the class of 2004-2005 - marsoft

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

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eturn is less th

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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 of Dry Bulk Return

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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 MtM Return





Median MtM Return


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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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)


+/‐25% from Actual 83% 42%

1‐Year Ahead Forecast Accuracy (1990‐2014)


+/‐25% from Actual 60% 40%

Confidence Interval and Turning Points

Confidential 17

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% Error (A

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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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the class of 2004-2005 - marsoft

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