Cullinane 'Shipping Economics' CH04 - Shipping Stock Returns PDF

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4. CROSS-INDUSTRY COMPARISONSOF THE BEHAVIOUR OF STOCKRETURNS IN SHIPPING,TRANSPORTATION AND OTHERINDUSTRIES

Manolis G. Kavussanos and Stelios N. Marcoulis

1. INTRODUCTION AND AIM OF THE PAPER

The aim of this paper is to review the empirical work in the area of shipping finance,which deals with the companies in the shipping sector that have taken the decisionto resort to the public capital markets in order to finance their activities. Morespecifically, the paper is concerned with the performance of listed companies instock exchanges around the world. The pricing of stocks in the financial marketsis a result of the collective action of investors analysing the stocks and takingaction while pursuing profit making opportunities. If markets work efficiently, thecharacteristics of stocks as determined in these markets should reflect past, presentand future prospects of the stocks and their sector, and must be the best indicatorupon which to base decisions.

To put things in perspective, the history of the shipping industry is inextricablylinked with the world economy and its economic and technological development.As Adam Smith (1776) notes, “shipping is one of the major catalysts of economicdevelopment. . . . shipping is a cheap source of transport which can open up wider

Shipping EconomicsResearch in Transportation Economics, Volume 12, 107–142Copyright © 2005 by Elsevier Ltd.All rights of reproduction in any form reservedISSN: 0739-8859/doi:10.1016/S0739-8859(04)12004-0

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108 MANOLIS G. KAVUSSANOS AND STELIOS N. MARCOULIS

markets to specialisation, offering shipment of even the most everyday productsat prices far below those that can be achieved by any other means.” Over 95%of world trade in volume terms moves by sea. Over the past years specializationof activities has taken place in shipping transportation itself. Ship design and shipbuilding technology, investment in infrastructure such as ports and port equipment,logistics and warehousing have allowed for that.

In discussing the structure of the industry, one should be concerned with the waybusiness is organised to achieve the efficient transport of different kinds of cargo.The major division within the cargo carrying part of the industry is between bulkand liner shipping. The former specialises in the transport of large cargo parcelswhich can be carried on a one ship one cargo basis. Thus, tramp ships travel on anysea-lane around the world in search of such cargoes to transport. Liner ships, on theother hand, specialise in the transportation of small cargo parcels, usually carriedin containers. These ships provide regular services on specific sea-lanes aroundthe world. Other segments of the water transportation industry include passengervessels, ferries, tugs and other ancillary services, among others.

The bulk, liner and other segments of the water transportation industry havetotally different approaches as far as the type of organisations involved and therespective shipping policies are concerned. For example, liner operators need toorganise the transport of many different parcels (usually in containers in our days)and need a large shore-based staff, capable of dealing with shippers, handlingdocumentation and planning the ship loading. Hence, due to their high overheadsand also the need to maintain a regular service even when a full payload of cargois not available, the liner business is vulnerable to uneconomic price competitionfrom other shipowners operating on the same trade routes.

Contrary to that, the bulk shipping companies operate under the principle of “oneship – one cargo” and hence handle fewer, but much larger cargoes. Therefore,their operations do not require a large shore-based activity. Nevertheless, thefew decisions that need to be made by a bulk shipping business are crucialand require the attention of the owner and/or vice-president. Large companies,shipping substantial quantities of bulk materials, often run their own shippingfleets to handle a proportion, or all, of their transport requirements. For example,according to Jacobs (1986), in 1984 the major oil companies collectivelyowned around 40% of the whole world tanker fleet. However, it should bestressed that industrial conglomerates do not necessarily become shipownersjust to optimise the shipping operation. It is also to ensure that basic transportrequirements are met at a predictable cost without the need to resort to the chartermarket.1

Another sector of the industry is cruise shipping. This sector is linked very muchto the tourist and leisure industry. Despite utilizing a ship, the service provided is

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Cross-Industry Comparisons of the Behaviour of Stock Returns 109

entirely separate from that of the cargo carrying sector; it is leisure and tourismprovided on board a ship. The ferry subsector of the shipping industry is alsoaffected by tourism and the movement of passengers, but also by the movement ofcargo. Despite its seasonal nature, part of the demand for its services emanate fromquite a steady movement of cargo carrying lorries in certain routes. The economicforces driving the demand and supply factors are distinct then. Other shippinglinked sectors involve yards and offshore companies, and these form importantparts of the total – the world shipping industry.

In our days, with the development of the concept of door to door service forproducts, specialized agents are involved in providing such logistical services.The final customer can stop worrying about arranging transportation by severalmodes of transport, the handling in between and the security issues involved beforegetting his hands to the final product. Of course this involves the question ofconsidering other modes of transport in the supply chain process. Again, the largesums of money involved in investment and infrastructure suggest that perhaps theperformance of listed companies in other modes of transport are relevant. Thisis discussed later on as well from the point of view of the investor, who is notnecessarily involved in the physical operation of the transportation service, but isinterested in investing in portfolios with transportation stocks.

A major question then throughout the years has been how to finance investmentsin this highly capital-intensive industry. Ships cost millions and such large sumsneed careful investment decisions. Methods of financing have varied over time andplace, as well as with the corporate structure of the company requiring funds toinvest in shipping. Thus, while traditional borrowing from banks2 has always beenprominent in the industry, charter backed finance3 has been very popular in thepost second world war period. This has been followed by asset backed finance inthe 1980s (e.g. ship funds), and since the 1990s – a lot of interest has been placed indrawing funds from the public. The latter may be materialised either by borrowingthrough bonds, or by offering part-ownership to the public through shares in thecompany.

This paper concentrates on this last form of finance of the industry. In particular,it concentrates on alternative valuation models of shares of water transportationcompanies. It examines single index models under which the market index alone isassumed to be driving market returns, and then extends this to multi index modelsas sets of micro economic and macroeconomic factors are considered as possibleadditional factors determining security returns. Security valuation models are alsoconsidered for stocks of other transportation industries and some other industries.International comparisons are also made by considering valuation issues throughthe formation of industries at the global level. At this global level, distinction ismade in the shipping industry of a number of subsectors, and the characteristics

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of these subsectors are compared between themselves. This investigation enablescomparisons of risk-return trade-offs between industries and subsectors of theinternational shipping industry, a process which takes place by investors whentaking practical investment decisions. The availability of listed companies in stockexchanges for each industry allows investors, by observing share price formation,to get a view of the market value of companies in each industry through the interplayof demand and supply forces driven by experts dealing in these stocks.

Despite the significance of the water and other transportation sectors and theuse of industry indices in investment decisions, there has been limited work inattempting to compare the risk-return performances of these industries: (1) in acapital markets context; and (2) in a comparative cross-industry context using assetpricing models. Equally limited attempts have been made to uncover factors, otherthan the market, that may influence returns in these industries. This might be dueto the fact that since the seventies the sectors which provided superior returns toinvestors were, according to Jones (1993), the finance sector and, after the crash of1987, the industrial sector. The exception to this lack of published work in the areais a series of studies by Kavussanos and Marcoulis (1997a, b, c, 1998, 2000a, b),Kavussanos et al. (2002) and Kavussanos et al. (2003).

Nevertheless, such industry analysis would be revealing for investors in the waterand other transportation industries, portfolio managers and corporate financierssince it would enhance investment decisions, possibly induce investors to place adifferent share of their investment funds in these industries and also shed somefurther light as to what drives values in these industries.

The significant amount of research produced in recent years on the concept ofefficient markets has shaped, to a large extent, the way academics and practitionersthink about the stock selection process. More specifically, there are two well-knownapproaches to analysing and selecting stocks, fundamental and technical analysis.Traditionally, the former approach has occupied the majority of resources devotedto the analysis of common stocks and it is concerned with the valuation of stocksaccording to fundamentals such as gearing ratios, book to market ratios, size andmany more. The other approach mentioned, technical analysis, deals with thesearch for identifiable and recurring stock price patterns and attempts to exploitmarket inefficiencies.

The research mentioned above deals with fundamental analysis, the study ofa stock’s value using basic data at microeconomic and macroeconomic level.Fundamental analysis is based on the concept that any stock has an intrinsic valuewhich is a multidimensional function of the general state of the economy (e.g.industrial production, inflation, oil prices etc.), the market, the structure of theindustry the company operates in, and the company’s fundamental microeconomicfactors (e.g. capital structure, book-to-market ratio, market capitalization, etc.).

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This paper is organised as follows. The next section discusses the decisionprocess of the investor when analysing and selecting stocks, and the determinantsof stock returns that enter his decision function. Section 3 discusses the decisionprocess by investors when analysing and selecting stocks at the micro and macroeconomic level. Section 4 extends further this process, outlining why industryanalysis makes sense and that it is part of the investors decision rule. Section 5describes the industry classification systems that may be used to define industrialsectors, such as the Standard Industrial Classification (SIC) system, the Bloombergand the Morgan Stanley classifications. Section 6 presents the major findings ofthe research in shipping economics and transportation industries. The final sectionconcludes and discusses the possibilities that this research presents for applicationsby industry practitioners.

2. THE DECISION PROCESS OF THE INVESTOR INANALYSING AND SELECTING STOCKS

It is important to understand the way investors analyse and select stocks duringtheir investment decision process. More specifically, it is important to understandthat investors perform industry analysis during their investment decision process.This in turn places the question of how the universe of companies can be classifiedinto industries. Kavussanos and Marcoulis (2001), for instance, classify stocks intoindustries based on the SIC (Standard Industrial Classification) index. An equallyimportant question is the identification of possible factors that drive returns forthese stocks.

To turn to the last question first, Kavussanos and Marcoulis (2001) dealwith fundamental analysis, the study of a stock’s value using basic data atmicroeconomic and macroeconomic level. Fundamental analysis is based on theconcept that any stock has an intrinsic value which is a multidimensional functionof the general state of the economy (e.g. industrial production, inflation, oil pricesetc.), the market, the structure of the industry the company operates in, and thecompany’s fundamental microeconomic factors (e.g. capital structure, book-to-market ratio etc.).

It is important to start any analysis dealing with stocks by assessing thestate of the economy which explicitly influences investors’ everyday investmentdecisions. For example, if a recession is likely, or under way, stock prices will beheavily affected (they are likely to drop) at certain times during the contraction.Conversely, if a strong economic expansion is under way, stock prices willbe heavily affected (they are likely to rise), again at particular times duringthe expansion. For instance, in 1997 when the world economy was performing

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exceptionally well, unprecedented rises were fuelled in stock markets all roundthe world.

Having completed an analysis of the economy an investment manager usuallyperforms industry analysis. King (1966) was the first to present evidence that thereis an industry factor affecting the variability in stock returns. Individual industriestend to respond to general market movements, but the degree of response can varysignificantly due to the fact that industries undergo significant changes over bothshort and long periods of time. Furthermore, different industries are affected, todifferent degrees, by economic recessions and expansions. For example, the heavygoods industries will be severely affected in a recession (e.g. the auto and steelindustries in the recession of 1981–1982). On the other hand, consumer goodsindustries might be much less affected during such a contractionary period.

Of course the opposite occurs when the economy is growing where income maybe spent on investment or consumption goods. According to Begg, Fischer andDornbusch (1987), when an increase in investment occurs, it raises income by alarge amount (the investment multiplier), which in turn may produce an increasein demand for the product (the income accelerator) generating demand for moreinvestment goods, so that the economic system expands rapidly. Eventually, labourand capital become fully utilised and the expansion is sharply halted, throwing thewhole process into reverse.

During a severe inflationary period, such as the late seventies and early eighties,regulated industries, such as utilities, were severely hurt by their inability to passalong all price increases. Finally, from time to time, there appear to emerge new“hot” industries which enjoy spectacular growth. Examples that come to mindinclude genetic engineering and synthetic fuels and more recently dot.coms.

Once the investment manager has performed economy, market and industryanalysis, he has to shift his emphasis on to company analysis. Security analystsfocus on a number of factors which are important in analysing a company. Thesecan be divided into two wide categories, qualitative factors and quantitative factors.Qualitative factors focus mainly on the managerial capacity of the company andits future prospects, which are fundamental to its success. Quantitative factorsfocus mainly on past and present income statements and the balance sheets ofa company and include variables such as earnings, price-to-earnings multiples,dividend yields, capital structure, book-to-market ratios, size etc.

3. THE DETERMINANTS OF STOCK RETURNS –GENERAL LITERATURE SURVEY

Experience has shown that stock analysis and selection procedure is not an easytask and as such it is not surprising that a substantial part of the financial literature

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has dealt with the issue i.e. the determinants of stock returns at the microeconomic,company level, and at the macroeconomic level. Numerous academics havefollowed the example of King (1966) who is believed to be the first to studythe determinants of stock returns. His study, utilising statistical methodologies ofthat time concluded that stock price changes can be expressed in terms of a market,an industry and a company effect. Effectively, what King proposed was that stockprices are shaped and determined by developments at the macroeconomic level,which in turn affect industries and the stock market in general, and by developmentsat the microeconomic level which affect the company’s fundamentals, hence itsvalue. King’s findings were extremely important and were going to be the basisfor a substantial amount of academic research which was to follow.

It is one of the aims of this part of the paper to very briefly review the voluminousliterature regarding the determinants of stock prices. One of King’s findings, that ofthe market effect, was to be presented in a more formal way by Sharpe (1964) andLintner (1965) and was to shape the way academics and practitioners perceivedasset returns for a long time to come. The reference point is the Capital AssetPricing Model (CAPM) which expresses the stock returns of any company as alinear function of just one factor, the return on the market portfolio of assets.The CAPM splits asset risk into two components, market or systematic risk,representing that portion of asset risk related to the riskiness of the market, andresidual or non-systematic risk, which is unrelated to market movements. Theseideas are summarised in Eq. (1).

Rjt = Rft + �j (RMt − Rft ) + �jt (1)

. . . where Rjt , Rft , RMt are the returns of company j, the risk-free rate and themarket return, respectively, measured over time, t, �j is a measure of market riskand �jt is the error term which captures residual risk.

However, during the late seventies and the early eighties, the discipline of financeand financial economics evolved and as computers became more powerful, anumber of theoretical and practical criticisms regarding the validity of the CAPMarose. For example, Roll (1977) criticised the CAPM on the grounds that thecomposition, let alone the return, of the true market portfolio is not known to theresearcher; what became widely known as the Roll Critique of the CAPM.

Others, such as Banz (1981), Basu (1977, 1983), Reinganum (1981), Lakonishokand Shapiro (1986), and Fama and French (1992), among others, observed thatsmall firms appear to consistently earn higher returns than big firms, the wellknown “size effect.” The economic rationale behind the “size effect” has been apuzzle and attempts have been made to explain it by arguing that: (1) returns forsmall firms have been computed in a way that biases them upwards; (2) the riskson small firms have been understated; and (3) size may act as a proxy for someother economic influence.

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Stattman (1980), Rosenberg, Reid and Lanstein (1985), Fama and French(1992), among others, found that there is a positive relationship between theaverage stock returns of U.S. stocks and their ratio of book value of equity,BE, to market value of equity, ME. A similar relationship is documented forJapanese stocks by Chan, Hamao and Lakonishok (1991). Companies with a highBE/ME ratio are believed to be “value” stocks, while companies with a low BE/MEratio are believed to be “growth” stocks. Generally speaking, “growth” stocks arestocks exhibiting rapid increases in earnings and that is why their market value ofequity, reflecting their hypothetical excellent prospects, may be significantly higherthan their book value of equity. On the other hand, “value” stocks are stocks whosemarket price seems to be low relative to their net worth. The empirical evidencesuggests that “value” stocks seem to outperform “growth” stocks.

However, “value” and “growth” stocks may also be categorised by their earningsto price (E/P) ratio, the inverse of the well-known and widely used P/E (PriceEarnings) ratio. Relatively low values of this ratio characterise “growth” stocks,while relatively high values characterise “value” stocks. Therefore, an interestingquestion that arises is whether there is any relationship between stock returnsand their E/P ratios. Ball (1978), Reinganum (1981) and Basu (1983) arguethat E/P ratios help to explain the cross-section of average returns on U.S.stocks.

Another variable which has been the subject of empirical tests regardingthe explanation of the cross-section of stock returns is leverage. Bhandari(1988) defined leverage as (Book Value of Total Assets minus Book Value ofEquity)/(Market Value of Equity) and found that stock returns are positively relatedto market leverage. Fama and French (1992) included leverage, among a numberof other variables, in an attempt to explain the cross-section of U.S. stock returnsand, in line with Bhandari (1988), found a positive relationship between marketleverage, defined as the ratio of Total Assets/Market Value of Equity (A/ME)and stock returns. Furthermore, they defined book leverage as the ratio of TotalAssets/Book Value of Equity (A/BE) and found a negative relationship betweenthis ratio and stock returns.

An excellent discussion of the above determinants or “anomalies” of stockreturns can be found in the seminal paper of Fama and French (1992), who employa multifactor model to estimate the influence of the aforementioned factors on thecross-section of U.S. stock returns.

The discussion so far has focused on a number of determinants of stock returnswhich, with the noticeable exception of the market, share a common characteristic,they all affect the company at the microeconomic level. An equally voluminousamount of literature exists with regards to macroeconomic factors which are alsobelieved to have a role to play in the determination of stock returns. As in the case

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of the microeconomic factors, it is not possible to review all the papers dealingwith macroeconomic factors and stock returns. However, an attempt will be madeto review a number of the most important ones so as to obtain an insight of therole of the macroeconomy in the explanation of stock returns.

The effect of macroeconomic factors on stock returns can be thought to be aconsequence of the pricing of stocks, as the stream of discounted expected futurecash flows from holding a security. According to Damodaran (1994), the generalstock valuation model is of the form presented in Eq. (2).

Price of Security =t=∞∑t=1

DPSt(1 + r )t (2)

. . . where DPSt denote expected dividends per share and r is the required rate ofreturn on stocks.

It is clear from Eq. (2) that any macroeconomic factors which affect eitherthe expectations of future cash flows to the investor (dividends per share) and/orthe rate used to discount them will indirectly, though effectively, influence stockreturns.

Chen, Roll and Ross (1986) were among the first to specify and test a set ofeconomic factors which, based on economic theory and intuition, should affectstock returns either through future cash flows or through the discount rate. Theyutilised the following factors: inflation; the term structure of interest rates; riskpremia and industrial production and found them to be significant in explainingstock returns. Although Chen, Roll and Ross (1986) can by no means claim thatthey have found the full set of variables for asset pricing they have most certainlymade an important step in the right direction. Their model is of course the base forthe well known Arbitrage Pricing Theory (APT).

Their work has been continued in a series of papers by Burmeister and Wall(1986) and Burmeister and McElroy (1987, 1988) who developed a multifactormodel and found that five macroeconomic factors, similar, though not identical,to those employed by Chen, Roll and Ross (1986) are statistically significant inexplaining stock returns. The factors they utilised were: default risk; the termstructure of interest rates; inflation; sales as a proxy for the profits of the economyand; the market.

In the spirit of the above work, other researchers have studied the explanatorypower of macroeconomic variables over stock returns in various stock exchangesacross the world. For example, among others, Poon and Taylor (1991) applied theideas discussed above to the U.K., Martinez and Rubio (1989) applied them tothe Spanish stock market, Hamao (1988) applied them to Japan and Wasserfallen(1989) applied them to a number of European countries. Most of the above studies

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find evidence in favour of the hypothesis that a set of macroeconomic factors hasexplanatory power over stock returns.

Chen and Jordan (1993), in a more recent study featuring the U.S. market,employed a set of factors similar to the one used by Chen, Roll and Ross (1986)but grouped the companies in their sample according to their industry classification,the significance of which is something which is discussed in great extent in Chap. 1of the book of Kavussanos and Marcoulis (2001). Chen and Jordan (1993) alsofind evidence in favour of macroeconomic factors being significant in the pricingof stocks. More specifically, they document that, apart from the market returns,changes in oil prices and inflation are possible sources of risk.

However, as in the case of microeconomic factors, the study of macroeconomicfactors and stock returns is not confined only to academics. Salomon Brothers(better known today as Salomon Smith Barney) have developed a macroeconomicmodel in the spirit of the macroeconomic models we have been discussingabove (Sorensen et al., 1989). This model uses seven variables to explain stockreturns. They are: economic growth, measured by changes in industrial production;the business cycle, measured by the difference between corporate bonds andU.S. Treasuries; long-term interest rates, measured by yield changes in the10-year Treasuries; short-term interest rates, measured by yield changes in the1-month Treasuries; inflation shock, measured by changes in the consumer priceindex; the U.S. dollar, measured by changes in a 15 country currency basket Vsthe dollar and; a market proxy. Salomon have been using their multifactor modelfor some time now and they claim that using monthly data, this model explainsabout 40% of the fluctuations in the returns of a sample of 1,000 stocks. Definitelythen, models of this type are very promising.

In addition to the above, following their invention of the APT model, Rolland Ross created the Roll and Ross Asset Management Corporation to translatetheir theory to practice. They begin by stating the systematic sources of riskthat they believe are relevant to capital markets. These, according to the Rolland Ross Asset Management Corporation are: the business cycle; interest rates;investor confidence; short-term inflation and; long-term inflationary expectations(Sharpe et al., 1995). Roll and Ross quantify these factors by designating certainmacroeconomic variables as proxies. For example, the business cycle factor isrepresented by changes in the industrial production index. At the heart of the Rolland Ross approach lies the assumption that each source of risk is subjected tovolatility and is entitled to some return. Moreover, Roll and Ross assume thatindividual securities and portfolios of securities possess different sensitivitiesto each source of risk. With these in mind, Roll and Ross attempt to constructinvestment portfolios which offer the most attractive risk-reward profile for theinvestor.

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Another institution of international prominence which is dealing withmultifactor models from a more practical angle is BARRA International. Theirprofessionals utilise both fundamental and macroeconomic factors to explainstock returns. For example, in 1993 BARRA International published a researchpaper (Engerman, 1993) whose aim was to compare a multifactor model utilisingfundamental factors to another multifactor model utilising macroeconomic factorsin the U.S. For the record, the fundamental factors were, the market; the stock’sprice momentum; size; trading activity; growth; the earnings-to-price ratio and;the book value-to-market value ratio. The macroeconomic factors were, interestrates; bond spreads; industrial growth; inflation; oil prices; gold price and; dollar’svalue. Their results indicate that both the microeconomic, fundamental factorsand the macroeconomic factors have a role to play in the explanation of stockreturns. They, moreover, claim that microeconomic factors are more effectivethan macroeconomic factors in accounting for the cross-sectional variation instock returns. In line with the above ideas, BARRA International is currentlyproviding multifactor models for several countries including the U.S., Japan, theU.K., Canada, France, Switzerland and many more. Reference to the BARRAmodels will be made again later in this paper, when the importance of industryclassification is discussed. Industry classification is indeed a prominent feature ofthe majority of BARRA’s models.

4. THE IMPORTANCE OF THE INDUSTRY EFFECT

Apart from King (1966), who as mentioned above was the first to argue that changesin stock prices can be explained by an industry effect as well as a market and acompany effect, others, such as Arditti (1967) and Nerlove (1968) find that industrydifferences are highly significant in explaining cross-sectional differences in stockprice returns in the U.S. stock market. Similar results are also produced in theindustry focused analyses of Saunders and Yourougou (1990), Ferson and Harvey(1991) and Isimbabi (1994), among others. Hence, an investor whose aim is to builda diversified portfolio could well be interested in these cross-industry differencessince he could utilise them to build a portfolio diversified across industries ofdifferent risk-return characteristics. The importance of the industry effect is alsoemphasised in a number of other studies focusing on the U.S. stock market. Forexample, Sorensen and Burke (1986) studied the relative price performance ofseveral industry groups and concluded that an investment strategy based on buyingand holding the best performing industry groups may enhance returns.

Kane and Unal (1988) and Neuberger (1991) focused on the risk returncharacteristics of the U.S. banking sector. They find banks to be greatly underpriced

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and exhibiting systematic risk that is lower than that of the “average” company.They also find that this systematic risk is increasing over time and that stocks oflarger banks exhibit higher risk than small ones. Gyourko and Keim (1993) focusedon another “popular” U.S. industry, real estate, and found that the average monthlyreturns of the industry are just under 1% with the associated standard deviationbeing over 7%. They then compared these figures with the corresponding figuresof the S&P 500 index and found that the average monthly returns of the real estateindustry are higher, but as might be expected are accompanied by higher standarddeviation.

Furthermore, the industry effect has not only been studied by academics.Prominent practitioners have also spent a lot of research time and resources toformulate models which encompass this effect. For example, BARRA Internationalwho was introduced above, very often estimates multifactor models for differentindustry groupings. One of their most widely used multiple-factor models,the BARRA E3 U.S. model (Kahn, 1994), utilises microeconomic factors andestimates a model for each of 55 industry groups. Furthermore, their Canadian(Roy, 1992) and Japanese models (Rosenberg, 1991), both released in 1992,also utilise microeconomic and macroeconomic factors to estimate models acrossseveral industrial groupings.

By considering some real world examples Kavussanos and Marcoulis (2001) inChap. 1 of their book argue that there is scope for undertaking industry analysisand that it pays to direct investment resources on to some industries at the expenseof others at different points in time. From the evidence observed, the authors goon to argue that it would have been beneficial for an investor to have performedcareful industry analysis at any point in time, from the post-war period to date.

Towards this end, Jones (1993) is cited, where a study spanning over the 48 yearperiod 1941–1989, indicates that from 1941 to 1973 the computer and businessequipment industry did extremely well (145 times what it was in 1941) and theelectronics industry also did well, rising to almost 69 times its 1941 level. Onthe other hand, during the same 32 year period, the lead and zinc industry wasless than twice its original level, and the sugar and textile apparel industries wereonly three times their original levels. Examples from the eighties cited in the samestudy, also indicate tremendous differences among industries’ performances asmeasured by the Standard and Poor’s Stock Price Industrial Indexes. For example,during the period 1982–1989, the drugs industry almost quadrupled, the broadcastmedia industry rose to almost six times its 1982 level while on the other hand thechemicals industry declined by almost 30%.

However, the most dramatic example during the period mentioned is thatproduced by the market crash of October 1987 when the Dow Jones Industrialsindex lost 23.2%. Industries such as toys, machine tools, leisure time, gold, and

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offshore drilling, among others, lost around 40% of their value while others suchas electric companies and telephone companies lost less than 10%. Jones (1998)clearly demonstrates that the losses suffered by investors varied widely accordingto the particular industries held.

5. THE ISSUE OF THE CLASSIFICATIONOF COMPANIES INTO INDUSTRIES

A practical question with industry analysis, concerns the classification ofcompanies into industrial sectors. The problem is not new. Kavussanos andMarcoulis (2001) utilise the SIC (Standard Industrial Classification) codes, toclassify the U.S. companies used in their research. The sectors used weretransportation industries, such as water transportation, air transportation, railtransportation and trucks and non transportation industries such as electricity,petroleum refining, gas and real estate. The rationale for selecting theaforementioned industries is that the air transportation, rail transportation andtrucks industries have been chosen due to the fact that they are transportationindustries. Hence, they might be argued to compete, in one way or another, withthe water transportation industry in the investor’s stock selection decision. Thepetroleum refining, electricity and gas industries were chosen due to their stablegrowth nature which is directly opposite to the very cyclical nature of the watertransportation industry. Finally, the real estate industry was included due to itscyclical nature which, is a characteristic of the water transportation industry aswell. For exact SIC definitions for each of the industries see Kavussanos andMarcoulis (2001), Appendix A of Chap. 1.

The SIC system which is based on Census data classifies companies intoindustries according to their final product or service. SIC codes have 11 letterdivisions, A to K and each division consists of several major industry groups,designated by a two digit numerical code. The major industry groups, withineach division, are further subdivided into three, four, and five-digit SIC codesto provide even more detailed classifications. The larger the number of digits inthe SIC system, the more specific the breakdown. For instance, Division E definestransportation. This consists of 10 major groups as per 2-digit number; 40, 41, . . . ,49. Major group 44 corresponds to water transportation which consists of sixfurther 3 digit groupings. Grouping 441 refers to deep-sea foreign transportationof freight, which only has one four-digit category under it. Other SIC codes for theother industry groups examined in the above book include 40 (Railroad Transport)and 45 (Air Transportation).

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SIC codes have significantly contributed towards bringing order to the industryclassification problem by providing a consistent basis for describing industries andcompanies. Analysts using SIC codes can focus on economic activity in as broad,or as specific, a manner as desired. For example, industry groupings in a number ofstudies dealing with industry stock return comparisons in the U.S. (e.g. Boudoukhet al., 1994; Eun & Resnick, 1992; Ferson & Harvey, 1991; Isimbabi, 1994), amongothers, have been formulated according to their SIC codes. Typically, the abovestudies have used 2–4 digit industry groupings.

Despite the fact that the SIC system of industry classification is the easiest touse and the most consistent system available, it is not the only one in actual use.Standard and Poor’s Corporation, as from the end of 1982, provide weekly stockindices on approximately 100 industry groupings, many of which go back 30 oreven 40 years. The Value Line Investment Survey covers roughly 1,700 companies,divided into approximately 90 industries. Another useful industry classificationsystem is that of The Media General Financial Weekly which divides stocks into60 industries.

Given its many advantages and its wide use among academics and practitionersthe SIC classification system was used by Kavussanos and Marcoulis (2001).Nonetheless, like any classification system, it faces certain limitations, the mostimportant of which is the classification of multiproduct, diversified companiesunder more than one heading. When performing cross industry comparison, inorder to avoid distorting the results during comparisons between the selectedindustries, companies that were found to be listed under more than one industrygroups were omitted. This resulted in industry groupings which were relativelyhomogeneous.

Morgan Stanley Capital International (MSCI) also have an industrial classi-fication system. They publish data on monthly price indices (with 1970: 1 = 100)for 38 International Industries. The MSCI price indices are value-weighted and aimfor 60% coverage of the total market capitalisation for each market. Companiesin the indices replicate the industry composition of each local market. The chosenlist of stocks, formed from the share prices4 of approximately 1600 securitiesin 22 countries, includes a representative sample of large, medium, and smallcapitalisation companies from each local market, taking into account the stocks’liquidity. Furthermore, stocks with restricted float or cross-ownership are avoided.Kavussanos et al. (2002) use this data set to perform industry analysis at theinternational level using global portfolios.

In yet another study, Kavussanos et al. (2003) focus on the internationalmaritime sector and its subsectors. The answer to the question of classification ofshipping companies into its subsectors is not readily available. Yet, such industryclassification can enhance further the understanding of the risk return trade offs

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Table 1. Maritime Industry Sectors.

Sector Description

Bulk Dry bulk, older type General cargo ships, excluding OBOContainer LOLO and some ROLOs with large container sectionCruise Cruise shipsDrilling Rig owners and operatorsFerry Passenger ferries including ROPAXOffshore Supply boats and anchor handlersShipping Companies with 90% or more of revenue derived from shipping or shipping

related activities but which could not be classified into any other sectorTanker Oil Tanker, excluding chemical and gas Tankers as well as FSPO. OBOs were

included when operated as oil TankersYard Shipyards excluding Rig yardsDiversified Companies with between 60% and 90% of revenue derived from shipping or

shipping related activities – the balance being derived from elsewhereAll All of the above sectors

Note: OBO – Oil Bulk Ore; LOLO – Lift On Lift Off; ROLO – Roll On Lift Off; ROPAX – Roll OnPassenger.

within the shipping industry. Kavussanos et al. (2003) identify every possiblemaritime company listed continuously in any stock exchange in the world over the3-year period July 1996 and July 1999, and classify it under pre-defined sub-sectorsof the industry (Table 1).

This, sampling of companies across stock exchanges (rather than focusingon companies listed in one exchange), gives the largest possible cross-sectionalsample of maritime companies in each sub-sector, and at the same time a sufficientlength of time series data for returns (36 monthly observations) to enable estimationand inferences.

The starting point was the Maritime Transport and Energy list of traded sharesappearing in the Financial World page of the Lloyds List, which in turn is basedon the Bloomberg classification list. This was supplemented by any other publiccompanies known to be involved in shipping or shipping related industries but notlisted there. In order to classify companies into sectors, a short questionnaire wassent to 250 of these companies in July 1999 asking them to classify the percentageof their core business activity in a number of predefined sectors. This informationwas supplemented by consulting their annual reports for 1998 and 1995. Therewas an approximately 20% response to the initial questionnaire, with a further30% replying after a reminder letter, which was sent four weeks later. Financialinformation for companies which did not reply was obtained from the WrightInvestors’ Service web page (http://www.wisi.com), from the Fairplay OnlineDirectory (http://www.wsdonline.com) and from individual company web pages.

http://www.wisi.com

http://www.wsdonline.com

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In order to make inferences for each sector which reflect the risk/return profile ofoperating in the specific sector, companies whose economic activity in shipping orshipping related activities was less than 60% were considered overly diversified andwere discarded from the sample.5 Companies for which there was no informationon revenue, companies involved in mergers, acquisitions and/or changes in theircore business during the sample timeframe, and companies for which stock datacould not be found on DataStream were excluded from the analysis.

To account for the possibility that different degrees of diversification havevarying effects on the risk-return profiles of sectors, the companies that remainedwere classified and analysed according to whether 60, 75 and 90% of their corebusiness activity was in the same sector. Specialised companies operating only inone sector were straightforward to classify. Companies whose core activity wasover 90% in more than one sector of shipping but for which no detailed breakdownof the percentages attributable to each sector were available were classified in ageneral category called “shipping.” Companies with diverse core business thatincluded over 10% of activities not shipping or shipping related were classifiedas “diversified.” The sectors “Reefer,” “Gas,” “Chemical Tankers,” “Brokers” and“Ports” had to be abandoned due to too few listed companies belonging to them.In total 108 companies made up the final sample used for analysis.

For analysis, the return on stocks must be defined, as well as other data onrelevant micro macro economic variables and the market. In the above studiesmonthly stock price and dividend yield (in percentage form) data for each sharewere collected from DataStream International Service. Logarithmic monthlyreturns for company i at time t, Rit , are calculated in percentage form using theequation:

Rit = 100 × ln

[(Pit + (Pit × DYit /1200))

Pit−1

]

Where Pit and Pit−1 are the stock prices of company i at time t and t−1,respectively, and DYit is the annualised dividend yield paid by company i attime t.

In calculating the CAPM regression and multifactor regressions, a questionof what is the relevant market is always raised. Because the sample includescompanies listed on stock exchanges in different countries the Morgan StanleyCapital International (MSCI) All Country World Index was used for analysisin the studies by Kavussanos et al. (2002, 2003). Also, given the practice ofevaluating industry specific funds by benchmarking on sectoral indices, the MSCIInternational Shipping Index was also used for analysis.

The MSCI All Country World Index is calculated as a market capitalisationweighted average of equity returns in 51 countries (23 developed and 28 emerging),

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and is quoted in gross form inclusive of dividends. The MSCI Shipping Index is oneof the 38 industry indices produced by Morgan Stanley. Companies are classifiedbased on their principal economic activity as determined by the breakdown ofearnings. If no detailed earnings data are available then breakdown of salesdata are used. In defining industries MSCI attempts to construct homogeneousgroups which are expected to react similarly to economic and political trendsand events.

A further practical question relates to how the risk free interest rate is defined.The U.S. three-month Treasury bill rate is generally considered as a measure ofthe global risk free rate of interest, RFt . It is instructive to view summary statisticsfor average equity returns by maritime sector and for the returns on the MSCIworld and shipping indices for the period July 1996 to July 1999, as estimated inKavussanos et al. (2003). These are presented in Table 2.

Yet another question is how to construct empirically the macro and microeconomic variables used as explanatory variables in a multifactor model, such

Table 2. Summary Statistics of Mean Monthly Returns of Each Sector byClassification Criteria; July 1996–July 1999.

Sector Classification Criteria

90% 75% 60%

Mean SD No Mean SD No Mean SD No

Bulk −2.18 1.22 6 −1.79 1.54 7 −1.88 1.45 8Container −0.85 2.90 7 −0.92 2.45 9 −0.92 2.45 9Cruise 3.04 1.32 3 3.04 1.32 3 2.93 1.10 4Drilling 0.32 1.12 7 0.32 1.12 8 0.33 1.05 9Ferry −0.05 2.73 11 −0.47 2.60 15 −0.14 2.61 17Offshore 0.17 1.67 7 0.17 1.54 8 0.25 1.38 10Shipping −1.68 3.79 34 −1.77 3.90 30 −2.01 3.76 30Tanker −2.53 2.50 12 −2.53 2.50 12 −2.46 2.41 13Yard 0.60 0.46 4 0.23 1.10 6 0.23 1.00 7Diversified −0.50 1.57 17 −0.12 1.72 10 N/A N/A N/AAll −0.92 2.85 108 −0.91 2.86 108 −0.91 2.86 107

Mean SD Skew KurtMSCI-All 1.42 4.50 −1.61 4.19MSCI-Sh 0.28 6.34 4.19 3.65

Notes: (1) SD = Standard Deviation, No = Number of companies classified under each sub-sector,Skew = Coefficient of Skewness, Kurt = Coefficient of Kurtosis, MSCI-All and MSC-Sh arethe Morgan Stanley All Country World Index, and the Shipping Index, respectively, (2) Underthe 60% criterion, the Diversified sector only contained one company (Wilh Wilhelmsen) andthis sector was therefore dropped.

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as that estimated in Kavussanos et al. (2002) and Kavussanos and Marcoulis(2000b). It is suggested that only the unanticipated part of the macro economicseries is relevant, as the anticipated part will be incorporated instantly into pricesin efficient markets. ARIMA models are used to filter out the anticipated partof the macro variables, with the unanticipated part being used for estimation inmultifactor models. For details see these last two papers.

6. MAJOR FINDINGS OF THE RESEARCH

It should be mentioned at this point that the reason Kavussanos and Marcoulis(1997a, b, c, 1998, 2000a, b, 2001) focused on the U.S. water transportationindustry is that the U.S. had the largest number of companies in this sector,relatively sufficient for meaningful analysis, compared to any other single country.Even for the U.S. the listing of water transportation companies did not have such along history, with most companies entering the stock exchange to raise funds in the1980s and 1990s. It is worth noting that currently, the vast majority of U.S. basedwater transportation companies still remain privately owned. The primary reason,given by both investor groups and investment bankers for this, is the industry’sperceived high level of risk.

This risk stems from the fact that on the one hand the water transportationindustry is a predominantly capital intensive industry which requires hugeinvestment outlays, while on the other hand it is subject to cyclicality which moreoften than not is beyond the industry’s control. This cyclicality is a result of theindustry serving the world economy through the transportation of world trade. It isa fact of life that the world economy goes through cycles and along with it worldtrade goes through cycles as well. Consequently, the water transportation industryis also subject to cycles whose amplitudes are a function of those of the worldeconomy and the demand and supply situation in the water transportation market.

Given the above risk profile of the water transportation industry, the primemotive of the studies have been to examine, and compare, this industry’s stockexchange risk perception over time and across industries of similar and differentrisk profiles.

In Kavussanos et al. (2002, 2003) the analysis was carried in a global setting.This goes along with the notion of investors operating in a transnational goods andcapital markets and forming global portfolios of industries across countries. It isa practice widely followed by big investment houses. At the same time, such ananalysis enables the increase in the sample of companies listed in each industryto a respectable level. As a consequence results are more reliable. Furthermore itallows the distinction of subsectors within the shipping industry, as in Kavussanos

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et al. (2003) – see Tables 1 and 2 and discussion in the previous section, somethingwhich would be impossible if restricted to a single country setting. Of course, asmore companies decide to become listed in stock exchanges, results can be evenmore reliable in future analysis.

To start with the findings of Kavussanos and Marcoulis (2001), Appendix A ofthis paper presents them, in tabular form, so that the reader can obtain a full pictureof the major findings of the research. Moreover, Appendix B of the paper presents asummary of the empirical evidence regarding the micro and macroeconomic factorsemployed in the book. Other studies similar to Kavussanos and Marcoulis aretabulated, which have also attempted to identify the determinants of stock returnsby using sets of either microeconomic or macroeconomic factors. Appendix B.1presents empirical evidence in the finance literature regarding the macroeconomicfactors employed in the book and compares it with the results of the book, whileAppendix B.2 repeats the exercise for the microeconomic factors employed in thebook.6

As can be seen in Appendices B.1 and B.2, there are similarities as wellas differences among the results of the book and the general literature. Mostdifferences show up with respect to the macroeconomic factors which, incidentally,tend to also exhibit differences across other empirical studies. However, whencomparing the results, one should keep in mind that the majority of publishedwork utilises a wide sample of companies, which have used portfolios as a basisfor asset pricing rather than industry classification as in our study.

6.1. Results from the CAPM

Kavussanos and Marcoulis (1997a, 1998) and Chap. 4 in Kavussanos andMarcoulis (2001) deal with the Risk and Return of U.S. Water TransportationStocks over time and over Bull and Bear market conditions and its resultsdo indicate a number of interesting aspects regarding the behaviour of watertransportation company stocks during the period 1985–1994. Firstly, the averagebeta of shipping companies was estimated to be numerically lower but statisticallynot different from the beta of the “average” company (unity). This result ofthe systematic, non-diversifiable, risk of the water transportation industry beingnot different from the average market risk could make the shipping industryan attractive candidate for potential investors. Furthermore, another attractivecharacteristic of the beta coefficient of the water transportation industry, fromthe investor’s point of view, is the fact that it appears to be stable over time.

Secondly, water transportation companies appear, with some notable exceptions,not to be underpriced over the full ten year period examined. Sub-period

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estimations of the CAPM coefficients suggest that underpricing did not occurduring the first five year period as well as during the second five yearperiod.

Thirdly, there appears to be a “size” effect in the shipping industry for the period1984–1989 in the sense that smaller shipping companies tend to exhibit higherreturns. These higher returns, as might be expected, were found to be accompaniedboth by higher total and systematic risk. However, this “size” effect disappears inthe period 1990–1994 possibly due to the shifting of several “small” caps to themedium and large groupings.

The chapter also examines whether underpricing, as measured by alpha, orthe systematic risk, as measured by beta, of the water transportation companiesincluded in the sample examined changed over bull and bear market conditionsduring the ten year period examined. It is found that alpha, not beta, tends to bemostly affected by upward or downward market movements. Therefore, investorsconsidering including shipping stocks in their portfolios need not worry aboutpossible changes in the systematic risk of these stocks during changing marketconditions.

Another aim of the chapter is to examine the risk-return relationship of the watertransportation industry along another dimension, that of comparing the systematicrisk of companies belonging to this industry to the systematic risk of companiesbelonging to other related and non-related industries. In this context, the beta ofthe water transportation industry is compared to the beta of the following sevenindustries: rail transportation; air transportation; trucks; electricity; petroleumrefining; gas and; real estate. To achieve that, the Capital Asset Pricing Modelis employed.

Results reveal some further interesting characteristics of the stock returns of thewater transportation industry in the U.S. during the period 1984–1995. The betaof the water transportation industry is significantly lower than the beta of the railtransportation industry and the beta of the real estate industry. It is statisticallysimilar to the betas of the five other industries employed in the chapter.

Looking at the findings of the chapter, one might conclude that stocks belongingin the water transportation industry do not appear to possess any risk characteristicthe investment community is not aware of. The industry average beta of 0.92 seemsto be in line with the average company’s beta, unity, and the average explanatorypower of the regressions of around 23% is also typical in these kind of estimations.Furthermore, tests suggesting that the industry beta does not appear to change overtime, despite the cyclical nature of the underlying industry, can only be “good”news for the industry’s stock market perception. Moreover, numerically speaking,the beta of the water transportation industry is the lowest of all transportationindustries’ betas.

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6.2. Microeconomic (Company specific) Factors as Determinantsof Equity Returns

Kavussanos and Marcoulis (1997b) and Chap. 5 in Kavussanos and Marcoulis(2001) undertake a comparative analysis of the stock market perception of the risk-return relationship of U.S listed water transportation stocks in relation to stocksbelonging to the other transportation and non-transportation sectors mentionedabove over the same time period. The significant difference between this analysisand the one in Kavussanos and Marcoulis (1997a) is that this one is carried out ina multidimensional risk environment. More specifically, apart from relating cross-sectional differences in the returns of companies belonging to different sectors tothe market, the model used in this chapter relates those differences to a number offundamental, company specific, factors, which according to intuition and academicresearch are believed to influence stock returns. These factors are: the market valueof equity (size), the book-to-market value of equity ratio; the earnings-to-priceratio; the asset-to-market value of equity ratio; and the asset-to-book value ofequity.

The methodology used by the authors to estimate the above relationship foreach industry is the Seemingly Unrelated Regression Model (SUR) due to itpossessing two significant advantages over the classic Ordinary Least SquaresModel (OLS). The first is that the sensitivities of each company’s returns to themarket (betas) are estimated simultaneously across companies together with theimpact of the fundamental variables and the alphas also allowing the impositionof cross-equation restrictions on the parameters. The second is that the SUR, incontrast to more classic methodologies utilised in the past in similar studies, adjustsfor the cross-sectional correlation in the residual returns across companies thusleading to parameter estimates which are more efficient than those given by OLSmodels, the gain being proportional to the correlation between disturbances fromthe different equations. This advantage is particularly important in studies of thisnature since companies grouped according to their industry classification are likelyto exhibit residual returns’ correlation.

The results of the study indicate that there appear to be factors, from themicroeconomic, company specific environment, which, in addition to the marketwhich remains the driving force behind returns, tend to influence the returns ofthe water transportation industry and the other industries. It should be noted,nonetheless, that the significance of the fundamental variables appears to varyacross sectors and over time. The book-to-market value, the asset-to-market and theasset-to-book value of equity ratios, and the market value of equity are significant insome industries but not in others while the earnings-to-price ratio has no role to playin any industry’s returns. Generally speaking, the coefficients of the fundamental

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variables come out with the expected sign, save the positive “size effect” in thepetroleum industry.

The returns of the water transportation industry appear to be influenced by onlyone factor, the asset-to-book value of equity ratio. The relationship is a negativeone and suggests that as shipping companies become more highly geared, in termsof book leverage, their stock market performance deteriorates.

As far as systematic risk (beta) is concerned and in line with the findingsof Chap. 4, the water transportation industry, along with three other non-transportation industries, exhibits lower than average systematic risk for the wholeperiod studied as well as for the two subperiods. Furthermore, the beta of the watertransportation industry, along with that of six other industries, does not vary fromsubperiod one to subperiod two while only the real estate industry exhibits a lowerbeta than the water transportation industry.

Finally, as in Chap. 4 in Kavussanos and Marcoulis (2001) the alpha of the watertransportation industry along with the alphas of all other industries analysed arepositive thus suggesting that these industries have been underpriced over the timehorizon studied.

6.3. Macroeconomic (Economy Wide) Factors as Determinants ofEquity Returns

Kavussanos and Marcoulis (2000a) and Chap. 6 of Kavussanos and Marcoulis(2001) is utilizing the traditional one-factor market model, augmented to include anumber of other economic factors believed to influence security returns. However,in this chapter, the factors used are macroeconomic, as opposed to microeconomic.Hence, cross-sectional differences in the returns of the companies in each industryare related to the stock market and the following set of macroeconomic factors:industrial production, the term structure of interest rates, oil prices, consumption,and inflation. The selection of this set of macroeconomic factors was driven bothby intuition, since the aforementioned factors affect both the future cash flows andriskiness of a company, as well as due to their popularity among academics (theyhave been widely used in previous studies).

Assuming efficient markets, only the innovations or unanticipated changesin the above macroeconomic variables should influence stock returns. HenceARIMA models were used to filter out the anticipated component of serieswith “memory.” Following that, Multiple Least Squares (MLSQ) regressionmethods were used to estimate the relationship of the unanticipated changesin the above factors to the stock returns of each industry over the period1985–1995.

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The results of this chapter, like the one preceding it, show that there are factorsother than the market which influence the returns of the water transportationindustry and other industries, thus justifying the use of multifactor models insteadof the traditional one-factor, market model. More specifically, in line with thefindings of Chaps 4 and 5, the beta of the water transportation industry is found tobe lower than the “average” beta of unity and it is also found to be among the lowestin the industries analysed. Moreover, the alpha of the water transportation industry,along with the alphas of the other industries analysed are significantly higher thanzero thus implying that these industries have, on average, been underpriced overthe period 1985–1995.

Regarding the macroeconomic factors, the authors find that their effect variesacross industries. This is probably the most interesting and important finding ofthe study, that different industries tend to react differently to different economicshocks. The investment manager could utilise this finding, and by examining thesensitivities of industry stock returns to the macroeconomy, make better investmentdecisions.

As far as the returns of the water transportation industry are concerned,they appear to be influenced by two macroeconomic factors, monthly industrialproduction and oil prices. The former exerts a negative effect which suggests thatincreases in monthly industrial production are accompanied by dropping returnsin the industry while the latter indicates a positive relationship which suggeststhat the returns of the water transportation industry are an increasing function ofincreases in oil prices.

6.4. Microeconomic and Macroeconomic Factors – A Unified Approach

Kavussanos and Marcoulis (2000b) and the seventh chapter in Kavussanos andMarcoulis (2001) relates cross-sectional differences in the returns of the companiesin each of the industries mentioned in the previous chapters to the set of themicroeconomic factors utilised in Kavussanos and Marcoulis (1997b) and theset of the macroeconomic factors utilised in Kavussanos and Marcoulis (2000a)simultaneously over the period 1985–1995. The chapter recognises that both microand macro economic factors may be determinants of stock returns across industriesand attempts to uncover the determinants of each industry’s stock returns in a moregeneral setting where both sets of factors are included. This practice is supportednot only by academics (Chen et al., 1986; Fama & French, 1992 among others)but also by practitioners (BARRA, amongst others).

Results from a Seemingly Unrelated Regression Model (SUR) regarding marketbetas indicate that the market, as expected, has a significant role to play in

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explaining the returns of all industries. The beta of the water transportation industryis, as in the previous chapters, the lowest among the transportation industries andthe third lowest, ranking behind electricity and gas, of all the industries analysed.Further inferences regarding market betas suggest that the market beta of the watertransportation industry, along with the market betas of the electricity, gas and realestate industries, are significantly lower than the “average” market beta which isof course one.

Moreover, the alpha of the water transportation industry, along with thealphas of the other industries analysed are significantly higher than zero thusimplying that these industries have, on average, been underpriced over the period1985–1995.

As far as the economic factors are concerned, the returns of the watertransportation industry were found to be positively related to oil prices and marketvalue of equity and negatively related to monthly industrial production and the totalassets-to-book value of equity ratio. The sensitivities of each industry’s returns tothe set of microeconomic and macroeconomic factors, as expected, varies acrossindustries. All factors, except the price to earnings ratio, appear to be priced inone industry or another. The estimation of this general model incorporating bothmicroeconomic and macroeconomic factors for each industry sheds some lightregarding differences both in the structure and sensitivities of each industry’s stockreturns to the set of factors employed.

A useful point that comes out of this analysis is that the stock returns of thewater transportation industry, for example, are positively affected by oil pricesand the market value of equity and negatively affected by monthly industrialproduction and the asset-to-book value of equity. The aforementioned factorscomprise a different set when compared to any other industry under analysisand hence the investment manager, by picking, or not picking, this industry, mayexpose, or not expose, his portfolio to the specific set of economic factors. Thesame holds of course for any other industry. Furthermore, the industry analystcan also compare the direction and magnitude of the sensitivities of the differentfactors employed in the analysis to the returns of each industry. For example, anunanticipated change in oil prices affects the gas industry much more than the watertransportation industry or the negative effect of market leverage is more profoundin the electricity industry than in the water transportation industry. Finally, it shouldbe noted that the sign of the microeconomic and macroeconomic factors utilisedis not always in line with the majority of the existing literature, thus pointingout that empirical results regarding the direction of the determinants of industrystock returns may differ, in some cases, to the direction of the determinants ofthe full universe of stocks. This is another important finding of this chapter andcertainly an area which could provide interesting research possibilities for theindustry analyst.

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6.5. Macroeconomic Factors and International Industry Returns

Given the increasing degree of integration in the capital markets internationally, aninteresting question is the identification of factors affecting the risk return profileacross industries at the global level. This is done in Kavussanos et al. (2002) andin Chap. 8 of the Kavussanos and Marcoulis (2001) book.

The objective of the paper was to present evidence, for the first time, of the abilityand the usefulness of world macroeoconomic news in explaining the variability ofglobal industry returns. The monthly risk variables employed in the study are: theexcess return on a world equity market portfolio, fluctuations in global exchangerates, global measures of inflation, industrial production growth and credit risk.

OLS regressions were used to estimate the relationship between unanticipatedchanges in the above factors and the excess returns of a set of 38 internationalindustries as compiled by Morgan Stanley Capital International (MSCI). Amongthe factors considered, the return of the world market portfolio affects significantlyall the 38 international industries under analysis. Moreover, it is by far the mostimportant factor in explaining the variation in international industry returns.Inclusion of macroeconomic factors marginally increases the explanatory powerof the model. Several significant relationships are detected with respect to theremaining factors that do not, generally, exhibit a consistent pattern in theway in which they affect returns of global industries. The long run impact afactor may have, can be positive on the returns of a particular industry, andnegative or insignificant on the returns of another, depending on industry specificcharacteristics. This finding is also consistent with evidence presented in chapters6 and 7 of the Kavussanos and Marcoulis (2001) book.

The practical implications of this study are important for portfolio managers. Theindustry integration or segmentation in the world economy, makes any evidence onthe sources of risk that may affect stock returns across industries at the internationallevel essential in adopting an optimal strategy for global investing.

The industrial classification of a given asset becomes crucial, as certainglobal industries develop to be homogeneous, and capital markets arebecoming increasingly integrated. The significant relationships between globalmacroeconomic factors and international industry stock returns detected in thispaper, are useful to the investor who can exploit these relationships in order toincrease his diversification capacity or speculate by timing his investment.

6.6. International Industry Returns for Subsectors of theShipping Industry

The paper by Kavussanos et al. (2003) gives yet another dimension to the analysis,as explained before. It compares the behaviour of shipping and shipping related

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company stock returns to reveal whether systematic risk differs from the averagein the market and across sub-sectors of the maritime industry.

Following an extensive collection of information through postal questionnairesurvey, 108 publicly listed shipping and shipping related companies, across stockexchanges of the world, are classified by sector according to their core businessactivity – see earlier discussion for this. The Capital Asset Pricing Model (CAPM)is employed for the period 1996 to 1999 to model stock returns and measure sector�s (systematic risk).

During the 1996–1999 period analysed, when the shipping industry was notdoing particularly well, companies in sectors were broadly overpriced, and averagereturns seemed to be negative. Market �’s for all the stocks in the industry appearedto be significantly lower than the market beta. The Drilling and the Offshoresectors were significantly higher than one, however all other average sector �’sappeared to be either equal or lower than the market average. The sectors thatappeared to have �’s which were significantly lower than the market are theShipping, Tanker, Ferry, and also Bulk and Containers mostly. It seems then that themaritime industry stocks do not carry above average market risk, at the internationalsetting.

In comparing the �’s amongst sectors it seems that the Drilling and Offshoresectors have the same proportion of systemic risk in them. The � values ofthese sectors do not differ significantly from each other but are significantlydifferent from all the other sector � values except for Cruise. However, the Cruisesector �, whilst not significantly different from Drilling and Offshore, is notsignificantly different from any other sector. There is no significant difference inthe � values of the remaining sectors: Bulk; Container; Ferry; Shipping; Tanker;Yard; Diversified and All. When regressed against the MSCI Shipping Index, theDrilling and Offshore sectors again appear to have the same degree of market riskin them. There is no significant difference in the � values of all other remainingsectors.

Finally, as more companies in the industry become public the scope for increasedsample sizes for each sub-sector of the maritime industry on which to baseinferences will also increase. Perhaps a further study when more data is availableand also when market conditions are different (on the upturn) may add to the bodyof knowledge established in this paper.

7. USEFULNESS OF THE FINDINGS

Having presented the review on the state of research regarding listed companies inthe shipping and other related sectors and subsectors of shipping, some comments

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are in order regarding the usefulness of these results. The focus is on two majorcategories of investors including portfolio managers and corporate financiers.

As far as the former category is concerned, traditionally, investors and portfoliomanagers’ strategies, regarding stock selection, are perceived as the choice of theproper mix of stocks in order to maximise returns subject to their risk profile. Inorder to achieve that however, they would need to identify what “features” reallymatter in a stock’s or an industrial sector’s performance. The research on shippingand transportation sectors answers this question by expressing the returns of theindustries analysed as a linear combination of each industry’s returns’ sensitivityto a number of microeconomic and macroeconomic factors times the risk premiumon this factor.

As might be recalled, every industrial sector analysed has its own pattern ofsensitivities to the different microeconomic and macroeconomic factors employed.This might be used by the architect of the portfolio’s investment strategy todetermine the most desirable exposure to each risk factor. Altering the mix ofindustries included in the portfolio will certainly affect the amount, and type, ofrisk exposure to each factor studied. For example, suppose the portfolio managerwishes to move away from any unanticipated change in the term structure risk (anunanticipated widening or narrowing of the long vs short -term interest rates) sincehe believes that there will be some turbulence in the future regarding this factor.Utilising the multifactor model, for example he could exclude the air transportation,trucks, electricity and petroleum refining industries from his portfolio.

Alternatively, the portfolio manager could employ the above model or any othermodel presented, to analyse the sensitivities of the factors employed to the returnsof each industry. Using each industry relevant equation, the investment managercan substitute his expectations of each microeconomic and macroeconomic factoremployed in order to arrive at the expected returns of each industry. Then,according to the confidence that he may be able to place in his expectations, hecan decide upon the proportion of stocks that belong to each industry that he willinclude in his portfolio. Furthermore, by comparing industry specific equations,the portfolio manager can diversify, more effectively, his risk with respect to thefactors employed.

As far as the second category is concerned, investment bankers, the usefulnessof the results centres around the concept of the cost of capital or discount rate,which is a critical factor used by corporate financiers in several projects whichhave discounted cash-flow valuation as their backbone (such as capital budgetingand the valuation of privately and publicly owned companies). Despite the factthat there is no consensus among practitioners regarding the right model to use forestimating the cost of capital, traditionally, most applications have been employingthe capital asset pricing model (CAPM) mainly due to its simplicity.

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NOTES

1. The charter market occupies a central place in the structure of the shipping industry.Both shippers and shipping companies may prefer to hire a vessel rather than buy it outright.This may be due to the high capital cost of the vessel or the need to either cover cyclicalpeaks in demand for shipping capacity or replace ships out of service.

2. When borrowing from banks owners offer equity as part financing in a mortgage.3. In charter backed finance shipowners borrow against the security of a long time charter

agreement they have with a charterer.4. Share prices included in the indices were adjusted for any rights issues, stock dividends

and/or splits.5. Accordingly, some companies known to have major shipping or shipping related

interests were excluded because they were too diversified elsewhere.6. For details of estimation methods and full set of results see Kavussanos and Marcoulis

(1997a, b, c, 1998, 2000a, b, 2001), and particularly Kavussanos and Marcoulis (2001).

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APPENDIX A

Summary of Major Findings in Kavussanos and Marcoulis (2001)

Chapter 4: Risk and return of U.S. water transportation stocks over time andover bull and bear market conditions.

Period Covered: January 1985 – December 1994Methodology: CAPMMajor Findings:

� Industry Average Beta: 0.9199 (= 1)� Industry Average Alpha: 0.00218 (> 0)� Parameters exhibit stability over time� No constant “size effect” over time� Shift of alpha, but not beta, over bull and bear market conditions

Note: Figures in parenthesis indicate statistical equality or non equality to thenumber in the parenthesis

Beta comparisons across industries – a water transportation industryperspective.

Period Covered: July 1984 – June 1995Methodology: CAPMMajor Findings:

� Industry CAPM Parameters

Industry Alpha Beta

Water transportation 0.0352 (> 0) 0.9411 (< 1)Air transportation 0.0124 (> 0) 0.9748 (= 1)Rail transportation 0.0150 (> 0) 1.0155 (= 1)Trucks 0.0206 (> 0) 0.9676 (= 1)Electricity 0.0668 (> 0) 0.9465 (< 1)Gas 0.0447 (> 0) 0.9581 (< 1)Petroleum refining 0.0039 (> 0) 0.9838 (= 1)Real estate 0.0260 (> 0) 0.6933 (< 1)

Note: Figures in parenthesis indicate statistical equality or non equality to the number in theparenthesis.

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� The beta of the water transportation industry is significantly lower than the betaof the rail transportation industry and significantly higher than the beta of thereal estate industry.

Chapter 5: The stock market perception of industry risk and microeconomicfactors: The case of the U.S. water transportation industry vs. other transportindustries.

Period Covered: July 1984 – June 1995Methodology: Multifactor Model employing fundamental microeconomic factorsMajor Findings:

� Industry Multifactor Model Parameters

Industry Alpha Beta ME B/M A/ME A/BE E/P *

Water transportation 0.0420 (> 0) 0.9410 (< 1) −Air transportation 0.0030 (> 0) 0.9760 (= 1) −Rail transportation 0.0080 (> 0) 1.0110 (= 1) +Trucks 0.0210 (> 0) 0.9680 (= 1)Electricity 0.0770 (> 0) 0.9420 (< 1) +Gas 0.0650 (> 0) 0.9520 (< 1) −Petroleum refining 0.0230 (> 0) 0.9760 (= 1) + + −Real estate 0.0280 (> 0) 0.6890 (< 1) +

Note 1: Figures in parenthesis indicate statistical equality or non equality to the number in theparenthesis. Note 2:Where the sign is positive, this means that there is a positive relationshipbetween that factor and returns. Where the sign is negative, the opposite holds. The magnitude ofeach factor is discussed in detail in the relevant chapter. Where no sign appears, the coefficient isstatistically insignificant.∗ME, B/M, A/ME, A/BE and E/P correspond to market value of equity, book to market, total assetsto market equity, total assets to book equity and earnings to price ratios respectively.

� The beta of the water transportation industry is significantly lower than the betaof the rail transportation industry and significantly higher than the beta of thereal estate industry.

� The only industry beta which exhibits significant temporal variability is that ofthe petroleum refining industry. In the water transportation and the other sixindustries no significant temporal change has occurred.

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Chapter 6: The stock market perception of industry risk andmacroeconomicfactors: The case of the U.S. water and other transportation stocks.

Period Covered: July 1985 – June 1995Methodology: Multifactor Model employing macroeconomic factorsMajor Findings:


Industry Alpha Beta MIP UTS UOG UCG UI*

Water transportation 0.0346 (> 0) 0.9449 (< 1) − +Air transportation 0.0103 (> 0) 0.9538 (= 1) +Rail transportation 0.0289 (> 0) 0.9875 (= 1) −Trucks 0.0176 (> 0) 0.9698 (= 1) + −Electricity 0.0642 (> 0) 0.9248 (< 1) − −Gas 0.0426 (> 0) 0.9579 (< 1) + −Petroleum refining 0.0320 (> 0) 0.9741 (= 1) + +Real estate 0.0348 (> 0) 0.7543 (< 1) −

Note 1: Figures in parenthesis indicate statistical equality or non equality to the number in theparenthesis. Note 2:Where the sign is positive, this means that there is a positive relationshipbetween that factor and returns. Where the sign is negative, the opposite holds. The magnitude ofeach factor is discussed in detail in the relevant chapter. Where no sign appears, the coefficient isstatistically insignificant.∗MIP, UTS, UOG, UCG, UI correspond to unanticipated changes in monthly industrial production,the term structure, oil prices, consumption and inflation respectively.

� The beta of the water transportation industry is not significantly different to thebeta of any other transportation or non – transportation industry.

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Chapter 7: The stock market perception of industry risk through theutilisation of a General Multifactor Model.

Period Covered: July 1985 – June 1995Methodology: Multifactor Model employing both micro and macroeconomicfactorsMajor Findings:


Industry Alpha Beta MIP UTS UOG UCG UI

Water transportation 0.0334 (> 0) 0.9438 (< 1) − +Air transportation 0.0206 (> 0) 0.9471 (= 1) + −Rail transportation 0.0289 (> 0) 0.9878 (= 1) −Trucks 0.0228 (> 0) 0.9593 (= 1) + −Electricity 0.0710 (> 0) 0.9264 (< 1) − −Gas 0.0603 (> 0) 0.9580 (< 1) + −Petroleum refining 0.0197 (> 0) 0.9676 (= 1) + +Real estate 0.0348 (> 0) 0.7543 (< 1) −Industry ME B/M A/ME A/BE E/PWater transportation + −Air transportation −Rail transportationTrucksElectricity +Gas −Petroleum refining + + −Real estate

Note 1:Figures in parenthesis indicate statistical equality or non equality to the number in theparenthesis. Note 2:Where the sign is positive, this means that there is a positive relationshipbetween that factor and returns. Where the sign is negative, the opposite holds. The magnitude ofeach factor is discussed in detail in the relevant chapter. Where no sign appears, the coefficient isstatistically insignificant.

� The beta of the water transportation industry is not significantly different to thebeta of any other transportation or non – transportation industry.

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Cross-In

dustry

ComparisonsoftheBehaviourofStockReturns

141APPENDIX B

Empirical Evidence Regarding the Macroeconomic Factors Employed in Kavussanos and Marcoulis (2001).

Factor Employed Study Findings of Study Findings of this Book Applicable Industry

Monthly growth of industrial production Poon and Taylor (1991) − ve effect − ve effect Water transportationBong – Soo Lee (1992) + ve effect − ve effect ElectricityChen, Roll and Ross (1986) + ve effectPearce and Roley (1985) n.s.Chen and Jordan (1993) n.s.

Unanticipated changes in term structure Chen, Roll and Ross (1986) + ve effect +ve effect Air transportationPoon and Taylor (1991) n.s. +ve effect TrucksChen and Jordan (1993) n.s. +ve effect Petroleum refining

− ve effect Electricity

Unanticipated changes in oil prices Chen, Roll and Ross (1986) + ve effect +ve effect Water transportationChen and Jordan (1993) − ve effect +ve effect Gas

+ve effect Petroleum refining− ve effect Trucks− ve effect Real estate

Unanticipated changes in consumption Rubinstein (1976) + ve effect − ve effect Rail transportationLucas (1978) + ve effect − ve effect GasBreeden (1980) + ve effectWasserfallen (1989) − ve effect

Unanticipated inflation Chen, Roll and Ross (1986) − ve effect n.s. All industriesHamao (1988) − ve effectWasserfallen (1989) − ve effectPoon and Taylor (1991) n.s.Martinez and Rubio (1989) n.s.

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142M

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APPENDIX C

Empirical Evidence Regarding the Microeconomic Factors Employed in Kavussanos and Marcoulis (2001).

Factor Employed Study Findings of Study Findings of this Book Applicable Industry

Market value of equity Banz (1981) − ve effect + ve effect Water transportationBasu (1977, 1983) − ve effect + ve effect Petroleum refiningReinganum (1981) − ve effect − ve effect GasLakonishok and Shapiro (1986) − ve effectFama and French (1992) − ve effect

Book to market value of equity Stattman (1980) + ve effect +ve effect ElectricityRosenberg et al. (1985) + ve effect +ve effect Real estateFama and French (1992) + ve effectChen et al. (1991) + ve effect

Asset to market value of equity Fama and French (1992) + ve effect +ve effect Rail transportationBhandari (1988) + ve effect +ve effect Petroleum refining

Asset to book value of equity Fama and French (1992) − ve effect − ve effect Water transportation− ve effect Air transportation− ve effect Petroleum refining

Earnings to price Ball (1978) + ve effect n.s. All industriesReinganum (1981) + ve effectBasu (1983) + ve effectFama and French (1992) n.s.

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Cullinane 'Shipping Economics' CH04 - Shipping Stock Returns PDF