2016 outlook – finding convexity - akshay agashe

2016 Outlook – Finding Convexity

A Variant View Akshay Agashe

[email protected]

2/1/16

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Note From the Author

Greetings,

What a crazy start to the new year. Between a week of “devaluation”, technical breakdowns, earnings misses and

maturing business lifecycles there has been a lot to deal with. All of a sudden a recession seems probable along with

the disaster that is EMEs, China is the new “big short”, and risk assets once again lag the intelligence of credit

markets. This is my effort at putting together a variant view analyzing the current chaos from a top down bottoms up

perspective; putting together a syllogism of turning gears even if forces are bidirectional and speeds unpredictable.

My goal is to try and quantify the current regime, not merely provide lip service, as seems to be the prerogative of

various talking head analysts and managers.

As with all market commentary, it is often difficult to stay ahead of every trend, and analysis can go stale/lose some of

its thunder. This outlook is no different, in the sense that while I began writing this in late December, reflexivity has

suddenly become a hot term in January. While I wrote that marginal impact of a single unit of QE/QQE would

necessitate further policy – likely with poor credibility, the BOJ went forward with negative rates last week. As we sit

here, I already see numerous shifts occurring namely one of my ideas up 30% last week, crude prices, production and

geopolitics coming to a head. Colombia was the first in what I predict as a number of EMEs forced to employ the

macroprudential hike as CPI deflates over the next few months, and real rates finally start to come into line.

That said, this deck is a manifestation of some of my best work and a physical representation of my deep rooted

commitment to finding a role within this industry. If you are so inclined, please distribute this to anyone who you feel

may find it interesting. My contact details are as follows for any feedback: [email protected].

Best,

Akshay Agashe

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Table of Contents

• The Reflexive Paradox 4-9

• Additional Distortions 10-18

• Results of the Reflexive Paradox + Distortions 11-25

• Resolving a Reflexive Paradox 26-88

• Where’s the Trade? – Variant View Russia Macro 89-113

• Two Names (YNDX + Mail.RU) 114-129

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Background

• Reflexivity (n.): Theory that seemingly independent variables share bidirectional relationships with one

another in such a manner that neither can be directly attributed causality.

• Since 2015 the world has endured a period I refer to as a reflexive paradox. A reflexive paradox is a

condition under which a codependent system self-promulgates in a manner that is bidirectional and

difficult to balance. During this time period we saw four such factors frequently caught at odds with one

another (1) a strengthening USD (2) a decline in commodity prices (3) declining global trade and (4)

monetary policy divergence.

• The reflexive paradox has been enhanced by a number of idiosyncratic distortions which have in

aggregate added uncertainty and fear to the market.

• The effects of this reflexive paradox have been obvious and painful.

• The following outlook presents a case for finding convexity within the chaos.

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Counterbalancing Forces

• All four reflexive factors are both counteracting and self promulgating:

• (1) Stronger USD TWI

• Cons: A stronger dollar is a drag on US large cap companies which derive significant sales

volume (40%) from abroad.

• Pros: A stronger dollar increases US nominal household wealth and improves consumer

borrowing power. The US is primarily a service economy, so some of the weaker demand

abroad is offset by rising household consumption expenditure and commodity price decline

dampens “real” effects.

• (2) Commodity Collapse

• Cons: Global trade is still primarily driven by commodity exports, which are declining in value

due to dollar denomination . Many poorer countries rely on SOE revenues to offset fiscal

liabilities and face budgetary imbalance. The resulting cycle weakens demand for commodity

currencies.

• Pros: USA, Japan, Germany, China, India etc. are eased by declining commodity prices and

as a result are able to unwind fiscal imbalance prerogatives (subsidies etc.) and bolster

household wealth. Some of the sales contraction from currency drag is offset by wider

operating margins.

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Reflexive Paradox Visualized

-5

0

5

10

15

20

25

01/2

014

03/2

014

05/2

014

07/2

014

09/2

014

11/2

014

01/2

015

03/2

015

05/2

015

07/2

015

09/2

015

11/2

015

Source: Federal Reserve

(1) King Dollar: USD Trade Weighted Indices

Major Currencies (YoY%, 1997=100)

Broad Currencies (YoY%, 1997=100)

$10

$15

$20

$25

$30

$35

$30

$40

$50

$60

$70

$80

$90

$100

$110

$120

10/2

9/2

009

02/2

8/2

010

06/3

0/2

010

10/3

1/2

010

02/2

8/2

011

06/3

0/2

011

10/3

1/2

011

02/2

9/2

012

06/3

0/2

012

10/3

1/2

012

02/2

8/2

013

06/3

0/2

013

10/3

1/2

013

02/2

8/2

014

06/3

0/2

014

10/3

1/2

014

02/2

8/2

015

06/3

0/2

015

10/3

1/2

015

(2) Global Commodity Collapse

ICE WTI Front Month (LHS) DJIC (DJUBS Index ETF, RHS)

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Counterbalancing Forces

• All four reflexive factors are both counteracting and self promulgating:

• (3) Global Trade Slowdown

• Cons: The resulting drag on EM economies has led to massive flight of risk assets and

depreciation of FX. This effects sov/corporate imbalances based on borrowing currency.

• Pros: Because the largest EM losers are net exporters, depreciation is CA positive. EMs have

amassed massive war chests of FX reserves and choose to intervene.

• (4) Monetary Policy Divergence

• Cons: Volatility surged as central banks struggled to understand the implications of tighter

financial conditions post exogenous shock. Hesitance to act accordingly to blueprint inverted

typical “easier for longer” flight to risk. Central Banks who were “behind the easing curve” such

as the ECB further deteriorated ToT conditions.

• Pros: For first time in years the FOMC showed that it believes its own economic guidance,

which is a powerful signal of improvement moving forward.

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Reflexive Paradox Visualized

-30%

-20%

-10%

0%

10%

20%

30%

40%

50%

1972

1975

1978

1981

1984

1987

1990

1993

1996

1999

2002

2005

2008

2011

2014

Source: IMF

(3) Global Trade Slowdown: World Exports (YoY%)

0.44

0.46

0.48

0.5

0.52

0.54

0.56

0.58

0.6

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

06/3

0/2

015

07/1

4/2

015

07/2

8/2

015

08/1

1/2

015

08/2

5/2

015

09/0

8/2

015

09/2

2/2

015

10/0

6/2

015

10/2

0/2

015

11/0

3/2

015

11/1

7/2

015

(4) Divergent Monetary Policy: 3m Bond Yields

United States (LHS) Japan (LHS)

United Kingdom (RHS)

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A Reflexive Paradox is Bidirectional

-2.0%

-1.5%

-1.0%

-0.5%

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

3.5%

03/2

013

06/2

013

09/2

013

12/2

013

03/2

014

06/2

014

09/2

014

12/2

014

03/2

015

06/2

015

09/2

015

12/2

015

Developed Market GDP (YoY%)

US EUA Japan

• These counterbalancing forces have an unclear aggregate effects on the three largest developed

economies. This can be easily visualized below. Though output remains around its three year central

tendency, headline inflation has collapsed placing existing QE programs (ECB, BOJ) at a crossroads.

-1.0%

-0.5%

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

03/2

013

06/2

013

09/2

013

12/2

013

03/2

014

06/2

014

09/2

014

12/2

014

03/2

015

06/2

015

09/2

015

12/2

015

Developed Inflation (YoY%)

US EUA Japan

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Additional Distortions • A reflexive paradox can be further distorted by unusual exogenous or structural forces. In this

case we have seen several:

• (1) China Uncertainty – Greater tail risk fears/ positioning

• (2) EM Reserve Sales – Rising to flat long term bond yields

• (3) Credit Market Cocktail – Rising short term bond yields / Cost of Balance Sheet

• (4) Herd Behavior – Larger hurdle rates (negative basis swap spreads), greater squeeze potential

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Additional Distortions: China Uncertainty

• Volatility surged cross asset in August 2015 as the PBoC devalued the CNY, causing global alarm

about the state of China’s economy. Despite the surge, the total dislocation in REER terms was no

bigger than past interventions in the late 1990s and early 2000s.

• The PBoC has since made repeated statements of no plans for further intervention…

-10.0

-5.0

0.0

5.0

10.0

15.0

20.0

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

Source: Fed Reserve

China vs US Real Broad Effective Rate

Relative Appreciation / Depreciation (YoY%)

Current Devaluation

5%

6%

7%

8%

9%

10%

11%

12%

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

01/0

1/2

015

01/2

2/2

015

02/1

2/2

015

03/0

5/2

015

03/2

6/2

015

04/1

6/2

015

05/0

7/2

015

05/2

8/2

015

06/1

8/2

015

07/0

9/2

015

07/3

0/2

015

08/2

0/2

015

09/1

0/2

015

10/0

1/2

015

10/2

2/2

015

11/1

2/2

015

Source: Bloomberg

Global Volatility

VIX Bond futures Exchange rates

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Additional Distortions: China Uncertainty

• Though offshore CNH versus onshore CNY spreads have yet to hit August highs, pressure continues to

amass following a poor opening week in 2016 for equity markets, and a large debt load (196% of GDP)

of which Fitch estimates 20% is non-performing.

• NFC USD Corporate debt makes up roughly 60% of GDP ($916.8B), creating worry that FX

intervention signifies impending defaults and a need to debase USD denominated liabilities.

$-

$200

$400

$600

$800

$1,000

$1,200

2000-1

.Q

2000-4

.Q

2001-3

.Q

2002-2

.Q

2003-1

.Q

2003-4

.Q

2004-3

.Q

2005-2

.Q

2006-1

.Q

2006-4

.Q

2007-3

.Q

2008-2

.Q

2009-1

.Q

2009-4

.Q

2010-3

.Q

2011-2

.Q

2012-1

.Q

2012-4

.Q

2013-3

.Q

2014-2

.Q

2015-1

.Q

Bil

lio

ns

Source: BIS

China NFC USD Credit

of which: local loan claims Debt securities Debt securities - offshore

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Additional Distortions: EM Reserve Sales

• Capital Flows out of EM have accelerated following the August CNY devaluation but actually started

with the initial commodity route in November 2014.

$(15)

$(10)

$(5)

$-

$5

$10

$15

03/2

012

06/2

012

09/2

012

12/2

012

03/2

013

06/2

013

09/2

013

12/2

013

03/2

014

06/2

014

09/2

014

12/2

014

03/2

015

06/2

015

09/2

015

Bil

lio

ns

3m Rolling Average Net Flows Into EM Portfolio Funds

Asia Latin America CEE and South Africa

$(70)

$(60)

$(50)

$(40)

$(30)

$(20)

$(10)

$-

$10

$20

$30

Q1 2

012

Q2 2

012

Q3 2

012

Q4 2

012

Q1 2

013

Q2 2

013

Q3 2

013

Q4 2

013

Q1 2

014

Q2 2

014

Q3 2

014

Q4 2

014

Q1 2

015

Q2 2

015

Bil

lio

ns

Source: IMF; Source: IMF *EM includes BR, CH, CO, MX, RU, ZA, TR, IN, ID, KR, MY, PH, TH, SA

Broad EM Financial Account Flows

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Additional Distortions: EM Reserve Sales

• In order to protect exchange rates and maintain government budgets, EM Central Banks have begun to

liquidate years of accumulated reserves principally concentrated in long end government bonds.

• China's official foreign reserves have fallen by 8.8% (USD321bn) since August as the authorities have

sought to blunt the depreciation of the CNY against a broader basket of currencies.

0%

1%

2%

3%

4%

5%

6%

7%

-200

-100

0

100

200

300

400

500

600

700

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

Bil

lio

ns

Source: World Bank, IMF, Fed Reserve; *EM includes BR, CH, CO, MX, RU, ZA, TR, IN, ID, KR, MY, PH, TH, SA; Annualized 2015 Q1 Reserve Sales

Ex China EM Reserves vs US 10yr yield

Net Change in Reserves (LHS)

Average 10yr Yield (Constant Maturity, RHS)

-100

0

100

200

300

400

500

600

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

Bil

lio

ns

Source: Worldbank, IMF; 1H2015 Change in Reserves

China Net Change in Reserves

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Additional Distortions: Credit Cocktail

• In 2015 fed funds futures implied probabilities were highly volatile in front of the eventual 25bp

December hike. As a result corporate bond issuance surged in 1H15, much of which was floating.

• Dealers found themselves caught trying to shorten duration and manage demand within a shrinking

interest rate derivatives market.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

06/3

0/2

015

07/0

7/2

015

07/1

4/2

015

07/2

1/2

015

07/2

8/2

015

08/0

4/2

015

08/1

1/2

015

08/1

8/2

015

08/2

5/2

015

09/0

1/2

015

09/0

8/2

015

09/1

5/2

015

09/2

2/2

015

09/2

9/2

015

10/0

6/2

015

10/1

3/2

015

10/2

0/2

015

10/2

7/2

015

11/0

3/2

015

11/1

0/2

015

11/1

7/2

015

11/2

4/2

015

Source: Bloomberg

Fed Funds Futures Implied Hike Probability

12/1/2015 Contract 3/1/2016 Contract

$(100,000)

$-

$100,000

$200,000

$300,000

$400,000

$500,000

-4%

-2%

0%

2%

4%

6%

8%

10%

12%

14%

1995

1996

1997

1998

2000

2001

2002

2003

2005

2006

2007

2008

2010

2011

2012

2013

2015

Mil

lio

ns

Source: Federal Reserve; Q1-Q2 2015 in red

US Nonfinancial corporate business; corporate bonds

YoY% Change From a Year Ago (RHS)

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• Note that the size of OTC interest rate derivatives market declined by 14% YoY while dealer inventories

surged, leading swap spreads negative. The resulting ugly cocktail has been misperceived by many as

a quality driven liquidity squeeze, which has placed additional upward pressure on yields.

$-

$100

$200

$300

$400

$500

$600

$700

$800

2007-2

.H

2008-1

.H

2008-2

.H

2009-1

.H

2009-2

.H

2010-1

.H

2010-2

.H

2011-1

.H

2011-2

.H

2012-1

.H

2012-2

.H

2013-1

.H

2013-2

.H

2014-1

.H

2014-2

.H

2015-1

.H

Tri

llio

ns

Source: TriOptima triReduce; BIS Derivatives Statistics

Global OTC Derivatives Market Notional Principal

Interest rate FX Equity Commodities CDS Unallocated

$0

$10

$20

$30

$40

$50

$60

$70

$80

$90

01/0

5/2

015

01/2

6/2

015

02/1

6/2

015

03/0

9/2

015

03/3

0/2

015

04/2

0/2

015

05/1

1/2

015

06/0

1/2

015

06/2

2/2

015

07/1

3/2

015

08/0

3/2

015

08/2

4/2

015

09/1

4/2

015

10/0

5/2

015

10/2

6/2

015

Bil

lio

ns

Source: BIS

Primary Dealer Net Positions: US Treasury Bonds

-20

0

20

40

60

80

12/2

006

04/2

007

08/2

007

12/2

007

04/2

008

08/2

008

12/2

008

04/2

009

08/2

009

12/2

009

04/2

010

08/2

010

12/2

010

04/2

011

08/2

011

12/2

011

04/2

012

08/2

012

12/2

012

04/2

013

08/2

013

12/2

013

04/2

014

08/2

014

12/2

014

04/2

015

08/2

015

Source: BIS

10yr Interest Rate Swap Spreads

Swap spread: US dollar (%) Swap spread: Euro (%)

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Regulatory effects have led to greater volatility in repo rates and complicated traditional relationship with

LIBOR. Bank repo activity has declined by >$1T over last three years while balance sheet capacity has

become scarcer and more expensive. For the sake of brevity all regulatory procedures will not be

comprehensively detailed but they are composed of a combination of:

• (a) Supplemental leverage ratio under Basel lll raises the Tier 1 ratio to 5% for bank holding

companies and 6% for insured depository institutions from previous 3%. A capital conservation buffer

and capital surcharge on global banks will begin to be adopted in 2016.

• (b) Money Market reforms requiring money market funds to float NAV by October 2016 or in certain

cases to impose liquidity fees on redemptions. Funds that hold all assets in government securities or

repo collateralized with government securities are exempt from these restrictions. As such money

market assets invested in government only funds has risen over last 2 years.

• (c) Incremental adoption of Volcker Rule, Liquidity Coverage Ratio (LCR), Net Stable Funding

Ratio (NSFR), and FDIC Assessments have all contributed to increasing balance sheet costs and

removing incentives to engage in low risk / low return complex balance sheet action

1 week General Collateral Rate 3m LIBOR Rate

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Additional Distortions: Herd Behavior &

Hurdle Rates

• Positioning has exacerbated volatility. Funding markets have become fragile as policy trades have

become more crowded and swap spreads further negative. There have been several instances of the

resulting blowout in daily cross asset volatility, most recently the December ECB meeting during which

the EURUSD surged on the slight hawkish guidance paired with US equities selling.

-120

-100

-80

-60

-40

-20

0

20

40

60

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

Bp

s

Source: BIS, Bloomberg

Cross Currency Basis Swap Spreads

EUR JPY GBP

0

20

40

60

80

100

120

140

160

$(250)

$(200)

$(150)

$(100)

$(50)

$-

$50

$100

$150

$200

$250

2005-1

2

2006-0

6

2006-1

2

2007-0

6

2007-1

2

2008-0

6

2008-1

2

2009-0

6

2009-1

2

2010-0

6

2010-1

2

2011-0

6

2011-1

2

2012-0

6

2012-1

2

2013-0

6

2013-1

2

2014-0

6

2014-1

2

2015-0

6

Bil

lio

ns

Source: BIS, Bloomberg

Levered Equity Positions

Credit balance (LHS) S&P 500, Jan 2000 = 100 (RHS)

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Results of Reflexive Paradox

• The effects of this reflexive paradox have been obvious and painful.

• Safe Haven Assets have been largely non-performing, providing investors with incentive to try their hands

in shorter term strategies with tight risk tolerance.

• Not only has volatility increased, but the week to week extremity in changes in volatility have reached

levels not seen since the global financial crisis.

• Nearly every tactical and allocation strategy underperformed in aggregate over 2015. These effects are

also self-promulgating as investors reduced risk by pulling allocations (thus feeding market liquidations)

and increasing cash.

• As far as positioning goes, investors oddly remained committed to trades defended by authorities (ECB /

BOJ) and strayed from confusing interaction of the paradox (EM equities / US equities). This can also

been seen in the rout of energy exporter EMFX and spike in 5 yr CDS implied default probabilities.

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Results of Reflexive Paradox – Safe

Haven Assets are Non- Performing

• Despite a backdrop of slower global growth and increasing policy divergence and future uncertainty,

flight to safety assets have underperformed along with risk assets.

• I opine this is due the reflexive effects.

-40%

-30%

-20%

-10%

0%

10%

20%

30%

40%

50%

60%

-60%

-40%

-20%

0%

20%

40%

60%

80%

100%

120%

2011-0

1-1

2

2011-0

3-1

2

2011-0

5-1

2

2011-0

7-1

2

2011-0

9-1

2

2011-1

1-1

2

2012-0

1-1

2

2012-0

3-1

2

2012-0

5-1

2

2012-0

7-1

2

2012-0

9-1

2

2012-1

1-1

2

2013-0

1-1

2

2013-0

3-1

2

2013-0

5-1

2

2013-0

7-1

2

2013-0

9-1

2

2013-1

1-1

2

2014-0

1-1

2

2014-0

3-1

2

2014-0

5-1

2

2014-0

7-1

2

2014-0

9-1

2

2014-1

1-1

2

2015-0

1-1

2

2015-0

3-1

2

2015-0

5-1

2

2015-0

7-1

2

2015-0

9-1

2

2015-1

1-1

2

Risk vs Risk-off Assets

10 Year Yield (LHS, Constant Maturity, Inverted, YoY%) Gold (RHS, YoY%) SP500 (RHS, YoY%)

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Results of Reflexive Paradox – Safe

Haven Assets are Non- Performing

• Most yield curve flattening occurred period to late year 2014 decline in commodities, and was

extremely small in the belly.

• Long rate volatility also exploded over short rates excluding pre-December FOMC, likely due to flow

distortions posed by EM reserve unwinds.

0.4%

0.5%

0.6%

0.7%

0.8%

0.9%

1%

1.1%

1.2%

1.3%

1.4%

1%

1.2%

1.4%

1.6%

1.8%

2%

2.2%

2.4%

01/0

2/2

014

03/0

2/2

014

05/0

2/2

014

07/0

2/2

014

09/0

2/2

014

11/0

2/2

014

01/0

2/2

015

03/0

2/2

015

05/0

2/2

015

07/0

2/2

015

09/0

2/2

015

11/0

2/2

015

UST Yield Curve

5s30s (LHS) 10s30s (RHS) 2s5s (RHS)

0%

0.02%

0.04%

0.06%

0.08%

0.1%

0.12%

1/2

9/2

014

3/2

9/2

014

5/2

9/2

014

7/2

9/2

014

9/2

9/2

014

11/2

9/2

014

1/2

9/2

015

3/2

9/2

015

5/2

9/2

015

7/2

9/2

015

9/2

9/2

015

11/2

9/2

015

Rolling 10day 10day UST volatility

2 yr 5 yr 10 yr 30 yr

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Results of Reflexive Paradox – Vol of Vol

is Surging

• A rising floor level in the VIX conceals the true nature of uncertainty within the current market.

• Rolling realized 1yr vol of vol has surged above 2011 and 2008 peaks, as investors are caught

between the defacto easy liquidity beliefs of the last 8 years and the recurring risks of material

economic disruption. This also supports the previous chart of rising cross asset correlations.

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Results of Reflexive Paradox – Investment

Strategies Are Failing

• Every major tactical strategy has underperformed YTD 2015 versus 2014. Several strategies

including multi-strategy equity, relative value credit, directional credit, and event driven can directly

blame the last 3 months for underperformance.

• The resulting fear can be seen in both HF performance and allocation (Evestment). Through Nov

2015, HF allocations are $18.78B less versus 2014. Much of the difference is explained by $10.7B in

redemptions between September and November 2015.

-8%

-6%

-4%

-2%

0%

2%

4%

6%

8%

HF Performance By Style

Last 3 Months YTD 2015 2014

-10.66

0.38

-16.38

0.75 3.62

-40

-20

0

20

40

60

80

100

All HF Fixed Income/Credit Multi-Asset

All

oc

ati

on

($B

)

Source: Evestment

Hedge Fund Allocation

Last 3 Months 2015 YTD 2014

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Results of Reflexive Paradox – De-risking

& Searching for Guidance

• Investors remain risk averse with cash remaining greater than 5% of total NAV for 5th straight month.

• There has been a shift towards positions with direct guidance from authorities– e.g. rising rates

(short USTs), or larger and longer QE programs (OW European equities, Japanese equities).

• Allocations with confusing behavioral and fundamental interplay (EM equities, US equities)

have seen major deallocation shifts due to the confounding nature of the paradox and other

idiosyncratic distortions.

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De-risking & Search for Guidance • With flight to safety assets such as bonds and gold underperforming against a weaker global backdrop,

investors have fled unguided risk assets indiscriminately, especially those with strong historical

relationships to the four factors or with history of crisis.

• The clearest and most vicious relationship can be seen between oil producing EMFX and Oil futures.

• BRL, COP, RUB, MYR have depreciated massively versus the USD, following linear

relationships with front month crude futures CL1.

• Unsurprisingly default risk has soared across oil producers

• Assuming a 50% LTV, 5yr CDS implied probabilities now sit at roughly 2x start of 2014 excluding

Russia which had its own idiosyncratic risk pricing during the Crimea conflict.

0%

1%

2%

3%

4%

5%

6%

7%

8%

9%

10%

09/1

9/1

4

10/1

9/1

4

11/1

9/1

4

12/1

9/1

4

01/1

9/1

5

02/1

9/1

5

03/1

9/1

5

04/1

9/1

5

05/1

9/1

5

06/1

9/1

5

07/1

9/1

5

08/1

9/1

5

09/1

9/1

5

10/1

9/1

5

11/1

9/1

5

An

nu

al

Pro

ba

bil

ity o

f D

efa

ult

5yr CDS Implied Default Probabilities

Brazil Colombia Malaysia Russia

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Resolving a Reflexive Paradox

• Due to its bidirectional nature, a reflexive paradox can rapidly unwind and result in regime

change.

• In order to understand the potential for regime change, we must understand the historical development of

the system. Though causality may be difficult to determine in the present, the timeline provides us

guidance as to what factors have led and how the conditions have developed.

• In this case consensus is that global weakness began with the sudden collapse of the crude oil in late

2014, and that the cause was oversupply following the start of the US Shale boom.

• My research suggest oil oversupply is the leading factor in the current reflexive regime, but the

USD is also responsible.

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Resolving a Reflexive Paradox – Causes:

Mild Oil Oversupply

• Oil is currently oversupplied, but the amount of oversupply is a very small percentage of total oil

production. EIA estimates that relative oversupply will average roughly 700,000 bpd over the course of

2016 versus 95 mbpd of production.

• The EIA estimates a consumption uptick of roughly 1mbpd over the course of 2016.

1.0

0.5

-0.8 -0.8

-0.3 -0.2

-1.2

-0.5

1.4

0.6

-0.8 -0.8

-0.6

0.2

-0.5

-0.8

0.5

0.7

0.3

1.8 1.6

2.0

1.8

1.4

0.4

0.7

0.5

0.7

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

80

82

84

86

88

90

92

94

96

98

Q1 2

010

Q2 2

010

Q3 2

010

Q4 2

010

Q1 2

011

Q2 2

011

Q3 2

011

Q4 2

011

Q1 2

012

Q2 2

012

Q3 2

012

Q4 2

012

Q1 2

013

Q2 2

013

Q3 2

013

Q4 2

013

Q1 2

014

Q2 2

014

Q3 2

014

Q4 2

014

Q1 2

015

Q2 2

015

Q3 2

015

Q4 2

015

Q1 2

016

Q2 2

016

Q3 2

016

Q4 2

016

EIA World Oil Supply/Demand

Implied stock change and balance (RHS, Mbpd) World production (LHS, Mbpd) World consumption (LHS, Mbpd)

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Causes: Mild Oil Oversupply

• Despite popular belief, US Shale oil production only accounts for ~3 mbpd in additional

production since 2012.

• Shale production was necessary to fill holes left by gulf and North African states following the Arab

Spring. Had it not been for the shale revolution, the world would have been roughly 1.5 mbpd

undersupplied in 2012 and 2013.

0

10

20

30

40

50

60

70

80

90

0

1

2

3

4

5

6

7

8

9

10

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

2012

2014

Mil

lio

ns

Mil

lio

ns

Global Oil Production

World Oil Production (Bpd, RHS)

United States Oil Production(bpd, LHS)

-1.5

-1

-0.5

0

0.5

1

1.5

2

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

Mil

lio

ns

US Oil Production vs Idled Production

Yemen Syria Libya Iran United States

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Causes: Mild Oil Oversupply

• Global demand weakness is also an overestimated factor.

• US energy appetite actually increased following the collapse (unlike demand shocks in 2008 and 2000),

as refiner throughput continued to rise post price collapse.

• China import volumes, though clearly in long term downtrend, had limited impact on price collapse.

-60%

-40%

-20%

0%

20%

40%

60%

02/2

010

06/2

010

10/2

010

02/2

011

06/2

011

10/2

011

02/2

012

06/2

012

10/2

012

02/2

013

06/2

013

10/2

013

02/2

014

06/2

014

10/2

014

02/2

015

06/2

015

10/2

015

Source: Bloomberg; CHHQMTTL Index

China Real Import Volume (NSA, YoY%, Previous Yr=100)

$-

$20

$40

$60

$80

$100

$120

$140

$160

11

12

13

14

15

16

17

18

02/2

006

09/2

006

04/2

007

11/2

007

06/2

008

01/2

009

08/2

009

03/2

010

10/2

010

05/2

011

12/2

011

07/2

012

02/2

013

09/2

013

04/2

014

11/2

014

06/2

015

Mil

lio

ns

Refiner Net Input vs. WTI Front Month Future Contract

Refiner Net Input WTI Price

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Resolving a Reflexive Paradox – Causes:

Appreciating USD TWI

• A stronger USD implies lower oil prices. Oil is indexed and settled in USD so sudden increases in

dollar value results in lower oil prices.

• The dollar rapidly appreciated in 2014 on the back of stronger US economic measures, increased DM

monetization of financial assets, and roll off of US QE policy, placing exogenous pressure on oil.

30

-150%

-100%

-50%

0%

50%

100%

-20%

-15%

-10%

-5%

0%

5%

10%

15%

20%

25%

10/2

007

01/2

008

04/2

008

07/2

008

10/2

008

01/2

009

04/2

009

07/2

009

10/2

009

01/2

010

04/2

010

07/2

010

10/2

010

01/2

011

04/2

011

07/2

011

10/2

011

01/2

012

04/2

012

07/2

012

10/2

012

01/2

013

04/2

013

07/2

013

10/2

013

01/2

014

04/2

014

07/2

014

10/2

014

01/2

015

04/2

015

07/2

015

10/2

015

Trade Weighted USD vs WTI Front Month Futures Contract

Trade Weighted USD Index: Broad Currencies (YoY%, LHS) ICE WTI Front Month Future (YoY%, RHS, Inverted)

Trade Weighted USD Index: Major Currencies (YoY%, LHS)

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Causes: Appreciating USD TWI

• Though US real personal consumption expenditure (RCE) has increased following the collapse

in oil prices versus demand shocks in 2000/2008, this is largely due the impact of a stronger

trade weighted USD.

• Adjusting RCE for the USD results in equivalent charts, further corroborating this effect.

-100%

-50%

0%

50%

100%

150%

200%

-4%

-3%

-2%

-1%

0%

1%

2%

3%

4%

5%

6%

7%

10/1

999

08/2

000

06/2

001

04/2

002

02/2

003

12/2

003

10/2

004

08/2

005

06/2

006

04/2

007

02/2

008

12/2

008

10/2

009

08/2

010

06/2

011

04/2

012

02/2

013

12/2

013

10/2

014

08/2

015


Consumption Expenditure vs Oil Prices

Real Personal Consumption Expenditures (LHS, YoY%, SA)

WTI (RHS, Yoy%, NSA)

-100%

-50%

0%

50%

100%

150%

200%

-20%

-15%

-10%

-5%

0%

5%

10%

15%

20%

10/1

999

08/2

000

06/2

001

04/2

002

02/2

003

12/2

003

10/2

004

08/2

005

06/2

006

04/2

007

02/2

008

12/2

008

10/2

009

08/2

010

06/2

011

04/2

012

02/2

013

12/2

013

10/2

014

08/2

015


Consumption Expenditure (USD Adjusted) vs Oil Prices

Real Personal Consumption Expenditure Adjusted for USD (LHS, YoY%)

WTI (RHS, YoY%)

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Resolving a Reflexive Paradox –

Regime Shift

• Shifts in either factor could result in regime change.

• Oil and commodity exports still account for a large percent of world GDP, but particularly among the

largest emerging markets. Since the decline in oil prices, EMEs have faced real recessions as terms of

trade deteriorated faster than core inflation. The resulting weakness has led to capital outflows and

further stress on FX (as already displayed). At the same time, the offsetting energy savings effect has

been much weaker than expected in the US due to the impact on energy and related manufacturing

industries.

• A declining USD eases financial conditions. Post GFC EMEs have borrowed huge amounts of USD

denominated corporate and government debt. A rising USD has deflated the value of these debts as

corporations have faced trade headwinds. Many of the largest EMEs have history of socialism and thus

governments find themselves on hook to plug deficits faced by state entities, leading to reserve

drawdown, higher credit risk, and further adding to the EM reserve sales distortion.

• Both factors are likely to shift in 2016

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Oil Price Shift Impact

• Rising Oil Prices provide immediate stimulus to struggling EM/FR economies.

• Oil Balance of Trade has historically accounted for a significant portion of output in EM and

Frontier economies. EM/FR with positive balance of trade averaged 8.8% and 11.6% of GDP in 2012.

• Stress has been magnified on capital account due to real recession due to rapid deterioration of terms

of trade vs core inflation. In the case of Brazil core inflation continued to rise with falling nominal GDP.

33

10.1%

8.8%

11.6%

-0.1%

4.4%

8.0%

-2%

0%

2%

4%

6%

8%

10%

12%

14%

All Oil EM (ex. CN, IN) Frontier

Source: EIA, Worldbank, +Trade Balance denotes countries which have balance > 0

Oil Balance of Trade Contribution (2012, %GDP)

+ Trade Balance All Trade

20%

18%

16%

14%

12%

10%

8%

6%

4%

2%

0

2%

3%

4%

5%

6%

7%

8%

9%

10%

01/2

015

02/2

015

03/2

015

04/2

015

05/2

015

06/2

015

07/2

015

08/2

015

09/2

015

10/2

015

Examples: Core Inflation vs. Terms of Trade

BR Core Inflation MX Core Inflation

BR ToT MX ToT

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Oil Price Shift Impact

• On a nominal basis large oil producer countries have seen material slowdown in output, as

terms of trade collapsed.

• The effect on the US economy has been mixed, as mining sector (incl. energy) contributed -80bps to

GDP over 1H2015. This is calculated as the absolute value of +55bps in Q4 2014 -33bps in 1H2015.

This effect is somewhat offset by higher consumption expenditure from energy savings.

34

-6%

-4%

-2%

0%

2%

4%

6%

8%

Q4 2

012

Q1 2

013

Q2 2

013

Q3 2

013

Q4 2

013

Q1 2

014

Q2 2

014

Q3 2

014

Q4 2

014

Q1 2

015

Q2 2

015

Q3 2

015

Source: Respective Central Bank

Oil Producer GDP (YoY%)

Brazil Saudi Arabia Russia

-1.5%

-1.0%

-0.5%

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

2005

2005

2006

2006

2007

2007

2008

2008

2009

2009

2010

2010

2011

2011

2012

2012

2013

2013

2014

2014

2015


Contribution to % Change in GDP: Mining Industry

Contributions to Percent Change in Real GDP by PrivateIndustries: Mining, Percentage Points, Quarterly, SeasonallyAdjusted Annual Rate

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USD Shift Impact

• A declining USD eases global financial conditions.

• Post GFC, EME’s borrowed huge amounts of USD denominated debt which along with high core

CPI constrains easing of monetary policy, and weakens real interest rates.

• At the center of the problem is China, which expanded NFC USD Debt by $260B post GFC, though this

is just a small fraction of aggregate debt balance.

35

$-

$200

$400

$600

$800

$1,000

$1,200

$1,400

$-

$50

$100

$150

$200

$250

$300

2000-4

.Q

2001-4

.Q

2002-4

.Q

2003-4

.Q

2004-4

.Q

2005-4

.Q

2006-4

.Q

2007-4

.Q

2008-4

.Q

2009-4

.Q

2010-4

.Q

2011-4

.Q

2012-4

.Q

2013-4

.Q

2014-4

.Q

Bil

lio

ns

Bil

lio

ns

Net NFC USD Denominated Debt Issuance (1999=0)

Brazil (LHS) China (LHS)

Mexico (LHS) Russia (LHS)

EME Non Banks (RHS)

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USD Shift Impact

• Rapid declines in output without the capability to ease have placed pressure on exchange rates

as investors accelerate capital outflows, crushing any advantageous carry.

• On the other side of the coin, rising US real rates versus declining EM real rates (growth led) have

further added pressure on capital flows. *Note that the worst performing EM and DM currencies have

been commodity exporters.

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Shift Mechanism: Oil Supply

• Supply is a function of (1) Nationalized Oil Companies and (2) Public Enterprise Oil Companies.

• NOC production makes up the vast majority of total production, encompassing 70%+.

• Of the top 25 largest oil companies in the world, public enterprise oil companies total 8 (32%) while

NOCs make the remainder (68%).

37

0

2

4

6

8

10

12

14

Sa

udi A

ram

co

Gazpro

m

Natio

nal Ir

ania

n O

il C

o

Exxon M

ob

il

Pe

tro C

hin

a

British P

etr

ole

um

Royal D

utc

h S

hell

Pe

mex

Chevro

n

Ku

wait N

atio

nal P

etr

ol

Ab

u D

hab

i N

atio

nal O

il C

o

So

natr

ach

To

tal

Pe

trobra

s

Rosn

eft

Iraqi O

il M

inis

try

Qata

r P

etr

ole

um

Lukoil

En

i

Sta

toil

ConocoP

hili

ps

Pe

trole

os d

e V

en

ezuela

Sin

op

ec

Nig

eria

n N

ation

al P

etr

ole

um

Pe

trona

s

Mil

lio

ns

Top 25 Largest Oil Producing Companies (mbpd)

Dark Blue = Public, Light Blue = NOC

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NOCs: Frontier Oil Production

• Frontier oil production is composed of nearly 100% NOCs.

• The following chart shows other NOC producers not counted within the top 25 largest.

• Total production is equal to 12 mbpd roughly 13% of total 94 mbpd production in 2015, and 6x the

supply gap of ~2 mbpd.

38

0.0

0.5

1.0

1.5

2.0

2.5

3.0

NN

PC

So

nangol

Ka

zM

unaiG

az

Ecopetr

ol

SO

CA

R

Pe

rtam

ina

ON

GC

Pe

troecuador

EG

PC

Lib

yan N

atio

nal O

il C

orp

ora

tion

Pe

trovie

tnam

Nile

Petr

ole

um

GE

Petr

ol

Tu

rkm

engas

YO

GC

Pe

trotr

in

SN

H

YP

FB

BA

PC

O

Other NOC Oil Producers (mbpd)

Dark Blue = EM, Light Blue = Frontier

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NOCs: Structural Trap Passthrough

• NOCs are able to produce at prices below economic equilibrium as they are implicitly backed by

government balance sheet. As a result NOCs are solvent until reserves are completely drawn down.

• Angel Ubide and Robert Dugger call this condition a structural trap: a condition under which capital

cannot shift from low return to high return sources due to external variable(s). In this case the

external variables are unemployment and government budget balance.

• UE in EM suggests that pass through of liabilities are very real. Despite recession Russia has seen

uptick in UE of only 50 bps, similarly Brazil 300bps. Compare to US GFC where UE peaked at > 10%.

• Russia is good example of impact of NOC passthrough. Despite recession UE rate is very

inelastic.

39

4%

4.5%

5%

5.5%

6%

6.5%

7%

01/2

012

04/2

012

07/2

012

10/2

012

01/2

013

04/2

013

07/2

013

10/2

013

01/2

014

04/2

014

07/2

014

10/2

014

01/2

015

04/2

015

07/2

015

10/2

015

Russia Unemployment Rate

-5%

-4%

-3%

-2%

-1%

0%

1%

2%

3%

4%

5%

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

Russia GDP (YoY%) w/ IMF Projection

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NOCs: Structural Trap Passthrough

• A common misconception is that the largest producers are the most externally exposed to

falling prices. The consensus short: RUB, SAR, AED, NGN etc.

• NOC passthrough effects are padded in larger EM economies which accumulated large reserve

balances and who can credibly issue new debt to plug holes in the near term keeping unemployment

levels relatively unchanged.

• Moreover, larger EM ex Middle East producers have smaller energy BoT as a percent of GDP,

and are thus less exposed to USD drag effects. The same cannot be said for frontier NOCs and

levered public enterprise oil companies.

• Historical socialist tendencies drag down stronger producers via budget contribution.

40

-2

0

2

4

6

8

10

12

Saud

i A

rabia

Ru

ssia

n F

ed

era

tion

Qata

r

Iran

, Is

lam

ic R

ep.

Kuw

ait

Un

ite

d A

rab E

mira

tes

Vene

zu

ela

, R

B

Alg

eria

No

rwa

y

Ca

nad

a

Ango

la

Nig

eri

a

Kazakhsta

n

Iraq

Lib

ya

Aze

rbaija

n

Om

an

Co

lom

bia

Equa

toria

l G

uin

ea

Suda

n

Bahra

in

Co

ngo

(B

razza

vill

e)

Trin

ida

d a

nd T

obag

o

Aru

ba

Gab

on

Bela

rus

Turk

men

ista

n

Bru

nei

Ch

ad

Lithu

ania

Arg

entina

Ecu

ado

r

De

nm

ark

Co

te d

Ivo

ire…

Ca

mero

on

Syri

a

Papu

a N

ew

Guin

ea

Uzbe

kis

tan

Surin

am

e

Millio

ns

Total Energy Production

All Energy Oil Only

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Shift Mechanism: Externally Facing NOCs

• The most threatened producers are a combination of substantially externally facing, important

to government budget, and incapable of consistently raising capital to plug deficits.

• For example, though Russia is the second largest oil producer in the world, it has a far smaller impact

on GDP than a smaller producer such as Equatorial Guinea where oil exports amounted to 77% of

GDP in 2012. At the same time oil revenues make up 52% of the government budget.

• A good starting point is observing external oil trade as a percent of GDP and oil rents (a proxy

for government budget contribution).

41

0%10%20%30%40%50%60%70%80%90%

Equa

toria

l G

uin

ea

Lib

ya

Ango

laG

ab

on

Aze

rbaija

nC

had

Iraq

Om

an

Kuw

ait

Saud

i A

rabia

Kazakhsta

nS

uda

nU

nite

d A

rab E

mira

tes

Alg

eria

Qata

rN

igeri

aIr

an

, Is

lam

ic R

ep.

Yem

en, R

ep.

Vene

zu

ela

, R

BE

cu

ado

rN

orw

ay

Beliz

eR

ussia

n F

ed

era

tion

Ma

urita

nia

Vie

tnam

Surin

am

eC

ongo

, R

ep

.C

am

ero

on

Turk

men

ista

nP

apu

a N

ew

Guin

ea

Me

xic

oT

un

isia

Co

lom

bia

Ca

nad

aM

ongo

liaE

sto

nia

Gua

tem

ala

Barb

ad

os

De

nm

ark

Arg

entina

Ma

laysia

Egypt, A

rab R

ep.

Bra

zil

Geo

rgia

Indon

esia

Source: EIA, Worldbank

Oil Trade Balance & Oil Rents (% GDP)

Oil Trade Balance %GDP (2012) Oil Rents(%GDP) 2012

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• On a primary balance basis, NOCs can produce at a loss as long as ex-energy sector government

receipts, reserve draws, debt issues, and cap flows are enough to plug operating deficits.

• A simple back of the envelope displays the extent of stress on current production levels assuming

entire budget financed by oil revenues and no additional debt issuance.

• Countries with less than 1yr of reserves contribute 11.50% of total production, nearly equal to

that of Russia (~11 mbpd), making frontier oil both substantial and fragile.

42

0%

2%

4%

6%

8%

10%

12%

14%

0

1

2

3

4

5

6

Ve

nezuela

Azerb

aija

n

Ecu

ad

or

An

gola

Nig

eria

Ma

laysia

Ka

zakhsta

n

Alg

eria

UA

E

Russia

Om

an

Norw

ay

KS

A

Me

xic

o

Colo

mb

ia

Bra

zil

Less than 1

yr

Source: IMF, Worldbank, DB Research Inputs

Primary Balance Reserve Drawdown

Years (LHS) Percent of Global Production NOC (RHS)

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• A more accurate estimation of reserve drawdown can be achieved by first netting operating

losses by the current portion of the country’s debt balance (in this case is converted to USD for

simplification).

• A final adjustment can be made to adjust operating losses by the historical share of

government revenue.

• Only a small cross section of frontier countries are represented below. Many blackbox weakest links

are absent such as Equatorial Guinea, Chad etc.

43

0

1

2

3

4

5

6

Ve

nezuela

, R

B

Azerb

aija

n

Ecuador

An

gola

Nig

eria

Ma

laysia

Ka

zakhsta

n

Alg

eria

United A

rab E

mirate

s

Russia

n F

edera

tio

n

Om

an

Norw

ay

Sa

udi A

rabia

Me

xic

o

Colo

mb

ia

Bra

zil

Reserve Drawdown (net current debt)

Years Years Assuming Debt

0.6

0.7

0.9

0.7

0.5

1.9

1.9

1.7

40.2

4.1

3.1

15.2

2.5

1.9

3.1

2.5

0

5

10

15

20

25

30

35

40

45

Ve

nezuela

, R

B

Azerb

aija

n

Ecuador

An

gola

Nig

eria

Ma

laysia

Ka

zakhsta

n

Alg

eria

United A

rab E

mirate

s

Russia

n F

edera

tio

n

Om

an

Norw

ay

Sa

udi A

rabia

Me

xic

o

Colo

mb

ia

Bra

zil

Reserve Drawdown (yrs) (net current debt, government budget)

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• Weaker EM/Frontier producers with limited capability to diversify/raise capital face two options:

• (1) Devalue/Depeg FX in order to improve terms of trade, but risk a surge in inflation and UE.

• (2) Continue to produce knowing that unsupportive capital flows and operating deficits

disadvantage NOCs in the long run versus larger economies and hope for exogenous shock.

• NOCs are engaged in both strategies, while carefully cutting excess production to extend a

reserve drawdown scenario.

• Note FX depreciation below. *Ecuador adopted USD; AED, OMR, SAR, VEF are USD pegged.

44

Country

Government

Revenue

Oil Rents

(%GDP)

BoT

(%GDP)

Primary Balance

Reserve Draw (Yrs)

FX Depreciation (since

11/14)

Current Debt (Principle &

Interest) Assuming all debt

USD denom at current FX

Venezuela, RB 45% 24% 15.41% 0.26 1% 11,892,714,478

Azerbaijan 53% 38% 50.94% 0.25 100%

Ecuador 33% 18% 15.27% 0.28 USD

Angola 79% 41% 61.15% 0.63 30%

Nigeria 75% 17% 18.44% 0.76 21% 32,218,767,774

Malaysia 30% 6% 0.58% 0.87 31.40% 15,209,580,578

Kazakhstan 39% 27% 24.59% 0.94 103% 2,200,701,939

Algeria 60% 24% 22.45% 1.05 24%

United Arab Emirates 4% 24% 23.48% 1.38 0%

Russian Federation 52% 15% 9.64% 1.88 65% 11,350,545,630

Oman 45% 36% 35.07% 2.15 0% 1,812,038,029

Norway 20% 9% 10.22% 3.48 33%

Saudi Arabia 80% 46% 34.23% 2.13 0%

Mexico 33% 7% 4.31% 4.07 32% 73,723,761,307

Colombia 20% 8% 4.03% 4.17 49% 10,629,322,621

Brazil 2% 2% 0.25% 5.24 60% 145,131,293,573

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• Devaluation is often a masking monetary policy which signals greater economic desperation.

• Devaluation is a double edged sword. While terms of trade improve and exports become more

competitive, domestic assets devalue in real terms decelerating the acquisition of financial assets and

creating additional funding pressure.

• Note rapid devaluations in the AZN and KZT also correspond with declines in financial account

asset flows, suggesting capital flows became more risk averse.

45

$(10)

$(5)

$-

$5

$10

$15

$20

$25

Q1 2

012

Q2 2

012

Q3 2

012

Q4 2

012

Q1 2

013

Q2 2

013

Q3 2

013

Q4 2

013

Q1 2

014

Q2 2

014

Q3 2

014

Q4 2

014

Q1 2

015

Q2 2

015

Bil

lio

ns

Financial Account Assets (flows)

Republic of Azerbaijan Republic of Kazakhstan

0.7

0.75

0.8

0.85

0.9

0.95

1

1.05

1.1

0

50

100

150

200

250

300

350

400

2006-0

5-2

4

2006-1

1-2

4

2007-0

5-2

4

2007-1

1-2

4

2008-0

5-2

4

2008-1

1-2

4

2009-0

5-2

4

2009-1

1-2

4

2010-0

5-2

4

2010-1

1-2

4

2011-0

5-2

4

2011-1

1-2

4

2012-0

5-2

4

2012-1

1-2

4

2013-0

5-2

4

2013-1

1-2

4

2014-0

5-2

4

2014-1

1-2

4

2015-0

5-2

4

2015-1

1-2

4

Kazakhstan/Azerbaijan FX

USDKZT USDAZN

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NOCs : Externally Facing NOCs

• Azerbaijan and Kazakhstan have clean financial account flows data, but they are barely alone in

devaluation. Angola, Algeria, and Nigeria have all devalued their currencies versus the USD.

• Forward markets are currently pricing substantial depreciation in USD pegged large producers:

OMR, SAR, QAR, BHD, AED.

46

50

70

90

110

130

150

170

190

210

230

20

06-0

1-0

2

20

06-0

8-0

2

20

07-0

3-0

2

20

07-1

0-0

2

20

08-0

5-0

2

20

08-1

2-0

2

20

09-0

7-0

2

20

10-0

2-0

2

20

10-0

9-0

2

20

11-0

4-0

2

20

11-1

1-0

2

20

12-0

6-0

2

20

13-0

1-0

2

20

13-0

8-0

2

20

14-0

3-0

2

20

14-1

0-0

2

20

15-0

5-0

2

20

15-1

2-0

2

USDAOA USDDZD USDNGN

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NOCs : Externally Facing NOCs • Given the low GDP/capita of many threatened oil producers, governments face substantial political risk

for rising unemployment , forcing production to continue at a rising marginal cost per laborer.

• Blackbox Central African producers are highly endangered. EURXOF is the pegged currency of

Central African French speaking nations: Equatorial Guinea, Chad, Gabon, Cameroon.

• Despite poor economic data, it is easy to argue a devaluation is probable within 1H16.

47

0

10,000

20,000

30,000

40,000

50,000

60,000

Chad

Cam

ero

on

Nig

eria

Alg

eria

An

gola

Ecuador

Azerb

aija

n

Tu

rkm

enis

tan

Lib

ya

Bra

zil

Ve

nezuela

, R

B

Ka

zakhsta

n

Russia

n F

edera

tio

n

Jap

an

Germ

any

United S

tate

s

GDP/Capita

Blue = EM/FR; Light Blue = DM

600

610

620

630

640

650

660

1999-0

9-0

6

2000-0

9-0

6

2001-0

9-0

6

2002-0

9-0

6

2003-0

9-0

6

2004-0

9-0

6

2005-0

9-0

6

2006-0

9-0

6

2007-0

9-0

6

2008-0

9-0

6

2009-0

9-0

6

2010-0

9-0

6

2011-0

9-0

6

2012-0

9-0

6

2013-0

9-0

6

2014-0

9-0

6

2015-0

9-0

6

EURXOF

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Externally Facing NOCs

• Devaluation conceals the reality that producer solvency is threatened regardless of physically

available data. Significant production cuts are a near certainty.

• Even assuming producers have the capacity to raise additional debt, EQUAL to the current amount of

outstanding FX reserves, several (Venezuela, Azerbaijan, Ecuador) would have to cut production in

order to survive 1yr at $35 oil.

• In total this would leave 5 countries (the previous 3, Nigeria, and Angola) within 1yr of default.

• At a base case respective cuts would reduce supply by 1.29 mbpd by the end of 2016. This

does not include probable cuts in countries such as those in Central Africa.

48

Base 2 x 2 x 2 x Months

NOC Country Reduction Base New Years

Years

(Debt)

Yrs (Debt,

Gov Budget)

Average

Survival Years

Years

(Debt)

Yrs (Debt,

Gov Budget)

Average

Survival Years

Years

(Debt)

Yrs (Debt,

Gov Budget)

Average

Survival

Saudi Aramco Saudi Arabia 0.00% 12.0 12.00 4.27 4.27 5.33 4.62 2.13 2.13 2.67 2.31 2.13 2.13 2.67 2.31 27.72

Gazprom Russian Federation 0.00% 9.7 9.70 5.16 4.78 8.60 6.18 2.58 2.39 4.30 3.09 2.58 2.39 4.30 3.09 37.07

Pemex Mexico 0.00% 3.6 3.60 8.14 2.99 3.96 5.03 4.07 1.49 1.98 2.52 4.07 1.49 1.98 2.52 30.20

Abu Dhabi National Oil Co United Arab Emirates 0.00% 2.9 2.90 3.59 3.59 89.87 32.35 1.80 1.80 44.93 16.18 1.80 1.80 44.93 16.18 194.12

Sonatrach Algeria 0.00% 2.7 2.70 2.09 2.09 3.49 2.56 1.05 1.05 1.75 1.28 1.05 1.05 1.75 1.28 15.36

Petrobras Brazil 0.00% 2.6 2.60 10.49 3.38 4.93 6.26 5.24 1.69 2.47 3.13 5.24 1.69 2.47 3.13 37.59

Statoil Norway 0.00% 2.1 2.10 6.96 6.96 34.82 16.25 3.48 3.48 17.41 8.13 3.48 3.48 17.41 8.13 97.50

Petroleos de Venezuela Venezuela, RB 50.00% 1.9 0.95 1.04 0.73 1.20 0.99 0.26 0.21 0.39 0.29 0.78 0.52 0.80 0.70 8.40

Nigerian National Petroleum Nigeria 0.00% 1.4 1.40 1.51 0.83 0.97 1.10 0.76 0.42 0.48 0.55 0.76 0.42 0.48 0.55 6.62

Petronas Malaysia 0.00% 1.4 1.40 1.74 1.52 3.94 2.40 0.87 0.76 1.97 1.20 0.87 0.76 1.97 1.20 14.40

Sonangol Angola 0.00% 1.7 1.74 1.26 1.26 1.60 1.38 0.63 0.63 0.80 0.69 0.63 0.63 0.80 0.69 8.25

KazMunaiGaz Kazakhstan 0.00% 1.6 1.63 1.87 1.75 4.04 2.55 0.94 0.87 2.02 1.28 0.94 0.87 2.02 1.28 15.32

Ecopetrol Colombia 0.00% 1.0 0.99 4.49 2.97 6.32 4.59 2.24 1.49 3.16 2.30 2.24 1.49 3.16 2.30 27.55

SOCAR Azerbaijan 35.00% 0.8 0.55 0.78 0.78 1.47 1.01 0.25 0.25 0.48 0.33 0.53 0.53 0.99 0.68 8.16

Petroecuador Ecuador 7.00% 0.6 0.52 0.60 0.60 1.82 1.01 0.28 0.28 0.84 0.47 0.32 0.32 0.97 0.54 6.45

Petroleum Development Oman Oman 0.00% 0.7 0.66 4.31 3.56 6.54 4.80 2.15 1.78 3.27 2.40 2.15 1.78 3.27 2.40 28.81

Total 1.29 99.5 98.2

Base Sensitivity

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• At $30 oil, production would need to be cut an additional 160,000 bpd bringing total cuts to 1.45 mbpd

in order to extend reserve drawdown to 1yr for Ecuador, Venezuela, and Azerbaijan.

• Thus oil price is itself a highly reflexive variable. While price can collapse while supply/demand remains

imbalanced, each additional dollar of weakness accelerates the eventual reversion of the system.

• The longer oil persists at low levels, the more difficult it becomes for weaker producers to ease

operating deficits and debt service ratios from the prior higher price equilibrium.

• As a result, a linear interpolation (as in no-arbitrage condition in forward price) for crude prices

is highly inaccurate as a forecast.

49



Years

(Debt)

Yrs (Debt,

Gov Budget)

Average

Survival Years

Years

(Debt)

Yrs (Debt,

Gov Budget)

Average

Survival Years

Years

(Debt)

Yrs (Debt,

Gov Budget)

Average

Survival



Pemex Mexico 0.00% 3.6 3.60 7.31 2.87 3.89 4.69 4.07 1.49 1.98 2.52 3.24 1.38 1.91 2.18 26.11




Statoil Norway 0.00% 2.1 2.10 6.09 6.09 30.47 14.22 3.48 3.48 17.41 8.13 2.61 2.61 13.06 6.09 73.13




Sonangol Angola 0.00% 1.7 1.74 1.17 1.17 1.48 1.27 0.63 0.63 0.80 0.69 0.54 0.54 0.68 0.59 7.04






Total 1.45 99.5 98.0

Base Sensitivity

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• A common argument made for “lower for longer” oil prices is the existence of an estimated $4.5T in oil

producing sovereign wealth funds, readily available inject liquidity and maintain budgets.

• The problem with this argument is that while risk assets may be liquidated, the most at risk oil

producers do not benefit, as they lack sizeable SWFs.

• CS estimates total SWF AUM as follows as of 2014:

50

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• SWF assets are not all readily available for sale, a general estimate is that 35% of assets are tied into

longer term development projects and real estate (though these may be foreign denominated

investments). Assuming 65% of SWF assets are “shadow reserves” we can recalculate survival.

• At a reserve multiplier of 2x, production is still forecast to be cut 1.12 mbpd as Venezuela and

Ecuador both lack SWFs, though Nigeria and Angola hang on to the 1 yr threshold by a hair.

51



Years

(Debt)

Yrs (Debt,

Gov Budget)

Average

Survival Years

Years

(Debt)

Yrs (Debt,

Gov Budget)

Average

Survival Years Years (Debt)

Yrs (Debt,

Gov Budget)

Average

Survival



Pemex Mexico 0.00% 3.6 3.60 7.48 2.93 3.98 4.80 4.07 1.49 1.98 2.52 3.40 1.44 2.00 2.28 27.37




Statoil Norway 0.00% 2.1 2.10 61.60 61.60 307.98 143.72 3.48 3.48 17.41 8.13 58.11 58.11 290.56 135.60 1627.16




Sonangol Angola 0.00% 1.7 1.74 1.32 1.32 1.67 1.44 0.63 0.63 0.80 0.69 0.69 0.69 0.87 0.75 9.03






Total 1.12 99.5 98.4

Base Sensitivity

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• Assuming a reserve multiplier of 1.5x and including 65% of SWF assets as liquid reserves,

production is forecast to decline 1.84 mbpd; driven by Venezuela, Ecuador, and Nigeria.

• Again it is worth noting, SWFs largely benefit nations with stronger fiscal balances and reserve

accounts. Moreover, using a 1.5x multiplier is a conservative estimate as struggling frontier

governments lack the ability to easily raise additional long term capital and continue the waiting game

extended by larger EM and DM producers.

52

Base 1.5 x 1.5 x 1.5 x Months


Years

(Debt)

Yrs (Debt,

Gov Budget)

Average

Survival Years

Years

(Debt)

Yrs (Debt,

Gov Budget)

Average

Survival Years Years (Debt)

Yrs (Debt,

Gov Budget)

Average

Survival



Pemex Mexico 0.00% 3.6 3.60 5.61 2.20 2.99 3.60 4.07 1.49 1.98 2.52 1.54 0.71 1.00 1.08 12.98




Statoil Norway 0.00% 2.1 2.10 46.20 46.20 230.98 107.79 3.48 3.48 17.41 8.13 42.71 42.71 213.57 99.67 1196.00




Sonangol Angola 0.00% 1.7 1.74 0.99 0.99 1.26 1.08 0.63 0.63 0.80 0.69 0.36 0.36 0.46 0.39 4.71






Total 1.84 99.5 97.6

Base Sensitivity

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Externally Facing NOCs: Geopolitical Risk

• Fiscally driven production cuts are easier to approximate, but additional upside risks exist.

• The longer oil prices remain low, the higher the probability of geopolitically motivated exogenous shock.

• While exogenous shocks are generally temporary in spot prices, this is a recent phenomenon.

• Over the last two decades, production cuts have largely provided opportunities to claim market share,

as opposed to outright supply disruptions. Material shocks have largely been demand driven.

• The NOC model suggests a structural correction similar to the early 1980s is more likely.

53 0

20

40

60

80

100

120

140

160

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

WTI Exogenous Shock

Gulf War Iraq War ll

Arab

Spring

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• Long term supply decline occurred through the late 1970s and early 1980s as a consequence of

the Iranian revolution.

• Iran was very similar to many current producing oil countries; a benefactor of rapid growth and inflation

during the period that disproportionately favored the rich in a low GDP/capita country.

• Post revolution the world reverted from surplus to major shortage for a period of years.

54 -8%

-6%

-4%

-2%

0%

2%

4%

6%

-4

-3

-2

-1

0

1

2

3

4

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

Mil

lio

ns

Production Reaction to Exogenous Shock

Production (bpd, LHS) YoY% (RHS)

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• Inklings of heightened geopolitical risk have begun to surface across the body of exporters, but

principally surround primary exporter Saudi Arabia:

• 1/8/16 – Saudi Arabia confirms intent to potentially IPO ARAMCO.

• 1/2/16 - U.S. State Department says it is concerned Saudi execution of Shia cleric Nimr Al Nimr

"risks exacerbating sectarian tensions“. This decision was denounced by Yemeni Houthis,

Lebanon Supreme Shia Islamic Council, Iraq MP.

• 12/28/15 – Raises domestic oil prices by 66%.

• This action has since been followed by Bahrain and Oman

• Subsidies account for 82% of cost of electricity & fuel in Venezuela, 80% -Libya, 79% -

Saudi Arabia, 74% -Iran, 56% -Iraq.

• Off budget contributions to regional stability

• 1/7/16 Report suggests KSA spending $12-14B monthly off-balance sheet on defense.

• KSA argues Iranians are waging proxy wars against the Saudis in Iraq, Syria, and Yemen and

aiding subversive elements in Bahrain, Kuwait, and the kingdom itself.

• Iran retorts KSA proxy wars waged in Yemen, Iran, Libya, Iraq. Including the 1/5/16

attacks on the Sidra, Libya’s largest oil terminal.

55

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• The following map shows ideological skew within the region – most Saudi Oil is concentrated

in primarily Shia regions. Black and red designate oil and gas fields. Given the blackbox nature of

geopolitics in the region, it is improbable to think current production conditions cant maintain a new

equilibria based on history.

56

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Shift Mechanism: Heavily Indebted Public

US Oil Companies

• While US production pales versus aggregate NOC output, highly levered US oil producers are unable

to passthrough debt obligations to the US government. As a result, they can produce up until operating

losses overwhelm debt service and value realized through asset sales.

• US Shale production fell throughout 2015. It is highly likely that production continues to

collapse at current price levels.

57 0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

Jan

-07

Ju

l-0

7

Jan

-08

Ju

l-0

8

Jan

-09

Ju

l-0

9

Jan

-10

Ju

l-1

0

Jan

-11

Ju

l-1

1

Jan

-12

Ju

l-1

2

Jan

-13

Ju

l-1

3

Jan

-14

Ju

l-1

4

Jan

-15

Ju

l-1

5

US Shale Oil Production (bpd)

Bakken Eagle Ford Niobrara Permian

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

Jan

-07

Jun

-07

Nov-0

7

Ap

r-08

Se

p-0

8

Fe

b-0

9

Jul-0

9

Dec-0

9

Ma

y-1

0

Oct-

10

Ma

r-11

Au

g-1

1

Jan

-12

Jun

-12

Nov-1

2

Ap

r-13

Se

p-1

3

Fe

b-1

4

Jul-1

4

Dec-1

4

Ma

y-1

5

Oct-

15

US Shale Oil Production (bpd)

Haynesville Marcellus Utica

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US Oil Companies

• At current 2yr forward prices nearly every major shale basin is underwater

• The Permian Basin, the largest supply of US shale energy is now below breakeven price level.

• In 2015 Permian production increased 12% YoY, while other basin (Eagle Ford, Niobrara,

Bakken) reduced output. Production is likely to mean revert.

• A 20% decrease in Permian output would result in a reduction of 400,000 bpd of production.

58

$69

$60

$43

$72

$41

$68

$34

$65

$47

$61

$42

$57

$63

$45

$52

$52

$53

$51

$50

$67

$91

$56

$71

$62

$81

$58

$- $20 $40 $60 $80 $100

Bakken - Divide

Bakken - Dunn County

Bakken - Messon

Bakken - Montana

Bakken - Mountrail

Bakken - North Nesson

Bakken - Sanish

Bakken - South McKenzie

Bakken - West Nesson

Eagle Ford

Niobrara - HZ

Niobrara - VT

Permian - Avalon

Permian - Bone Spring

Permian - Cline

Permian - Delaware Sands

Permian - Wolfberry

Permian - Wolfbone

Permian - Wolfcamp

Three Forks - Feather Edge

Three Forks - Mckenzie

Three Forks - Nesson

Three Forks - Northern

Three Forks - Sanish

Ulinta

Utica

Breakeven Price (Full lifecycle, %/bbl)

1/16 - 2 Yr Forward WTI Price

12/14 - 2 Yr Forward WTI Price

-12%

-23%

-8%

3%

-21%

12%

55%

22% 26%

1% 1%

7%

41%

2%

-40%

-20%

0%

20%

40%

60%

Ba

kken

Ea

gle

Fo

rd

Haynesvill

e

Ma

rcellu

s

Nio

bra

ra

Pe

rmia

n

Utica

Shale Oil Production 12/2015

Production (%YoY) % Total

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US Oil Companies

• The cost of raising additional capital has exploded higher as 2015 brought a wave of high yield energy

bankruptcies. The Energy HY sub index Index trades at an OAS 800 pts wider than the aggregate

Barclays HY Index.

• Haynes and Boone detail a partial list of US Shale Oil bankruptcies throughout 2015. Note that the

number and size increased each month. This trend is likely to hold in 2016.

59

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US Oil Companies

• A partial look (37.5% of universe) of distressed energy bonds due in 2017 shows a gross liability of

$21B. Gross liability reaches an apex in 2019 of $32B (35.14% of universe).

• Assuming homogenous capital structure for unaccounted for bonds, the total liability would total $57B in

2017 and $92B in 2019.

• While mostly insignificant independently, distressed O&E makes up ~2.5 mbpd of production annually.

60

0%

10%

20%

30%

40%

50%

60%

$-

$5

$10

$15

$20

$25

2017

2018

2019

2020

2021

2022

2023

Bil

lio

ns

Source: Bloomberg, Morningstar

Global HY Energy Bonds

Principal Interest % of Total Distressed Bonds

706,3

00

182,0

00

167,0

00

125,0

00

108,0

00

87,7

00

82,7

68

74,8

75

65,0

00

64,8

00

58,5

00

54,5

00

53,9

20

53,3

00

52,8

84

50,5

46

50,2

61

44,1

13

44,1

00

38,3

33

35,1

79

32,6

00

30,5

00

30,0

00

29,0

00

26,8

21

21,1

11

21,0

00

14,4

45

13,2

00

13,1

00

12,0

00

10,0

00

10,0

00

5,8

71

5,0

00

4,3

12

3,6

00

3,5

00

3,2

35

2,6

33

2,2

09

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

Chesap

eake E

ne

rgy C

orp

Pio

ne

er

Energ

y S

erv

ices C

orp

Wh

itin

g P

etr

ole

um

Corp

Cre

stw

ood M

idstr

eam

Part

ne

rs L

P / C

restw

ood

…

Calif

orn

ia R

esourc

es C

orp

SandR

idge E

nerg

y Inc

ME

G E

ne

rgy C

orp

Denb

ury

Resourc

es In

c

EP

L O

il &

Gas Inc

Lin

n E

nerg

y L

LC

/ L

inn E

ne

rgy F

inance C

orp

Energ

y X

XI G

ulf C

oast In

c

Bre

itB

urn

Energ

y P

art

ne

rs L

P / B

reitB

urn

Fin

ance C

orp

Sanchez E

nerg

y C

orp

EP

Energ

y L

LC

/ E

vere

st A

cq

uis

itio

n F

inance Inc

Seventy

Seven E

nerg

y Inc

Oa

sis

Petr

ole

um

Inc

Key E

ne

rgy S

erv

ices Inc

Bayte

x E

nerg

y C

orp

Sto

ne E

ne

rgy C

orp

Fre

eport

-McM

ora

n O

il &

Gas L

LC

/ F

CX

Oil

& G

as Inc

Lig

hts

tream

Re

so

urc

es L

td

Mid

sta

tes P

etr

ole

um

Co

Inc / M

idsta

tes P

etr

ole

um

Co L

LC

Halc

on R

eso

urc

es C

orp

Ge

nera

l E

xp

lora

tion P

art

ne

rs In

c

Bonanza C

reek E

nerg

y Inc

Sw

ift E

nerg

y C

o

Kosm

os E

nerg

y L

td

Chap

arr

al E

ne

rgy Inc

Nort

hern

Oil

an

d G

as Inc

Tri

angle

US

A P

etr

ole

um

Corp

Cla

yto

n W

illia

ms E

ne

rgy I

nc

Bella

trix

Exp

lora

tion L

td

Eclip

se R

esourc

es C

orp

Sunshin

e O

ilsand

s L

td

Wa

rren

Resou

rces In

c

Ric

e E

nerg

y Inc

Lon

esta

r R

esourc

es A

merica In

c

Leg

acy R

eserv

es L

P /

Le

gacy R

eserv

es F

inance C

orp

Go

odrich P

etr

ole

um

Corp

Eagle

Rock E

nerg

y P

art

ners

LP

/ E

agle

Rock E

nerg

y…

EV

Energ

y P

art

ne

rs L

P / E

V E

nerg

y F

inance C

orp

Rex E

nerg

y C

orp

Distressed O&E Production (bpd)

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US Oil Companies

• 90 US Energy Companies have negative EBITDA–CAPEX with gross debt totaling $422B.

• This is substantially larger than the extrapolated HY Energy debt universe which stands at ~$50B in

gross debt (from sample of 11 companies totaling $35B in HY bonds).

• Sandridge Energy Inc., Energy XXI Ltd. and Halcón Resources Corp. all paid more than 40% of Q3

revenue toward interest.

61

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US Oil Companies

• As margins and FCF shrivel, PE remains mostly on the sidelines, anxiously awaiting the

opportunity to buy assets in firesales.

• In the short run, production resumes after assets are acquired in bankruptcy.

• According to GS, only 9% of total $20B of capital raised across dedicated energy PE is deployed.

• With generalist PE such as CG, BX, KKR, a very small portion of dry power is dedicated to Energy.

62

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US Oil Companies

• Barclays argues the downside risk to US HY energy defaults is due to rise as hedges expire. 59% of

US Oil production is nearly completely unhedged at all in 2016, 70% of US Nat Gas production is

unhedged.

• At very best, even if Permian production remains flat YoY, at 2015 rates of decline US Shale

production would fall by 460,000 bpd assuming all other basin growth rates remain fixed.

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Shift Mechanism: What is the Market

Missing?

• Since the original rout, oil prices have largely relied on interpolation of the futures curve driven

by spot market supply / demand imbalance.

• Given the opaque nature of supply – demand balance and sudden nature of production increases, the

futures curve has seen downward parallel shift.

64

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Missing?

• Market participants continuously discounted an additional ~4.25 mbpd of production from

activation of offline Iran, Libya, Iraq, and Kuwaiti supply along with an estimate of 2 mbpd

excess KSA capacity.

• In reality the market remained only 1.5 mbpd oversupplied in the physical market with a 200,000 bpd

range of error in 2015.

• The EIA estimates an additional 1 mbpd of reduction of oversupply, but this appears conservative.

65

1.7

6

1.6

3

1.4

5

0.4

4

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

Q1 2

010

Q2 2

010

Q3 2

010

Q4 2

010

Q1 2

011

Q2 2

011

Q3 2

011

Q4 2

011

Q1 2

012

Q2 2

012

Q3 2

012

Q4 2

012

Q1 2

013

Q2 2

013

Q3 2

013

Q4 2

013

Q1 2

014

Q2 2

014

Q3 2

014

Q4 2

014

Q1 2

015

Q2 2

015

Q3 2

015

Q4 2

015

Q1 2

016

Q2 2

016

Q3 2

016

Q4 2

016

mb

pd

Implied stock change and balance

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Missing?

• A true supply shock is a rare occurrence which many market participants have never

experienced.

• A historical lookback starting with earliest EIA supply/demand data in 1994 shows market has

historically risen on 6m period regardless of supply/demand mismatch.

• Using a threshold of n=10 or greater for any over/under supply level accentuates this point as price

changes remains positive on a 6m basis across the body of data.

66

14.4

%

12.4

%

9.2

%

3.2

%

-1.7

%

-0.6

%

-4.4

%

7.4

%

1.1

%

0.0

% 3

.5%

7.3

%

3.0

%

-10%

-5%

0%

5%

10%

15%

20%

0

2

4

6

8

10

12

14

16

18

20

-1.3

-1.2

-1.0

-0.8

-0.7

-0.5

-0.3

-0.2

0.0

0.2

0.3

0.5

0.7

Monthly 6M Rolling Supply - Demand (mbpd)

WTI vs. World Supply/Demand Oil Imbalance

Instances WTI (6m %Change Avg. Monthly Price)

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Missing?

• The same is not true when observing the entire sample. The right hand tail is largely skewed positive

due to mean reversion in 2009 and rapid growth in the pre-GFC period.

• Extreme imbalances (either supply/demand) have led lower prices but occurred during recessions.

• The recent 6m decline of 44% implies roughly 4 mbpd of oversupply. Interestingly all instances

within this sample occurred during 1998 Asian Financial Crisis.

• A 4mbpd oversupply roughly concurs with market expectation of total production with KSA

capacity, Iran, Libya, Kuwait, and Iraq re-entry.

67

-3.6

%

0.0

%

0.1

%

-3.2

%

-14.0

%

-13.3

%

1.0

% 8.0

%

18.2

%

23.0

%

22.2

%

25.3

%

14.4

%

12.4

%

9.2

%

3.2

%

-1.7

%

-0.6

%

-4.4

%

7.4

%

1.1

%

-2.3

%

3.5

%

7.3

%

3.0

% 10.0

%

-5.8

%

27.6

%

-5.7

%

20.5

%

14.6

%

10.3

%

-22.4

%

-30%

-20%

-10%

0%

10%

20%

30%

40%

0

2

4

6

8

10

12

14

16

18

20

-3.3

-3.2

-3.0

-2.8

-2.7

-2.5

-2.3

-2.2

-2.0

-1.8

-1.7

-1.5

-1.3

-1.2

-1.0

-0.8

-0.7

-0.5

-0.3

-0.2

0.0

0.2

0.3

0.5

0.7

0.8

1.0

1.2

1.3

1.5

1.7

1.8

2.0

6M Rolling Supply - Demand (mbpd)

WTI vs. World Supply/Demand Imbalance

Instances WTI (6m %Change Avg. Monthly Price)

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Missing?

• Of the estimations made of renewed supply, two seem spurious.

• Despite committing to overproduction, Saudi Arabian rig count stabilized after an initial surge in 2015. It

is interesting that KSA increased rig count and then proceeded to cut production into end of year.

• Libyan supply restoration has been announced several times since early 2015, but rig count has

continued to decline. 1.1 mbpd would be ~80% of Libya’s total 1.4 mbpd capacity, denoting roughly

12.5 rigs by proportion.

68 0

2

4

6

8

10

12

14

16

18

1/7

/201

4

2/7

/201

4

3/7

/201

4

4/7

/201

4

5/7

/201

4

6/7

/201

4

7/7

/201

4

8/7

/201

4

9/7

/201

4

10

/7/2

014

11

/7/2

014

12

/7/2

014

1/7

/201

5

2/7

/201

5

3/7

/201

5

4/7

/201

5

5/7

/201

5

6/7

/201

5

7/7

/201

5

8/7

/201

5

9/7

/201

5

10

/7/2

015

11

/7/2

015

12

/7/2

015

Libya Rig Count

9

9.2

9.4

9.6

9.8

10

10.2

10.4

10.6

10.8

80

85

90

95

100

105

110

115

120

125

130

1/7

/201

4

3/7

/201

4

5/7

/201

4

7/7

/201

4

9/7

/201

4

11/7

/20

14

1/7

/201

5

3/7

/201

5

5/7

/201

5

7/7

/201

5

9/7

/201

5

11/7

/20

15

Saudi Arabia Rig Count vs. Production

Rig Count (LHS) Production (mbpd, RHS)

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Missing?

• In total proprietary model suggests a base case of 1.45 mbpd of production cuts in NOC assuming

average reserve lifespans are extended to 1yr.

• US HY Energy production base assumes a halving of Permian and Utica growth rates while keeping

other rates fixed. Best case assumes a 0% YoY growth rate in Permian and 5% Utica. Worst assumes

equal production change YoY.

• I forecast a base case net reduction in imbalance of -160,000 bpd in 2016 versus the EIA

projection of +660,000 bpd of oversupply. Historically a change in imbalance of this magnitude

denotes 8% upside on a 12m basis, but this number is likely undershot for numerous reasons.

69

Model Output: EOY 2016 Oil Supply - Demand Imbalance

Reserves Multiplier 3.0 x 2.0 x 1.5 x

Worst Base Best

Base (w/

KSA -

2mbpd)

Production Cuts:

NOC 0.91 1.45 2.44 1.45

US HY 0.264 0.381 0.5 0.381

Total 1.17 1.83 2.94 1.83

Incoming Supply:

KSA 2 1 0.5 2

Kuwait 0.25 0.25 0.25 0.25

Libya 1.1 0.55 1.1 0.55

Iraq 0.125 0.125 0.125 0.125

Iran 0.75 0.75 0.75 0.75

Total 4.225 2.675 2.725 3.675

Aggregate Change

+/-Demand/Supply 3.05 0.84 -0.21 1.84

+/-Consumption 0.75 1 1.25 1

Net Imbalance 2.30 -0.16 -1.46 0.84

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Shift Mechanism: Technical Support

• Positioning is likely to enhance any price move in oil.

• From a positioning standpoint hedge fund WTI and Brent short positions have continued to grow into

the price rout, with no signs or catalyst for unwind.

• Implied volatility has surged in crude oil options as shorts have piled into puts at lower and

lower strikes.

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• Exogenous production unwinds will result in an enormous short squeeze.

• Though implied volatility has surged, expression has been extremely skewed to the downside (smirk).

• Puts are also trading expensive relative to realized volatility, with volatility spread both widening and

holding levels.

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• Technical Indicators support that we may be approaching a bottom near term in oil price.

• 1st to 4th month contango >10% has consistently predicted bottoms or near bottoms in oil.

• The last time contango matched magnitude and front month price level, WTI ended the year

50% higher.

72

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• Crude Oil to gold ratio stands at levels last seen in the depths of the 1986 crude bear market.

• The last time crude oil/gold reached such levels, crude oil rallied 69% over the year into 1987.

73

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• Crude oil has well defined 30yr seasonality of lows in late Jan, with rally through mid June.

• Independently these technical factors may be insufficient, but when combined with the fundamental

rebalancing, the story becomes very compelling.

74

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• Assuming a fundamental base case of 160,000 bpd reduction in supply-demand imbalance, we find an

average 1yr return of 7.5%. Adjusted for cases where undersupply drifted to oversupply (the opposite of

the current scenario), 1yr return averaged 12.5% (27% excluding 2001).

• Under the best case scenario, 1yr return averaged 56%.

75

21.73%

30.89%

-33.45%

30.50%

11.49%

-70%

-60%

-50%

-40%

-30%

-20%

-10%

0%

10%

20%

30%

40%

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

1994

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2009

2010

2011

2012

2013

mb

pd

Source: EIA

Supply/Demand as Predictive Model - Base Case

Instances

12m Supply-Demand Change (LHS)

WTI (RHS, YoY%)

72.78%

46.74%

47.36%

-40%

-20%

0%

20%

40%

60%

80%

100%

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2005

2006

2007

2008

2009

2010

2011

2012

2013

mb

pd

Source: EIA

Supply/Demand as Predictive Model - Best Case

Instances

12m Supply-Demand Change (LHS)

WTI (RHS, YoY%)

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• An average of historical model and technical factors provides a baseline estimate of a 12 month

forward price for WTI Crude of $45.56 per barrel, about 33% higher than predicted by the

current crude futures curve for January 2017.

• Though the model and technical indicators may provide some basis, these are incomplete details given

the highly intertwined nature of the reflexive regime.

76

Base Best Gold/Oil 1-4 Contango

Starting Price 12.50% 56.00% 69.00% 70.00%

30 33.75 46.8 50.7 51

Implied (12m forward price)

45.56$

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Shift Mechanism: USD Strength

• The second important factor within the reflexive regime, and highly correlated with oil production

surplus is the rapidly strengthening USD over the course of 2014 and 2015.

• USD strength is a function of:

• (a) stronger US growth, employment, inflation in 2014 and greater likelihood of a Fed hike

• (b) the beginning ECB QE and expansion of BOJ QQE

• (c) surreptitious unwind of the Fed balance sheet in front of a higher EFFR

• (d) declining commodity prices and flight to the USD as global trade decelerated.

77

0.0

2.0

4.0

6.0

8.0

10.0

12.0

-4.0

-2.0

0.0

2.0

4.0

6.0

8.0

01/2

006

04/2

006

07/2

006

10/2

006

01/2

007

04/2

007

07/2

007

10/2

007

01/2

008

04/2

008

07/2

008

10/2

008

01/2

009

04/2

009

07/2

009

10/2

009

01/2

010

04/2

010

07/2

010

10/2

010

01/2

011

04/2

011

07/2

011

10/2

011

01/2

012

04/2

012

07/2

012

10/2

012

01/2

013

04/2

013

07/2

013

10/2

013

01/2

014

04/2

014

07/2

014

10/2

014

01/2

015

04/2

015

07/2

015

10/2

015


US Economic Data

CPI (YoY%, LHS) GDP (YoY%, LHS) Unemployment (%, RHS)

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Shift Mechanism: Divergent Monetary

Policy

• (b) The USD was strengthened by the start of ECB QE and expansion of BOJ QQE.

• Starting in 2014 the ECB began a number of extraordinary measures; cutting the benchmark interest

rate twice to 5bps and deposit rate to -20 bps (which was since cut to -30 bps).

• In March of 2015 the ECB began to buy government bonds at a rate of 60B EUR per month (1.1T EUR

over course of the program).

• Japan has been engaged in a massive QE program since 2012, with additional invective in 2014 to

enlarge the monetary base by 80T Yen per month from the previous 60T to 70T per month.

78 0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

3,500,000

4,000,000

4,500,000

70

80

90

100

110

120

130

140

12-1

998

12-1

999

12-2

000

12-2

001

12-2

002

12-2

003

12-2

004

12-2

005

12-2

006

12-2

007

12-2

008

12-2

009

12-2

010

12-2

011

12-2

012

12-2

013

12-2

014

12-2

015

Mil

lio

ns

Yen

BOJ QE (Balance) vs. USDJPY

USDJPY (LHS) BOJ Assets (RHS)

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

3,500,000

0.80

0.90

1.00

1.10

1.20

1.30

1.40

1.50

1.60

1.70

01-1

999

01-2

000

01-2

001

01-2

002

01-2

003

01-2

004

01-2

005

01-2

006

01-2

007

01-2

008

01-2

009

01-2

010

01-2

011

01-2

012

01-2

013

01-2

014

01-2

015

Mil

lio

ns

Eu

ro

ECB QE (Balance) vs. EURUSD

EURUSD (LHS) ECB Assets (RHS)

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Policy

• (c) While BOJ and ECB expanded assets, the Fed surreptitiously did the opposite.

• In 2014 the Fed began to engage in larger and more frequent reverse repo operations in order to drain

additional liquidity to facilitate higher GC rates, and set the stage for a higher Fed funds rate.

• Consequently nearly 6% of the Fed’s $4.2T of core (UST, Agency, MBS) long dated holdings are due to

mature within 1yr as the impact of monetization fades.

79 $-

$500,000

$1,000,000

$1,500,000

$2,000,000

$2,500,000

$3,000,000

$3,500,000

$4,000,000

$4,500,000

0%

10%

20%

30%

40%

50%

60%

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

Mil

lio

ns

Fed Core Holdings

% of Total Core Holdings Maturing <1yr (UST, Agency, MBS)

Core Fed Holdings (UST, Agency, MBS)

$-

$100,000

$200,000

$300,000

$400,000

$500,000

$600,000

$700,000

0.00%

0.05%

0.10%

0.15%

0.20%

0.25%

0.30%

0.35%

0.40%

01/2

014

03/2

014

05/2

014

07/2

014

09/2

014

11/2

014

01/2

015

03/2

015

05/2

015

07/2

015

09/2

015

11/2

015

01/2

016

Mil

lio

ns

Fed RRP vs. Fed Funds Rate

Effective Fed Funds Rate (LHS)

Reverse Repo Held By Fed (RHS)

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Policy

• The net impact of Fed operations can be seen in the Wu-Xia Shadow Federal Funds Rate, which

denotes a tightening of nearly 250 bps over the course of 2014 and 2015.

• Shadow Fed Funds Rate bolsters the argument that the opportunity cost of USD yield is smaller than

the market may perceive on a nominal basis.

80

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Shift Mechanism: Growth/Inflation

Mismatch & Flight to USD

• (d) As explained in previous sections, falling commodity prices create growth/inflation

mismatch leading to lower real rates and greater proclivity to USD long positions.

• Historically the the Fed Funds rate has tracked both world GDP and inflation, with US inflation and

tightening leading to tightening abroad. This is also functionally displayed by historical real interest

rates for BRICS economies versus YoY% change in the USD TWI.

• In the current cycle, negative real rates spurred inflation abroad and encouraged overinvestment. As

output declined with commodity prices, EM real rates came under pressure due to resilient inflation and

monetary paralysis.

81 -10%

-5%

0%

5%

10%

15%

20%

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

USD vs. BRICS Real Interest Rate

USD Broad TWI (YoY%) Average BRICS Real Interest Rate

-10%

-5%

0%

5%

10%

15%

20%

25%

07/1

954

05/1

957

03/1

960

01/1

963

11/1

965

09/1

968

07/1

971

05/1

974

03/1

977

01/1

980

11/1

982

09/1

985

07/1

988

05/1

991

03/1

994

01/1

997

11/1

999

09/2

002

07/2

005

05/2

008

03/2

011

01/2

014

World Macro vs. Fed Funds Rate

Effective Fed Funds Rate World Inflation (YoY%)

World GDP (YoY%)

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Missing?

• In turn I see several reasons for a USD unwind in 2016 (at least on a narrow TWI basis):

• (a) Declining marginal effect per unit of QE in Japan and EUA.

• (b) Crowded EUR/JPY funded carry trades finally coming undone cross body – SPX etc.

• (c) Basing inflation expectations making additional easing more difficult to justify.

• (d) Decreased risk aversion as Chinese trade benefits from narrow dollar compression on a trade

weighted basis and is more easily able to target a CFETs index level.

82

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Missing?

• Though the ECB and BOJ have continued to monetize assets and follow through with QE, the

marginal unit of currency depreciation per unit of QE has rapidly decelerated.

• There are a number of reasons for this starting with market sentiment, but principally the cause is likely

immense crowded levered carry US equity long positions, and related carry trade unwinds as asset

managers curtail risk.

83

-30%

-20%

-10%

0%

10%

20%

30%

-600,000

-400,000

-200,000

0

200,000

400,000

600,000

800,000

1,000,000

12/1

999

11/2

000

10/2

001

09/2

002

08/2

003

07/2

004

06/2

005

05/2

006

04/2

007

03/2

008

02/2

009

01/2

010

12/2

010

11/2

011

10/2

012

09/2

013

08/2

014

07/2

015

BOJ QE (flows) vs. USDJPY

BOJ Assets (YoY flows) USDJPY (YoY%)

-30%

-20%

-10%

0%

10%

20%

30%

-1,000,000

-500,000

0

500,000

1,000,000

1,500,000

01/2

000

12/2

000

11/2

001

10/2

002

09/2

003

08/2

004

07/2

005

06/2

006

05/2

007

04/2

008

03/2

009

02/2

010

01/2

011

12/2

011

11/2

012

10/2

013

09/2

014

08/2

015

ECB QE (flows) vs. EURUSD

ECB Assets (YoY flows) EURUSD (YoY%)

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Missing?

• We are starting to see a fundamental (interest rates spreads) versus behavioral (ECB forward

guidance) divergence manifest in the EURUSD as marginal value per unit of QE diminishes.

• Outside of computer driven resistance, a higher EURUSD at 1.10 or greater seems very likely.

84

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Missing?

• The biggest non-China carry trade has been between EUR and JPY funded US equities

positions. As basis has gotten larger, these positions have become difficult to initiate in a

higher volatility world.

• While the magnitude of these explicit positions is unclear, the relationship is best described by the

increasing negativity of basis swap spreads and net credit vs SPX (previously shown on Slide 18).

• On an Index basis there is a clear divergence between the broad and narrow USD TWI, as the

EURUSD and USDJPY have reversed despite the rout in EMFX.

85

80

90

100

110

120

130

140

1999

1999

1999

2000

2000

2001

2001

2001

2002

2002

2003

2003

2004

2004

2004

2005

2005

2006

2006

2006

2007

2007

2008

2008

2009

2009

2009

2010

2010

2011

2011

2011

2012

2012

2013

2013

2014

2014

2014

2015

2015

Source: Federal Reserve, CFTC

Broad vs. Narrow USD

Broad USD TWI (YoY%, 1995=100) DXY Index (YoY%, 1995=100)

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Missing?

• As inflation rebounds real rate spreads between developed markets will converge, putting

further downward pressure on the USD.

• Unlike past cycles, the Fed has effectively tightened prior to raising nominal rates. In past cycles the

USD generally rallied into the first hike, to sell off after. In this case the USD rallied through 2 years of

shadow hike prior to a nominal hike. As such the USD is very mature versus past cycles.

• Going into the next 4 months (1/16 – 4/16), for the first time in years, many different DM

countries/regions including the US and EUA, will see a rise off a deflated CPI base from 2015.

We have already seen CPI (YoY%) December 2015 bounce 66 bps off December 2014.

86

96

97

98

99

100

101

102

103

104

110

112

114

116

118

120

122

124

01/2

012

04/2

012

07/2

012

10/2

012

01/2

013

04/2

013

07/2

013

10/2

013

01/2

014

04/2

014

07/2

014

10/2

014

01/2

015

04/2

015

07/2

015

10/2

015


CPI Basing Visualized (2005=100)

US CPI (LHS) EUA CPI (LHS) Japan CPI (RHS)

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Missing?

• The USD is inextricably tied to EM Real rates but has faced downward pressures from two

angles due to the commodity nature of route:

• (1) Falling commodity prices a negative shock to output in many EM,

• (2) Countries that are not producers are mostly net importers and face disinflation.

• Disinflation should support real rates for importers, but many share trade relationships with China which

is experiencing its own secular slowdown, and consequently inciting global capital outflows.

• The most interesting takeaway of diminishing QE value is the impact on Chinese trade. Japan

and Europe make up 30% of total Chinese exports.

• China’s slowdown has been the cause of massive global capital outflows, which principally began with

the devaluation of its currency in August 2015.

• As the PBoC moves to target a basket of currencies, a weaker DXY is sufficient to allay some of the

outperformance of the Broad USD, and stem some fear concerning the CNY.

87

China Export Destination

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Missing?

• Difficulties guiding the EUR and JPY weaker against the USD would provide dividends to the PBoC

which announced in December that it would target a CFETS basket as opposed to a pure USD peg.

• The PBoC has held up its commitment by defending the index near its 100 level.

• Europe and Japan make up roughly 30% of total Chinese exports, and contribute 7% of BoT.

• Easing the CNY would in turn ease global slowdown fears and capital flow effects on other

related emerging markets, establishing a new regime for risk.

88

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About Convexity: Where’s the Trade?

• Convexity in the context of the present market means finding opportunities tied to a potential

regime shift, but relatively protected from its continuation.

• During this regime “baby out with the bathwater” has been a major theme as investors have been

unable to understand the impacts of reflexivity and distortion on asset prices, and have decided to steer

clear of uncertainty.

• The best opportunities are in financial assets in macro economies which have absorbed the reflexive

regime but with misrecognized unrelated exposures or natural internal hedges.

• I present a sample case from my portfolio, top down/bottoms up variant perception of one of the most

despised EME’s (Russia) with specific quality R/R exposure.

• The Russian Internet Sector provides a superb balance of secular trends, cultural, and

opportunistic support:

• Long

• YNDX – Yandex – current price $13.42

• MAIL:LI – Mail.RU – current price $21.60

89

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About Convexity: Russian Macroeconomy

• Russia Macro Economy – a misinterpreted victim of the reflexive system. • Consensus:

• Russia is a geopolitical and economic mess with long term recession risk, if not collapse

for following reasons:

• (1) Oil Economy

• Russia is an oil exporter which is losing huge amounts of money and suffering a

recession.

• (2) Vladimir Putin

• Russia’s megalomaniac leader is a kleptocrat and policy is mismanaged.

• (3) Revolution/Communism

• Significant geopolitical risk exists from continued economic weakness.

• (4) Cannot defend RUB

• Sign of weakness. Russia has too much debt, Central Bank of Russia is overwhelmed.

Disaster looming.

• Variant Perception:

• Russia is following a careful policy strategy of allowing for FX depreciation due to:

• (1) Historical aversion to credit / highly reactionary population.

• Small USD denominated short term external debts across public, private, and household

balance sheets

• Superb central bank FX reserve adequacy.

• (2) Pure terms of trade advantage from RUB depreciation.

• Impact on government budget versus rising import costs.

• (3) Effective monetary policy / demographic protections.

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About Convexity: Historical Background

91

• Russia is better prepared for the current environment than other commodity focused economies.

• Following its incursion into the Crimea region of Ukraine, Russia was hit by a wave of sanctions from

Europe and the US, placing pressure on trade balance, and increasing inflation and accelerating USD debt

unwinds by Russian corporations.

• When oil prices collapsed in 2014, short term interests rates surged, as Rosneft gathered a $10.8B injection

from the CBR which began to unwind foreign exchange reserves in order to restore order.

• As short term rates declined, the CBR stopped defending USDRUB. This is due to the nature of (1)

credit and (2) trade within the country.

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About Convexity: Debt Aversion

92

• Since the 1998 Ruble crisis, Russia has been a relatively risk averse external borrower, and

corporations have been quick to react to credit denomination mismatches resulting from FX pressures.

• As sanctions and commodity collapse began to take a hold on the USDRUB, both private and public sector

entities began to unwind short term dollar denominated external debts. Since the Q3 2014 peak, nearly

40% of all short term external debts have been unwound.

• As can be seen, most external USD debt is long term., and as such is a much smaller draw on FX reserves.

19.7 26.7 27.7

24 25.6 27.1 26.5 24.8

15.8 14.4 13

33.8

31.8 32.9 32.6

33.1 32.3 28.9

28.8

28.9

21.8 20.9

$-

$10

$20

$30

$40

$50

$60

$70

12/2

012

03/2

013

06/2

013

09/2

013

12/2

013

03/2

014

06/2

014

09/2

014

12/2

014

03/2

015

06/2

015

Bil

lio

ns

Source: Central Bank of Russia

Russia Short Term USD Denom External Debt

Public Sector Private Sector

188.5 226.8 229.5 235.5 237.4 241.1 236.4 227.5 212.6

191.9 185

134.3

130.3 129.6 130.3 132.7 133 131.2 128.5

123.2

118 114.4

$-

$50

$100

$150

$200

$250

$300

$350

$400

12/2

012

03/2

013

06/2

013

09/2

013

12/2

013

03/2

014

06/2

014

09/2

014

12/2

014

03/2

015

06/2

015

Bil

lio

ns

Source: Central Bank of Russia

Russia USD Denom Long Term External Debt

Public Sector Private Sector

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About Convexity: Debt Aversion

93

• Russian external debt is also overstated on a gross basis. Unlike other struggling commodity

driven economies like Brazil, Russia has been a net creditor since 2014.

• Note Russian portfolio flow liabilities actually declined through 2014 and Q1/Q2 2015.

• Russian fiscal prudence has been to delever into economic slowdown, rather than simply borrow more at

longer maturity as has been the case in Brazil. Geopolitics also indirectly benefited Russia, as it has lent to

other struggling EZ countries in a bid for greater political mobility and leverage against existing sanctions.

(15)

(10)

(5)

0

5

10

15

20

2012

Q1 2

012

Q2 2

012

Q3 2

012

Q4 2

012

2013

Q1 2

013

Q2 2

013

Q3 2

013

Q4 2

013

2014

Q1 2

014

Q2 2

014

Q3 2

014

Q4 2

014

Q1 2

015

Q2 2

015

Bil

lio

ns

Source: IMF

Russia Net Incurrence of Liabilities - Portfolio Investment

(Debt Securities, USD Flows)

Deposit Taking Corp Government

Other Sectors Other Financial Corps

-10

-5

0

5

10

15

20

25

30

35

2012

Q1 2

012

Q2 2

012

Q3 2

012

Q4 2

012

2013

Q1 2

013

Q2 2

013

Q3 2

013

Q4 2

013

2014

Q1 2

014

Q2 2

014

Q3 2

014

Q4 2

014

Q1 2

015

Q2 2

015

Bil

lio

ns

Brazil Net Incurrence of Liabilities - Portfolio Investment

(Debt Securities, USD Flows)

Deposit Taking Corp Government

Other Sectors Other Financial Corps

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About Convexity: Debt Aversion / CBR

Reserve Adequacy

94

• Debt aversion also extends to the household sector where Russia (in red) ranks among the lowest EMEs in

terms of household debt as a percent of GDP.

• Russian USD denominated short term external debt stands at a paltry 9.07% of FX reserves, down ~2.5%

from 2014 levels. On a public sector basis adequacy is even greater at 3.5% of total FX reserves.

11.52% 11.57%

9.07%

$-

$100

$200

$300

$400

$500

$600

0%

2%

4%

6%

8%

10%

12%

14%

2013 2014 Q2 2015

Bil

lio

ns

Source: Central Bank of Russia, IMF, Worldbank

Russia USD Denom Short Term External Debt to FX Reserves

ST Debt/ FX Reserves (LHS)

FX Reserves (RHS)

Total ST USD Denom External Debt (RHS)

0%

10%

20%

30%

40%

50%

60%

70%

80%

03/2

005

09/2

005

03/2

006

09/2

006

03/2

007

09/2

007

03/2

008

09/2

008

03/2

009

09/2

009

03/2

010

09/2

010

03/2

011

09/2

011

03/2

012

09/2

012

03/2

013

09/2

013

03/2

014

09/2

014

03/2

015

Source: BIS

EM Household Debt (%GDP)

CN MY TH BR MX

PL RU ZA TR

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About Convexity: Russian Trade

95

• Given limited short term credit risk tied to FX, the CBR has allowed the RUB to depreciate against

various currencies in order to strengthen terms of trade.

• While dramatic depreciation of the ruble has been negative in the short run on a real basis (REER), as a

percent of GDP trade balance has remained fairly consistent at ~5% of GDP.

• One may question why a country with such a relatively small trade balance would allow for a ~42%

depreciation in USDRUB since early 2015. The true concern is fiscal ties to nominal oil revenues.

$(10,000)

$(8,000)

$(6,000)

$(4,000)

$(2,000)

$-

$2,000

$4,000

$6,000

$8,000

$10,000

$12,000

$-

$10,000

$20,000

$30,000

$40,000

$50,000

$60,000

$70,000

Q2, 1994

Q2, 1995

Q2, 1996

Q2, 1997

Q2, 1998

Q2, 1999

Q2, 2000

Q2, 2001

Q2, 2002

Q2, 2003

Q2, 2004

Q2, 2005

Q2, 2006

Q2, 2007

Q2, 2008

Q2, 2009

Q2, 2010

Q2, 2011

Q2, 2012

Q2, 2013

Q2, 2014

Q2, 2015

Mil

lio

ns

Mil

lio

ns

Source: Central Bank of Russia, Federal Reserve

Russia Real vs. Nominal Trade Balance

Error (RHS) Real (LHS) Nominal (LHS)

0%

1%

2%

3%

4%

5%

6%

7%

Q4, 1995

Q3, 1996

Q2, 1997

Q1, 1998

Q4, 1998

Q3, 1999

Q2, 2000

Q1, 2001

Q4, 2001

Q3, 2002

Q2, 2003

Q1, 2004

Q4, 2004

Q3, 2005

Q2, 2006

Q1, 2007

Q4, 2007

Q3, 2008

Q2, 2009

Q1, 2010

Q4, 2010

Q3, 2011

Q2, 2012

Q1, 2013

Q4, 2013

Q3, 2014

Q2, 2015

Source: Central Bank of Russia, OECD

Russia Trade Balance (%GDP)

Real BoT (%GDP) Nominal BoT (%GDP)

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96

• The Russian government derives 52% of its revenue from taxes on oil companies within its borders.

• Based on 2012 estimate, Russia exports roughly 40% of total crude oil produced, accounting for 35% of

total exports. An additional 31% of exports are comprised of refined petrol and petrol gas products bringing

energy share of exports to 66%.

• A simple back of the envelope example shows how depreciation of the ruble directly impacts government

revenues received. Assuming a 37% depreciation, the Russian government saves ~358.8 rubles per

barrel in Federal Revenue. This can be compared to a pegged exporter such as KSA.

USDRUB USDSAR

Deprectn Starting 55 3.75

36.36% Ending 75 3.75

Oil ($/bbl) RUB SAR

USD/bbl 60 3300 225

USD/bbl 30 2340 112.5

-960 -112.5

Ending 55 3.75

Oil ($/bbl) RUB SAR

USD/bbl 60 3300 225

USD/bbl 30 1650 112.5

-1650 -112.5

Savings 690 0

Gov Savings 358.8 0

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97

• As long as ruble denominated exports continue to rise, the government remains capable of paying

fixed RUB denominated expenses. Despite declining USD BoT, government revenue derived from

exports grew 9% YoY in Q3 2015.

• The risk to rapid depreciation of currency is rocketing inflation and rising costs of majority

imported staples.

• Russia has a highly diversified import mix which is both a blessing and a curse.

• Benefits from small staple imports e.g. food makes up 12% of total imports, versus discretionary

imports such as machinery and transportation (44% of imports).

• Where adequate domestic products are not available such in medical goods, consumers are forced

to pay much higher prices along with additional costs passed via sanctions.

-100%

-50%

0%

50%

100%

150%

Q2

, 20

01

Q1

, 20

02

Q4

, 20

02

Q3

, 20

03

Q2

, 20

04

Q1

, 20

05

Q4

, 20

05

Q3

, 20

06

Q2

, 20

07

Q1

, 20

08

Q4

, 20

08

Q3

, 20

09

Q2

, 20

10

Q1

, 20

11

Q4

, 20

11

Q3

, 20

12

Q2

, 20

13

Q1

, 20

14

Q4

, 20

14

Q3

, 20

15

Russia Trade Balance

Trade Balance (RUB, Quarterly, YoY%)

Trade Balance (USD, Quarterly, YoY%)

Energy Contribution to Government Budget (RUB, Quarterly,YoY%)

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About Convexity: Inflation

98

• As expected sudden depreciation of the RUB led a surge in inflation, topping at 15% in Q1 2015.

• The CBR responded swiftly by hiking the key rate, and the impact is starting to appear as CPI sees some

decline via basing effects from end of year 2015.

• Russia also benefited from enforcing larger VAT and sanction related passthroughs on imports. Whereas

export volumes were mildly weaker in 2015, import volumes were extremely weak, suggesting

consumer shifts away from foreign to domestic products or saving.

-40%

-30%

-20%

-10%

0%

10%

20%

30%

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

Source: IMF; current through 10/2015 w/ forecast

Russia Trade Volumes

Import volume of goods and services (YoY%)

Export volume of goods and services (YoY%)

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About Convexity: Inflation/Labor Market

99

• The net impact of inflation has also been allayed by the labor market replacement cycle. Inflation

and unemployment respectively have had smaller than estimated impact:

• (1) Russian demographic shift eases real wages as younger workers supplant older labor. Save for

post GFC returning labor, Russian labor force has been shrinking while participation rate rises.

• (2) Lagged monetary policy is finally beginning to take hold as inflation hit a 12 month low in

December. Simultaneously, real wage growth has begun to stabilize.

65.0%

65.5%

66.0%

66.5%

67.0%

67.5%

68.0%

68.5%

69.0%

69.5%

-0.5%

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

2006

2007

2008

2009

2010

2011

2012

2013

2014

Source: Federal Statistics Service

Russia Labor

Economically Active Population (LHS, %YoY)

Labor Force Participation Rate (RHS, %)

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About Convexity: Geopolitical Risk

• Historically investors have feared investing in Russia due to geopolitics. This fear was

exacerbated when Russia annexed the Donbass region of Ukraine.

• These fears are likely overstated for numerous reasons:

• (1) Though Russia and Ukraine have both been entirely non-compliant with the original Minsk

Protocol, Russian geopolitical influence has grown in light the growing violence and militarization

in Syria and Iraq.

• The Minsk ll agreement from February 2015 has also not been enforced, yet weight within

the EU and US decision calculus has been reduced.

• At Davos leaders from US, DE, FR echoed that sanctions could be lifted within 3 months

if Minsk agreement were honored.

• (2) Sanctions are a vis a vis punishment for both Russia and the EU.

• Initially sanctions were seen as a mechanism to weaken Russian geopolitical hold on

European energy imports, but this effect as eroded as oil prices have collapsed, and

energy importer terms of trade have improved.

• Russia introduced retaliatory counter sanctions on a broad array of US and European

goods. The impact has been clear as import volumes have contracted in 2014 and 2015.

• Both parties are trying to navigate through a period of economic slowdown.

• (3) Russia has benefited geopolitically from its outsider status from OPEC.

• Both member states and non-member states have reached out in order to target one

another with cooperation from the largest outside producer.

• I estimate a strong likelihood that sanctions are lifted by mid year 2016.

100

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Russia Macro Summary

• The Russian macro economy is likely to continue to contract in 2016, but the extent of the long

term damage is overestimated, as it has been a victim of the current reflexive regime.

• Russia should benefit from exogenous variables .

• (1) Removal of sanctions – both on Russia from EU/US and on Iran.

• (2) Reflexive Regime Shift

• It is likely that Russia continues to allow the ruble to depreciate in order to maximize terms of trade and

ruble government revenues as long as oil prices remain below initial $50 budget basis.

• Lack of short term debt overhang should highlight business cycle basing effects, providing investors an

opportunity to buy profitable companies at a long term discount.

101

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Why Russian Internet Companies:

• Secular Tailwinds:

• (1) PC / Mobile OEM Adoption Growth

• Historical trend of adoption by affordability.

• Declining ASPs cross technology in CIS markets.

• Tech oriented culture

• Efficiency driven, first adopter mindsets in Russia.

• Terminal ownership underestimated

• Other CIS still immature PC markets

• Mobile Penetration rates low, fit profile for rapid adoption.

• (2) PC / Mobile Utilization

• Greater mobile data availability via LTE and faster mobile networks.

• Above average utilization rates per user.

• (3) Evolving Monetization

• Online advertising growth uncorrelated + countercyclical.

• Shift towards mobile as advertising mix.

102

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Secular Tailwinds: PC Adoption

103

• Russia already has the largest internet using population in Europe, and growth has been explosive.

• PC ownership per household lags developed Europe due to lower household incomes and affordability.

• Both trends are uncorrelated with the business cycle, with penetration rates rising despite the global

financial crisis.

0

20,000,000

40,000,000

60,000,000

80,000,000

100,000,000

120,000,000

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

Source: Worldbank

Internet Users

Russian Federation Germany

France United Kingdom

Italy Spain

0.0

0.5

1.0

1.5

2.0

2.5

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

Source: Worldbank; 2013/2014 linearly extrapolated estimation

PCs per Household

Russian Federation Germany

France United Kingdom

Italy Spain

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Secular Tailwinds: Consumer Culture

104

• Unsurprisingly, Russian internet usage has risen as ASPs for PCs have declined.

• Uniquely, Russian internet penetration his risen much faster than affordability compared to other

similar GDP/capita countries, suggesting trend is more powerful and secular and only partially explained

by cost, and likely driven by culture.

0%

10%

20%

30%

40%

50%

60%

70%

80%

0%

2%

4%

6%

8%

10%

12%

14%

16%

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

Source: Worldbank

Russia Internet Adoption

PC ASP (LHS, %GDP per Capita)

Internet Penetration (RHS, % per 1000)7.48%

5.53%

1.38%

1.94%

1.03%

2.47%

0%

1%

2%

3%

4%

5%

6%

7%

8%

RussianFederation

Chile Malaysia Lithuania Poland Hungary

Source: Worldbank, comscore

Secular Technology Adoption

5yr Avg Change Internet Penetration Rate - Change PC Affordability

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105

• 2013 Global Consumer Technology Trends Report by the Consumer Electronics Association (CEA)

provides additional justification that Russian tech adoption is driven by more than affordability.

• In 2013, the average Russian household spent more on technology than an average household in the US,

UK, France, Spain, and Denmark.

• This occurred during a year where affordability changed only 40 bps over the prior year.

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106

• Russians (with access) spend more hours on internet than residents of developed countries.

• Russians (as of 2013) still viewed technology purchases as efficiency driven as opposed to developed

countries which primarily viewed technology as entertainment, displaying the potential next step in the

product lifecycle for Russian consumers.

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107

• The same survey also concluded that Russian consumers rank second only to the Chinese as “first

adopters” of new technology.

• By logical extension, actual adoption volume is largely explained by availability and affordability, though

Russian consumers are willing to spend more than people of similar wealth in other countries.

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Secular Tailwinds: Mobile Penetration

108

• Despite having a high propensity for technological adoption, smartphone penetration rate is low in

Russia versus European and US average.

• While 74% of Russians have mobile phones, only 55% of this total is estimated to be smartphone users (an

aggregate penetration rate of 41%) .

• This rate should rise exponentially as smartphone prices decline faster than PC prices over the next 5

years. Since 2015, the relative affordability of smartphones vs PCs has increased ~50bps and even faster

within other CIS countries..

40%

45%

50%

55%

60%

65%

70%

75%

2015 2016 2017 2018

Source: US Census, Emarketer

Smartphone Penetration (% total population)

Western Europe United States Russia

0.27%

0.40%

0.47%

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

0.00%

0.10%

0.20%

0.30%

0.40%

0.50%

0.60%

0.70%

0.80%

2013 2014 2015E

Source: Emarketer

Affordability Spread PC - Smartphones (% GDP per capita)

Russian Federation Kazakhstan

Turkey Belarus

Ukraine (RHS)

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Secular Tailwinds: PC/Mobile OEM

Terminal Growth Rates

109

• Consensus PC and Mobile OEM adoption rates for Russia are generally conservative, and linearly

extrapolate consumption based on affordability, which is contrary to cultural analogue.

• Assuming PC/household ratios matching developed Europe of 2.25 and required CAGR of 7%, implied PC

sales would total 3.1M units in 2015. In reality, CAGR over last 5 years averaged 14% (closer 6M units).

• Assuming .5 new users per computer shows only a marginal 1.5% increase in internet users, but this

ignores the time intensity of the Russian user. For an equal level of unit growth and new users, Russian

internet usage is still 60% greater than French usage.

0

2,000,000

4,000,000

6,000,000

8,000,000

10,000,000

12,000,000

2015

2016

2017

2018

2019

2020

2021

2022

2023

2024

Source: Worldbank, Emarketer

Projected Russia PC Annual Sales Volume

Base (7% CAGR) Model (12% CAGR)

5

10

15

20

25

30

t1 t2 t3 t4 t5 t6 t7 t8 t9

Ch

an

ge in

Usag

e H

rs P

er

Day

*Assume Index 100 at t1, equal growth and users per new PC

Internet Usage Intensity

Russia (4 hrs per day) France (2.5 hours per day)

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110

• Realistically, while new internet users may be a marginally declining measurement, usage intensity

per new PC remains high as current PC/household ratio of 1.21 lags household size of 2.71 people.

• Smartphone terminal growth rates will not be achieved until usage mirrors than of European and US peers,

as shown on slide 108. Again, adoption rates are linearly extrapolated and ignore extra-affordability factors.

• Mobile traffic is rapidly moving away from USB dongles (PC) to mobile as data availability increases,

making it easier for Russians to justify smartphone ownership.

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111

• Both PC and Mobile OEM adoption trends are even more exaggerated in secondary CIS markets in

which Russian companies enjoy meaningful market share.

• Both Internet and smartphone penetration lag Russia.

70.5

54.9

43.4

51.0

59.0

87.4

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

Internet Penetration (Users per 1000)

Russian Federation Kazakhstan

Ukraine Turkey

Belarus United States

0%

10%

20%

30%

40%

50%

60%

2015 2016 2017 2018

Source: Emarketer

Smartphone Penetration (% total population)

Russia CEE

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Secular Tailwinds: Online Monetization

112

• Online advertising has been a secular trend in Russia as people spend more time online.

• Though aggregate advertising expenditures are forecast to decline in current macro environment, online

advertising is primed to capture greater market share with projected CAGR of 8% even with aggregate

CAGR of 0% in ex-online advertising.

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Secular Tailwinds: Online Monetization

113

• Russian online ad spend still lags stalwart UK and China as a percent of total ad spend.

• Online advertising growth rates are projected to remain positive even in a declining environment due to cost

effectiveness. At 2015 Barcelona MS TMT conference, participants noted Russia macro trends were being

meaningfully offset by rising share of online advertising.

• Text based online advertisement is best positioned, as it captures market share from display advertising.

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About YNDX

• Yandex is the largest search engine in Russia total 57.2% of market share. It is also active in

Ukraine, Belarus, Turkey, and Kazakhstan.

• Yandex operates in 4 primary segments.

• O&O Text Based Advertising

• Partner Text Based Advertising

• Display Advertising

• Other Auxiliary Services

• Yandex Dot Services: Mail, Maps, Market, Taxi etc.

114

Yandex (YNDX)

Valuation Measures

Market Cap (intraday)5: 4.22B

Enterprise Value (Jan 28, 2016)3: 3.84B

Trailing P/E (ttm, intraday): 9.2

Forward P/E (fye Dec 31, 2016)1: 18.38

PEG Ratio (5 yr expected)1: 2.06

Price/Sales (ttm): 6.25

Price/Book (mrq):

Enterprise Value/Revenue (ttm)3:

Enterprise Value/EBITDA (ttm)6: 14.01

Financial Highlights

Fiscal Year

Fiscal Year Ends: 31-Dec

Most Recent Quarter (mrq): 30-Sep-15

Profitability

Profit Margin (ttm): 25.55%

Operating Margin (ttm): 20.12%

Management Effectiveness

Return on Assets (ttm): 7.52%

Return on Equity (ttm): 24.87%

Income Statement

Revenue (ttm): 1.82B

Revenue Per Share (ttm): 5.71

Qtrly Revenue Growth (yoy): 18.20%

Gross Profit (ttm): 607.20M

EBITDA (ttm)6: 557.85M

Net Income Avl to Common (ttm): 464.26M

Diluted EPS (ttm): 1.44

Qtrly Earnings Growth (yoy): -2.20%

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Why YNDX:


• (1) PC / Mobile OEM Adoption Growth

• Declining ASPs cross technology in secondary CIS markets


• YNDX Specific Trends:

• (1) One off USD Capex and SBC expenses which damaged FCF yields in 2015.

• (2) Antitrust resolution:

• September FAS ruling forces unbundling of applications and search

• Mobile OEM search shares exaggerated in later waves.

• EU legislation catalyzed by decision

• (3) OEM Adoption impact on market share favorably asymmetric

• Less likely: Sanctions and VAT delay adoption of new devices where market share spread

exaggerated.

• More likely: Removal of sanctions speeds up adoption of unbundled OEMs as per ruling.

• (4) Anti-Google Sentiment / Partnerships

• Browser Market Share

• (5) Cost Saving Environment favors VCG auction

• (6) Rising auxiliary segment revenues

115

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(1) One Off Factors & Current Comp

116

• YNDX FCF yield was hit in 2015 due to a combination of one off USD denominated capex in Q1

and Q2 2015 and SBC expenses.

• While capex and SBC are adjusted for, USD denominated SG&A expenses are not. In aggregate Q1/Q2

2015 displayed meager results despite a 14% revenue upside over prior years Q2 guidance.

• The results can be seen below on adjusted EBITDA margins, which collapse before stabilizing within

historical levels in Q3.

• On a USD adjusted TTM basis, YNDX trades at multiple discounts to primary search competitor google on

a sales, ex-TAC, and adjusted EBITDA basis.

• P/S levels indicate pricing of a mature tech company (<4x), though sector analysis suggests the internet

trend still has strength left. On a ruble basis the comp is cleaner at 6.25x vs. GOOG at 6.82x.

Current Share Price 13.22

Shares Outstanding 322.1 325.6 742.95

USD Bn (FX Adjusted) YNDX GOOG

Q3 2015 Q2 2015 Q1 2015 Q4 2014 Q3 2014 Q2 2014 Q1 2014 FY 2014 TTM Multiple

Revenues 233.09$ 250.70$ 211.05$ 260.71$ 331.51$ 361.52$ 305.01$ 1,258.74$ 1,160.32$ 3.67 x 6.82 x

Ex-TAC revenues2 183.54$ 196.28$ 164.58$ 205.69$ 261.38$ 282.69$ 233.05$ 982.82$ 904.97$ 4.71 x 16.13 x

Income from operations 48.05$ 39.55$ 25.42$ 79.60$ 114.15$ 107.97$ 76.16$ 377.88$ 311.78$

Adjusted EBITDA2 90.90$ 86.76$ 61.08$ 108.04$ 149.92$ 149.48$ 113.26$ 520.70$ 461.68$ 9.22 x 21.67 x

Net income 64.59$ 7.62$ 36.38$ 134.59$ 111.00$ 71.24$ 75.10$ 391.94$ 345.52$

Adjusted net income2 52.95$ 50.27$ 38.47$ 70.51$ 99.37$ 98.66$ 71.51$ 340.06$ 293.63$

Adj EBITDA Margin 39.00% 34.60% 28.94% 41.44% 45.22% 41.35% 37.13% 41.37%

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(1) One Off Factors & Current Comp

117

• As can be seen below, capex surged in first two quarters of 2015 has already surpassed 2014 total.

• D&A expense should diminish as new installation are completed and FCF yield should also rise. FCF is

particularly important in the current macro environment as YNDX faces USD denomination risks from rent

and employee compensation expense.

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(2) FAS Anti-Trust Ruling

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• A significant portion of declining valuation story for YNDX is a question of aggregate market

share. YNDX dominates desktop search at 57%, but makes up only 40% of mobile search.

• YNDX won an anti-trust suit arguing that the loss is mobile search was directly related to unfair bundling

practices employed by google with regards to default application installations – preventing the installation

of similar YNDX applications even if desired by the user.

• GOOG appealed the case in November, but has been a growing target of protectionist sentiment which

has been worsened by anti-government dispositions including setting searches of people and locations

within Russia to comical Lord of the Rings inspired names, and refusing to keep public records at behest

of the the government, instead choosing to shut down Russian engineering operations.

• The implications for this ruling are dramatic as the vast majority of lost market share suffered by YNDX

has occurred in later wave Android products, and GOOG faces a 1-15% fine on 2014 Russian revenues.

• Energized by the FAS ruling, similar suits have been brought in front of the EU by Portuguese app store

Aptoid, US tech firm Disconnect, and lobby group Fairsearch which represents Microsoft, Expedia,

Twenga, and TripAdvisor.

• At worst the current ruling has provided YNDX and opportunity to protect search market share,

while GOOG is unable to push newer OEMs which are incompatible with the FAS ruling.

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(3) Mobile OEM Impact Favorably

Asymmetric

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• Either:

• (1) Russian protectionism as discussed prior has helped YNDX maintain market share as pass

through of sanctions and VAT on US/EU goods has decreased demand for imported smartphones,

forcing additional online advertising further into internet search.

• OR (2) FAS ruling has also helped YNDX in terms of future market share as secular adoption of

smartphones will now occur with “unbundled” applications, allowing new YNDX applications to gain

a foothold and improve mobile search share.

• Via MS, internet search divergence on mobile devices has been a relatively recent phenomenon

beginning with wave 6 smartphones. YNDX mobile monetization has been strong historically,

suggesting fair application privileges will be monetized.

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(4) Anti-Google Sentiments/Partnership

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• Analysts have questioned whether YNDX can maintain stable TAC in an unbundled and greater

mobile environment, but the last 6 months suggest that cooperation against GOOG has helped

foster stronger partnerships:

• In October 2015, YNDX announced it had partnered with Microsoft as the default search option for

Windows 10 across its core markets of Russia, Ukraine, Turkey, Belarus, and Kazakhstan.

• IE (Microsoft default) is still the most widely used browser globally and benefits from

incumbency effect among new internet users in immature markets.

• Starting March 30, 2016 YNDX will be default search for all new versions/updates to Mozilla Firefox

browser in Turkey.

• Contrary to estimates, TAC has remained relatively fixed as percentage of revenues over time, and there

is no indication of near term pressure based on mobile negotiation.

Q3 2015 Q2 2015 Q1 2015 Q4 2014 Q3 2014 Q2 2014 Q1 2014 FY 2014 TTM

TAC:

Related to the Yandex ad network 2,333 2,113 1,866 2,102 1,825 1,804 1,789 7,520 8,028

Related to distribution partners 949 909 851 993 937 847 779 3,556 3,568

Total TAC 3,282 3,022 2,717 3,095 2,762 2,651 2,568 11,076 11,596

Total TAC as a % of total revenues 21.30% 21.70% 22.00% 21.10% 21.20% 21.80% 23.60% 22% 23.40%

Other cost of revenues 1,036 960 996 912 808 776 764 3,260 3,488

Other cost of revenues as a % of revenues 6.70% 6.90% 8.10% 6.20% 6.20% 6.40% 7.00% 6% 5.90%

Total cost of revenues 4,318 3,982 3,713 4,007 3,570 3,427 3,332 14,336 15,084

Total cost of revenues as a % of revenues 28.00% 28.60% 30.10% 27.30% 27.30% 28.20% 30.60% 28% 28.38%

Ex TAC 2,739 1,795 854 2,983 3,143 2,376 1,474 9,976

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(5) Cost Saving Environment

121

• In Q2 2015 YNDX initiated a new VCG auction facility for its Yandex.Direct pay-for-click search

platform in order to drive out “cheapest position in ad block” strategy and facilitate lower and

more efficient CPCs.

• The second-price auction pushes advertisers to pay for their clicks a price that is defined by their closest

competitors. In the VCG auction, the cost-per-click price is based on the difference between the amount of

traffic in different ad positions. If an ad in the top position yielded 15% more clicks than it would have done

in the second position, the advertiser would pay only for these additional clicks if their ad moved up from

the second position to the top. In contrast to the second-price auction, the cost of baseline clicks in the

VCG auction remains the same regardless of the ad's position. The average cost per click grows in

proportion to the increasing amount of traffic, making advertisers compete for additional traffic.

• The VCG auction has been a great success as CPCs have remained low in a contractionary macro

environment, while CTR has remained near historical average ~15%. Number of advertisers using

Yandex grew 18% in Q3 2015 vs Q3 2014.

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(6) Auxiliary Revenue Streams

122

• While YNDX has traditionally made nearly 90% of its revenue from O&O and partner text and display

advertising, it has continued to diversify its business into separate high growth streams offsetting potential

risks to TAC expense.

• Other revenue grew to 371B rubles (+155% over Q3 2014), and now encompasses 2.5% of total

revenues. Other revenues totaled 11.3% of TAC in Q3 2015 versus 5.3% in Q3 2014.

• Other revenues include upstart segments including Yandex.Taxi and Yandex.Market which have capability

to extend into other CIS markets and see immediate and explosive growth off low base.

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YNDX Valuation Summary

123

• On a near term basis I expect YNDX to trade at trailing 5.7x sales on a FY15 basis, with a valuation upside

to historical forward 7.5x sales into year end 2016, this would imply a value of $16.50 after Q4 2015

earnings, $23.70 through year end 2016. For conservative measure an average of both multiples

provides a target value of $20.00 per share. Adding in DCF assuming historical average 40 ruble per

share EPS at 65 USDRUB, 5.5% terminal growth, and 9.5% WACC provides implied value of $17.75.

Average of all valuations provides target price of $19.32.

• **Note DCF prepared based on basis of YNDX provided non-GAAP financials

USDRUB 71 66.2367 55.524 58.4643 56.2584 39.3866 33.6306 35.6871

In RUB millions

Q4 2015 Q3 2015 Q2 2015 Q1 2015 Q4 2014 Q3 2014 Q2 2014 Q1 2014 FY 2014 Q4 2013

Revenues 17013.55 15,439 13,920 12,339 14,667 13,057 12,158 10,885 50,767 12,086

Ex-TAC revenues2 13299.55 12,157 10,898 9,622 11,572 10,295 9,507 8,317 39,691 9,258

Income from operations 3714 3,183 2,196 1,486 4,478 4,496 3,631 2,718 15,323 3,921

Adjusted EBITDA2 6924 6,021 4,817 3,571 6,078 5,905 5,027 4,042 21,052 5,148

Net income 3714 4,278 423 2,127 7,572 4,372 2,396 2,680 17,020 3,346

Adjusted net income2 3714 3,507 2,791 2,249 3,967 3,914 3,318 2,552 13,751 3,519

EBITDA Margins 40.70% 39.00% 34.60% 28.94% 41.44% 45.22% 41.35% 37.13%

Adjusted EPS 11.53 10.89 8.67 6.98 12.32 12.15 10.30 7.92

In RUB millions

Q4 2015 Q3 2015 Q2 2015 Q1 2015 Q4 2014 Q3 2014 Q2 2014 Q1 2014 FY 2014 Q4 2013

Advertising revenues:

Text-based advertising

Yandex websites 10961.5 10,503 9,371 8,444 9,965 9,310 8,559 7,394 35,228 8,006

Ad network 3597 3,740 3,342 3,013 3,270 2,772 2,705 2,663 11,410 2,830

Total text-based advertising 14558.5 14,243 12,713 11,457 13,235 12,082 11,264 10,057 46,638 10,836

Display advertising

Yandex websites 697.4 634 739 525 997 716 674 647 3,034 1,015

Ad network 210.1 191 109 81 184 114 101 76 475 137

Total display advertising 907.5 825 848 606 1,181 830 775 723 3,509 1,152

Total advertising revenues 16373.5 15,068 13,561 12,063 14,416 12,912 12,039 10,780 50,147 11,988

Other 640.05 371 359 276 251 145 119 105 620 98

Total revenues 17013.55 15,439 13,920 12,339 14,667 13,057 12,158 10,885 50,767 12,086

In RUB millions

Q4 2015 Q3 2015 Q2 2015 Q1 2015 Q4 2014 Q3 2014 Q2 2014 Q1 2014 FY 2014 TTM

TAC:

Related to the Yandex ad network 2522.4 2,333 2,113 1,866 2,102 1,825 1,804 1,789 7,520 8,028

Related to distribution partners 1191.6 949 909 851 993 937 847 779 3,556 3,568

Total TAC 3714 3,282 3,022 2,717 3,095 2,762 2,651 2,568 11,076 11,596

Total TAC as a % of total revenues 21.30% 21.30% 21.70% 22.00% 21.10% 21.20% 21.80% 23.60% 22% 23.40%

Other cost of revenues 1094.4 1,036 960 996 912 808 776 764 3,260 3,488

Other cost of revenues as a % of revenues 6.70% 6.70% 6.90% 8.10% 6.20% 6.20% 6.40% 7.00% 6% 5.90%

Total cost of revenues 4808.4 4,318 3,982 3,713 4,007 3,570 3,427 3,332 14,336 15,084

Total cost of revenues as a % of revenues 28.00% 28.00% 28.60% 30.10% 27.30% 27.30% 28.20% 30.60% 28% 28.38%

Ex TAC 2,739 1,795 854 2,983 3,143 2,376 1,474 9,976

Average EPS $10.09 Current $13.20

USDRUB LT 65 12m Valuation Trailing 5.7x Forward 7.5x DCF Average

WACC 9.50% $16.50 $23.70 $17.75 $19.32

Terminal Growth 6.0% Implied Upside 25% 80% 34% 46%

Implied Value $17.75

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Why Mail.Ru:


• (1) Mobile OEM Adoption Growth

• Declining smartphone ASPs in Russia increase penetration rates

• Smartphone ownership lags PC – historical analogue for exponential growth


• Increased mobile data availability via LTE networks

• Mail.Ru Specific Trends:

• (1) Owns largest Russian social networks which will benefit from greater exposure and price

point advantage on CPM basis with greater number of adopters.

• (2) Mail’s online gaming platform my.com presents opportunity to earn external revenues and

presents enormous upside at high margins.

• Armored warfare has potential to dislodge current leading franchise World of Tanks which

currently generates ~$500M per year.

• (3) Mail is co-owned by Alisher Usmanov Russia’s richest man and shares potential future

strategic value

• Usmanov is also an investor in Uber, which is currently 3rd in market share behind

Yandex.Taxi and Gett.

• Wife is considered a close friend of Vladimir Putin, allowing for potential political upside.

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(1) Russian Social Networks

• Mail.Ru owns the three largest social networks in Russia: Vkontakte (VK), Odnoklassniki (OK),

and My World Mail.Ru (MW).

• Despite being active in Russia for last 10 years, Facebook has gained barely 5% market share, while

VK usage has soared.

• Usage via mobile phones has been a strong secular trend, with mobile now accounting for about the

same number of users as desktop. Via MS:

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(1) Russian Social Networks

• Mail.Ru is the best play on a structural shift towards mobile monetization, as it enjoys high

growth rates in adoption of its application via smartphones, a trend which is expected to see

5% annual penetration growth over the next 5 to 10 years.

• CPM is traditionally cheaper on mobile devices, but has been rising through 2015 versus desktop; a

reason why I feel confident recommending both YNDX and Mail.

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(2) Online Gaming

• Mail.Ru also controls an extremely popular gaming platform Games.Mail.Ru, which seen its

userbase continue to grow to 1.8m users as of year end 2014, at 15% in Q3 2015 over Q3 2014.

• Gaming has potential for massive growth as Mail pushes to self- publish more franchises and expand

geographically. Unlike social networks, gaming platforms are not bound by language.

• Mail’s most recent self-published title, Armored Warfare has received strong reviews and has potential

to challenge leading franchises World of Tanks and War Thunder. World of Tanks generates ~$500m

per years in sales, and it is likely that Mail can eat into the gap at a high margin (MS estimate of 50%).

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(3) Ownership Structure

• Mail.Ru is co-founded by Alisher Usmanov, the richest man in Russia and husband to gymnastics

coach Irina Viner, who is considered to be a close Putin confidant. Usmanov is also a close friend of

Roman Abramovich, who was personally consulted in the selection of both Vladimir Putin and Dimitry

Medvedev. A long trope in Russia has been to always invest with the oligarchs.

• While this may be easy to overlook, Mail is likely to enjoy discounted political risk and greater

protectionism than other companies which lack similar political capital, and in this sense its long term

growth rates should more closely follow secular trend.

• Usmanov was revealed this month as also being an investor in Uber, which may help Mail both

generate strategic partnerships without being subject to the protectionist obstacles faced by other

operators such as the Israeli company Gett.

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Mail.RU Valuation Summary

• Mail.Ru valuation is somewhat straight forward simply because it trades at internet versus social media

multiples rather than a blend. Mail trades at a forward P/E of 29.1x which is roughly the same as FB

28.99x INCLUDING non recurring items, 8.5x sales versus FB which trades at 16x.

• While its unrealistic to think Mail will ever trade at current FB multiples, I expect Mail to target about 12x

sales and 35x forward P/E (both roughly mid/early cycle FB multiples)

• At these levels I see a target price of $28.27 by end of FY2015. A simple back of the evelope

DCF using FY 2015 estimated EPS, an exchange rate of 65 USDRUB, 9.5% WACC, and 6.5%

terminal growth provides a value of $24.66 per share. An average of all methods provides a

value of $27.07 per share.

• ** Note Mail.Ru quarterlies were not found online, instead all measures were extrapolated from annual

and semiannual reports.

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USDRUB 65 42 35 35 Per Share Sales Current Price 21.6

FY 2015E 1H 2015 FY 2014 FY 2013 FY 2015E 1H 2015 FY 2014 FY 2013

Online Advertising 12778 6389 12454 9246 Online Advertising0.89 0.69 1.62 1.20

MMO Games 8016 4008 8414 6654 MMO Games 0.56 0.43 1.09 0.86

Community IVAS 12674 6337 11870 8697 Community IVAS 0.89 0.69 1.54 1.13

Other revenue 3042 1521 3041 2807 Other revenue 0.21 0.16 0.39 0.36

Total Revenue 36510 18255 35779 27404 2.553147 1.975649 4.646623377 3.558961

Multiple 8.46 x 4.65 x 6.07 x

Net gain on venture cap investments 8

P/S forward P/E P/E TTM

Personnel -9306 -4653 6577 5332 FB 16.72 x 28.99 x 86.98 x

Office & Rent -1896 -948 1643 1262 TWTR 5.50 x 13.36 x N/A

Agent/Partner Feeds (TAC) -4854 -2427 4550 2968 MAIL.RU 8.46 x 29.19 x 13.29 x

Marketing Expenses -842 -421 1164 842 Estimated 12 35 Average

Server Hosting Expenses -2296 -1148 2227 866 30.64$ 25.90$ 24.66667 27.07$

Professional Expenses -378 -189 349 274 DCF

Other opex -1386 -693 971 773 WACC 9.50%

Total OpEx -20958 -10479 17481 12317 Terminal 6.50%

24.66667

EBITDA 15568 7784 18297 15087

Depreciation & Amortization -7114 -3557 2154 1141

Impairment of intangible assets -118 -59

Share of profit of equity accounted associates40 20 258

Finance income 600 300

Finance expenses -2486 -1243

Other non-operating income 68 34 166 442

Net gain on financial assets & liabilities at profit or loss over the equity222 111

Net gain on disposal of shares in equity accounted associates

Net FX (losses)/gains -822 -411

Profit before income tax expense 5958 2979 15978 14646

Income tax expense -1896 -948 3460 3193

Net Income 4062 2031 12518 11453

Attributable to

Equity Holders of the parent 4504 2252 12518 11453

non controlling interest 44 22

EPS (RUB)

Basic EPS 48.1 9.6 56.9 52.05909

Diluted EPS 48.1 9.6 56.9 52.05909

0.74 1.625714 1.487403

P/E forward 2015, trailing 29.19 x 13.29 x 14.52 x

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2016 outlook – finding convexity - akshay agashe

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