Shipping is an industry which suffers from frequent cycles approaching faster as time goes by. Table 1 below mentions 24 shipping cycles since 1741 and till

Source: data from Stopford (2009), p. 106.

2020 (280 years). This means one cycle for every ~12 years. We also calculated cycles’ duration, on which we have focused our interest hoping to find a pattern, with a view to pre-identify them. The periods examined here are separated in 3 groups, depending on the main state of propulsion technology: Sails, Tramps and Bulks.

World suffered also from 9 at least major wars, from which two global, since 1741! Wars are beneficial and harmful at the same time for world shipping: in wars ships are required to transport all kinds of means to those fighting, and for reconstruction afterwards, increasing thus seaborne demand; but also, ships are lost due to war causes.

Our prediction, however, is that local wars will continue… Now an Armenia-Azerbaijan war begun. In fact, our epoch (since 1945) is full of local wars (7 wars at least). Global wars, most probably, will be avoided, as no one will get out alive in a worldwide nuclear war… something well-known by global leaders. World peace is maintained by the fear of nuclear death…

Moreover, the duration of shipping cycles fell over 3 technological periods: from ~20 years to ~7 on average (Table 1). This suggests that technology—the factor which has changed, and no doubt, affected cycles, made the duration of recessions and depressions shorter…! Of course, it is useful to know that cycles now are coming sooner to get prepared and react properly (Goulielmos, 2020). In future, a shipping cycle may be even shorter, as technology continuously improves… Shipowners have to learn to live with cycles as Greeks learned to live with earthquakes and… COVID-19!

Our reference to technology is to that of building ships, including their mean of propulsion, speed, unloading/loading means, fuel etc., and how fast large and faster ships, or sophisticated ones, can be built! Can we blame shipbuilding for delivering ships to shipowners fast, even 1 say within 3 months? Or have we to blame shipowners for over-ordering ships and demanding to get delivery of them as soon as possible to get advantage of the rising market?

There are also involved “economies of scale”—a matter again of technology—by which supply can increase by leaps and bounds! To avoid a possible misunderstanding, economies of scale depend on the existence of an analogoussized demand, called “shipment”¹ or unit-load, which determines the maximum size of a vessel among other factors. Passenger ships are built marginally larger than the estimated demand per voyage to face a future increase.

To understand the magnitude of errors which can be committed by shipowners during a depression, we take the one between 1981 and 1987, where ~48 m dwt of tankers were ordered, ~221 m dwt were laid-up and ~93 m dwt were scrapped, out of an estimated fleet of 400 mdwt… A great waste of valuable resources, no doubt! This demonstrates sadly the failure of shipowners/and maritime economists to predict cycles!

The purpose of this paper is to calculate the exact duration of shipping cycles, first historically, par excellence between 1945 and 2020, and then mathematically. Also, we want to remind reader of the reasons that caused those cycles. Analysis here is based on cycle’s definition provided by economic theory (i.e. from a good economic dictionary). We will refer also to a paper, which tried to forecast shipping cycles to see if this was possible before working on the assumption that forecasting is impossible in maritime economy… held also by the most powerful Greek shipowners.

In the shipping (game)², we believe, that one at least… Joker is always in the pack of cards.

The paper is organized in 4 parts, after literature review: Part I, deals with the definition of business and shipping cycles; Part II, presents the 4 cycles in freight markets of tankers and dry cargoes, between 1947 and 2020, in an historical analysis; Part III, deals with the cycles in nonlinear Time Series; Part IV deals with the “impossibility theorem” of forecasting shipping cycles. Finally, we conclude.

Research on cycles started in UK in 1815 (Stopford, 2009: p. 96)! Moreover, Schumpeter (1954) (1883-1950) noticed that the world “cycle” was used first by the English economist Petty W (1623-1687) in 1662. Schumpeter (1954), moreover, related cycles to technology, approach adopted also by Stopford (2009). Observers at old times came to the conclusion that crises formed part of a wave-like mechanism, acting on economy. They started then to call them “cycles” though a wave is not cyclical… but re-occurring.

Cournot (1927) (1801-1877) distinguished cycles, in 1838, as those lasting 60 years (long cycles), in seasonal, and in short lasting 5-10 years. In shipping, tanker demand e.g. is par excellence seasonal among other things as in severe winters assumes naturally higher levels.

Economists were not in agreement about a unique cause of cycles. A voluminous literature (494 pp) published in 1950 (American Economic Association, AEA, 1950), classified cycles in 12 different main theories! There, 449 famous authors are mentioned³, including Keynes, with one or more publications. After 1950, we believe, the subject of business cycles attracted a lesser interest, and theories of growth of GDP took its place as more important and also novel.

Modern economists were brought-up as we did with Keynes’ General Theory in 1936, triggered by the 1929-1933 depression we believe, they were occupied or rather fascinated by the task to transforming Keynes “General theory” into a long run dynamic growth theory, (e.g. the “Harrod-Domar” growth model in 1940s was characteristic). Moreover, growth is something that all political parties agree, unlike “distribution of income”.

Stopford (2009; chapter 3) gave a detailed presentation of shipping cycles, and in addition, recorded the freight rates of dry cargo ships, and their cycles, since 1741 (Figure 1). Table 1, moreover, has summarized these 22 cycles and updated them to 2020.

As shown, 22 cycles recorded along 259 years, till 2007. The “Wind/Sails period” lasting till 1871, i.e. 130 years, since 1741, gave a cycle duration of ~18.5 years on average over 7 cycles. Interesting is the fact that after 1945, and till 2020, only 10 shipping cycles occurred over 76 years, reducing⁴ the average duration of cycles to 7.6 years from ~18.5!

Stopford (2009) argued that from 1945, and for 50 years, the mechanization of “bulk⁵” and “liner⁶” shipping, using also bigger⁷ ships, and more efficient cargo-handling technology, produced a fall in real freight rates. This is quite true that efficiency came through economies of scale and better mechanization in ports and stronger unloading-loading means in both ships and ports. This is so because time everywhere is the secret factor in shipping to the extent that “economies of time” is a very important chapter.

Summarizing, the problem of cycles occupied economists very early, since 1600s. Schumpeter (and Stopford) were first to connect cycles with technology. We, connected, cycle’s duration with technology… To predict the coming of a

cycle proved to be a hopeless endeavor for centuries. Its duration is also not pre-known. These are 2 points where forecasters have failed.

Stopford (2009) described the above situation as the one where 30,000 shipowners play “poker”; we insist that the situation is not one of playing poker, but one of including a “Joker” in the pack of maritime cards. Joker manifests, after all, the “Free Will” of businessmen.

Greek shipowners realized the above situation and invented a win-win strategy (Goulielmos, 2020), that always works in their benefit, and ends to a higher competitive advantage. Surely, perfect timing in decision making is not accomplished by all humans… but Greeks achieved 86% accuracy in attempting it…

To give an idea of how shipping cycles are created, we present Demand (=seaborne trade) (Figure 2), which clearly fell in 1974-5 for the first time, due to the “Yom-Kippur war” between Israel and Egypt in October 1973. This caused the 2^nd and longer closure of the Suez Canal, and brought a structural depression till 1988, hitting par excellence tankers, with the only exception: year 1979. Tankers hit as they were by majority passing Suez Canal. The 1981-1987 depression, which started in 1979 for tankers, was due to the oil price increase, enforced by OPEC at that time.

As shown, it is clear that the fleet does not follow always trade, as in 1950-1964 and in 1975-1995. Shipowners over-ordered ships during peak periods, so that ships to be in numbers above of what was required (blue line above red one).

A cycle, (called also business or trade), are 2.5 time periods of fluctuation (say in the level of economic activity, proxied e.g. by GDP), of a regular pattern with an expansion followed by a contraction, and again by an expansion… (a definition modified slightly by us from that found in Pearce, 1992).

Economists conceived trade cycles as regular, excluding thus all sudden appearances, caused, e.g. by wars (1914; 1939); by collapses of Stock Exchanges (1929); by collapses of USA Banks (2008); and by a Pandemic (2020; Covid-19)! The reason was that economists did not like to take them as dealing with random events⁸, like the weather. But are cycles random manifestations or are representations of deterministic chaotic time series?

The trade cycle, diagrammatically, is drafted as follows (Figure 3).

As shown, a cycle has a peak: at B, and a trough: at D. Cycle’s period is: either from A to E, or from B to F. A cycle starts from the trend line and returns to it (A-E). As shown, a cycle retards the uprising of GDP. This can be seen, as during the movement of GDP from A to E, had first to pass from C to E, before reaching E, and thus delaying. In other words, if GDP could avoid part CE, and the time required to pass it, then GDP could reach faster E, say in 1/2 time. The trend, however, may be downward as well! The cycle is not really a full one, but a sum of 2 halves… Cycle’s amplitude is BG.

What is wrong, and dangerous, however, is to assume AC = CE (BD = DF). In other words, expansion and contraction phases are not equal… In shipping we know very well that good times are much shorter than bad times!

Humans, in their inability to understand complex phenomena, tried to simplify economics by excluding a great part of reality (“reductionism”). Linearity is

closer to human brain, no doubt, when un-helped by artificial intelligence. This mentality fits exactly to the case of cycles.

For centuries, observers tried to make cycles regular, symmetric-up and -down (Peters, 1994: Chap. 6) and harmonic. Pioneers in this approach were Ancient Greeks-par excellence Euclid, Aristotle, Ptolemy-believing in a divine shape: the circle! They thought that Earth is and “follows” a perfect circle.

Also, Newton, Fourier and Spectral Analysis had the same belief. But this was far away from reality! Stars have an elliptic trajectory for wise reasons. Einstein realized that who constructed Universe made it beautiful, harmonic, and stable, and theories that are not beautiful are wrong… Humans were excluded from such situation to act under absolute… freedom to the extent to disobey also their Father.

People misled by the harmony, accuracy and beauty of the Universe, failed to understand the existence of free will and its implications. Humans behave differently than Universe, which obeys to eternal laws. In shipping, e.g. about 30,000 human decisions make up supply, and about 8 billion human decisions make up demand!

Human decisions for centuries destroyed e.g. global climate. Moreover, humans though they pay the consequences of climatic destruction do nothing to reverse it. Humans contributed also so that everybody to live under an inferior quality. Humans decide freely, and so do business-men, but business-men need par excellence information of what is going-on in order to decide correctly. Technology has been all along at their service. Non sentimental machines, will take all important decisions…?

Goulielmos (2019) used a “nonlinear prediction method”, called “radial basis functions”, (due to Casdagli, 1989), predicting the value of freight rate index for dry cargoes, and coefficient “alpha”¹⁸ for the next 20-years, 2013-2032¹⁹.

Many scientists say that Professors when they are making predictions they say: “As I predicted 10 years ago”, but only if they were successful…

As shown (Figure 18), the freight market was expected to be at its lowest point in 2019 (7^th year), and in 2032 (20^th year). The Joker will appear in 2022, given the lowest alpha (~1.16). The shipping cycle is from A to C (2015-2028) lasting 14 years (ABC)! The freight rate index of dry cargoes will have a decadence of 5 years (2015-2019); and an up-rising of 3 years 2020-2022 and a decadence again of 6 years, 2023-2028. The actual values of the freight rate index for 2013-2015 were 336, 322 and 216. Thus, our prediction was right for only 2015… So, we concluded that forecasting was unsuccessful…

Greek shipowners who do not trust predictions, when the prices of ships (newly built and 2^nd hand ones) are at rock bottom, proceed to build and buy them, larger and newer. After a while, they get rid of older and smaller ships they own (Goulielmos, 2020). Thus, Greek shipowners achieve: 1) Economies of scale; 2) capital cost economies, covering 1/2 the total cost; 3) economies of age and 4) economies of perfect timing (rock-bottom prices). All these 4 economies reward them so that to stay on the top worldwide position, as shown. Random?

Shipping time series—as shown beyond doubt—had an intense and frequent cyclicality, since 1741 to 2020. Their handling, however, was all along wrong, when this attempted by linear statistical methods like “Normal distribution²⁰” and non-linear “GARCH”²¹.

Our main conclusions may be summarized in a Table 4.

The average historical duration of shipping cycles from 1951 to 2020 was 6 years (5.78). Chaos theory gave a cycle duration of 6 (5.67) years on average of a descending duration since 1945!

It was a sad finding, however, to see that technology instead of improving business life made it worse… by bringing cycles nearer!

The theory of business cycle is indeed confusing: are the 2 semicircles equal? Are bad times equal to good times, or are the former are longer? The facts revealed by the text should not be ignored, however, but shipowners have to be prepared to get advantage of them. We showed them how…

Shipping crises (recessions; depressions) are not in the hands of shipowners exclusively, as many may believe, but shipbuilders help as well! Shipowners

Source: author from text.

order ships, no doubt, and they bear the prime responsibility for it. Shipbuilders, however, offer finance and offer low prices, as the case may be, and faster delivery times. But, they cannot be blamed.

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

Goulielmos, A. M. (2020). An Anatomy of Cycles in Shipping Industry, 1946-2020. Modern Economy, 11, 1671-1695. https://doi.org/10.4236/me.2020.1110116

Appendix 1: Rescaled Range Analysis ( Peters, 1994: p. 55 and thereafter; Steeb, 2008: p. 96 and thereafter).

Assume that we have a time series x :   = x 1 , ⋅ ⋅ ⋅ , x n { 1 } with n consecutive values. Because time series are transformed into stationary, we use first logarithmic differences, r = n − 1 . We need also the mean of the series, i.e. sum divided by n. We need also the standard deviation of n − 1 series. Rescaled range is calculated by first “normalizing” data by subtracting the mean. Einstein (1905) proved that D = √time {2}. Hurst (1951) found a more general formula including {1} as a special case: D = c ∗ n k { 3 } , where c is a constant and time = n (number of observations over time), k is a power receiving values from [0 to 1] and for honor to Hurst k = H (root of time); D = Range (rescaled) indicating distance by subtracting from maximum value the minimum value. Thus: R / S = c ∗ n H { 4 } . Einstein’s formula comes if D = R/S, n = t and k =1/2. For practical reasons: we take logs of {4} and get log ( R / S ) = log c + H log n { 5 } . The values of H in the above closed interval characterizes time series as persistence, anti-persistence and random. S is local standard deviation.