_{1}

^{*}

The purpose of this research was to forecast next maritime depression beyond 2018. For this we used the nonlinear forecasting method: “Radial Basis Functions”
[1] through the computer program NLTSA
[2] allowing a prediction for 20 steps ahead. Forecasting applied to a freight rate dry cargo index since 1741
[3] and to alpha
^{1}
coefficient. The lowest alpha predicted was 1.01 in 2038. Stopford’s dry cargo index forecast will be at its lowest point, of 114 (100 = 1947) units, in 2034 and 2035. Three cycles forecast to last 5, 5, and 4 years (2019-2038). Thus shipping has to learn to live with cycles… and depressions, but perhaps it is better if knowing them in advance.

Shipping suffers from frequent recessions, i.e. one every twelve years (on average) [

Shipping suffers also from… Jokers. The Joker is an external shock such as a war (e.g. the Yom Kippur war in 1973; the Gulf war in 1990; the Iraq war in 2003; and so on), or a canal closure (e.g. the Suez Canal closed short term in 1956 and long term in 1967-1975). But the King is seaborne trade.

Alternatively, shipping is a privileged industry, because sea covers 2/3 of Earth and the dominant centers of production are far away, by sea, from the main areas of consumption (the “distance effect”). Sea transport adds spatial utility to products, and so it is necessary. In recent decades shipping became more important with the participation of China (6.6% growth in real GDP in 2018; IMF data) and India (7.3% growth): two highly populated nations of more than two billion consumers!

Worth noting is that maritime economic literature, after World War II, did not focus on depressions, but on growth of the fleet, and on the expansion of seaborne trade, following the global economic growth achieved (1945-end-1973). But at the end of the day, few, if any, maritime economists explain successfully how to pre-know the coming of a depression, its exact duration and what to do in advance to protect businesses from it…

Moreover, we believe, that the dominance of oil (and of “fossil fuels”) by 2050, will end, and it will be substituted fully by gas, and other friendly sources of energy to environment. Relevant is the recent decision of EU. We believe that the world will be forced to pass on to sources of energy exclusively friendly to environment under the pressure of the high cost of physical destructions including human deaths. Shipping is and will be affected as a result of the climatic change.

The paper is structured as follows: First is a literature review followed by methodology. In Part I, a historical analysis of the two main shipping depressions (1929-2008) is carried out. In Part II, the last shipping depression (2009-2016) is analyzed in more depth. In Part III, we present certain reliable features of shipping markets. In Part IV, two chartists’ theories about forecasting secular economic and medium shipping cycles are critically presented. In Part V, we forecast next shipping depression. In part VI, we set an outline for further research. Finally, we conclude.

Our aims were to provide an anatomy of the two last dry cargo shipping depressions (since second half of 1981) and show a method to improve their forecasting. In fact, we forecast next shipping depression (2019-2038). Moreover, we aimed at revealing the method dealing with non-periodic depressions and at describing a method to find-out depression’s (average) duration.

Stokes [^{2} years (or longer). “A depression is an abnormal phenomenon produced by the collapse of an investment bubble”… he wrote. McConville [^{3} of the endless growth in oil transport to meet global oil consumption was transformed suddenly into a nightmare by the two energy crises (1975; 1979).

Mandelbrot and Hudson, (2006/2008 preface), [

Goulielmos [^{4} numbers [

Engelen et al. [^{5} results validate that freight rate forecasting is feasible; due also to returning phenomena of cycles of three to four years, or of long-range dependence, and lack of a time-variabilit…

Stiglitz [

Anonymous (2013)^{6} investigated the cyclical properties of the annual growth of BDI (Baltic Dry Index) (1993-2012) using 231 months and found cycles lasting from three to five years. The method applied was due to Harding & Pagan in 2006 and Harding in 2008, while the forecasting method was a trigonometric regression. Goulielmos [

Zheng and Lan [^{7} analysis using nonparametric specifications to deal with nonlinear and non-stationary time series, characterized by fat-tails^{8} in probability distributions and volatility clusters. They used a generalization of the “de-trended fluctuation analysis” (due to Kantelhardt et al. [^{9} exponent varied from a minimum of 0.40 (for Handy) to a maximum of 1.00 (for Handy, Panamax and Suezmax) and a dominance of memory… . The crude oil market found an oligopoly^{10} of… nations ( [

Summarizing, Koopmans [

We mentioned Joker (Peters, p. 60, [^{11}, embargos and all international events disrupting the workings of supply and demand. The post Second World War period (1947-2008) produced 9 Jokers (part VI). As argued by Peters ( [

In [^{12} diminished as data’s frequency increased (over same days, weeks and months)… i.e. as the duration of data increased, the more random the same data became… Let us take the box representing 2048 days, (about 8 years), from [^{13} here is 0.70 (round.), and though it varied over time, this characterized the whole time series (Peters [^{14}. The persistent time series is the exclusive candidates for a depression, and for this reason is important.

In [^{15} = 1/H. As argued by Mandelbrot & Hudson [^{16}

A robust method we used is the Rescaled Range Analysis. This is a methodology in non-parametric statistics due to Hurst [

Hurst worked extensively on a Nile River dam, as hydrologist, who undertook from UK Government to build an efficient and effective dam there ( [

Einstein [

Hurst [^{17} (rescaled by S). The Hurst exponent provides a criterion for three cases: if H = 1/2 = random walk = independent series (white noise). If 0 ≤ H ≤ 0.5, series is anti-persistent (pink noise) and if 0.5 < H ≤ 1, series is persistent (black noise). In Nile, H = 0.91, meaning that River’s waters indicated a speed higher than random and so previous flows influenced next, and present and future flows remembered previous overflows, i.e. they have a memory. Given that H = 0.69 or 0.70 here, maritime series, if found decreasing, is most likely to continue to fall rightly next; the reverse is also true. This phenomenon is called Joseph effect, as it indicates seven years of fortune followed by seven years of famine (Bible). Moreover, these series has only the potential of sudden catastrophes, called apropos Noah effect (Bible; names coined by Mandelbrot) as happened.

To estimate H, we take logs of (2): log ( R / S ) = log ( k T H ) = log ( k ) + H log ( T ) (3)^{18}, where log(T) is the independent variable, log(R/S) is the dependent variable and log(c) is the intercept. We run regression (3) using NLTSA [

Given that R / S = V ∗ n (4) and solving (4) for V : V = ( R / S ) / n (5) (this is the V-statistic)^{19}. The V-statistic works particularly well in the presence of noise (Peters, p. 92, [

Important is that H is connected with alpha. To estimate alpha there is the original methods of Mandelbrot [_{1} the initial value; i.e. the sum of n values of R scales by n 1 / α times initial value. Taking logs: log R n = 1 / α log n + log R 1 (7), and solving for alpha: alpha = log n / log R n − log R 1 (8); (8) equals 1/H, if (logRn − logR_{1}) is replaced by (logR/S) given that (logR/S) = (logn)H, and so alpha = 1 / H (9). (9) gave us an estimated alpha coefficient for shipping time series equal to 1.43 (round.).

According to Rescaled Range Analysis, H exponent determines the order of events: good years follow good ones and bad ones follow bad ones. Moreover, shipping time series produces trends and cycles. Alpha exponent measures how wildly freight rates vary and how fat the tails of the distribution of freight rates’ changes are. It is a proper tool to recognize violent freight markets.

Mandelbrot and Hudson, (2006/2008 preface) [

The bubbles (depressions) flow from the entwined effects of a long-term dependence (measured by Hurst exponent), and by a discontinuity, (measured by alpha). This explanation (due to [

First, we will restore optimism among shipowners.

1) The decline in seaborne trade^{20} is the main cause of a shipping depression, because seaborne trade—given distances—is shipping demand (derived demand). 2) The world supply of ship space cannot be coordinated, despite various programs, from time to time (e.g.: scrap 2 tons, build 1). 3) Shipping supply is in the hands of about 30,000 shipping companies, the majority of which are small, owning three vessels (on average). The ships are managed mostly by private, independent, managers. 4) Global demand for ship space is in the hands of those many thousands importing and exporting companies by sea. Here it matters what promotes sea trade. 5) The oversupply of ship space, relatively to demand, is the other main cause of a shipping depression.

The picture of “shipping dry cargo market” between 1741 and 2019 (March) is as

As shown, three main shipping peaks occurred: in 1808-1813 (during Napoleonic Wars); in 1918 (end of the Great War), when almost all commercial ships destroyed; and in 2003-2008, due mainly to China’s trade. We have noticed that in 2007 demand for dry bulk carriers was intensified and was related with the imports of iron ore and steel by China (380 mt iron ore) and Japan.

In 1929 (end) the Wall Street crashed causing a shipping depression. The trade fell 26%, while world fleet… increased (1931-1934); 21% of total tonnage of world fleet (14 million GRT-gross registered tonnage) was laid up (1932), i.e. 5 times higher than the normal. A heavy scrapping took place; second hand prices fell by 50% in 1930, and further in 1933. In 1933, prices of second hand ships were called distress prices due to their low level. Five million GRT of ships

scrapped (7.5% of the 1932 world fleet) (1935-1937).

The dry cargo freight rates fell below operating cost (1981-1987) causing losses (

The sharp fall of dry cargo freight market started in 1981 (end), when a Panamax ship earned $8500/day in December from $14,000 in January (61% less). The Joker here was the strike of coalminers in the USA, which collapsed the whole Atlantic market. The freight rates/day further halved to $4200 (end-1982)… Technology contributed in replacing (old) ships with ships having fuel-saving main engines, after the serious double increases in fuel cost (1975; 1979). Bunker costs covered 41% of annual operating cost (including voyage expenses) by 1980 of a newly-built tanker!

A large number of ships were laid-up (1975-1985) (

As shown, the laid-up tonnage peaked-up in 1982 (84 m dwt). The overall total was 317 m (1981-1985), if we add the amounts corresponding to yearly peaks. This prevented the market from reaching equilibrium, causing freight rates to go up and down, till oversupply absorbed. Laid-up tonnage is a stand-by supply removed from sight, but not from fight (market)!

We assume that all ships with no hope of earning anything above operating costs in next three^{21} years, they end-up in scrapping yards. As shown (

Moreover, over 145 m dwt of tankers scrapped (1977-1985). The majority scrapped, (in 1985), concerned tankers of over 175,000 dwt each (i.e. 76%: 18.4 m dwt) (data from Asian Shipping [

1) Why valuable ships are scrapped?

There are six reasons: a) high age: ships scrapped are old; b) high cost: ships scrapped had e.g. an expensive mean of propulsion (which could not be economically replaced); in 1985 steam turbine ships had a poor economic performance; c) legal obsolescence: IMO required then the: inert gas system, crude oil washing and dedicated clean ballast tanks; d) low market: it plays an important role (economic obsolescence). In 1982, tankers had a surplus of 139 m dwt (40%) on a 349 m total supply… In 1985, dry bulks had a surplus of 48 m (21%) on a 225 m total supply; e) high scrap price: ships can earn a satisfactory price

[

Shipowners adopt various methods to reduce oversupply—for which they are… personally responsible. Ships in order to reduce fuel costs, during a depression, they steam slowly (

As shown, the bulk carriers falling between 10,000 and 39,999 dwt, (more in this than in any other class), slow-steamed. They peaked in March 1982 (14 m dwt). In tankers, the greater numbers of slow-steamed ships were in sizes of 150,000 dwt and over, with a peak of 43.3 million (June 1981)… This was the revenge of… economies of scale, we may say, because shipowners ignored the golden rule: “an economy of scale is a good thing, if there is a good cargo”, i.e. enough cargo.

The surplus^{22} in tankers varied from 80 m dwt (1981) to 130 m (mid-1983) and 80 m (1985). The supply of tankers was 265 m dwt (end-1985) and the demand was only 180 m! There was a surplus of 85 m (32% of supply). This surplus was: slow steaming: 30 m or 35%; laid-up: 30 m or 35%; used as storage: 25 m or 30%. In bulk-carriers, the fleet was 225 m dwt (end-1985) and the demand only 175 m. The surplus was 50 m (22% of supply). Slow steaming in bulk-carriers accounted for 50% of their surplus.

The market situation between 1998 (May) and 2008 (Nov.), 10 years prior to Global Financial Crisis, is next presented (

As shown, the vertical fall in BPI in 2008 (6^{th} August)—is dramatic and unexpected: pushing shipowners into the 2009-2016 depression. Profitable markets emerged before this catastrophe, between 2003 (6^{th} August) and 2005 (6^{th} August) and exceptionally profitable from 2005 (6^{th} August) to 2008 (6^{th} August) (a five years boom).

As shown (

As shown, the peak in deliveries appeared four years after the peak in orders. Ship-owners in a depression, try to postpone deliveries… and cancel as many orders as possible. Shipyards, however, recorded large orders between 2009 and 2012. Dry cargo ships delivered to owners, between 2009 and 2013, were exceptional many and varied from 7.5% (2009) to 16% (2012) of existing fleet. Orders increased, and as a result deliveries increased… though not equally. The construction time is a flexible variable depending on the intensity of demand and the availability of berths. This manifested that the cause of shipowners to order was the amount of revenue entered into companies’ vaults, and not the crisis flowing around due to GFC.

Scrapping is the only equilibrating mechanism between demand and supply; it reached 100 m dwt between 2009 and 2013, i.e. between 2% and 5% of existing fleet (

Scrapping, despite of what is believed, is not the effective mechanism to remove

surplus tonnage quickly (exception: the period mid-1981-mid-1987). Scrapping removed only 1/3 of the tonnage delivered. Thus a market based on scrapping for its improvement needs time (three years), given demand.

Deliveries (1963-1982) surpassed scrapping by 50 m dwt (max.) during most of this period. Scrapping intensified (during the depression) from 1981 to 1987. Scrapping fell by an almost steady amount of 10 m dwt/year below deliveries (1988-2003: 15 years). The gap between deliveries and scrapping widened sharply in 2006 to 70 m dwt. A strong demand reduces scrapping and magnifies deliveries in a serious level.

As shown, a short-term shipping cycle (3 - 4 years) unfolds in 8 stages, lasting from 2008 to 2010 (31 months) (instead of 36 theoretically). Freight rates varied from $4000 to $39,000, but they had to return to $4000 according to theory (2010; 12/09).

Year | BDI | Year | BDI |
---|---|---|---|

2010 | $4200 | 2011 | $~2000 |

2012 (*) | $~1,000 647 units = collapse of the index (**) | 2015 end | 504 units |

2016 (April); We reckon 2016 to be the end of the 2009 depression (which lasted 8 years). | 500 units (lowest) | 2017 May | 994 units |

2018 | 1,109 units | (*) A gloomy year with record deliveries, dismal earnings and record scrapping | (**) Though seaborne trade increased by ~4% in 2011 and ~6% in 2012 |

Source: author from various publications.

Over-ordering of ships during a shipping boom did not benefit shipping as much as it did to shipyards… Scrapping on the other hand is shipping’s psychological law, i.e. the hope of shipowners for a better day. But the 1979-1991 tanker depression took more than 13 years for the market to absorb the large surplus of some 100 m dwt mainly via scrapping [

At the end of a depression, companies have to build-up “depression reserves” to cope with next one, which is expected with a high degree of certainty (our opinion). Moreover, ship-owners should not charter ships at all in a very bad market, but better lay-them-up [

Part (a) shows that the change in shipping net revenue (1986-2007), (net of operating costs), varied from $1 m (1986) to $16.5 m (2007)! In 2007, annual revenue was greater than the 1/3 of the value of the vessel (part b). Moreover, buying a ship in 1986, at $13.5 m, and selling her in 2007, at $48 m, one gained $34.5 m… So, one good sale of a ship is equivalent to three-four years of (net) profit from operations.

Zannetos [

Zannetos’ paradox is still valid today as shown by the time charter rates in force in 1999-2009 (

Under depressed freight rates (end-1982), a large number of orders placed, first by Japanese, and then by Greeks, Norwegians and others, reaching a total of 32 m dwt! These orders initiated by the “Sanko Steamship Company of Japan”, which secretly ordered 123 dry cargo ships of a total of 4.5 million dwt [^{24} was to order (dry cargo) ships at the end of 1982, expecting depression to last two years, i.e. till end-1984. So, ships’ delivery would coincide with the next upturn of the market, expected to be in January 1985, (a genius idea)… but market improved 2.5 years later^{25}…

Symmetry in the period of a depression is rooted in ancient Greeks, who assumed circle to be the divine shape. Moreover, Greeks believed that the Sun moves round the Earth in a circle… In the Fourier analysis, the irregularly shaped time series are the sum of a number of periodic sine waves, each with different frequencies and amplitudes. Spectral analysis attempted to break an observed irregular time series, with no obvious pattern, into sine waves, and impose an unobserved periodic structure on the observed non-periodic time series [

Goulielmos [^{26}, because a world depression is coming, similar to that of 1929-1937. This statement based on the “theory of long waves”, advanced first by the Russian economist Kondratieff (in 1926 in German; and in 1935 in English) [

As shown, the world economy passed through 5 lows: 1788, 1842, 1896, 1950, and 2004 (2008 really), and it will pass another one at 2058. The symmetry of the waves is apparent^{27}. By 2031 (2004 + 27 years) world economy is expected to reach a new high.

Hampton [

As shown, freight rates form six pyramids, over two equal chronological phases, unfolding from zero time (to 8 years) or to 12 years and from 12 years

(to 16 years) or to 24 years, in six equal chronological periods of 4 years maximum each (4 × 6 years^{28}). Every pyramid shows a different level of freight rates. Pyramids start from a low freight rate, reach a top, and return to a higher low than that in their start (one to three stages). Each previous peak is lower than the next. The climbing-up phase describes the evolution of an actual freight market improving as demand increases. Given that supply reacts with a delay due to construction time, freight rates continue to rise (absorbing any laid-up ships). After third pyramid, suddenly, market collapses^{29}, and falls down to the lower stage four. Thus, a correction phase starts. Long-term corrections come after certain years of rising freight rates, where optimism caused a surplus of newly-built ships.

The three boom pyramids, are followed by three, equal chronologically, recessions, the first being lower (step four) and the next two (five, six) higher. Steps five and six are at the same (low) level as that of step four. Step sixth cannot be higher than third step, because market resists. This should mean that tonnage is coming-in from lay-up. In shipping, a disharmony between the decisions of shipowners to provide the means of transport and those of importers/exporters by sea to provide cargoes is possible, due to man’s free will. Some argue that freight rates trigger supply. Different people interpret differently a rising freight market, and moreover they act differently when they decide to order ships. However, a rising demand heals all wounds and covers all owners’ mistakes. But a depression exterminates the heavy wounded and reveals any serious past mistakes (appendix two presents such a case-study). A shipowner in Homeric language means a person prepared (=εφοπλιστης in Greek). Goulielmos and Goulielmos [

Let us use one of Hampton’s charts to re-present the crucial period 2003-2018 (

As shown, the last boom started in 2003 (August), at a time-charter rate of $10,727/day (Panamax). The freight rate then rose to: $47,000 (2004), $51,000 (2005) and finally $96,000 (maximum) (January 2008)! The sudden fall happened on 5^{th} December, 2008, down to $4,058: this is the catastrophe of the

freight rates market.

The freight rate then rose to $36,000 and fell to $12,000 (early 2011). In 2018 (24^{th} May) Panamax Baltic time charter/day (close to the above index) was $9692^{30}. This is higher than the $5562 average (YTD) in 2016. We consider this to be another sign indicating that last depression ended.

The $96,000/day peak, and the previous record freight rates, induced—as expected—shipowners to… form long queues… (a metaphor) outside world shipyards to order these extremely profitable ships. In such cases it is expected new shipowners to enter the market and existing shipowners to increase their fleet. But owners are (wrongly) backed by shipyards, bankers and Governments alike in such decisions, as maritime history showed. This is so for over-ordering on delivery forces markets to collapse… given demand and distances!

The building-up phase took place between 2003 and end-2008, lasting 65 months, i.e. 31 months shorter than the theoretical period argued by Hampton (i.e. 96 months or 8 years)… Moreover, the correction-phase had to start in January 2009, and expected to end in Jan. 2016, according to Hampton. It delayed eleven months.

H exponent (yearly) found, as mentioned, equal to 0.689849 (for n ≥ 10 and n = 270-9-1 = 260 years) (

As shown, H exponent regressed around random walk (0.50) for 246 years (229 + 9 years skipped + 8 years missing due to Second World War)! This justifies maritime economists arguing that shipping market follow a perfect competitive model. But is this due to the nature of data used or is this a real phenomenon? H rose finally to 0.70 (round.) by 2018. So the “freight rates dry cargo index” became persistent and extremely dangerous since 1987, capable of creating catastrophes, as the one in 2008 (05/12)!

A nonlinear prediction method, i.e. radial basis functions^{31}-RBF [^{32} (

As shown, the “dry cargo freight rates index” will fall six times below 148 units since 2019, but mainly after 2034 (83%) (128 units, on average). Lower levels (103 units on average: 1982-1986) occurred also during 1981-1987 (depression). There will be also three unequal cycles: AB (5 years), BC (5 years) and CD (4 years).

As shown, (

During 2019-2033 it is advisable for owners to stay away from new buildings and spot markets. Years 2034-2035, will offer a good opportunity for the above. Moreover, when risk is fair (alpha tends to 2) one should decide to enter the market; when alpha tends to one, a shipowner has to stay away from it (2027-2030); alpha can also help shipowners in their best timing. When alpha indicates that a high volatility is coming, then a shipowner should not be idle, but pass on to asset playing! Years 2021; 2023; 2025 and 2028 will offer rock bottom prices proper to buy or sell or order!

A proper model, we reckon, is the representation of a persistent time series (H > 0.50 ≤ 1) with randomness (H = 0.50) and a Joker… In 2006, we applied Rescaled Range Analysis [

As shown, the Joker appeared nine times since 1947: one due to Korean War (1950); the Suez Canal short closure (1956-1957); the Suez Canal long closure (1967-1975); the Iranian revolution (1979); the Iran-Iraq war (1982); and the Iraq-Kuwait war (1990). There were also the crises in Asia (1997); the dot.com (2001) and the GFC (2008).

We suggest, however, before modeling, one has to answer four questions: 1) Do freight rates fully reflect all relevant information? 2) Is Random Walk the best metaphor to describe maritime markets? 3) Can one beat maritime markets? 4) Can we take the efficient market hypothesis not any more as hypothesis, but as real?

Every shipping depression has its own duration and depth, and each one should be forecast afresh. The 2009 depression was due to speculative bubbles, fueled by credit expansion and lax monetary policy followed in the USA since 2000. Shipping was this time one of the victims. A shipping cycle is not periodic, and its duration is not fixed. Different papers above produced different durations in years for the same shipping cycles! We better have to forecast a shipping cycle using V-statistic.

Over-ordering of ships is the Achilles’ heel for the happiness of shipowners. Wide fluctuations in asset values showed that the asset speculation is a better way to make profits in shipping, from time to time, than operations. Economies of scale—a basic economic principle—is understood, and pursued by shipowners, even if not educated in economics. Shipping helped the world by reducing the cost/ton of sea transport, by creating serious economies of scale. The cost of transport of one ton of coal from Wales to Singapore (1871) or from Brazil to Japan (1990) (equal distance) fell by more than 18% [

Shipowners are rational, but their actions cannot be based on an accurate prediction^{33}. This is a responsibility of Academia. Moreover, we consider certain parallel actions to be due to this inability to forecast: a life-time experience (or past history) affects shipowners in their investment decisions, though history may not be repeated in shipping. In addition, small shipping companies copy larger and more successful ones.

The duration of all depressions was considered wrongly symmetrical. Reality, and nonlinear theory, demonstrated that the equal periodicity of cycles is a dangerous myth. Boom periods are (rarely) longer than crises, but there were exceptions, both in the past and recently. Shipping is… a “joker in the pack”, where its appearance is the random element. We mentioned at least nine jokers that appeared since 1947.

Chartist Hampton, for shipping economy, and Kondratieff, for global economy, failed to forecast crises as they deviated from their theoretical timing by at least ±10% on their theoretical forecasting period. Kondratieff predicted a depression in 2004, but it came in end-2008…

During the 1981-1987 depression shipyards reduced prices to attract orders. Banks held a large amount of liquid assets and investors were looking somewhere to invest abundant credit. Depressions, moreover, are strangely described as symmetrical in economic dictionaries as well. This assumption led shipping companies to fatal mistakes during the 1981-1987 depression. Best-timing is the major managerial tool for achieving success in shipping, but best-timing can only be based on best-forecasting, and on predicting alpha, the modern yardstick of risk.

Scrapping failed to be an effective and fast equilibrating mechanism… and to avoid illusion; it takes 75% more time than would be necessary for it to be effective. Similarly, tonnage laid-up is a pseudo-solution, as it removes only from sight—but not from market—about 1/3 of surplus tonnage. Shipowners should not put all their eggs in one basket (tankers or dry cargo).

Moreover, time charters, we believe, act as a proxy for long term profitability. These influence shipowners more than spot rates in their decision to order new ships… and this gets us closer to Keynes. Greeks have an all times right—though empirical—investment policy as we have advanced this elsewhere [

The author declares no conflicts of interest regarding the publication of this paper.

Goulielmos, A.M. (2019) Forecasting the Next Dry Cargo Shipping Depression beyond 2018. Modern Economy, 10, 1684-1712. https://doi.org/10.4236/me.2019.107110

On every point—in a phase space-we place one center. The X j ( x ) = { x − x j } 2 + c n } β (A1) is the “radial basis function”, where x = a vector and c = the average of distances of x from x_{j}, and β > 0. For last point, x_{τ}, we select the k nearest neighbors x_{j} (where j = 1(1)k)). One center is placed on every nearest neighbor. We construct a linear system of k equations with k unknowns. Line i of the matrix of A coefficients is X_{j}(x_{i}) from (A1). The vector of the results b is the determinant m of the vector x_{j}_{+1}. To have b average = 0, we subtract the mean from every determinant, and solve the linear system Ac = b using S vectors decomposition. The predicted values come from the internal outcome of X_{j}(x_{τ}) with c, plus the mean subtracted above: x N + 1 ( pred . ) = b ( average ) + ∑ j = 1 k c k X k ( x τ ) (A2).

Company’s fleet (end-1979) was^{35}: 1 VLCC 250 k; 3x 138 k tankers—4 years; 4x 26 k bulkers—3-4 years; 2 SD14; 50% credit; $8 m cash; a rather young fleet. Company’s emphasis was on tankers, i.e. 664 k of tankers or 82% (1^{st} mistake). Notable is that 1979-1987 was a disaster for tankers^{36} (Iranian Revolution; price of oil from $11/b increased to $40/b; oil trade fell from 1.4 bt to 0.9 bt)!

Ship-owners’ decisions were:

Remarks: Company assumed… twice that depression would last two years. It lasted six… Company tempted twice by low prices to order ships… of wrong type. It had then to sell the wrong ships, because of their better prices (due to company’s low liquidity). It did not restructure loans or delayed installments. It did not keep crisis reserves directly or via high depreciation. No use of balloon practice (*). The company bet on wrong horses. He had to know that tankers were in depression since 1979, and bulk carriers will also enter into it (in the second half of 1981)! No proper management of cash flow mentioned. Creating debts of $95m during a depression is a dangerous decision; no laid-up of ships mentioned. The % of debt at 60% to banks did not help. (*) When market is unpredictable beyond say first x years, and the loan lasts y years (y > x), then the amount of loan at the end of y-x years is left—in a lump sum—to be renegotiated when future is clearer.

Suggestions: If one believes that a shipping crisis will happen, he/she has to create a “crisis reserve” retaining say a minimum of 10% of the profits from boom period. When crisis comes one has to sell only unprofitable ships, after reducing their cost as much as possible. Increasing companies’ debts attracted by low new building or low second hand prices is not allowable. If one thinks that company’s ships will be profitable in near future, then he/she lays-them-up. As a crisis creates liquidity problems, as a rule, one has to renegotiate at once with Bankers past loans. Company’s loans must follow always market, not the bank! Acquisitions of ships are allowed, but strictly only at rock bottom prices, and larger and younger than hitherto; own funds (%) to be as high as possible and the rate of interest to be as low as possible. New-buildings are avoided when entering into a depression and allowed when market exits minus construction time. Sale of ships is allowed, but only if they are older and smaller than hitherto. Forecasting-even inaccurate—is better than no forecasting!