Ship Financing

External Financing and Capital Structure

Leontios Chaviaras

University of the AegeanDepartment of Shipping Trade and TransportCourse Shipping FinanceProfessor Ksydeas Evaggelos

Chios

2016

Introduction

“Shipping finance has changed beyond recognition in the post-crisis period, with a huge increase in the role of export credit agencies and the emergence of new opportunities in areas such as liquefied natural gas”

Valentino Gallo, global head of export& agency finance at Citi

No one can ignore the importance of shipping in the global economy, as it plays a central part in it, stretching back many years. Their close relationship has been well documented by many acknowledged economists throughout the years. However, shipping finance has been generally neglected in applied financial research. This is surprising, given the fact that the shipping sector is characterized by capital intensive investments. The main reason for the lack of research is due to the introvert approach that shipping companies usually follow in financing their investment plans, as well as the recent introduction of other methods of fund raising such as the stock markets, which gave researchers, access to more information, as listed companies must disclose their financial statements.

In that context, by reviewing some of the latest developments in global shipping finance, especially after the 2008 financial crisis, it is clear that there has been a major lack of bank financing. Alternative financing is becoming more common, as companies are struggling to mediate cost pressures and secure the funding for new vessels to stay competitive. The change in the financing landscape can be gauged by the Lloyd’s List’s1 categorization of the financiers in shipping. Among the top 10 of them, at least four belong to alternative funding organizations2.

Export Credit Agencies or ECAs have become critically important after the crisis. ECAs are of major importance as they provide a stable source of finance that is crucial in the shipping industry, where cyclicality tends to destabilize long term investment plans of ship-owners and shipyards. They operate as a tool of economic policy and have a mandate to support exports. The proportion of shipping and offshore related debt finance involving ECAs has increased from around 10% before the crisis to more than a third of all financing currently, with annual volumes totaling around US$15bn a year. 3

An alternative viable solution of external financing includes capital markets. Capital Markets play a key role in the promotion of shipping business growth and value creation providing an advantage to shipping companies as they are able to issue debt with longer maturities and fixed interest. According to a Dealogic estimate, capital market financing, with the issuance of equity and bonds, accounts for 30% of the total financing extended to the shipping industry. Another important factor is the institutionalization of shipping finance which is leading to improved reporting and also corporate governance structures which lead to transparency for upcoming future finance partners. In this way, institutional investors, who prefer liquid investments in shipping, are becoming more interested in initial public offers IPOs. Shipping

1 Top 10 in ship finance, Lloyd’s List, 20142 KPMG, Shipping Insights Briefing Issue 1, 20153 Shipping Finance : A new model for a new market, Global Trade Review, March-April 2014

IPOs are slowly picking up pace after a stagnant period in 2008-2009. However, as freight rates remain low, the attractiveness of IPOs, especially by companies with commodity shipping as dry bulk, is low.Meanwhile as shipping companies struggle with low freight rates and oversupply, private equity (PE) firms are focusing on opportunistic investment options4. PE funds provide flexibility in extending credit. They also extend loans to riskier projects that banks are restricted from providing. However, the possibility of high PE investment affecting the demand-supply equilibrium by creating oversupply is a growing concern, as shipping companies are investing heavily in buying new vessels. This impending overcapacity is expected to make PE exit less profitable.

In this paper, we are going to examine the financing methods used in the shipping sector and analyze the capital structure of shipping companies by finding which of the prevailing capital structure theories applies the most. Greek shipping remains the leading shipowning country with Greek companies accounting for more than 16% of the world industry followed by companies from Japan, China, Germany and Singapore5, therefore it is important to examine the theory on the leaders and also to compare it with the other countries.

4 KPMG, Shipping Insights Briefing Issue 1, 20155 UNCTAD Review of Maritime Transport 2015

Financing methods, tools and markets

Investment decisions in the shipping industry bear a significant element of business uncertainty mainly due to the derived nature of demand for shipping services. Cyclicality in freight rates and vessel prices are highly correlated with the world economic growth. In that context, shipping companies have to adjust to a dynamic and rapidly changing environment and carefully plan their investment plans in order to minimize their exposure to risk. This careful planning is gradually changing the culture of shipping companies, shifting their long term goal of profit maximization to an increase in firm market value6. To achieve this, shipping companies have to constantly focus on promoting investment plans that bring growth and have positive returns that perform more than the required costs undertaken. Most of the investment plans of the shipping companies, involve building new vessels, purchasing secondhand or leasing. Since all these actions involve acquiring assets that have a fluctuating price and long lifecycle, that means that no mistakes can be made.

The first separation that can be made in examining capital financing in shipping is between internal and external financing. Internal financing is based on robust profitability and entails that retained earnings are sufficient to finance new investment projects. On the other hand external financing, involves fund raising from international capital markets. Since 2003, when the shipping markets began to raise asset prices and charter rates, external financing and alternative sources of equity capital begun to raise such as vessel leasing, public equity offerings, subordinated debt and high yield bonds in order to finance ships. Capital markets play a key role in the promotion of shipping business growth as they provide a more stable source of funding than internal financing. Greek shipping companies employ a combination of internal and external financing using various financing tools as those mentioned before.

In this chapter we are going to briefly analyze the methods of shipping finance, mainly focusing on external funding.

Bank Lending

The most widely accepted form of company financing is through loans, particularly the ones that use a combination of funding projects and assets. There are some basic types of borrowing:

1. The standard ship mortgage loan, with or without the assignment of charter income2. Financing up to 100% through a lease or bareboat/hire purchase agreement3. The fixed interest credit for newbuildings advanced on behalf of the shipbuilder by a

bank with the backing of a state guarantee

A number of core issues are important for shipping loans. Using external finance sources companies have to come up to a level that prospective investment cash flows can sufficiently meet financing expenses. Freight rates trends, newbuilding price trends and second-hand vessel price prospects are closely related to that point. The cost of funding, which is reflected in ship lending interest rates, is an issue of major concern. Due to the high volatility

6Theodore Syriopoulos, Financing Greek Shipping, Modern Instruments Methods and Markets, Research in Transportation Economics, 2007

and cyclicality of the shipping industry, risk premiums on shipping loans have remained relatively tight. Another important issue is currency risk, closely related with potential credit facilities originating from shipyards. To overcome and control foreign exchange risks, modern hedging instruments, including currency derivatives and swaps of varying durations are used.7Despite banks’ difficulties providing loans, due to the credit markets’ systemic problems after the financial crisis, bank loans remain as the premier method of funding for Greek and international ship owners. This condition might not hold for long, as banks are slowly beginning to limit their exposure, especially on relatively risky sectors such as shipping, a sector that bears unpredictable short term fluctuations. Shipping companies do not seem to help this situation, mainly smaller ones, as they have difficulties meeting their obligations; most of them rescheduling their loans.

The most common form of ship finance is the syndicated loan provided by international and/or Greek financial institutions for the (re)financing of

1. The construction of one or more new vessels2. The acquisition of one or more second hand vessels.

The value of new or second hand vessels, on their according markets plays an important role, and their respective fluctuations has led individual banks to pursue the sharing of lending obligation in shipping loans, thus minimizing their exposure. There has been an apparent drop in the prices of newbuilding for both bulk carriers and tankers where on the other hand prices in secondhand vessels have been rising since 2012 for tankers whereas in bulkers, prices continue to plummet.

By examining the Greek shipping loan portfolio for the last 15 years we can see that there are different trends in terms of exposure between Greek and international banks. International banks without a Greek presence seem to steadily escalate through the whole period showing and impressive increase of 17.23% . On the other side, Greek bank lending rose to a peak of $16.944 mln in 2008 and has since declined . Overall, International banks with a Greek presence are still in the lead, though as they follow a steady decline for the last 6 years (2008-2014), it appears that they will be overtaken by international banks without a Greek presence in the next years. (Petrofin Bank Research 2015)

In general there seems to be an overall increase by 4.1% in Greek ship finance; the first since 2009, with the total Greek loans booked both in Greece and worldwide as of 31/12/2014 rising to $64.019 bn compared to $61.498 in 2013.(Petrofin Bank Research 2015)

7 Theodore Syriopoulos, Financing Greek Shipping, Modern Instruments Methods and Markets, Research in Transportation Economics, 2007

1. Source: Petrofin Bank Research 2015

European banks continue to account for the vast majority of total loans. Royal Bank of Scotland remains the major lender for years, while the remaining top five banks in the list are Credit Suisse, DVB, Piraeus Bank and the National Bank of Greece.

A regular type structure of a term-loan in ship finance includes the loan agreement and the security documents: a mortgage on the vessel(s), general and specific assignment of earnings, assignment of insurance, bank account pledges, share pledges, guarantees by the holding and managing companies and all or certain individual shareholders, who are the ultimate beneficial owners. Against the advance of the loan amount, the bank requires as primary security a mortgage on the ship. This practice although has a ground basis, in times of financial distress, where the market prices of ships do not reflect their intrinsic value, poses a significant danger to banks. Those dangers, in conjunction with the enforcement of

the Basel II directives on increased sensitivity on capital requirements against asset risk exposures, might force banks to limit their exposure to ship financing to the underlying volatility in ship prices.

In Greece in order for the mortgages to be more attractive, L.D. 2687/1953 was introduced. This law essentially allowed the mortgagee to take over the management of the ship in case of default. In the year 1958 another law was introduced, the L.D. 3899/1958 which initiated the concept of preferred mortgage, which further enhanced the rights of mortgagees. In case of a preferred mortgage the mortgagee may liquidate the mortgaged asset in a private sale or auction and also has the right to assume management of the vessel.

Alternative Forms of Financing

Leasing

Another method in shipping finance that presents similarities to bank lending, is leasing. Leasing has been popular in shipping finance since 1960s, at first in the US then at UK and afterwards to the rest of Europe. What makes leasing tempting is that it represents 100% finance without requiring additional security in the form of mortgages on other ships.

A general definition of leasing is that «it is the process by which one party obtains the use of a fixed asset for which it must pay a series of contractual periodic rentals to the owner of the fixed asset.» The one obtaining the use of the asset is called “Lessee” and the one providing the asset is called “Lessor”.

When applying this definition to shipping, the financing institution (legal owner of the vessel) provides full financing for the user (shipowner) over an extended period in return for much narrower security than it would demand as an ordinary security lender. Whereas leasing is similar in nature to borrowing, as mentioned before, it does not require a charge on any of the shipping company’s assets, although it does impose a continuing charge on income. Another attractive aspect of leasing is the period obtainable. The lessor wants to fully amortize his initial capital outlay on the asset by leasing it on a certain period that enables him to provide for his borrowing costs as well as obtaining a profit. In cases of capital intensive underlying assets (such as vessels), the period of the primary lease is usually much longer, most of the times more than 10 years, since the lessor acknowledges that the lessee’s ability to earn money on the asset is harder and depends on his ability. In most cases the period is mutually agreed and is based on lessor’s view about revenue-earnings potential of the underlying asset and the professional record of the lessee. From a shipowners view, leasing, allows him to plan his cash flows, as the required level of earnings level that has to be reached in order to cover costs is pretty straightforward. As a result a common arrangement is a fixed term lease that cannot be affected by changes in tax or money cost. A fair arrangement would be based on a back-to-back deal, whereby the lessor can structure the period and terms of the lease precisely to match the revenues earned by the lessee. In general the motivation for entering into a leasing transaction could be based on a number of different factors, such as funding diversification, cost, cash management and other.

Equity Markets & IPOs

Apart from leasing, other forms of financing include listing, private equity and bonds. The constant changes in the shipping business environment have pushed many shipping companies to adapt and grasp the new opportunities of using those alternative financing methods to boost their growth. Some of the factors that prompted this shift were the big wave of replacement of old ships, which caused a necessity of substantial capital that could be more easily accessed by public markets, the contraction of banking finance and the erosion of the capital reserves in many shipping companies. Furthermore by examining the period 2001-2005, developments in the macroeconomic environment such as the growth rate of the Chinese economy, the import growth of goods in US, the turmoil in oil market as well as a general increase in world demand of wet and dry bulk that surpassed supply in the spot markets (leading to high levels of freight income), resulted in a mass IPO wave of 23 maritime companies when in 2001 the number of listed companies were only four.

Shipping business in Greece has traditionally been cash rich and family-run, avoiding the dilution of company control and the disclosure of sensitive company information. Shipping stocks as well, are generally considered unattractive to investors, due to the volatility in cash flows and also the absence of dividend payments. In recent years though, a number of major Greek shipping firms were listed on international stock exchanges mainly in the US. The main source was Initial Public Offerings (IPOs) as well as Follow-on Offerings and Bond issuances. Some examples were Costamare’s initial public offering which took place in November 2010 ($12/share) and a secondary offering in March 2012 offering at $14 mln as well as Capital Maritime Partners which issued 2.048.823 common units on March 2008 at $22.94 per unit8.

Shipping IPOs differ from those of industrial companies. The close connection between the market value of a shipping company and the value of the physical assets (ships) that the company owns, plays a major role especially in pricing the new issues. Cash flow earnings that are generated by the ships do not actually reflect in most cases their actual value in the second hand market. As a consequence new issues cannot fully reflect their intrinsic value. Despite the problem in pricing new issues, equity financing in stock markets seems to be an attractive source of capital for shipping companies mainly because of its low cost of capital compared to different sources of funding. In addition, shipping companies follow a “no dividend” policy, alleging that retained earnings have to be channeled back to fleet replacement, due to the capital intensive nature of the business and investors accept this practice. On the other hand, investors demand higher returns on equity of 15% - 20% per annum given their risk exposure.

8 Schinas O, Grau C, Johns M, HSBA “Handbook on Ship Finance” , Springer 2015

Private equity funding

Another funding method, complementary to that of equity markets is private equity funding. Private equity funds or PEFs have become increasingly popular, especially through the last 5 years driven, and the reason behind their rising appearance is the perception that shipping presents attractive profit making opportunities. The contribution of PEF investments in shipping cannot be accurately measured due to the secrecy of such investments, though according to estimates they range at about $7 to $10 billion. PEFs are primarily interested investing in newbuilding and maybe the large investments made in 2013-2014 play a little part in today’s oversupply problem in the dry bulk sector. The reason PEFs are interested in investing in newbuilding is that they represent an investment in a real economic asset with an intrinsic value and cash flow. As with other methods though that rely on the intrinsic value of the assets, a major concern is that usually in secondhand market, ships may not reflect their actual value, with their prices fluctuating above or below that. As a consequence, investing in ships is a risky investment.

Private Equity Funds work alongside with shipping companies, enjoying good operating chartering and technical skills. They provide a large amount of funds and require average returns between 15% and 25% per annum. Such returns are relatively high and it is obvious that in a market fall, shipping cannot provide these returns (in the long term).

Capital structure

In the previous chapter we analyzed the main methods of financing in shipping companies. It seems fairly obvious that most, if not all, shipping companies heavily rely on debt capital as the major source of external financing. We also concluded that throughout the last decade, shipping companies were interested in alternative ways of funding, such as IPOs, Private Equity Funds and leasing , therefore widening their financing toolset. Capital structure decisions are crucial for the survival and growth of a company, ultimately affecting its market and book value. Finding the perfect amount of debt is a difficult decision and it depends on many factors such as taxation, industry specific reasons, the borrowing cost etc.

In this manner, it is quite interesting to investigate how shipping companies manage their capital structure, if they follow a particular capital structure model and which factors apart from macroeconomic ones, affect their decisions. The first step that should be taken, to lay the groundwork for the following analysis, is to provide the theoretical framework of the prevailing capital structure theories.

Capital structure theories

Since 1958, when Franco Modigliani and Merton Miller published one of the most influential finance article ever written (“The Cost of Capital, Corporation Finance and the Theory of Investment”) in which they concluded under various significant assumptions that “The market value of any firm is independent of its capital structure and is given by capitalizing its expected return at the rate ρ appropriate to its risk class”, various theories have been introduced such as the trade-off theory and the pecking order theory.

The trade-off theory assumes that capital structure strategies rely on a trade-off between the benefits and the costs of debt.9 As we mentioned before companies are partly financed with debt and partly with equity. When companies choose debt financing, they receive a better tax treatment than when financing with equity. This benefit of tax treatment exists to partially mediate the costs of financing with debt, the costs of financial distress including bankruptcy costs of debt. In the Modigliani and Miller original theory, one of the main assumptions was that there are no bankruptcy costs, whereas in reality, bankruptcy costs can be very costly for the company (Staff leaving, high legal and accounting expenses, difficulties retaining customers and suppliers). From an agency point of view the bankruptcy risk involves agency costs such as underinvestment, even when projects have a positive NPV. 10 The trade-off theory essentially summarizes that the value of a levered firm is equal to the value of an unlevered firm plus the value of the “tax shield” and the expected costs due to financial distress. 11 Therefore according to this theory there is an optimal leverage ratio which is based on the opposing attributes of favorable tax treatment and the financial and agency costs of equity.

In trade-off theory, it is assumed that an optimal leverage ratio exists that mitigates bankruptcy costs. However it does not take into consideration (as the original Modigliani-Miller theory) that in reality investors and managers do not have the same information with the latter having better information. This is called asymmetric information and it has an important effect on capital structure. This effect is covered in the Pecking-Order theory 12. In that theory it is assumed that managers prefer to finance projects by using first internal funds, then debt and finally as a “last resort” raise new equity. The reason issuing new equity is less preferred as a mean to raise capital is because investors believe that when managers (who supposedly have better information) announce a stock offering it signals that the firm’s prospects as seen by its management are not good. Therefore the observed capital structure of a firm is the result of the company’s financing requirements over time and its attempt to rank the financing sources according to the degree they are affected by information asymmetry.

(The third theory that contradicts the other two that were previously mentioned is the Market Timing hypothesis13 . According to this theory (which is classified as behavioral finance literature), companies choose their way of funding, at first by paying attention to the market conditions when the decision is to be made. For example they issue equity when the stock market is perceived to be more favorable. In this theory it is generally perceived that firms do not care about which source of funding they use as long as it is seems to be the most valued by financial markets at the time. Therefore the capital structure of the firms and the historical changes of it, reflect the attempt to time the equity markets and not readjustments to a specific ratio (Trade-off theory) nor the requirements over time (Pecking Order theory))

9 Kraus, Alan, Litzenberger, Robert H., 1973. A state-preference model of optimal financial leverage. Journal of Finance10 Myers, Stewart C., 1977. Determinants of corporate borrowing. Journal of Financial Economics11 Eugene F. Brigham, Michael C. Ehrhardt-Financial Management Theory and Practice, 13th Edition-Cengage Learning (2010)12 Myers, Stewart C., Majluf, Nicholas S., 1984. Corporate financing and investment decisions when firms have information that investors do not have. Journal of Financial Economics13 Baker, Malcolm, Wurgler, Jeffrey, 2002. Market timing and capital structure. Journal of Finance

Empirical Evidence and Capital Structure Factors

Although hundreds of papers have been testing the applicability of the capital structure theories mentioned before, the empirical evidence does not provide a clear sign of which theory is the most accurate as it largely depends on many factors such as the industry in which the firms belong, the size, the asset tangibility etc. As a consequence the results obtained are largely mixed

In support of the trade-off theory, firms with tangible assets and large profits to shield exhibit higher debt ratios than unprofitable companies with intangible assets, high expenditures and unique products that depend on equity finance ( De Angelo, Masulis 1980) ( Harris, Raviv 1991)14 15 . An interesting study by professors Mehotra et al (2003)16 which examined the capital structure of spinoff firms, newly created firms that are not influenced by past capital structure decisions, concluded also, that more profitable firms (which have a low bankruptcy probability) and asset intensive ones, have higher levels of debt. Another study by Huang and Ritter(2009) which looked at a sample of publicly traded US firms concluded that firms turn to equity when the cost of equity is low, and also found a relationship between past decisions and the current capital structure of those firms. Their study indicates the existence of both market timing theory as well as a trade-off model. Flannery and Rangan (2006) by using a sample of publicly traded firms found evidence that is inconsistent with the static optimal target capital structure implied in the trade-off theory. They found that the volatility of the stock prices, which cause a deviation on a firm’s market based debt ratio, do not cause firms to immediately return to their target ratio. Instead they tend to make a partial adjustment every year, thus supporting the existence of a more dynamic trade-off theory.On the other hand Frank and Goyal (2009)17 who tested the Pecking Order theory on US publicly traded firms, found that on average, net equity issues commonly exceed net debt issues and argued that the large sized firms in the early years exhibited support for the pecking order theory.

It generally appears that firms try to gain the positive attributes of debt financing through the tax benefits while avoiding financial distress costs. That does not impose that they have to abide to a certain target ratio implied by the trade-off theory but can also deviate from it depending on the market conditions as the market timing theory implies. Finally, it appears that firms with growth opportunities or problems with informational asymmetry maintain a reserve borrowing capacity.

Capital Structure in the Shipping Sector

In this analysis it is important to note that the shipping sector has some specific elements that make it differ. At first it is important to note that only listed companies provide sufficient data in order to analyze them, though the listed companies are only a fraction of the total shipping firms that operate worldwide. Those listed companies also differ greatly as they usually are holding companies with as many as 50 or more ships ( which are considered

14 DeAngelo, H., & Masulis, R. (1980). Optimal capital structure under corporate andpersonal taxation. Journal of Financial Economics15 Harris, M., & Raviv, A. (1991). The theory of capital structure. The Journal of Finance16 V. Mehotra, W. Mikkelson, and M. Partch, “The Design of Financial Policies in Corporate Spinoffs,”Review of Financial Studies, Winter 200317 Frank, Murray Z., Goyal, Vidhan K., 2009. Capital structure decisions: which factors are reliably important? Financial Management 38,

as individual companies) each. Another important characteristic of the shipping industry as mentioned before is that it is strongly dependent on the international economic environment thus it does not follow specific cycles but rather global economic ones.

Another distinct feature of the shipping companies that is of great significance when analyzing their capital structure is that they have twice as high leverage ratio compared to other listed industrial firms highlighted by Drobetz et al. (2013)18 . In their analysis using a sample of 115 publicly traded shipping companies, they argue that the industry appears to have high levels of leverage which seem to be adjusted counter-cyclically. Among their findings was that traditional capital structure variables have a significant impact on the variation of leverage ratios ( Market & Book ) as in other industries though their magnitude of their impact is different due to the characteristics of the shipping industry. Specifically, asset tangibility was found positively related to leverage and its impact was more pronounced when compared with other industries. On the other hand, asset risk and operating leverage were inversely related to leverage, indicating that financial managers in the shipping sector use operational and financial hedges as complements in their corporate risk management considerations.

Capital Structure Factors

Based on past empirical literature, a number of factors has been selected in order to explain the firms’ structure and financing decisions. These factors used by Rajan and Zingales19 are the following ones:

Tangibility: Asset tangibility is the ratio of fixed asset value to total asset value. Tangibility can be used as a measure for the level of a firms’ collateralizable value. When examined from a trade-off angle , it is expected that firms with a high tangibility ratio are subjected to lower costs of financial distress due to smaller loss of value if the firm bankrupts. Furthermore tangible assets can be easily valuated from the external environment, thus contributing to low information asymmetry and smaller agency costs of debt. As a result, and in line with most empirical studies, the positive relationship between asset tangibility and leverage support the trade-off theory.

Profitability: Profitability is measured as the ratio of operating income before depreciation to total book assets. According to the static trade-off theory, a positive relationship between profitability and leverage is expected due to lower costs of financial distress when firms are more profitable. It is also assumed from past empirical studies that firms with high profitability seem to have high levels of leverage in order to avoid and reduce agency conflicts. On the other hand, most empirical studies, conclude that profitable firms prefer internal funds over external financing, finding lower levels of leverage in profitable firms, thus supporting the pecking order theory.

Firm Size: Firm size is usually measured as the natural logarithm of total book assets. Size can have a positive relationship with leverage as larger firms are generally more diversified and have a lower default risk. This conjunction supports the trade-off theory. On the contrast, under the pecking order theory, the growth of a company’s size essentially means 18 Drobetz, W., Gounopoulos, D., Merikas, A., Schroder H.,2013. Capital structure decisions of globally-listed shipping companies, Journal of Transportation Research Part E 19 Rajan, R , Zingales, L, 1995. What do we know about capital structure? Some evidence from international data. Journal of Finance 50

that more information is provided to external investors, creating information asymmetry between insiders and capital markets and lower cost for the issuance of equity. Most empirical tests find a positive relationship, which supports the trade-off theory.

It is important to note that when considering the factors, a basic exclusion was made. That exclusion involves the tax factor which is omitted for various reasons. Primarily, the main reason is that in most countries, shipping companies have a different tax treatment than other companies. The main taxation regime in many countries including Greece, is the tonnage tax, in which companies pay taxes based on the tonnage of the vessels and not accounting profits from the exploitation of the vessel. Another reason is that in other countries there are special tax incentives for shipping companies that have reduced taxes or complete tax redemption. Also many shipping companies prefer to list their ships to tax-havens where there are favorable regimes for the shipping companies. Finally the existing literature on which this project is based does not take into account into their core models the tax factor, so the same pattern is followed in this project. (Drobetz et al. (2013), Frank and Goyal (2009))

Data

Sample of Listed Companies

The sample used in this analysis consists of 38 listed shipping companies from various countries. The data were retrieved from Thompson Reuters Datastream20 database and cover the period 2004-2014 on an annual basis in US dollars. The software that was used for the analysis was E-views 8.0 and Microsoft Excel. The selected companies were chosen upon specific conditions, such as that they must have non missing data for total book assets, they should be present at the market for at least a decade and should have consolidated balance sheet data in order to account for their total liabilities. Also the companies have been selected in such a way to include all operation sectors of shipping (Bulk, Tankers, Gas carriers, Containerships). The sample is consisted of 418 firm-year observations. It is worth noting that 11 of the companies are Greek-owned. The full list of the companies and their respective countries are listed in the following table.

Shipping Company Name Country Stock Exchange MarketCompagnie Maritime Belge Belgium SYDTsakos Energy Navigation Bermudes NASDAQTeekay Partners Bermudes NASDAQAlgoma Corp Canada TORCompania SUD Americana Chile SANCompania Chilena De Navegacion

Chile SAN

China Merchants Energy Shipping Co Ltd

Christmas Islands SHG

China Shipping Container Lines

Christmas Islands SHG

China Shipping Haisheng Christmas Islands SHGCOSCO Shipping Co LTD Christmas Islands SHGAP Moller-Maersk A/S Denmark CPHDampskibsselskabet Denmark CPH

20 http://online.thomsonreuters.com/datastream

Norden A/STop Ships Inc Greece NASDAQHellenic Carriers Greece NASDAQGoldenport Greece NASDAQFreeseas Greece NASDAQEuroseas Ltd Greece NASDAQDry Ships Greece NASDAQDiana Shipping Greece NASDAQDanaos Corp Greece NASDAQAegean Marine P Greece NASDAQSeaspan Corp Hong Kong NYSEDaiichi Chuo Japan TYOAzuma Shipping Co Ltd Japan TYOMitsui OSK Japan TYOKawasaki Kisen Kaisha Japan TYOIsewan Terminal Service Japan TYOHyundai Merchant Marine Co Ltd

Korea SEO

Golar LNG Ltd Norway NASDAQHavila Shipping Norway OSLNational Shipping Company of Saudi Arabia

Saudi Arabia RYD

Neptune Orient Lines Singapore SINConcordia Maritime AB Sweden STOEvergreen Marine Corp Taiwan LSEMatson Inc. US NYSEKirby Corp US NASInternational Shipholding Corp

US NYSE

Genco Shipping US NASDAQ

Descriptive StatisticsDebt Ratio Profitability

RatioSize Tangibility

Mean 0.382199 0.142471 7.242.083 0.647503 Median 0.381211 0.118468 7.360.555 0.654451 Maximum 0.833088 0.849241 1.121.868 0.977506 Minimum 0.000000 -0.332616 2.906.901 0.011445 Std. Dev. 0.196425 0.143380 1.502.446 0.180058 Jarque-Bera 8.586.605 8.637.921 0.217566 1.541.907 Probability 0.013660 0.000000 0.896925 0.000449 Observations 418 418 418 418

The above table provides the descriptive statistics of all variables. In order to put these findings into a broader perspective, they are can be compared to other relevant empirical findings as that of Drobetz, et.al.(2013) and the thesis paper of Kakkava O. (2011). Although in their analysis, they have introduced more variables and their sample has more companies and firm years, debt ratio, profitability ratio, size and tangibility can be compared.

Debt Ratio

The mean of debt ratio of the 38 companies in the sample is 0.382 or 38.2% which is close to the findings of the other studies mentioned. More specifically the mean of their debt ratio was 40.7% and 43% accordingly. All these figures verify the fact that shipping companies have a high debt ratio, which is what we expected in the first place. These high ratios are comparable to those of the REIT industry, presumably because both industries are characterized by high intensity in fixed assets21. Also the difference between the mean and the median is very small, even though the sample is relatively small and there is a wide range of companies of various sizes, indicating that the debt ratio is indifferent to the size of the company. The standard deviation is small and at close levels to other researches (0.206 Drobetz et.al.).

Profitability

The mean of the profitability is also at close level with findings of other researches. In this case, the profitability ratio is at 0.142 or 14.2%, while in other researches the results are 11.3% (Drobetz et.al) and 5% (Kakkava). That level of profitability is generally similar to other industrial firms.

Size

Size, which is the natural logarithm of total assets is at also at close levels with other researches although it is a bit higher. The mean is 7.242 while in other researches it is at 6.355, meaning that the total assets of our sample companies are higher.

Tangibility

In line with the fact that shipping companies have a high leverage ratio due to investments in fixed assets, tangibility mean is at 0.647 ,meaning that tangible assets account for 64.7% of firms total assets. In other researches this ratio is 63% (Drobetz) and 88% (Kakkava).

The shipping industry is characterized as a capital intensive, with relatively unpredictable changes in supply and demand prices. Standard deviation prices, prove this fact, almost on all variables that are examined. From the Jarque and Bera test results, it appears that the null hypothesis of normal distribution of the variables is rejected, as probability is below 0.05, except in size variable where it is 0.89.

From the following correlation matrix between the depended ( Debt ratio ) and independed variables ( profitability, size and tangibility) that are used in the examined model it appears that the values are generally at low levels. Debt is negatively correlated with profitability meaning that companies with high leverage ratios tend to be less profitable. Tangibility and

21 Harrison, David M., Panasian, Christine A., Seiler, Michael J., 2011. Further evidence on the capital structure of REITs. Real Estate Economics

size are positively correlated with debt, although at different levels. Tangibility has a higher correlation with debt, which means that companies that have many fixed assets have a higher debt. On the other hand the size of the firm seems to be generally very little correlated with all other variables. In general these findings are consistent with other researches.

Correlation Matrix

Debt Ratio Profitability Ratio

Size Tangibility

Debt Ratio 1.000.000Profitability Ratio

-0.184821 1.000.000

Size 0.073489 -0.021315 1.000.000

Tangibility 0.462558 -0.061790 0.004099 1.000.000

Econometric Model

Panel Data Method

In statistics and econometrics, the term panel data refers to multi-dimensional data frequently involving measurements over time. Panel data contain observations of multiple phenomena obtained over multiple time periods for the same firms or individuals. Time series and cross sectional data can be thought of as special cases of panel data that are in one dimension only (one panel member or individual for the former, one time point for the latter). An example of panel data (used in this research ) is given on the following table:

Company Year Book Leverage

Tangibility Profitability Size

Top Ships 2004 0,361363 0,707724 0,142434 6,291384Top Ships 2005 0,575084 0,905189 0,203996 6,888471Top Ships 2006 0,451008 0,689142 0,322673 6,19624Hellenic Carriers

2004 0,414545 0,752727 0,312364 3,314186

Hellenic Carriers

2005 0,680702 0,666667 0,342105 3,349904

Hellenic Carriers

2006 0,641949 0,802966 0,214407 3,854394

Panel data can be balanced or unbalanced. At the former table the data are balanced meaning that individual data ( Book Leverage, Tangibility, Profitability and Size) are collected for different firms ( Top Ships and Hellenic Carriers) and different years, although they are collected for the same time period ( 2004-2006). An unbalanced panel data set means that, firm years might differ for each company ( for example while data for Top Ships covers the period 2004-2006, Hellenic Carriers covers the period 2004-2008 ). In our analysis, the data set is balanced covering 10 firm years for 38 different companies of various sizes and regions around the world.

The econometric model of this research is based on panel data for various reasons. The main one is that the nature of the data. The ratios used in this analysis can be measured on a year or quarter basis, because they derive from the income statement and balance sheet of the examined firms. Although by using quarterly data, the number of observations per firm can be substantially more, there are little differences between each quarter on these ratios. Thus year to year changes in those ratios are more appropriate for research. Another major reason for using panel data is that most shipping companies that are listed, are relatively new. Only a few companies have data that trace back before 2000. That means that annual observations for each company are limited, thus it is better to analyze them as a whole. But beyond the difficulties in the nature of our data, panel analysis gives the advantage to researchers to overcome the limiting hypotheses of linear regression models. Panel data can be used to overcome the disadvantages of time series that are autocorrelation, multicollinearity as well as the disadvantages of sectoral analysis as heteroscedacity. They also provide more precise estimations because the number of observations is more than double than those of cross section or time series. Panel data also have a great degree of heterogeneity by combining cross section and time series data.

However, using panel data does have some significant drawbacks. First there is the risk of losing information which makes the research less effective. For example many shipping companies did not manage to stay listed until 2014. That means that in order to have more precise information, if many companies get delisted in 2014, the sample that will be taken for all companies, has to end on 2013, in order to be balanced.

The basic form is the following:

Y ¿=β0+β1X ¿ ,1+β2X ¿ ,2+….+βk X¿ ,k+a i+u¿

Where :

Y ¿is the observation i of the dependent variable Y for i=1,2,…N ( in this case N=418 observations) and t=1,2,…T ( where T= 10 , 2010-2014)

Xit,j is the observation i of the independent variable X j for i=1,2,…N, t=1,2,…T and j=1,2,…K

Ai is the non observed factors that affect the dependent variable and do not change over time.

Uit is the error term of the non observed factors that affect the dependent variable over time. Where:

Uit=μι + λτ

Μi= non observed cross section individual specific effect

λ I = non observed time invariant effect

When μ and λ are stable than the model is a Fixed Effects model, while when they are random the model is a Random Effects model.

Fixed Effects Model (FE)

A Fixed effects model is a model that essentially represents the observed quantities in terms of explanatory variables that are treated as if the quantities are non random. The Fixed effect assumption is that the individual specific effect is correlated with the independent variables. The term μi is stable. Through the change of the fixed terms it is possible to use all data, that results in including the effects of other missing independent variables. The Fixed effects model is the following:

Y ¿=β0+β1X ¿ ,1+β2X ¿,2+….+βk X¿ ,k+a i+u¿ ,Cov (α i , X ¿)≠0

The problem that arises is the estimation of the fixed effect model when the covariance of the independent variables and the non observed factors that affect the dependent variable are remain constant over time and different than zero.

Random Effects Model (RE)

The second category of panel models in that of Random effects, where a it term is random. In contrast with the Fixed effect model where the goal is to eliminate the non observed effects, in Random effects model the unobserved effect is considered that is irrelevant with the explanatory variables in each period. The random effect model considers that the fixed term that were mentioned before are random variables. This model is preferred when the explanatory variables are irrelevant with cross section and time effects. The estimation of the parameters is achieved with the method of Generalized Least Squares (GLS). The Random effects model is the following:

Y ¿=β0+β1X ¿ ,1+β2X ¿,2+….+βk X¿ ,k+a i+u¿ ,Cov (α i , X ¿ )=0

Hausman Test

In order to determine which of the previous models is the appropriate, the Hausman test is used. According to the Hausman criterion the null hypotheses Ho is that in the model the non observed variables μI and λt are irrelevant with the explanatory variables X jit. The alternative hypothesis is that the FE model is appropriate. Therefore the Hausman test will determine which model is the appropriate one.

H0 : the RE model is statistically significant

H1 : the FE model is more effecttive.

Emprirical Analysis

The panel dataset of this analysis consists of annual data from 38 companies over the period 2004 2014 . The null hypothesis concerning the capital structure is the following:

H0: The capital structure factors ( Size, Tangibility, Profitability and the debt ratio of the previous period) are statistically important and explanatory of the dependent variable of Debt ratio.

The estimated model after several modifications is the following:

BLit = β0 + β1Profitit + β2Sizeit + β3Tangit + β4BL(-1)it + εit (1)

where:

BLit = The debt ratio of company i at time t

Profitit = The profitability ratio of company i at time t

Sizeit= The natural logarithm of the T. assets of company i at time t

Tangit= The tangibility of company i at time t

BL(-1)it= The debt ratio of company i at time t-1

εit= The error term at time t

Empirical ResultsThe first step was to conduct a regression analysis without the using the FE or RE models as can be seen from the following table

Dependent Variable: BOOK_LEVERAGEMethod: Panel EGLS (Cross-section weights)Date: 03/11/16 Time: 18:49Sample: 2005 2014Periods included: 10Cross-sections included: 38Total panel (balanced) observations: 380Linear estimation after one-step weighting matrixWhite cross-section standard errors & covariance (d.f. corrected)

Variable Coefficient Std. Error t-Statistic Prob.  

C -0.052905 0.011723 -4.512982 0.0000PROFITABILITY -0.150079 0.024580 -6.105711 0.0000

SIZE 0.008476 0.001935 4.380288 0.0000TANGIBILITY 0.141011 0.021777 6.475240 0.0000

BL1 0.788129 0.027317 28.85117 0.0000

Weighted Statistics

R-squared 0.886098    Mean dependent var 0.566022Adjusted R-squared 0.884883    S.D. dependent var 0.413315S.E. of regression 0.105298    Sum squared resid 4.157911F-statistic 729.3257    Durbin-Watson stat 1.799279Prob(F-statistic) 0.000000

Unweighted Statistics

R-squared 0.695868    Mean dependent var 0.381921Sum squared resid 4.293414    Durbin-Watson stat 1.934922

The existence of many undetermined factors that affect the capital structure of the shipping companies, obligate the usage of Fixed and Random Effects models and compare them .

The results from this first model show that the independent variables used are statistically significant as the p-values are below 0.5. Adjusted R square is 88.48 %, which means that the model used fits the data and that the number of independent variables used for determining the capital structure of the companies is sufficient. The serial correlation in the residuals has been accounted for by using White period Standard errors.

Before continuing with the analysis of Fixed and Random Effects, the Hausman test is conducted, in order to determine which of the two model is the most appropriate for the research, as can be seen in the following table. At 5 % significance level we can accept the null hypothesis that is that Random effects model is appropriate and the alternative hypothesis is that fixed effect model is appropriate.

H0= Random effects model is appropriateH1= Fixed effect model is appropriate

To conduct the test, first we estimate the model equation using cross section random effects as can be seen in the following table:

Dependent Variable: BOOK_LEVERAGEMethod: Panel EGLS (Cross-section random effects)Date: 03/13/16 Time: 18:05Sample: 2005 2014Periods included: 10Cross-sections included: 38Total panel (balanced) observations: 380Swamy and Arora estimator of component variances

Variable Coefficient Std. Error t-Statistic Prob.  

C -0.039971 0.031095 -1.285427 0.1994PROFITABILITY -0.142565 0.036071 -3.952357 0.0001

SIZE 0.008864 0.003355 2.641761 0.0086TANGIBILITY 0.146944 0.030511 4.816128 0.0000

BL1 0.737221 0.027615 26.69687 0.0000

Effects SpecificationS.D.   Rho  

Cross-section random 0.000000 0.0000Idiosyncratic random 0.095268 1.0000

Weighted Statistics

R-squared 0.698455    Mean dependent var 0.381921Adjusted R-squared 0.695239    S.D. dependent var 0.192997S.E. of regression 0.106544    Sum squared resid 4.256895F-statistic 217.1489    Durbin-Watson stat 1.832381Prob(F-statistic) 0.000000

Unweighted Statistics

R-squared 0.698455    Mean dependent var 0.381921Sum squared resid 4.256895    Durbin-Watson stat 1.832381

Then the results from the Hausman test are seen in the following table:

Correlated Random Effects - Hausman TestEquation: UntitledTest cross-section random effects

Test SummaryChi-Sq. Statistic Chi-Sq. d.f. Prob. 

Cross-section random 113.947374 4 0.0000

** WARNING: estimated cross-section random effects variance is zero.

Cross-section random effects test comparisons:

Variable Fixed   Random  Var(Diff.)  Prob. 

PROFITABILITY -0.065820 -0.142565 0.002494 0.1243SIZE 0.047226 0.008864 0.000128 0.0007

TANGIBILITY 0.257936 0.146944 0.001846 0.0098BL1 0.410785 0.737221 0.001184 0.0000

From the Hausman test, it appears that p value is less than 0.05 and Chi Sq is 113.94> Critical value (11.07 for 4 degrees of freedom). Therefore we reject the null hypothesis that is RE are appropriate and accept the alternative hypothesis that FE model is appropriate.

Fixed Effects Model

Dependent Variable: BOOK_LEVERAGEMethod: Panel Least SquaresDate: 03/13/16 Time: 18:14Sample: 2005 2014Periods included: 10Cross-sections included: 38Total panel (balanced) observations: 380

Variable Coefficient Std. Error t-Statistic Prob.  

C -0.279551 0.091665 -3.049693 0.0025PROFITABILITY -0.065820 0.061603 -1.068466 0.2861

SIZE 0.047226 0.011807 3.999863 0.0001TANGIBILITY 0.257936 0.052696 4.894800 0.0000

BL1 0.410785 0.044124 9.309861 0.0000

Effects Specification

Cross-section fixed (dummy variables)

R-squared 0.782694    Mean dependent var 0.381921Adjusted R-squared 0.756335    S.D. dependent var 0.192997S.E. of regression 0.095268    Akaike info criterion -1.760316Sum squared resid 3.067692    Schwarz criterion -1.324823Log likelihood 376.4600    Hannan-Quinn criter. -1.587511F-statistic 29.69301    Durbin-Watson stat 1.617348Prob(F-statistic) 0.000000

From the results from the fixed effects model that are presented in the previous table we can see that the R squared value has dropped at 78.26% as the adjusted R square value

which has dropped to 75.63%. We can see that all variables are statistically significant except profitability which at this case is insignificant.

The coefficients of the independent variables are positively related with the debt ratio of the company except profitability which is negative. These results come in line with other researches that find that profitability is negatively related to leverage, whereas the size, tangibility and previous leverage are positively related.

Findings- Conclusion

Analyzing the results from the precious table and comparing them with other studies, we see that the results are matching, not only for shipping companies but for other industries as well. The high positive relationship (0.257) between tangibility and book leverage ratio means that companies with many fixed assets tend to have a higher leverage. These assets are used as collateral in order to limit the bank risk exposure. This result comes in line with the trade-off theory which predicts a positive relationship, as companies with high value assets have a higher degree of leverage and in periods of financial distress they liquidate their assets to repay their debt as tangible assets suffer from small of value in that situation. In contradiction, the pecking order theory would suggest that due to lower information asymmetry ( tangible assets are easier to value for outsiders ) equity issuance is less costly therefore shipping companies with more tangible assets should have a lower leverage ratio, implying a negative relationship between them. This, however is not supported in our cases, as well as in other cases which strongly support the trade off theory in tangibility-leverage relationship.

Firm Size, has a small positive correlation with leverage ratio (0.047). This positive relationship supports the trade off theory because as companies grow larger they tend to be more diversified and exhibit a lower probability of default, thus implying an inverse relationship between size and bankruptcy costs and a positive relationship between size and leverage. Under the pecking order theory size is regarded as a proxy for information asymmetry between insiders and capital markets, so as companies grow bigger , equity costs are lower (Debt is more expensive) thus a negative relationship should exist. Other empirical tests also support the trade off theory.

Profitability which is not statistically significant, has a negative relationship with leverage ( -0.0658) which contradicts the trade off approach which implies a positive relationship between them. This negative relationship supports the pecking order theory in the sense that more profitable firms have prefer internal over external financing.

Finally the lagged leverage ratio of the previous period (0.410) seems to have the biggest positive effect. This effect is in line with the trade off theory which suggests that capital the leverage ratio of the company is greatly affected from past leverage.

As it appears the results suggest that the prevailing capital structure theory that applies to the sample of shipping companies used in this research is the trade off theory. The traditional capital structure variables exert a significant impact on the cross sectional variation of the book leverage ratio, although their magnitude is different than other industries probably due to the different characteristics of the shipping industry. In a further research it would useful to add country specific variables and their effect on capital structure as well as market to book ratios to test the market timing theory. It would be also better to

add more shipping companies in the sample from various countries in order to have a more complete and detailed results.

References

Top 10 in ship finance, Lloyd’s List, 2014KPMG, Shipping Insights Briefing Issue 1, 2015Shipping Finance : A new model for a new market, Global Trade Review, March-April 2014UNCTAD Review of Maritime Transport 2015Theodore Syriopoulos, Financing Greek Shipping, Modern Instruments Methods and Markets, Research in Transportation Economics, 2007Schinas O, Grau C, Johns M, HSBA “Handbook on Ship Finance” , Springer 2015Kraus, Alan, Litzenberger, Robert H., 1973. A state-preference model of optimal financial leverage. Journal of FinanceMyers, Stewart C., 1977. Determinants of corporate borrowing. Journal of Financial EconomicsEugene F. Brigham, Michael C. Ehrhardt-Financial Management Theory and Practice, 13th Edition-Cengage Learning (2010)Myers, Stewart C., Majluf, Nicholas S., 1984. Corporate financing and investment decisions when firms have information that investors do not have. Journal of Financial EconomicsBaker, Malcolm, Wurgler, Jeffrey, 2002. Market timing and capital structure. Journal of FinanceDeAngelo, H., & Masulis, R. (1980). Optimal capital structure under corporate andpersonal taxation. Journal of Financial EconomicsHarris, M., & Raviv, A. (1991). The theory of capital structure. The Journal of FinanceV. Mehotra, W. Mikkelson, and M. Partch, “The Design of Financial Policies in Corporate Spinoffs,”Review of Financial Studies, Winter 2003Frank, Murray Z., Goyal, Vidhan K., 2009. Capital structure decisions: which factors are reliably important? Financial Management 38,Drobetz, W., Gounopoulos, D., Merikas, A., Schroder H.,2013. Capital structure decisions of globally-listed shipping companies, Journal of Transportation Research Part E Rajan, R , Zingales, L, 1995. What do we know about capital structure? Some evidence from international data. Journal of Finance 50Kakkava Ourania, Kefalaiaki Diarthrosi Naftiliakon Eterion,2011 University of The Aegean Jeffrey M. Wooldridge, Econometric Analysis of Cross Section and Panel DataAgiakloglou C. , Mpenos T. , Introduction to Econometric Analysis