Forecasting the Next Dry Cargo Shipping Depression beyond 2018

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.


Introduction
Shipping suffers from frequent recessions, i.e. one every twelve years (on average) [3]. A shipping depression, however, is not as frequent, because it needs a serious percentage (greater or equal to 20%) of existing fleet to be laid-up (the shut point in economics), due to lack of demand (=seaborne trade). Shipping self-correction mechanism, lasting a painful, but necessary period of adjustment of ten 2 years (or longer). "A depression is an abnormal phenomenon produced by the collapse of an investment bubble"… he wrote. McConville [5] argued that the 1973 shipping oil depression removed the presumption for a consistent and underlying expansion of oil trade. The dream 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 andHudson, (2006/2008 preface), [6] argued that conventional economics about investment bubbles, (or shipping depressions for us), were wrong and that these are irrational deviations from norm, caused by rapacious speculators or mass greed. But they suggested that investment bubbles can be entirely rational. Kavussanos [7] did not expand on financial-credit tsunami caused by the end-2008 depression. Soros [8] [9], argued that the undisputed faith in market forces, made us blind to see crucial instabilities. The dominant paradigm, that financial economic markets tend to equilibrium, and that deviations are simply random, is wrong and misleading.
Goulielmos [10] tested Hampton's hypothesis, using econometric tools, and called apropos Hampton's theory a "maritime technical analysis" based on the mystery of Fibonacci 4 numbers [11]. Stopford [3] argued that a (shipping) crisis removes the imbalances in Supply and Demand, and it lasts so as to achieve this.
On average, a crisis takes about ten years. He argued that shipping depressions are caused by a falling demand and an increasing supply. He also [3] argued that a shipping crisis is a poker game with a dealer (=the market). The market dangles the prospect of riches on each turn of cards, while shipowners struggle through the dismal recessions and raise the stakes as the cash rolls-in during booms. Ship-owners bet on ships… Engelen et al. [12] applied, the "Rescaled Range Analysis" and the "De-trended fluctuation analysis" (due to Kantelhardt et al. [13] to LPG market. They undermined the efficient market hypothesis… Then they found three cycles: one four years (1993-4 to 1996-97); one six years (1998)(1999)(2000)(2001)(2002)(2003) and also one six years (2003)(2004)(2005)(2006)(2007)(2008). They argued that shipping cycles may not fully materialize due to stochastic events. They stated that shipping cycles' scaling and multifractal 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 [14] argued that the global depression in end-2008 showed that the state (USA), could not force markets to price risk correctly or to draft regula- 2 We warn reader to ignore any such estimation! 3 A dream of Onassis, which came out to be true till his death! 4 In 1202 Leonard of Pisa (~1180-1250), (known as Fibonacci), published a book called Liber Abbaci, (book of abacus), dealing with the calculating methods with the new arithmetic numbers coming from Arabs. 5 There is a plethora of papers https://researchgate.net/publication/323256722_Multifractal_Detrended_Analysis (downloaded 29/11/2018) using fractals. tions to minimize the damage caused by wrong estimates. Goulielmos and Psifia [15] showed that the tool used to measure risk by maritime economists, (i.e. standard deviation), is conservative and the risk is much higher than that predicted by σ of normal distribution [16].
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 [17] rejected the idea that ship-owners are irrational, following an analysis based on Keynes. Moreover, he rejected [17] Hampton's [10] argument that groups of investors, meaning also shipowners, do not, necessarily, act rationally. Zheng and Lan [18] applied to tankers a multifractal 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. [13]. Their model [18] applied to 6 tanker types: VLCC (very large crude carriers); ULCC (ultra large crude carriers), Suezmax, Aframax, Panamax and Handy, using daily returns. They concluded that tanker markets are frac-tal… [19]. They used rolling windows, and found that the Hurst 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 ( [18], p. 558) and the tanker market found highly competitive ([18], p. 558) [20]… Summarizing, Koopmans [21] argued that Tinbergen was wrong in assuming equal cycles up and down, as tanker booms were shorter, but he was also wrong, because one boom in 1988-1997 lasted 10 years (Stopford p. 106, [3]) and one in 2003-2008 lasted 5 years! Sanko Steamship Co of Japan committed a historical mistake in 1982 by investing massively in new dry cargo ships, believing in a shipping cycle of two years up and two years down (Couper,[22]; Stopford, p. 126, [3]). Copying other shipowners is also a symptom of the inability to forecast shipping markets.
We mentioned Joker (Peters, p. 60, [23]). The Joker represents also strikes 11 , 6 "The Baltic Dry Index: Cyclical Analysis and Forecasting"; probably published in Logistics and Transportation Review, Part E, in 2013. 7 Mandelbrot-Hudson's, ( [6], p. 217), model is a representation of the fractional Brownian motion of multifractal time, or a multifractal model of Assets Returns in Brownian motion, expressed by an equation. The trading time is expressed by f(α). Its purpose is to re-distribute time. Time is shortened and stretched… The main variable: price, becomes a function of trading time, a function of clock time; here the end is to manage wild fluctuations, and volatility, which clusters. 8 Characteristic applicable to shipping time series as well. 9 Due to Hurst, indicating cycles etc. in time series. H = 0.50 stands for Random Walk, where alpha = 2. 10 Oligopoly of governments: i.e. those of OPEC, Russia, Venezuela etc. 11 In "Seatrade" (monthly shipping journal), an article about the re-opening of the Suez Canal in June 1975 titled apropos: "Suez: the joker in the pack"! 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 ([23] p. 61), if a market is a Hurst process, as shipping is, it exhibits trends that persist till an economic equivalent of a Joker arises to change its bias in magnitude, in direction or in both… In [18], the time-dependent Hurst exponent 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 [18], then daily H is 0.62, the weekly is 0.51 and the monthly is 0.43 (applying "Rescaled Range Analysis").
How the same time series are persistent in days and weeks, and anti-persistent in months? The reverse had to be also true. The maximum H 13 here is 0.70 (round.), and though it varied over time, this characterized the whole time series (Peters [23]) 14 . The persistent time series is the exclusive candidates for a depression, and for this reason is important.
In [18] high Hs found, but low α… But alpha 15

An Historical Account
Hurst worked extensively on a Nile River dam, as hydrologist, who undertook from UK Government to build an efficient and effective dam there ( [23]; Mandelbrot & Hudson, (2006/2008), [6]; Steeb [25], p. 108). Egyptians supplied Hurst with extremely long time records, i.e. of 847 years! Hydrologists, before Hurst, assumed that the inflow of water into reservoirs was a random process. To Hurst's surprise data did not represent a random structure, and the statistical tools indicated no correlation between various observations. Hurst developed a 12 In methodology. 13 Rounded from 0.689849 for n ≥ 10 and n = 260 (278-1-9-8) years. 14 The longer was about 15 years. Data that last less than 20 years cannot reveal cycles of 20 years or longer. Looking at the 5 graphs in [18], of a variable time duration of H, almost all Hs were ≥ 0.50 and ≤ 1.00 (Handy only had H = 0.40) (Oct. 2011). 15 A coefficient indicating volatility and risk; alpha is also the measure of the "peak-ed-ness" of the probability density function. 16 Exarchou-Moutafidis-Simitsis-Tzouvara and Adamidis in 2013 found (2007-2012) that the 1132 daily observations of the indices of the Stock Exchanges of France, Germany, Spain, Portugal, UK and Greece (AGI), showed for all-but Portugal-to be inefficient. They used the "Portmanteau test" due to Q-statistic of the Ljung-Box. "Normality", "Random Walk" and "efficient market hypothesis" were rejected (Value Invest, 2013, issue 6, www.valueinvest.gr). set of new tools in statistics (mentioned below) to examine data deviating from a Gaussian distribution.

Einstein's Contribution
Einstein [26], during his highly productive phase, did an extensive study on Brownian motion, (stated first in 1828 by Robert Brown-a botanist, and remained unsolved since then): i.e. what is known as the model of random walk. Einstein proved that the distance covered by a random particle, undergoing random collisions from all sides, is directly related to the square root of time: where R stands for distance, k is a constant and T is the index of time.

Hurst's Contribution
Hurst [24] generalized Brownian motion to be applicable to a broader class of time series: (2), where R is the range of a time series, H is the relevant exponent (or power coefficient), and S is the (local) standard deviation. Equation (2) scales as time increment increases by a power law. R indicates the distance of a time series; R/S is a timeless and dimensionless ratio 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.

H's Estimation
To estimate H, we take logs of (2): 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 [2] computer program and took results for T = n ≥ 10, i.e. nine results are ignored [25], and one observation is used to get first log differences. The range (R) of a time series is the difference between its maximum and its minimum value (indicating the total distance covered by time series).

V Statistic
Given that R S V n = * (4) and solving (4) for ( ) (5) (this is 17 Ingenious act. the V-statistic) 19 . The V-statistic works particularly well in the presence of noise (Peters, p. 92, [23]). Equation (5) gives a precise measurement of a depression's length in calendar time. Rescaled range analysis provides a graphical method to calculate the time, which a depression lasts (Peters, p. 92, [23]). The discontinuities in the plot of (V/logn) are sought. The non-periodic depressions are shown when the (V-statistic's) plot starts to flatten-out, and its slope diminishes at the end of each depression.

The Relationship between Alpha and H
Important is that H is connected with alpha. To estimate alpha there is the original methods of Mandelbrot [27] and Fama [28]. We will follow Peters, p. 212, [23]): let the sum of a (stable) variable R, in an interval n, be: where R 1 the initial value; i.e. the sum of n values of R scales by 1/ n α times initial value. Taking logs: level of alpha indicates that a Noah Effect, or the rapid reversal of trends, is likely. The Noah effect is the tendency of (a persistent) time series to produce abrupt and discontinuous changes, or apropos for shipping depressions as in Dec. 2008.

The Bubbles
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 [6]) supports our argument that ship-owners are rational, like other investors, and more important is that we can use alpha. Financial markets and shipping ones behave the same way.

Part I: Analysis of Two Shipping Depressions, 1929-2008
First, we will restore optimism among shipowners.

Five Universal Truths about Shipping Markets
1) The decline in seaborne trade 20 is the main cause of a shipping depression, because seaborne trade-given distances-is shipping demand (derived de-mand

The Picture of about 300 Years of the Dry Cargo Shipping Market
The picture of "shipping dry cargo market" between 1741 and 2019 (March) is as

The 1929-1937 Shipping Depression
In

The Freight Rates Market
The dry cargo freight rates fell below operating cost (1981-1987) causing losses ( Figure 2).
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

Scrapping
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 (Figure 4) they reached a top, (1985), of 44 m dwt. Comparing Figure 3 with Figure 4, we see that a massive lay-up emerged first, and then-after three years-a substantial scrapping followed. Worth noting is also that scrapping (44 m peak) covered almost 1/2 of the laid-up tonnage (84 m peak). In total, 231 m tons scrapped (1979)(1980)(1981)(1982)(1983)(1984)(1985)(1986)(1987).
Moreover, over 145 m dwt of tankers scrapped (1977)(1978)(1979)(1980)(1981)(1982)(1983)(1984)(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 [29]). These tankers were built with a dream in the mind of their owners of a cheap and abundant oil lasting for ever. OPEC had a different opinion.  [30]; f) high scrap funds: the greater the size of ships (increased by leaps and bounds to reap economies of scale), the more serious became the funds coming from scrapping!

Slow Steaming etc.
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 ( Figure 5). Moreover, tankers can be used for storage of oil, and if cleaned-up, to carry grain! As shown, the bulk carriers falling between 10,000 and 39,999 dwt, (more in this than in any other class), slow-steamed. They

The 1998-2008 Situation
The market situation between 1998 (May) and 2008 (Nov.), 10 years prior to Global Financial Crisis, is next presented (Figure 6), as a prelude to last depression.

Part II: The Last Shipping Depression, 2009-2016
As shown (Figure 7), the orders for dry cargo ships fell to 50 m dwt (end-2008-2013). This fall started in 2010, and continued in 2013, (in 2013 orders were 50 m dwt, i.e. 6% of existing fleet), and beyond. Our question was as to why orders did not stop completely… as one would expect during a depression? 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.     Table 1 presents the evolution of BDI, closer to index of Figure 9, since 2010.

The over Ordering of Ships
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 psycho-

Depression Reserves/Lay-Up
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 [4] [33].

Zannetos' Paradox
Zannetos [34] saw the abrupt changes occurring in tanker ship-owners' expectations, varying from elastic to inelastic, and back to elastic, in relation to orders placed and monthly spot rates (1949)(1950)(1951)(1952)(1953)(1954)(1955)(1956)(1957)(1958). He was surprised. He argued that operators have definitely… lost their memory. A sketch (Figure 11    (July), but re-started during 2009 (second half), when time charter rates reached $80,000/month! Keynes [17] argued that the current price, i.e. the spot freight rate for shipping, influences expectations about an investment, but he insisted that this is not the exclusive, or even the dominant, cause. So, time charters, as shown, by having an amount of long-term expectations in them… are the do- 23 A Cape transports bulk cargoes, but is too wide to transit Panama Canal; she travels via Cape, deriving her name from this. In 2009 a Cape varied in size from 170,000 to 180,000 dwt, but she may

Shipping Chartists' Medium-Term Recessions (16 -24 Years)
Hampton [38] [39] argued that shipping exhibits a (long) recession cycle of 16 -24 years, unfolding in two equal phases: a building-up phase and a correction phase ( Figure 14). Again this model adopts symmetry.
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 24 To order ships and buy used ones (younger, larger and dispose thereafter the smaller, older) is the Greek investment policy, at rock bottom prices. 25 Shipowners, in all cases we have studied, never estimated the impact of their decisions to order on freight rates on delivery! The orders of 32 m dwt placed in the case of Sanko and others, of course, made worse an already depressed market.  (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 28 Following Fibonacci. 29 This can be due in either a fall in demand or a rise in supply or in both. 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 [40], argued that if a shipowner wants to apply "best timing" in his/her investment and chartering decisions, this can be done only through "best forecasting". 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.

Mapping the End-2008 Depression
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!
As shown, (Figure 18(b)), predicted alphas will reach their lowest point, i.e. 0.90 (round.), in 2029. This characterizes a Cauchy distribution. The freight market will have its lowest points in 2035 and in 2036, six and seven years afterwards. The industry will remain dangerous after 2036, because alpha will reach eventually 1.10 (rounded). Alpha = 1.10 means H = 0.90 (round.) (i.e. high dependence of the current changes of the freight rate index on its past changes).
The market will enter into a new depression in 2033 and it will remain there till 2038, but the higher risk will emerge in 2020, and it will remain there by 2029. Is this an early warning?

Best Timing Using Predicted Alphas
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!

Part VI: Further Research
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 [41] to shipping, but the above needs a mathematical dexterity. A simpler model will be the one which will succeed to remove the jokers from the picture, and to deal with the remaining deterministic part (H > 0.5 ≤ 1.00). Figure 19 shows the nine appearances of the Joker (1947-2008).  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)(1968)(1969)(1970)(1971)(1972)(1973)(1974)(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?

Conclusions
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
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 ap- 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 [42]. Worth noting is that Alpha helps us to decide best timing when deciding for chartering or ordering new buildings or asset playing!

Conflicts of Interest
The author declares no conflicts of interest regarding the publication of this paper. 33 There is a theory that owners anticipate futures freight rates to predict where the physical market is going… Another wrong way?