on stock is 10% lower than the return on the benchmark.
More recent studies by Tuch and O’Sullivan (2007) and Campa and Hernando (2004) confirm that in almost all cases companies find negative cumulative average abnormal returns in the 5 years following a merger. This has not only been in the US, but also around Europe.
Besides the fact that the announcement effect should already include all the expectations of investors, it seems that the results on the long-term are either substantially biased, or the expectations turn out to be too positive in general.
The literature is not one-sided on this subject and while there is very few information
available that holds uniquely for the airline industry, it is not easy to predict the outcomes of the data analysis in this paper. Because of the unique characteristics of the airline industry, e.g. the fact that it is slowly adapting to market changes and that it is unclear how the changes turn out to be, could make a difference in this case. In this paper the same methodology is used as in the papers focusing on general mergers and acquisitions to investigate how the effects of mergers and acquisitions in the airline industry develop in the long run. If the airline industry would move in the same way as the entire market, negative or insignificant returns would be expected over the 5 years following the merger.
2.6 Factors that influence announcement effects
There are multiple factors that can influence the effects on stock-prices after the merger announcement. Discussed in this thesis are momentum, way of financing and relative firm size.
Rosen (2006) speaks of momentum. He claims that if the current merger market is hot and many successful mergers have taken place recently, the returns on short time rise, where on the longer term the abnormal returns drop below zero. In fact this means that in a hot merger market mergers are more often overvalued in the short term. The airline merger market over the last decades can be called a quite hot merger market.
Another potential influencing factor is the way a merger or acquisition is financed.
Managers that plan to engage in a stock-based acquisitions have an incentive to temporarily increase a stocks purchasing power (Heron and Lie, 2002). Heron and Lie (2002) used a large sample of mergers between 1985 and 1997 and found that however there is no significant difference in firm performance between methods of payment, the short-term and long-term returns are lower for stock-based acquisitions.
Relative firm size can also influence the effect on stock price returns after a merger announcement. Moeller et al (2004) investigated this effect using a sample of 12,023
acquisitions of public firms between 1980 and 2001. It appears that relatively small firms are significantly better than large firms when they announce a merger or acquisition. They found that relatively small firms score up to 2 percentage higher returns on the short-term after the announcement.
3. Methodology and data 3.1 Hypotheses
For this thesis the following hypotheses have been created:
H0: During 5 years after the merger airlines are not able to generate a (positive or negative) abnormal result compared to the industry
H1: During 5 years after the merger airlines are able to generate a (positive or negative) abnormal result compared to the industry
H0: During the first year after the merger airlines are not able to generate a (positive or negative) abnormal result compared to the industry
H1: During the first year after the merger airlines are able to generate a (positive or negative) abnormal result compared to the industry
During the literature review it became clear that the former literature is broadly divided on this subject, as one puts more emphasis on effects that occur in the short run, such as market power effects, where others put more emphasis on longer-run effects, such as efficiency effects. It also became clear that in a lot of former mergers and acquisitions in general the abnormal returns turned out to be negative, so these could also be expected for the airline industry.
3.2 Methodology
The effects on performance are analyzed by the cumulative average abnormal return method used by Agrawal et al. (1992), better described by Dimson and Marsh (1986). This method calculated the abnormal returns that are generated compared to a benchmark portfolio of other airlines. Using this method instead of other methods for calculating the
cumulative average abnormal returns a part of the bias for longer term effects, as it catches the systematic risk of the entire airline sector. Firm-specific risk and thus some bias will however remain to exist.
As the benchmark the NYSE ARCA Airline Index (XAL) is used. This index contains data on multiple European and American airlines. As the market index the S&P500 index is used. The information used is analyzed for the 60 months following the merger, using monthly data.
Thus, following Agrawal et al. (1992), abnormal return is given by: 𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖 = 𝐴𝐴𝑖𝑖𝑖𝑖− 𝐴𝐴𝑠𝑠𝑖𝑖− (𝛽𝛽𝑖𝑖− 𝛽𝛽𝑠𝑠)(𝐴𝐴𝑚𝑚𝑖𝑖− 𝐴𝐴𝑓𝑓𝑖𝑖) Where:
𝐴𝐴𝑖𝑖𝑖𝑖 equals the return of security i for month t
𝐴𝐴𝑠𝑠𝑖𝑖 equals the return of the benchmark portfolio (XAL) 𝛽𝛽𝑖𝑖 is the beta of security i and estimated using monthly data 𝛽𝛽𝑠𝑠 is the benchmark portfolio (XAL) beta.
𝐴𝐴𝑚𝑚𝑖𝑖 is the return on the market index (S&P500) 𝐴𝐴𝑓𝑓𝑖𝑖 is the risk free rate in month t
The average abnormal return (AAR) over all stocks is given by:
𝐴𝐴𝐴𝐴𝐴𝐴 = 𝑁𝑁1
𝑖𝑖� 𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖 𝑁𝑁𝑡𝑡
𝑖𝑖=1
The cumulative average abnormal return (CAAR) from month t1 to t2 is given by:
𝐶𝐶𝐴𝐴𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖21 = � 𝐴𝐴𝐴𝐴𝐴𝐴𝑖𝑖
𝑖𝑖2
𝑖𝑖=𝑖𝑖1
The significance of the cumulative abnormal returns is tested for by calculating the t-values, The method for calculating these used is the one described by Brown and Warner (1980). In formula the t-value equals:
𝑡𝑡 = 1 𝑁𝑁 ∑𝑁𝑁𝑖𝑖=1𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖 1 𝑁𝑁 ��(𝑑𝑑𝑑𝑑 − 1) ∑1 𝑖𝑖+𝑖𝑖=𝑖𝑖−�𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖− �∑𝑖𝑖+𝑖𝑖=𝑖𝑖−〖 𝐴𝐴𝐴𝐴(𝑑𝑑𝑑𝑑 + 1)�𝑖𝑖𝑖𝑖 �〗2�� 1 2 Where:
𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖 equals the abnormal return for security i in month t
𝑑𝑑𝑑𝑑 equals the degrees of freedom (number of months used for sample - 1)
First of all the monthly returns of the stock are calculated using the information on the stock-prices and dividends paid. Then the betas of the stock of the airline and the XAL index are calculated for the time-period of 5 years after the merger. Using this data the Abnormal Returns for every month can be calculated and so the Average Abnormal Returns and the Cumulative Average Abnormal Returns.
Besides calculating these factors for the complete 5 years after the merger ([+1,+60]), the CAAR is also analyzed for the five separate years after the merger ([+1,+12], [+13,+24], [+25,+36], [+37,+48], [+49,+60]). First the data is analyzed for the separate firms. Averages for the Cumulative Average Abnormal Returns are also calculated and analyzed.
As also stated by Agrawal et al. (1992), unfortunately this method does not allow any shifts in the beta between months, so the assumption is made that the beta stays constant for all years after the merger. This can result in some bias.
3.3 Data
For this paper data on 17 different mergers and acquisitions of different sizes and from different continents in the airline industry and of different magnitude was collected, of which the data of 15 was used. Only 100% mergers were included. In the analysis section parts of the dataset are individually analyzed.
As can be seen in table 4a, there are two mergers that produce extreme Cumulative Average Abnormal Returns using the method described earlier; The Qantas – Star Track merger and the Utair – Avialinii Chuvashii merger. It is a possibility that these two airlines do not match the benchmark index that well, as the Australian and Russian markets are not included in the NYSE ARCA Airline Index (XAL). This is way it is decided to leave the data on these mergers out of the analysis.
Acquirer Target Announced Deal value(€)
1 Air France (FR) Cityjet (IE) 01/02/2000 2,459.00
2 Ryanair (IE) Buzz (NL) 31/01/2003 20,100.00
3 Air France (FR) KLM (NL) 01/10/2003 784,000.00
4 Qantas (AU) Star Track (AU) 19/12/2003 603,225.00
5 Skywest (US) Atlantic Southeast Airlines (US) 15/08/2005 342,210.00
6 Utair (RU) Avialinii Chuvashii (RU) 18/08/2005 unknown
7 Air Berlin (GB) LTU (DE) 27/03/2007 340,000.00
8 Gol Linhas (BR) Varig (BR) 28/03/2007 205,792.00
9 Norwegian (NO) Nordic (NO) 24/04/2007 15,830.32
10 Easyjet (GB) GB Airways (GB) 25/10/2007 138,789.06
11 Air France-KLM (FR) VLM (BE) 24/12/2007 180,000.00
12 Delta Airlines (US) Northwest Airlines (US) 14/04/2008 2,266,348.27
13 Vueling (ES) Clickair (ES) 07/07/2008 175,000.00
14 Lufthansa (DE) Germanwings (DE) 08/12/2008 unknown
15 China Eastern Airlines Shanghai Airlines (CN) 10/07/2009 933,858.38
16 Skywest (US) Expressjet (US) 04/08/2010 83,689.80
17 Southwest Airlines (US) Airtran (US) 27/09/2010 2,307,031.91
Data on mergers and acquisitions in the airline industry is collected from the ZEPHYR database. The stock-prices, dividends paid and index returns are downloaded from the Datastream database, using monthly data. All the prices are downloaded in dollars, so the risk-free rates of the fed could be used. These risk-free rates are downloaded from the federal reserve’s website.
A note has to be made that for the last two mergers included not all data was available for the following 5 years, as the mergers happened less than 5 years before this paper is
written. As a result in the 5th year of the data of the merger between Skywest and Expressjet only 10 months were used for the analysis. The same holds for the Southwest – Airtran merger, where only 9 months were used in the analysis.
4. Analysis 4.1 Results
The results of the calculations of the Average Abnormal returns, as well as the Cumulative Average Abnormal Returns for all airlines are provided in the appendix (tables 4a and 4b). Below tables are provided with the equally-weighted averaged results of all airlines,
including t-values. Also included are the averages of just the European and American airlines.
What can be seen in the airline specific results is that the results are not one-sided. Where several airlines present positive Cumulative Average Abnormal Returns over the 5 year period following the merger, others present a significant negative CAAR over the complete 5 year (60 month) period. All of them do have a significant CAAR at the 5% level, and most of them even at the 1% level, except for the two mergers in which Skywest is the acquirer. It is notable that all merged firms have a significant positive CAAR during the first 12 month period or an insignificant one. In this sample no proof has been found of the existence of negative Abnormal returns during the first year after the merger.
During the second 12 month period ([+13,+24]) the same can be noticed, however much more airlines present an insignificant CAAR and there are three major exceptions; For merger 13, 15 and 16 (see appendix for details) substantial negative returns are reported. For the third period ([+25,+36]) the mergers that happened until the beginning of 2007 mainly report positive returns, while the mergers after 2007 report mainly significant negative returns. This actually shows there are lots of other factors that need to be
calculated for, but higher returns in the third year, if they can be derived from the merger, could mean the synergies gained from the merger are higher than expected.
The fourth and fifth period are quite diverse.
Analyzing the averages calculated (table 1) shows that on average a highly significant positive Cumulative Average Abnormal Return is reported for the complete 5 year period after the merger, for both the total sample set as the separate European and American subsets.
No big differences are found between the European and American market, they both report substantial abnormal returns during the first year, where the second year abnormal return is significantly negative. In the third year a small rise in returns is reported, after which the returns get small and insignificant in the fourth year. Only the European market seems to get a small boost in the fifth year, where the American market does not.
In the total sample average it is very notable that on average the highest abnormal returns are reported during the first year after the merger announcement. The first year’s returns include the improved market power effect after a merger, which creates a rise in prices, and thus higher firm performance. If this is higher than expected the returns will also end up higher. During the second period no significant CAAR is found.
Based on this thesis it seems that airlines in general are able to generate a positive abnormal shareholder return during the 5 years after a merger, as a significant positive CAAR was found on the total 5 year period following the merger.
Total Average European Average American Average AAR [+1,+60] 0.0065 0.0086 0.0069 AAR [+1,+12] 0.0231 0.0226 0.0229 AAR [+13,+24] -0.0117 -0.0097 -0.0112 AAR [+25,+36] 0.0110 0.0109 0.0139 AAR [+37,+48] -0.0022 -0.0023 0.0048 AAR [+49,+60] 0.0123 0.0213 0.0039 CAAR [+1,+60] 0.3893 0.5142 0.4118 t-value (9.7810) (11.5226) (8.1699) CAAR [+1,+12] 0.2774 0.2716 0.2750 t-value (4.9468) (5.0773) (3.9245) CAAR [+13,+24] -0.1403 -0.1164 -0.1345 t-value (-3.0485) (-2.1503) (-2.2891) CAAR [+25,+36] 0.1322 0.1314 0.1671 t-value (4.3538) (3.1730) (4.0135) CAAR [+37,+48] -0.0269 -0.0277 0.0575 t-value (-1.0250) (-0.9805) (1.3374) CAAR [+49,+60] 0.1470 0.2553 0.0468 t-value (3.7811) (5.0685) (1.0445)
Table 1: Average CAAR calculations for the sample-set , European mergers and American mergers (AAR: monthly, CAAR: cumulative over all months)
4.2 Cross-sectional Domestic Mergers Cross-border Mergers AAR [+1,+60] 0.0049 0.0097 AAR [+1,+12] 0.0284 0.0125 AAR [+13,+24] -0.0183 0.0016 AAR [+25,+36] 0.0103 0.0124 AAR [+37,+48] -0.0029 -0.0009 AAR [+49,+60] 0.0069 0.0229 CAAR [+1,+60] 0.2927 0.5826 t-value (6.3514) (9.7708) CAAR [+1,+12] 0.3409 0.1504 t-value (5.2807) (1.8771) CAAR [+13,+24] -0.2201 0.0192 t-value (-4.7161) (0.2449) CAAR [+25,+36] 0.1239 0.1487 t-value (2.5543) (3.5851) CAAR [+37,+48] -0.0349 -0.0109 t-value (-1.0359) (-0.2992) CAAR [+49,+60] 0.0830 0.2751 t-value (2.5371) (3.8877)
Table 2: Average CAAR calculations for Domestic and Cross-border mergers (AAR: monthly, CAAR: cumulative over all months)
It also notable that the cross-border mergers (1, 2, 3, 7, 11) show lower abnormal returns (or insignificant returns) during the first year. This could be because the market power effect for cross-border mergers is lower, due to the absence, or minimal amount of routes in common.
Low Value Mergers High Value Mergers AAR [+1,+60] 0.0079 0.0027 AAR [+1,+12] 0.0168 0.0201 AAR [+13,+24] -0.0154 -0.0076 AAR [+25,+36] 0.0191 0.0009 AAR [+37,+48] -0.0184 0.0070 AAR [+49,+60] 0.0376 -0.0070 CAAR [+1,+60] 0.4759 0.1618 t-value (8.0150) (3.5328) CAAR [+1,+12] 0.2020 0.2415 t-value (2.4139) (4.4589) CAAR [+13,+24] -0.1853 -0.0916 t-value (-3.8276) (-1.4575) CAAR [+25,+36] 0.2288 0.0110 t-value (3.2188) (0.3158) CAAR [+37,+48] -0.2211 0.0845 t-value (-8.4182) (2.3270) CAAR [+49,+60] 0.4516 -0.0835 t-value (-1.8423) (-1.8423)
Table 3: Average CAAR calculations for Low value (below €200,000) and High value (above €200,000) Mergers (AAR: monthly, CAAR: cumulative over all months)
Interesting to see seems that all mergers with a deal value below €200,000 seem to get insignificant results. Relatively it could be that these target companies do not account for a large part of the total company return, which could make the total abnormal return lower or equal to zero, as could be expected from literature as well.
However, if equally weighted averages are taken for both the low and high value deals, it is clear that the lower value mergers present more significant results than the higher value mergers.
5. Discussion and Conclusion
In this paper an attempt was made to answer the questions: Do mergers in the airline industry create positive effects on longer-term shareholder value? And how do these effects hold in the longer run? First a review on existing literature was done, where it was noticed that there is no existing research available on Stock-price effects in the longer run for the airline industry. These questions were tested for using the Cumulative Average Abnormal Returns (CAAR)-method described by Agrawal et al. (1992) and Dimson and Marsh (1986).
5.1 Abnormal returns over 5 years
As presented in the analysis section, all separate merged airlines out of the sample set report significant abnormal returns over the complete 5 year period following the merger, except for the two mergers in which Skywest was involved. Looking at the averages it can be concluded that during the first five years following a merger substantial and highly
significant abnormal returns can be generated by the merged company. This is not in line with most of the literature found on mergers and acquisitions in general. However, it was noticed, for example in Knapp (1990) and Morisson (1996) that the airline industry is quite unique in terms of absence of competition in the airline merger and acquisition market, adaption speed and unpredictability. These factors make it possible to earn positive
abnormal returns after mergers, also in the longer run. From this thesis it seems that airlines are often undervalued by investors regarding the announcement effect, as it seems possible for them to generate positive shareholder returns on the longer run.
5.2 Abnormal return over the first year
Looking at the separate abnormal returns during the first year, it is notable that they are either positive or insignificant. It was also concluded before, that mainly for the mergers with a lower deal value it holds that the results during the first year are insignificant. These results are in line with the findings of Jarrel and Poulsen (1989), who stated that the amount and significance of abnormal returns are dependent on the relative size of the target firm compared to the acquirer.
On average the highest abnormal returns are found in the first year after the merger. This could be because of market power effects that multiple papers (e.g. Kim and Singal, 1993) describe, but it is hard to conclude anything about the reasons behind the positive return.
At least the results in this thesis imply that it is possible to generate positive returns in the longer run.
5.3 Changes in abnormal returns
During the years after the merger the abnormal returns are highly volatile, so it is very hard to say anything on the changes in abnormal returns during the separate 5 years after the merger. It is clear that in the first year the cumulative average abnormal returns are the highest. In the averages it can be seen that during the second and fourth year after the merger the abnormal returns do not differ significantly from zero, while during the other years the abnormal returns are positive on average. There are enough reasons to believe that airline mergers in general are not expected to be followed by negative abnormal returns.
5.4 Conclusion
Based on this thesis it seems that airlines are able to generate positive results on the longer run after a merger. In most background literature the authors stated that a lot of mergers and acquisitions are not able to contribute to positive shareholder returns in the longer run. The airline industry however is unique in terms of the absence of competition in the merger market, as well as its low ability to quickly adapt to changes in the market. While the highest abnormal returns are obviously earned during the first year after the merger, during the years afterwards the returns get more volatile. In some years the abnormal returns are insignificantly different from zero, while in others they remain positive. At least there is no evidence to suggest that the returns after an airline merger on the longer run would be expected to be negative, which, as said earlier, is not exactly in line with literature.
It is hard to say anything more on the effects that contributed to the reported results. From literature it became known that there are multiple effects that contribute to returns from a merger. On the very short term there are effects expected from increased market power, while on the middle to long-term increased efficiency effects are also possibly contributing to positive results. If the market is efficient all the investors’ expectations should have been included in the returns, thus it is not expected to generate abnormal returns in the longer run. As the highest returns are reported in the first year it is possible that the increased market power would have a higher effect than generally expected in the airline industry.
The same holds for improved efficiency effects, as it can be seen that in the third year the abnormal returns get positive again.
More information on the nature of all different local airline markets and internal structure of the airlines for the mergers would be needed to investigate the source effects of all results. Based on the findings of this thesis it could be said that airlines in general are able to generate positive returns in the longer run, which would imply they are overvalued at the time of the merger announcement. This is not in line with the findings in literature of mergers and acquisitions in general.
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Appendix
Mergers on the tables on the next pages:
AAR = Average Abnormal Return (monthly) CAAR = Cumulative Average Abnormal Return
1 Air France (FR) Cityjet (IE)
2 Ryanair (IE) Buzz (NL)
3 Air France (FR) KLM (NL)
4 Qantas (AU) Star Track (AU)
5 Skywest (US) Atlantic Southeast Airlines (US)
6 Utair (RU) Avialinii Chuvashii (RU)
7 Air Berlin (GB) LTU (DE)
8 Gol Linhas (BR) Varig (BR)
9 Norwegian (NO) Nordic (NO)
10 Easyjet (GB) GB Airways (GB)
11 Air France-KLM (FR) VLM (BE)
12 Delta Airlines (US) Northwest Airlines (US)
13 Vueling (ES) Clickair (ES)
14 Lufthansa (DE) Germanwings (DE)
15 China Eastern Airlines (CN) Shanghai Airlines (CN)
16 Skywest (US) Expressjet (US)
17 Southwest Airlines (US) Airtran (US)
Table 4a: CAAR Calculations 1 2 3 4 5 6 7 8 9 AAR [+1,+60] 0.0236 0.0216 0.0323 0.0714 0.0026 0.0560 -0.0222 0.0220 0.0118 AAR [+1,+12] -0.0037 -0.0061 0.0322 0.0640 0.0199 0.0775 0.0265 0.0992 0.0423 AAR [+13,+24] 0.0182 0.0017 0.0065 0.0639 0.0048 0.0735 -0.0326 -0.0313 -0.0201 AAR [+25,+36] 0.0704 0.0358 0.0433 0.0800 0.0386 0.0876 -0.0552 0.0513 0.0266 AAR [+37,+48] 0.0061 0.0319 0.0371 0.0997 -0.0022 0.0262 -0.0218 -0.0322 0.0080 AAR [+49,+60] 0.0271 0.0444 0.0426 0.0496 -0.0483 0.0151 -0.0278 0.0231 0.0023 CAAR [+1,+60] 1.4178 1.2944 1.9409 4.2855 0.1532 3.3590 -1.3303 1.3224 0.7091 t-value (11.7989) (10.9400) (17.4978) (34.0196) (1.4810) (14.3026) (-8.8341) (6.6607) (4.3543) CAAR [+1,+12] -0.0445 -0.0726 0.3869 0.7674 0.2388 0.9298 0.3180 1.1909 0.5077 t-value (-0.4494) (-0.5074) (5.3160) (8.3201) (1.7414) (5.8769) (2.0955) (7.3461) (3.6278) CAAR [+13,+24] 0.2187 0.0209 0.0781 0.7664 0.0575 0.8822 -0.3917 -0.3757 -0.2410 t-value (3.2832) (0.1635) (0.7673) (7.4759) (0.8461) (5.8153) (-1.5689) (-1.6252) (-0.9998) CAAR [+25,+36] 0.8453 0.4295 0.5197 0.9599 0.4630 1.0511 -0.6620 0.6154 0.3188 t-value (4.3037) (3.8262) (6.8538) (7.2130) (5.2827) (6.1195) (-4.4914) (2.0461) (1.6087) CAAR [+37,+48] 0.0737 0.3830 0.4450 1.1961 -0.0266 0.3143 -0.2613 -0.3858 0.0956 t-value (0.5345) (6.0442) (5.8577) (20.7689) (-0.1976) (0.7657) (-3.3820) (-3.1493) (1.4441) CAAR [+49,+60] 0.3247 0.5336 0.5111 0.5956 -0.5795 0.1816 -0.3334 0.2775 0.0280 t-value (3.6377) (3.2301) (2.4437) (2.6584) (-6.0451) (0.6933) (-2.8023) (1.6486) (0.1638)
Table 4b: CAAR Calculations continued 10 11 12 13 14 15 16* 17* AAR [+1,+60] 0.0041 -0.0068 0.0050 -0.0092 0.0202 -0.0227 0.0002 0.0155 AAR [+1,+12] -0.007 0.0137 0.0384 0.0038 0.0679 0.0058 0.0181 -0.0041 AAR [+13,+24] -0.0153 0.0142 -0.0007 -0.0522 -0.0062 -0.0224 -0.0434 0.0035 AAR [+25,+36] -0.0428 -0.0324 -0.0404 0.0146 0.0244 -0.0139 0.0365 0.0084 AAR [+37,+48] 0.0332 -0.0579 0.0149 -0.0585 0.0390 -0.0346 -0.0516 0.0549 AAR [+49,+60] 0.0527 0.0283 0.0129 0.0462 -0.0240 -0.0484 0.0499 0.0147 CAAR [+1,+60] 0.2463 -0.4100 0.2995 -0.5538 1.2141 -1.3609 0.0139* 0.8834* t-value (1.7926) (-2.4923) (3.2480) (-2.8432) (13.1386) (-10.5463) (0.1286) (16.8903) CAAR [+1,+12] -0.0872 0.1642 0.4608 0.0454 0.8151 0.0697 0.2175 -0.0500 t-value (-0.4572) (0.7391) (3.1940) (0.1360) (7.0071) (0.4050) (3.1102) (-0.8590) CAAR [+13,+24] -0.1838 0.1699 -0.0094 -0.6263 -0.0744 -0.2682 -0.5212 0.0416 t-value (-1.0602) (0.8035) (-0.1073) (-2.5156) (-0.7878) (-2.7148) (-4.7379) (0.8101) CAAR [+25,+36] -0.5136 -0.3888 -0.4853 0.1753 0.2930 -0.1667 0.4382 0.1007 t-value (-3.9917) (-2.4812) (-5.9045) (1.6499) (3.9685) (-0.9787) (3.4943) (2.7548) CAAR [+37,+48] 0.3980 -0.6948 0.1790 -0.7024 0.4680 -0.4148 -0.6192 0.6588 t-value (4.0324) (-7.0728) (2.8716) (-7.4031) (4.7834) (-3.2025) (-8.3547) (16.1067) CAAR [+49,+60] 0.6328 0.3395 0.1544 0.5542 -0.2876 -0.5810 0.4986* 0.1323* t-value (6.5274) (2.2394) (1.9711) (4.2121) (-3.8676) (-5.8004) (2.8114) (1.8883) 29
