The Class of 2004/2005Marsoft/Lorange Shipping Markets & Strategy, Zurich
Shipping Returns are Cyclical...
230 April 2015
CONFIDENTIAL
The annual investment returns over the 1980‐2014 period using the mark‐to‐market approach highlight the cyclical nature of the shipping industry.
Median Returns
Tanker: 11%Dry Bulk: 15%Container: 13%
All Shipping: 10%S&P: 12%
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Prob
ability One
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eturn is less th
an Target R
eturn
Target Return
Shipping Sector Returns for One‐Year Investment Periods (1980‐2014)Cumulative Distribution Curves for Quarterly Observations
Tanker Dry Bulk Container
... With A Few Instances of Very High Returns
330 April 2015
CONFIDENTIAL
The cumulative distribution curve highlights the probability of each shipping sector generating a level of return based on the historical one‐year returns during the 1980‐2014 period.
The dry bulk sector has delivered the most volatile returns, while offering the highest probability of exceptional returns of all shipping sectors.
Shipping returns assume 35% leverage
Return Analysis: Median vs. Average Returns
430 April 2015
CONFIDENTIAL
Median return is a better measure of investment performance than average return.
The consistently lower median returns compared to average returns are a reflection of a few periods of exceptionally high returns (>80%), as highlighted by the histograms below.
• The periods of exceptionally high returns for any sector were during the late 1980’s and mid 2000’s
• The only period of exceptionally low returns (<80%) for any sector was in 2009
Additionally, average returns are distorted as a rise in the index will produce a larger absolute % change than a similar fall.
Median return Is used to measure investment performance
TANKER DRY BULK CONTAINER
Above: Histograms of One‐Year Investment Returns Collected Quarterly during the 1980‐2014 Period
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Frequency Distribution of Index Returns, 1 year investment, 35% debt, floating interest rate
Frequency of Tanker Return
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Frequency Distribution of Index Returns, 1 year investment, 35% debt, floating interest rate
Frequency of Dry Bulk Return
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Frequency Distribution of Index Returns, 1 year investment, 35% debt, floating interest rate
Frequency of Container Return
Median Annual Return
Average Annual Return
Std. Dev. Annual Return
Median MtM Return
Tanker Market 11% 20% 44% 9%Dry Bulk Market 15% 34% 75% 13%Container Market 13% 24% 50% 15%All Shipping 10% 21% 45% 11%S&P 500 12% 11% 15% 11%
Median Annual Return
Average Annual Return
Std. Dev. Annual Return
Median MtM Return
Tanker Market 5% 15% 40% 7%Dry Bulk Market 10% 33% 78% 11%Container Market 11% 17% 42% 10%All Shipping 9% 18% 41% 12%S&P 500 12% 10% 15% 10%
Median Annual Return
Average Annual Return
Std. Dev. Annual Return
Median MtM Return
Tanker Market 29% 25% 47% 21%Dry Bulk Market 25% 53% 96% 23%Container Market 14% 19% 52% 9%All Shipping 17% 23% 51% 21%S&P 500 8% 5% 15% 8%
1980‐2014
1990‐2014
2000‐2014
Shipping Returns with 35% Leverage
Baseline
• Imagine you are embarking upon career as a shipowner – just ten years ago.
• At that time you were considering buying a 5 year old Supramax in 2004, with 50% leverage but no charter cover.
• Presume no crystal ball. How likely is it that you would need to call upon cash reserves to offset a shortfall in charter rates? How much would you need to hold in reserve to eliminate default risk?
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Shipping in the Public Markets
• 16 Shipping Companies were public or Went Public In 2004/05
• 7 of 16 were forced into restructuring since 2011– 1 more restructured in 2008– About $8 billion in enterprise value lost
• 5 Survived– But their prospects are unclear
• 4 Outperformed their Peers– What made them so special?
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Share Price Performance
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People
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Distinction between Winners & Less Winners?
• Management Experience & Training• Size & Access to Capital• Market Concentration• Too Much Debt• Too Little Charter Cover• Investment Timing• …
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Larger, on Average, Seems A Little Better
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Dec‐05 Dec‐07 Sep‐14 Trend
Navios 263 925 943Golden Ocean 139 1,695 661
Seaspan 564 1,257 2,152Teekay 3,303 3,887 4,366
Average 1,067 1,941 2,030DHT 4,645 389 444
Knightsbridge 434 456 648Dryships 185 2,784 1,410
Tsakos Energy 725 1,423 633Diana 490 1,686 848
Average 1,296 1,348 796OSG 1,988 2,323 210Eagle 508 1,256 21Excel 226 805 1
Genmar 1,570 961 1Torm 1,690 2,573 65Genco 441 1,636 35
Frontline 2,595 3,632 158Average 1,196 1,584 59
(in $MM USD)
Stars
Survivors
Re‐structured
Too Much Debt & Too Little Liquidity?
11Liquidity = ratio of current assets to current liabilities.
2007 Debt to Equity 2007 Liquidity
Navios 39% 188%Golden Ocean 234% 103%
Seaspan 155% 829%Teekay 186% 110%
Average 154% 308%OSG 83% 377%Eagle 116% 1534%Excel 92% 451%
Genmar 247% 232%Torm 82% 29%Genco 144% 380%
Frontline 84% 180%Average 121% 455%
Fiscal Year 2007
Stars
Re‐structured
Too Narrowly Focused? Too Diluted?Fleet Composition as of end‐2007
Dry Tank Con‐tainer LNG LPG Other
Stars
Navios X X1
Golden Ocean X
Seaspan X
Teekay X X X X2
Re‐structured
OSG X X X3
Eagle X
Excel X
Genmar X
Genco X
Frontline X X4
Torm X X
121) Logistics; 2) Shuttle tankers, FSO, FPSO; 3) Jones Act, Lightering; 4) OBOs
SSW: Cost of Capital, No Risk, Design Pioneers
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Seaspan “Saver” Design –Extremely Fuel Efficient
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Transportation Cost, Asia/Europe
Bunker: $650/tonne
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12 knots 22 knots
Bunker: $150/tonne
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5100 teu 10,000 teu 14000 teu
Industry Dilemma: Speed Up?
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Navios Maritime Q2 2014 Investor Presentation
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Navios controls 63 bulkers,
Handy thru Cape
As of 2014Q3, for forecasts published between 1990 and 2014 across the dry bulk, tanker, and containership markets, actual 1‐year TC rates have been +/‐25% from Marsoft’s Base case 1‐year ahead TC forecasts 60% ‐ 80% of the time.
An alternative reversion‐to‐mean forecast would have been within that range just 40% of the time.
Marsoft’s Base case 1‐year TC forecasts have accurately predicted market upturns and downturns 72% of the time on a 1‐year ahead basis. In contrast, the turning‐point accuracy of a mean reverting methodology is just 43%.
1‐Year Ahead Market Upturn/Downturn Forecast Accuracy (1990‐2014)
Marsoft’s Base Case Mean Reverting
Container 74% 36%
Dry Bulk 68% 45%
Tanker 73% 47%
All 72% 43%
6‐Month Ahead Forecast Accuracy (1990‐2014)
Marsoft’s Base Case Mean Reverting
+/‐25% from Actual 83% 42%
1‐Year Ahead Forecast Accuracy (1990‐2014)
Marsoft’s Base Case Mean Reverting
+/‐25% from Actual 60% 40%
Confidence Interval and Turning Points
Confidential 17
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‐7%
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2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
% Error (A
ctua
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arsoft Forecast)
Marsoft's 1‐year‐ahead 1‐year‐TC Rate Forecast Accuracy: Dry Bulk
1‐yr‐ahead Forecast Error
This track record includes data for Cape, Panamax, and Handymax charter rates and all Marsoft’s Base Case scenarios published from 1990Q1.
In 2006 – 2009 Marsoft’s Base case forecasts underestimated the strength of the Chinese steel boom and consequences for charter rates.
Excluding those years, actual 1‐year TC rates have been between ‐15% and 21% from Marsoft’s Base case forecasts.*
If we expand the analysis to include all data points from 1990, the 1‐year‐ahead forecast error median would be 1.7% over the past 24 years.
10‐year Track Record for the Bulker Market
Confidential 18*) Based on are median quarterly forecast errors.
Marsoft’s Low and High cases supplement the Base case by highlighting the areas of greatest uncertainties and presenting possible strong and weak market alternatives.
A comparison of actual rates and Marsoft’s forecast rates for a cape size bulker shows that our Base, High, and Low case forecasts in 2007 successfully captured the timing and magnitude of the upcoming market crash. Marsoft has helped our clients save millions of dollars in market cycles by the application of our expertise.
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Marsoft Forecast Accuracy for Cape Size, 170,000 dwt, 1‐Year TC Rate
2007 Base Case Forecast 2007 High Case Forecast
2007 Low Case Forecast Actual
Anticipate Uncertainties beyond the Base Case:High and Low Case Risk Analyses
Confidential 19
Another way to benchmark our track record is to compare our forecast performance with that of other analysts. We recently sampled analyst projections published between Dec‐13 and Feb‐14 by 6 research analysts, including DNB Markets, Fearnley Securities, Jefferies, Morgan Stanley, Pareto, and RS Platou.
The table below compares Marsoft forecasts of spot rates in our 2014Q1 scenario to analysts’ projections for 2014 average rates, to the adjusted 10‐year average, and to actual rates (actual average of the relevant Baltic Dry Sub‐Index through September). The leftmost four columns show the forecast and actual rates. The rightmost three columns show the deviation between the actual rates and the realized rates. The average forecast error (across all four vessel benchmarks) is shown at the bottom of the deviations section.
Over this period Marsoft’s forecast were about 10% above realized market conditions. In contrast, the analysts were nearly 50% higher than actual market conditions and 85% above for the historical benchmark.
Marsoft’s superior forecast performance helped our clients make better decisions.
Marsoft vs. Research Analysts
$ Per Day Projected/Realized Rates % Higher/(Lower) than Realized Rates
BALTIC INDEX MARSOFTPROJECTIONS
2014 ANALYSTPROJECTIONS
10YR AVG(EXCL 07/08)
REALIZEDRATES
MARSOFTPROJECTIONS
2014 ANALYSTPROJECTIONS
10YR AVG(EXCL 07/08)
Cape $13,950 $23,467 $31,664 $14,206 (1.8%) 65.2% 122.9%Panamax $10,744 $14,383 $18,325 $7,886 36.2% 82.4% 132.4%Supramax $10,850 $13,267 $15,897 $10,481 3.5% 26.6% 51.7%Handy $8,405 $10,175 $11,479 $8,578 (2.0%) 18.6% 33.8%
AVERAGE 9.0% 48.2% 85.2%Confidential 20
Marsoft Helps Clients Anticipate Market Threats and Opportunities and
Act to Maximize Risk‐Adjusted Returns
www.marsoft.com – [email protected]
Boston London Oslo Singapore
155 Federal St, Suite 1000 132 Buckingham Palace Road Inkognitogaten 33 Marsoft (Singapore) PTE. Ltd. Boston, MA 20110 London SW1W 9SA 0256 Oslo 105 Cecil Street #11‐16United States United Kingdom Norway Singapore 069534T: +1 (617) 369‐7800 T: +44 (0)20 74 93 38 80 T: +47 22 04 94 50 T: +65 6521 2910
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