Abstract
Funding conditions do not remain the same. The corporate finance literature documents that variations in funding conditions, for instance in the form of shifts in interest rates, affect banks’ and firms’ access to capital, as well as investors’ security pricing behaviour. The high levels of leverage in the shipping industry make it particularly susceptible to fluctuations in funding conditions, exerting a significant impact on shipping companies’ investment decisions. In this paper, we examine the link between funding conditions and investment quality in the shipping industry, focusing on mergers and acquisitions (M&As). We employ the event study methodology to obtain acquirer returns around M&As announcement dates, and multivariate regression to reveal the link between M&As and funding conditions. By using 352 completed acquisitions announced by international shipping companies between 1987 and 2020, we find that shipping companies engage in less value-creating deals in favourable funding conditions; a finding that supports the capital rationing theory. We report that a unit increase in our measure of funding conditions, on average, reduces shareholder value by 1.2% during the deal announcement window. Higher profitability moderates the negative effect of favourable funding conditions on shareholder value. Uncertainty of economic policies in acquirer’s nation is associated with even lower deal quality during times of favourable funding conditions, emphasising the inseparable relationship between the economic landscape and shipping. The paper contributes to the shipping M&As literature by showing that the macroeconomic environment can have a great impact on the outcomes of M&A deals, as well as company and deal characteristics. The paper offers several policy implications for shipping companies with M&As intentions, shipping investors, and banks that support shipping.
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Data availability
The data that support the findings of this study are available from Standard & Poor’s Compustat Database, retrieved from Wharton Research Data Service. Restrictions apply to the availability of these data.
Notes
The figure is calculated using M&A data from Thomson SDC and includes all mergers and acquisitions that took place in the shipping industry over the period 2008–2018.
The top three banks with the highest exposure to the shipping industry are BNP Paribas, KfW, and Exim Bank of China, with more than $50bil in total.
See Sect. 3 for a detailed discussion on the construction of the funding conditions measure.
It is important to note that deals in both favourable and unfavourable funding conditions increase value for their shareholders, with average returns of 0.9% and 2%, respectively. Our references to value destruction throughout the paper refer to the lower or higher value created in one set of conditions relative to alternatives, as deals do not destroy value on average in absolute terms.
Despite the highly fragmented structure of the dry bulk and tanker industries, aiming at an increased presence in specific routes may still be desirable to investors.
Defined here as the average of national indices showing the frequency of newspaper articles that involve the terms economic, policy, and uncertainty together (Baker et al. 2016).
China economic growth slowest in 25 years, “https://www.bbc.com/news/business-35349576”.
The sample size in shipping M&As studies is inherently restricted by data unavailability. The most comprehensive shipping M&As study to date, Alexandrou et al. (2014), does not impose any restrictions to the sample apart from the requirement of the acquirer company being public.
“Are shipowners ready for higher interest rates?” Drewry Research, June 2018.
Using Federal Reserve Policy rates instead of LIBOR does not change the direction of our results.
Changing the time span of funding conditions prior to an acquisition announcement does not affect our results.
These countries are Australia, Brazil, Canada, Chile, China, Colombia, France, Germany, Greece, India, Ireland, Italy, Japan, Mexico, the Netherlands, Russia, South Korea, Spain, Sweden, the United Kingdom, and the United States. Our sample covers all the represented countries except Colombia.
The data for Global Economic Policy Uncertainty Index starts since 1997, leaving 40 deals excluded from our regressions.
We do not include year-fixed effects in any of our models since they are highly correlated with our funding conditions measure.
We follow this procedure in the rest of the paper as well.
For brevity, we do not include a discussion about the control variables as their impact is found to be largely in line with the literature.
This result has clear policy implications not only for shipping companies but also for commercial banks that support the shipping industry. The decisions of banks on lending terms and capital availability significantly affect companies’ strategic decisions to involve in M&A deals. The fact that shipping companies exhibit less corporate vigilance calls for banks to consider another risk factor when lending to shipping companies.
The figure is based on the full specification in model III in Table 2.
The figure is based on the full specification in model VI in Table 2.
The reason we still report the tests in Panel A is that the non-ship-owning industries can still be affected by earning levels in the ship-owning industry. Furthermore, Alexandrou et al. (2014) include the Baltic Dry Index, a much less comprehensive proxy for earnings levels, in their likelihood of merger models that comprise all subsectors in the shipping industry.
The interpretation of the main effects is not insightful in the presence of an interaction term since they directly depend on the values of each other. For instance, the isolated impact of funding conditions in Table 3 can only be extracted when ClarkSea equals to 0, i.e. when the interaction term disappears from the model. Since the variable ClarkSea cannot take a value of 0 (See Table 1), average isolated inferences cannot be drawn.
References
Acharya, Viral V., and Lasse H. Pedersen. 2005. Asset pricing with liquidity risk. Journal of Financial Economics 77 (2): 375–410. https://doi.org/10.1016/j.jfineco.2004.06.007.
Adra, Samer, Leonidas G. Barbopoulos, and Anthony Saunders. 2020. The impact of monetary policy on M&A outcomes. Journal of Corporate Finance 62: 101529. https://doi.org/10.1016/j.jcorpfin.2019.101529.
Albertijn, Stefan, Wolfgang Bessler, and Wolfgang Drobetz. 2011. Financing shipping companies and shipping operations: A risk-management perspective. Journal of Applied Corporate Finance 23 (4): 70–82. https://doi.org/10.1111/j.1745-6622.2011.00353.x.
Alexandridis, George, and Manish Singh. 2016. Mergers and acquisitions in shipping. In The international handbook of shipping finance: Theory and practice, ed. Manolis G. Kavussanos and Ilias D. Visvikis. London: Palgrave Macmillan.
Alexandridis, George, Manolis G. Kavussanos, Chi Y. Kim, Dimitris A. Tsouknidis, and Ilias D. Visvikis. 2018. A survey of shipping finance research: Setting the future research agenda. Transportation Research Part E: Logistics and Transportation Review 115: 164–212. https://doi.org/10.1016/j.tre.2018.04.001.
Alexandridis, George, Nikolaos Antypas, Arman Gulnur, and Ilias Visvikis. 2020. Corporate financial leverage and M&As choices: Evidence from the shipping industry. Transportation Research Part E: Logistics and Transportation Review 133: 101828. https://doi.org/10.1016/j.tre.2019.101828.
Alexandrou, George, Dimitrios Gounopoulos, and Hardy M. Thomas. 2014. Mergers and acquisitions in shipping. Transportation Research Part E: Logistics and Transportation Review 61: 212–234. https://doi.org/10.1016/j.tre.2013.11.007.
Baker, Scott R., Nicholas Bloom, and Steven J. Davis. 2016. Measuring economic policy uncertainty*. The Quarterly Journal of Economics 131 (4): 1593–1636. https://doi.org/10.1093/qje/qjw024.
Becher, David, Tyler K. Jensen, and Tingting Liu. 2020. Acquisitions and funding conditions. Journal of Corporate Finance 65: 101760. https://doi.org/10.1016/j.jcorpfin.2020.101760.
Bernanke, Ben S., and Alan S. Blinder. 1992. The federal funds rate and the channels of monetary transmission. The American Economic Review 82 (4): 901–921.
Bhagwat, Vineet, Robert Dam, and Jarrad Harford. 2016. The real effects of uncertainty on merger activity. The Review of Financial Studies 29 (11): 3000–3034. https://doi.org/10.1093/rfs/hhw061.
Biddle, Gary C., and Hilary Gilles. 2006. Accounting quality and firm-level capital investment. The Accounting Review 81 (5): 963–982.
Bonaime, Alice, Huseyin Gulen, and Mihai Ion. 2018. Does policy uncertainty affect mergers and acquisitions? Journal of Financial Economics 129 (3): 531–558. https://doi.org/10.1016/j.jfineco.2018.05.007.
Bouwman, Christa H. S., Kathleen Fuller, and Amrita S. Nain. 2009. Market valuation and acquisition quality: Empirical evidence. The Review of Financial Studies 22 (2): 633–679.
Brown, Stephen J., and Jerold B. Warner. 1985. Using daily stock returns: The case of event studies. Journal of Financial Economics 14 (1): 3–31. https://doi.org/10.1016/0304-405X(85)90042-X.
Brunnermeier, Markus K., and Lasse H. Pedersen. 2008. Market liquidity and funding liquidity. The Review of Financial Studies 22 (6): 2201–2238. https://doi.org/10.1093/rfs/hhn098.
Di Maggio, Marco, and Marcin Kacperczyk. 2017. The unintended consequences of the zero lower bound policy. Journal of Financial Economics 123 (1): 59–80. https://doi.org/10.1016/j.jfineco.2016.09.006.
Dixit, Avinash K., and Robert S. Pindyck. 1994. Investment under uncertainty. Princeton University Press.
Drobetz, Wolfgang, Dimitrios Gounopoulos, Andreas Merikas, and Henning Schröder. 2013. Capital structure decisions of globally-listed shipping companies. Transportation Research Part E: Logistics and Transportation Review 52: 49–76. https://doi.org/10.1016/j.tre.2012.11.008.
Drobetz, Wolfgang, Malte Janzen, and Ignacio Requejo. 2019. Capital allocation and ownership concentration in the shipping industry. Transportation Research Part E: Logistics and Transportation Review 122: 78–99. https://doi.org/10.1016/j.tre.2018.09.010.
Erel, Isil, Rose C. Liao, and Michael S. Weisbach. 2012. Determinants of cross-border mergers and acquisitions. The Journal of Finance 67 (3):1045–1082. https://doi.org/10.1111/j.1540-6261.2012.01741.x.
Faccio, Mara, and Ronald W. Masulis. 2005. The choice of payment method in European mergers and acquisitions. The Journal of Finance 60 (3): 1345–1388.
Fuller, Kathleen, Jeffry Netter, and Mike Stegemoller. 2002. What do returns to acquiring firms tell us? Evidence from firms that make many acquisitions. The Journal of Finance 57 (4): 1763–1793.
Fusillo, Mike. 2009. Structural factors underlying mergers and acquisitions in liner shipping. Maritime Economics and Logistics 11 (2): 209–226. https://doi.org/10.1057/mel.2009.3.
Gregoriou, Andros, Binh D. Nguyen, Tung D. Nguyen, Huong Le, and Robert Hudson. 2021. Economic policy uncertainty and cross-border mergers and acquisitions. International Review of Financial Analysis 78: 101911. https://doi.org/10.1016/j.irfa.2021.101911.
Haider, Jane, Ou. Zhirong, and Stephen J. Pettit. 2018. Predicting corporate failure for listed shipping companies. Maritime Economics and Logistics 21 (3): 415–438. https://doi.org/10.1057/s41278-018-0101-4.
Harford, Jarrad, Sandy Klasa, and Nathan Walcott. 2009. Do firms have leverage targets? Evidence from acquisitions. Journal of Financial Economics 93: 1–14. https://doi.org/10.1016/j.jfineco.2008.07.006.
Harrison, Jeffrey, Matthew Hart, and Derek Oler. 2014. Leverage and acquisition performance. Review of Quantitative Finance and Accounting 43 (3): 571–603.
Hoshi, Takeo, Anil Kashyap, and David Scharfstein. 1991. Corporate structure, liquidity, and investment: Evidence from Japanese industrial groups. The Quarterly Journal of Economics 106 (1): 33–60.
Jensen, Michael C., and William H. Meckling. 1976. Theory of the firm: Managerial behavior, agency costs and ownership structure. Journal of Financial Economics 3 (4): 305–360. https://doi.org/10.1016/0304-405X(76)90026-X.
Jensen, Gerald R., and Theodore Moorman. 2010. Inter-temporal variation in the illiquidity premium. Journal of Financial Economics 98 (2): 338–358. https://doi.org/10.1016/j.jfineco.2010.05.008.
Jensen, Gerald R., Jeffrey M. Mercer, and Robert R. Johnson. 1996. Business conditions, monetary policy, and expected security returns. Journal of Financial Economics 40 (2): 213–237. https://doi.org/10.1016/0304-405X(96)89537-7.
Jiménez, Gabriel, Steven Ongena, José-Luis. Peydró, and Jesús Saurina. 2012. Credit supply and monetary policy: Identifying the bank balance-sheet channel with loan applications. American Economic Review 102 (5): 2301–2326. https://doi.org/10.1257/aer.102.5.2301.
Jiménez, G., S. Ongena, J.L. Peydró, and J. Saurina. 2014. Hazardous times for monetary policy: What do twenty-three million bank loans say about the effects of monetary policy on credit risk-taking? Econometrica 82 (2): 463–505. https://doi.org/10.3982/ECTA10104.
John, Kose, and Eli Ofek. 1995. Asset sales and increase in focus. Journal of Financial Economics 37 (1): 105–126. https://doi.org/10.1016/0304-405X(94)00794-2.
Ju, Huizhu, Qingcheng Zeng, Hercules Haralambides, and Yimeng Li. 2023. An investigation into the forces shaping the evolution of global shipping alliances. Maritime Policy & Management. https://doi.org/10.1080/03088839.2023.2180549.
Kang, Hyung Koo, Grace WY. Wang, Hee-Seok. Bang, and Su-Han. Woo. 2015. Economic performance and corporate financial management of shipping firms. Maritime Economics and Logistics. https://doi.org/10.1057/mel.2015.8.
Kilian, Lutz. 2009. Not all oil price shocks are alike: Disentangling demand and supply shocks in the crude oil market. The American Economic Review 99 (3): 1053–1069.
McWilliams, Abagail, and Donald Siegel. 1997. Event studies in management research: Theoretical and empirical issues. Academy of Management Journal 40 (3): 626–657.
Merikas, Andreas, Dionysios Polemis, and Anna Triantafyllou. 2011. Mergers and acquisitions in the shipping industry. Journal of Applied Business Research 27 (4): 9–22. https://doi.org/10.19030/jabr.v27i4.4653.
Moeller, Sara B., Frederik P. Schlingemann, and René M. Stulz. 2004. Firm size and the gains from acquisitions. Journal of Financial Economics 73 (2): 201–228. https://doi.org/10.1016/j.jfineco.2003.07.002.
Moeller, Sara B., Frederik P. Schlingemann, and René M. Stulz. 2005. Wealth destruction on a massive scale? A study of acquiring-firm returns in the recent merger wave. The Journal of Finance 60 (2): 757–782.
Nejadmalayeri, Ali, and Aaron Rosenblum. 2022. Distressed acquirers and the bright side of financial distress. International Review of Financial Analysis 83: 102303. https://doi.org/10.1016/j.irfa.2022.102303.
Nguyen, Nam H., and Hieu V. Phan. 2017. Policy uncertainty and mergers and acquisitions. The Journal of Financial and Quantitative Analysis 52 (2): 613–644.
Panayides, Photis M. 2006. Maritime logistics and global supply chains: Towards a research agenda. Maritime Economics & Logistics 8 (1): 3–18. https://doi.org/10.1057/palgrave.mel.9100147.
Panayides, Photis, and Xihe Gong. 2002. The stock market reaction to merger and acquisition announcements in liner shipping. Maritime Economics and Logistics 4: 55–80. https://doi.org/10.1057/palgrave/ijme/9100030.
Petrofin Research. 2020. Key developments and growth in global ship finance. In Petrofin global bank research, ed. Ted Petropoulos. Athens.
Renneboog, Luc, and Cara Vansteenkiste. 2019. Failure and success in mergers and acquisitions. Journal of Corporate Finance 58: 650–699. https://doi.org/10.1016/j.jcorpfin.2019.07.010.
Rhodes-Kropf, Matthew, and S. Viswanathan. 2004. Market valuation and merger waves. The Journal of Finance 59 (6): 2685–2718. https://doi.org/10.1111/j.1540-6261.2004.00713.x.
Schwert, G. William. 2000. Hostility in takeovers: In the eyes of the beholder? The Journal of Finance 55 (6): 2599–2640. https://doi.org/10.1111/0022-1082.00301.
Serdar Dinc, I., and Isil Erel. 2013. Economic Nationalism in Mergers and Acquisitions. The Journal of Finance 68 (6): 2471–2514. https://doi.org/10.1111/jofi.12086.
Su, Chi-Wei., Kai-Hua. Wang, Qi. Shao, and Ran Tao. 2019. Are there bubbles in the shipping freight market? Maritime Policy & Management 46 (7): 818–830. https://doi.org/10.1080/03088839.2019.1619946.
Syriopoulos, Theodore, and Ioannis Theotokas. 2007. Value creation through corporate destruction? Corporate governance in shipping takeovers. Maritime Policy & Management 34: 225–242. https://doi.org/10.1080/03088830701342973.
Travlos, Nickolaos G. 1987. Corporate takeover bids, methods of payment, and bidding firms’ stock returns. The Journal of Finance 42 (4): 943–963. https://doi.org/10.2307/2328300.
Turki, Aymen. 2019. Dividend policy and stock acquisition announcement returns: A test of asymmetric information theory. Journal of Financial Research 42 (1): 115–145. https://doi.org/10.1111/jfir.12164.
UNCTAD. 2021. Review of Maritime Transport edited by Peter Stalker. Geneva.
Uysal, Vahap B. 2011. Deviation from the target capital structure and acquisition choices. Journal of Financial Economics 102 (3): 602–620. https://doi.org/10.1016/j.jfineco.2010.11.007.
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Appendix: variable definitions
Appendix: variable definitions
CARs | Acquirer’s cumulative abnormal returns over the event window around announcement day 0. The announcement event window is [− 1, + 1] |
Funding dummy | A dummy variable takes a value of 1 (− 1) if the most recent change in monthly LIBOR rates is a decrease (increase). The variable takes a value 0 if no change is observed in the rates |
Funding conditions | The average of Funding dummy over the twelve months prior (acquisition negotiation period) to the acquisition announcement date |
Funding tercile 1 | A dummy variable takes a value of 1 if the values lie within the first tercile of Funding conditions |
Funding tercile 2 | A dummy variable takes a value of 1 if the values lie within the second tercile of Funding conditions |
Funding tercile 3 | A dummy variable takes a value of 1 if the values lie within the third tercile of Funding conditions |
GEPU Index | Natural logarithm of Global Economic Policy Uncertainty Index from https://www.policyuncertainty.com/global_monthly.html |
ClarkSea Index | The average of the natural logarithm of Clarksea Index values over the twelve months prior (acquisition negotiation period) to the acquisition announcement date |
Toehold | A dummy variable takes a value of 1 if an acquirer has an ownership stake in the target company of 5% or more prior to the acquisition announcement |
Attitude | A dummy variable takes a value of 1 if the deal attitude is recorded as “Friendly” on SDC |
Cross-border | A dummy variable takes a value of 1 if cross-border flag is recorded as “Y” on SDC |
Tender | A dummy variable takes a value of 1 if tender flag is recorded as “Y” on SDC |
Public target | A dummy variable takes a value of 1 if the publicly listed status of the target is recorded as “Public” on SDC |
All other | A dummy variable takes a value of 1 if the payment method is recorded as 100% other/unknown on SDC |
All cash | A dummy variable takes a value of 1 if the payment method is recorded as 100% cash on SDC |
All stock | A dummy variable takes a value of 1 if the payment method is recorded as 100% stock on SDC |
Diversifying | A dummy variable takes a value of 1 if the target does not share the same SIC code with the acquirer |
M&A liquidity | The ratio of total deal value to total assets in a given industry and year |
HHI | The sum of squared terms of the market share percentage of companies in a given industry and year |
Size | Natural logarithm of book value of total assets |
Leverage | The ratio of short- and long-term debt to book value of total assets |
Cash | The ratio of cash and cash equivalents to book value of total assets |
Profitability | The ratio of operating income before depreciation to book value of total assets |
Market-to-book | The ratio of the market value of assets to book value of total assets |
Dividend payer | A dummy variable takes a value of 1 if the company pays dividends in a given year |
Acquirer runup | Acquirer’s annual stock return measured in the previous year of acquisition announcement |
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Gülnur, A., Antypas, N. Favourable funding conditions: friend or foe of shipping M&As?. Marit Econ Logist 25, 728–754 (2023). https://doi.org/10.1057/s41278-023-00272-y
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DOI: https://doi.org/10.1057/s41278-023-00272-y