innovation and technology · 02/05/2005 · investor time horizons wvar wstatistical problems...

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The Impact ofInnovation and Technologyon Bond Manager Style

M E T R O P O L I T A N W E S T A S S E T M A N A G E M E N T

May 2005 w Laird Landmann, Brian Cone

© 2005 Metropolitan West Asset Management LLC


I. New Bond Market Dynamicsw Growthw Proliferation of Complex

Securitiesw Rise of Preference Arbitrage

II. Reinforcing New Dynamics,Innovation, and Technologyw Options Models, Correlation

Analysisw Supports Proliferation of Securitiesw Model Assumptionsw Example: Mertonian Problems

III. The Shortening ofInvestor Time Horizonsw VARw Statistical Problemsw Impact on Dealersw Impact on Investors

IV. The Impact onFixed Income Managersw Approach to

Opportunities/Riskw Momentum Approachw Value Approach

V. The Benefits ofStyle Developmentw Diversificationw Investor Choice

VI. Conclusion:Value Manager Outlookw Interest Ratesw Creditw High Yield

CONTENTSThe Impact of

Innovation and Technologyon Bond Manager Style

1


I. New Bond MarketDynamicsw Growthw Proliferation of Complex











2

© 2005Metropolitan WestAsset Management LLC

3M E T R O P O L I T A N W E S T A S S E T M A N A G E M E N T

w Approximately $1 Trillion in size.

w Dominated by large simple asset classes. 45% US government bonds 43% High quality corporate bonds 12% Others

w Pricing controlled by large oligopolistic dealers.

w Buyers diffuse and small.

w Information highly controlled by dealers. High Bid/Ask spreads Moderate volatility

OldU.S. Bond

Market



w $20+ Trillion in size.

w Diverse, customized, and complex claim set.

w Complex new markets that transfer risk (disintermediation). High yield, distress and bank debt Credit default swaps CMOs, CBOs, CLOs, ABS, synthetic structured CDOs

w Pricing determined by large marginal buyers of asset classes. Insurance companies Huge money managers Foreign buyers (banks, insurance, gov’t agencies, pensions)

w Pricing information rapidly disseminated. Smaller Bid/Ask Larger price swings Dealers more risk averse

CurrentBond Market

Dynamics



CurrentBond Market

Dynamics

1000’s of Special Vehicles Created to “Arbitrage Buyer Preferences”US TresuriesT-BillsT-NotesT- BondsTIPSSTRIPSTIGeRsCATSFlower BondsGNMA

US AgenciesFHLMCFNMAFHLBBermuda CallEuropean CallBermuda, European

Call With a FixedCoupon

Stepped NotesFlip NotesRange Accrual noteInverse FloaterIndex Amortizing

NotesCPI Linked NotesPSA Linked NotesCAP FloatersCanary NotesCurrency LinkedEquity Linked Notes

US AgenciesBrady BondsREFCOFICOTVAPEFCOHUDNational ArchiveOPICAID Bonds

US CorporateCapital Structure:Bank Debt1st Mortgage BondsSecured

EETCs (EnhancedEquipment TrustCertificates)

ETCs (Equipment TrustCertificates)

PTCs (Pass-Thru TrustCertificates)

Opco DebtHoldco Debt

Senior NoteDebenturesTrust PreferredsRetail Preferreds

US CorporatePrincipal Type:BulletSinkingCallablePerpetual

Tier 1Tier 2

Coupon Type:FixedFloatingStep-ups

GICsFunding AgreementsSurplus Notes

OtherHigh TidesSenior Secured Bank DebtSenior Unsecured Bank Debt1st Lien Debt2nd Lien DebtSenior NotesSenior Subordinated NotesSubordinated DebenturesTrust PreferredConvertible Subordinated

DebenturesPerpetual Preferreds w/Step-Up

US CorporatePreferredWarrantsEquityCredit-linked NotesExchangable Variable Rate

Notes (EVRNs)Usable BondsSpringing IssuesPay-in-Kind Securities (PIKS)Debtor-in-Possession (DIP)

LoansCommercial PaperCredit Default SwapsTotal Return SwapsInverse FloatersDeferred Coupon BondsPeriodic Auction Reset

Securities (PARS)Preferred Equity Redemption

Cumulative Stock (PERCs)



CurrentBond Market

Dynamics

1000’s of Special Vehicles Created to “Arbitrage Buyer Preferences”ABSABS Mezzanine CDOABS High Grade CDOFirst to Default Synthetic BasketCDO-SquaredSingle-Tranche Synthetic CDOTrust Preferred CDOCommercial Real Estate CDOLeveraged Loan CDOHigh Yield CDOMulti-Sector CDOBalance Sheet CDONet Interest Margin SecuritiesABS Pay-As-You-Go SyntheticsABS Pay-As -You-Go Synthetic CDOCMBS Pay-As-You-Go SyntheticsCMBS Index Total Rate of Return SwapsCMBS Conduit StructuresCMBS Large Loan StructuresCMBS Transitional Property StructuresCMBS Credit Tenant Lease StructuresCMBS Single Asset StructuresCMBS Single Borrower StructuresCMBS Rake StructuresCommercial Mezzanine LoansCommercial B NotesAutomobile ABSDealer Floor Plan ABSBank Credit Card ABSPrivate Label Credit Card ABSEquipment ABSTobacco Settlement ABS

ABSLegal Settlements ABSStranded Cost ABSHome Equity ABSManufactured Housing ABSHigh LTV ABSSecond Lien ABSUK Mortgage ABSAustralian Mortgage ABSDisaster Recovery ABSShipping Loan ABSRoyalty ABSStudent Loan ABSHealthcare ABS12B-1 Fee ABSFranchise Loan ABSFuture Flow ABSSecured Lease Aircraft ABSTax Lien ABSTimber Receivable ABSRailroad ABSInsurance Settlement ReceivablesLiquidating Trust ABSRe-performing Loan ABSRecreation Vehicle ABSMarine ABSGrantors Trust StructuresOwners Trust StructuresWarehouse Revolver StructuresNAS IO'sPrepayment Penalty ReceivablesSubordinate Turbo Bonds

MBSCMO/REMIC Class Types:AD Accretion Dir. AFC Available Funds ARB Ascending Rate Accel Security CALL Call Option Controlled Am. Complex Component Coll. Strip Rate Date Callable Non-Zero Delay Descending Rate Extended Delay Exchangeable Excess P&I Floater Fix to Variable Hazard Bond Hard Bullet Index Amort Inverse Floater Interest Only Liquidity Mezzanine Mandatory Redemp. Non-accel Security Non-paying Resid. Non-sticky Jump Notional Prin .



CurrentBond Market

Dynamics

w Many new structures are difficultor impossible to hedge.

w These new instruments gothrough cycles of over/undersponsorship.

MBSCMO/REMICClass Types:Planned Amort.Payment ExchangePrincipal OnlyPass-ThroughPutPartial AccrualResidualRatio StripRetailSoft BulletStructured CollScheduled PaySegmentSemiannual PaySequential PaySticky JumpScheduled MtySenior StripStep Rate BondProrata Prin StrSubordinatedSupportSwapTargeted Amort.Wtd Avg CouponIndex Alloc.Extended ResetAccrual

MBSMortgage Agencies: FHLMC, GNMA, FNMAGNMA I GNSF 30-yr Fixed GNJO 15-yr Fixed GNMHB Mobile Home B GNMHD Mobile Home D GNGP Grad Payment G N M A I I G2SF 30-yr Fixed G2JO 15-yr Fixed G2AR ARM G2GP Grad Payment FNCL 30-yr Fixed FNCI 15-yr Fixed FNCX 30/7 Balloon FNCT 20-yr Fixed FNCN 10-yr Fixed FNGL FHA/VA Guar FNCOF COFI ARM F H L M C 75-Day FHLMC 30-yr Fixed FHCI 15-yr Fixed FHTPM Tiered Payment FGLMC 30-yr FixedFGCI 15-yr FixedFGFB 30/5 BalloonFGSB 30/7 BalloonFGTW 20-yr Fixed

MBSPass-Through TypesFixed: 30yr, 15yr, 20yr, 10yr, 5yr and 7yr

BalloonsARM: Cofi, 1yr UST, MTA, LIBOR,

Negative AmortizationHybrid: 3yr, 5yr, 7yr, 10yr

Non-Agency:PrimeAlt AScratch and DentSubprime

1000’s of Special Vehicles Created to “Arbitrage Buyer Preferences”



CurrentBond Market

Dynamics

EXAMPLEInvestment: CMO: Jump Z Tranche Price: 97.5 Coupon: 4.5%

Prepayment Per Year: 3% 4% 5% 6% 10%

Yield: 4.84% 4.84% 4.87% 6.37% 10.57%

Duration: 11.34 10.89 9.65 1.45 0.42

Spread: 0.20% 0.20% 0.23% 3.10% 7.43%

Source: MWAM

Diffusion of Claimsw To meet investor preferences.w Allows customized cash-flows to meet user needs/preference.w In this process, bonds are created with difficult risk profiles.

Conclusionw Great short-term investment if housing turnover is above 6%.w Awful long-term investment if housing turnover is 5% or less.

How does one hedge a 1% difference in housing turnover?



w Large overall market, made up of diverse and complex underlying bonds.

w Large buyers can easily overwhelm any Broker/Dealer.

w Information flows are fast.

w Bid/Ask generally more efficient.

w Individual security volatility can be high.

w Liquidity is mercurial – best bet is diversification.

Liquidity and volatility in individual securities depends on:

CONCLUSION:

CurrentBond Market

Dynamics

Time horizon & holdingrequirements of the buyers

Ability to hedgesecurity’s riskSize/Depth of sponsor




II. Reinforcing New Dynamics,Innovation, and Technologyw Options Models, Correlation Analysisw Supports Proliferation of Securitiesw Model Assumptionsw Example: Mertonian Problems








10



Growth Driven By:w Economic Growth/Deficits

w Disintermediation

w Innovation and Preference Arbitrage

Growth ofUS BondMarkets

0

5

10

15

20

25

1970

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

Outst

andin

g De

bt ($

Trillio

ns)Outstanding Debt

Outstanding Debt / GDP

w

Source: Bureau of Economic Analysis, Bond Market Association

Outstanding Debt (% of GDP)

250%

200%

150%

100%

50%

0%

Outstanding Debt / GDP

ABS

Money Markets

Fed Agencies

Corporate

MBS

Treasury

Municipals



w Tools such as “options analysis”, CAPM, and correlation/covarianceanalysis encourage: Financial innovation Preference arbitrage Bespoke risk-taking Mertonian-based credit ratings

By allowing risk analysis of complex securities/structures.

w Key Model Inputs Normal distributions Volatility Capital costs/interest rates Covariances (volatility/correlation)

All tools focus investors on short-term arbitrage conditions.

QuantitativeTools

AccelerateTrends inInnovation



w The 1970s/1980s saw the introduction of new technologies: Correlation analysis Black/Scholes closed form options pricing Binomial options pricing CAPM Mertonian capital structure analysis

w Techniques represent huge advancements; but have limitations: Assume asset returns are Gaussian, or normal. Assume complete markets/perfect liquidity/no transaction costs. Assume future will look like the past. Assume symmetric information sets.

w In most markets, these assumptions are not debilitating.

w Inputs are mostly short-term “efficient market” variables.

The Advent ofQuantitative

Models



w In volatile markets, the main assumption of “normality” fails.

w Markets become scaler (think: school of fish) when: Market participants all have same opinions/fears. Independence of the model assumptions are violated.

This happens when market participants and analysts use the samemodels to analyze markets.

QuantitativeModel Failures

I.VolatilityRises

II.Prices inDecline

III.More Analysts

Sell

IV.Models Indicate

More Risk

Analysts Sell

More SellRecommendations

Volatility Rises Prices Decline

w Lack of “independent” analysis takes market from normal to scaler.



QuantitativeModelsSupport

Proliferationand

“PreferenceArbitrage”

w Provide quantitative risk measures for:

CMOs ABS Structured Notes CLOs, CBOs

SWAPS Total Return Swaps Capital Structure

Arbitrage Yield Curve Notes

PIK Bonds Callable Bonds Bank Debt Synthetic Tranches

w Risk measures based on short-term “efficient market” arbitrage conditions.

w Allowed the creation of hundreds of unique securities.

UniqueSecurityCreated

ValueBuyersAppear

Sold toSpecializedSponsor

OtherSponsors Sell

“Due to Models”

SponsorNeeds

Liquidity

Security NotEasily Hedged

SecurityEasily Hedged

Sells,Pays Bid/Ask

Analyzed W

ith

Model

Placed In Porfolio

Asks

For Bid

Price Falls

Price Falls More



RecentFailures of

NewTechnologies

Askin Capital Managementw Options Analysisw Correlation Analysis

Long-Term Capital Managementw Options Analysisw Correlation Analysis

1998 Brokerage Crisisw Value At Risk Correlation analysis Options analysis

1998 High Yield CBOsw Value At Riskw Credit Ratings Analysisw “Mertonian Analysis”

2001-2002 Mertonian-Based Ratingsw “Efficient Market” Models of Ratingsw Options Analysis



Mertonian Rating ModelsMain Input is Market Value of Equity/Debt

ANDEquity Volatility

w Provides reasonable proxy of solvency in normal environments.

w During periods of high equity volatility or illiquidity: Models produce a high number of false positives. Models emphasize SHORT-TERM financial indicators.

w Rating agencies have increasingly been emphasizing Mertonian models.

MertonianCredit Rating

Models

EXAMPLE



Vicious Cycle:

MertonianModels

EXAMPLE

MarketVolatilityRises

Sellers Emerge& Volatility

Rises

EquityDeclines DueTo Downgrade

Sellers Emerge& Volatility

Rises

Downgrade Warnin

gs

Issued

AgenciesDowngrade

AgenciesDowngradeAgency

Issues

New Downgrade

Watch

w When rating agencies choose market-based short-term models overlong-term fundamentals, the ratings lose independence.

w Strict reliance on short-term equity volatility distorts the ratings process.



Consequenceof “MertonianTechnology”

EXAMPLE

Rating Agencies Respond to Equity Market VolatilityMoody's U.S. Corporate Downgrade-to-Upgrade Ratio

1.601.91 1.73

2.04

3.96

2.70 2.63

1.340.76 0.93 0.75 0.91

1.21 1.18 1.35

2.27

8.40

0

2

4

6

8

10

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

Ratio

recession

recessionSource: Moody's, Lehman Brothers

w Comparing 1990 with 2002: Has the world really changed that much?

w Ratings agencies are focused on short-term indicators.



CONCLUSION:

QuantitativeModels

w Supported/Enhanced disintermediation, securities proliferation,and preference arbitrage.

w Models work well when assumptions are understood.

w Models fail in scaler markets: When all market participants are on the same side. When all market participants use the same technology.

w Models can influence market behavior. Reinforce volatility trends. Reinforce investor behavior.

w Hence, models are not always independent(i.e. Mertonian Credit Ratings)

w Models can generate vicious/virtuous cycles. Risk opportunities for total return investors. Creates excessive price volatility relative to underlying value.













21



EXTENDING OPTION& CORRELATION

ANALYSIS:

Value At Risk-Shortening

InvestorTime Horizons

w Value At Risk - Primary Risk Management Tools: Broker/Dealers Prime Brokers Hedge Funds

w Being rapidly adopted by longer-term investors through “tracking error”: Pension Funds Mutual Funds Endowments

Should long-term investors use short-term volatility-based measures to allocate risk?



VALUE AT RISK(VAR):

Tracking Error(for Bonds)

VAR = f ( )Covariance Matrix

) VAR = f 1. Risk Measures - Often Using Options Models2. Interest Rate / Spread Volatility3. Correlation to other Assets

(

w Correlation and volatility generally based on 200 day weighted moving averages. Emphasizes market changes over the last 90 days.

w Assumes market returns are normal.

w Are correlations based on good statistical theory?

w Tracking error is a derivative of VAR (% expected maximum loss versus index.)



VOLATILITY:

Changes(a lot) VIX* Index (S&P 500 Volatilty)

0

5

10

15

20

25

30

35

40

45

50

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

Index Leve

l

Source: Bloomberg

*VIX refers to theChicago Board Options

Exchange SPX VolatilityIndex.



ASSET CLASSCORRELATIONS:

Stable/Predictable?

6-Month Correlations

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

Jun-92

Jun-93

Jun-94

Jun-95

Jun-96

Jun-97

Jun-98

Jun-99

Jun-00

Jun-01

Jun-02

Jun-03

Corre

lation

S&P/Corp S&P/MBS MBS/Corp

Source: Bloomberg



VICIOUS CYCLES:

VAR-The Impact onBroker/Dealers

w Trading Desk exposure based in VAR dollars.

w When volatility spikes higher:

1. Prices decline / volatility increases.

2. VAR rises dramatically. 3. Trading desks sell “highest VAR” positions.

w Short-term volatility becomes self-fulfilling.

w Cycle does not break until long-term investors step in. Pension Funds Endowments Mutual Funds Insurance Companies

w What happens if long-term investors use the same technology?

Is VAR an Independent Risk Measure?

“ShorteningInvestor TimeHorizons”



IMPACT OFCHANGING

CORRELATIONS/VOLATILITY:

UnintendedConsequences

w Aggregate risk in market can change dramatically when volatility surges

w What happens if Broker / Dealers keep their risk constant?

TargetedBroker /Dealer

Position

TargetedBroker /Dealer

Position

High Volatility Environment

Broker / Dealer Position

Total Market Risk

Low Volatility Environment

Total Market Risk

w What happens to prices and volatility when Broker / Dealers try to reduce risk toprevious aggregate amount?

“Short-TermApproach toInvestmentsExacerbates

MarketVolatility”



VOLATILITYVS. VALUE:

WilliamsCompany(WMB)

EXAMPLE

w Bond prices fall in summer of 2002.

w Volatility soars!

w Rating agencies downgrade.

w Equity declines.

w Assets remain unchanged.

May 2002Market Value of Debt $14 B

Market Value of Equity $ 8 B

Total Enterprise $22 B

Asset Value: $18 B - $25 B

FCF = $2.5 B

August 2002Market Value of Debt $ 5 B


Total Enterprise $ 6 B


FCF = $2.5 B

August 2003Market Value of Debt $13 B


Total Enterprise $18 B


FCF = $2.2 B

Source: Williams Company Documents, MWAM













29



w Largest, most diverse and complex markets in the world. Lots of risk/opportunity. Diverse claim sets create arbitrage and volatility.

w Wall Street is constantly engaged in “Preference Arbitrage”. Uses quantitative techniques and models to sell new securities.

w When models fail and markets are not “normal”: Large price swings occur. Investors react differently depending on investment philosophy. Investors “employ” models for different reasons/goals.

U.S. FixedIncomeMarkets

& InvestorBehavior



Investor Style& MarketDisruption Momentum Investors

w Use models to avoid volatility.

w Tend to believe models are “accurate” and, hence, “market efficient”.

w Buy uptrends in prices and declining volatility.

w Sell spikes in volatility/sell on sharp downward price moves.

w Buy upgrades, sell downgrades - ratings have “new” information.

Value Investorsw Use models to stress test securities.

w Believe model assumptions are challenging and markets are not efficient.

w Buy downtrades below fundamental value/sell at fair value.

w Treat volatility as a long-term opportunity.

w Rating agency information is “in the market” and can be incorrect.



w Vicious/Virtuous cycles generated by reliance on short-term indicators.

w Price swings far exceed swings in fundamental value.

Innovation/Technology/

New DynamicsExacerbateDifferences

in Style

w Preference arbitrage- and volatility-based models further exacerbate price swings. Can make securities trade at large discounts to intrinsic value.

Expansion Recession Recovery

Value $

Time

Market Value

Intrinsic Value

Source: MWAM Illustration



MomentumManagers

w Alpha in bull markets (long part of the cycle); mild underperformance in bear markets.

w Momentum managers exit early on volatility.

w Momentum managers enter late after the “all clear” signal. Low Volatility Upgrades Return of Key Sponsors


Value $andPrice

Time

Price

Intrinsic Value

Momentum Buy

Momentum Sell

Momentum Buy




ValueManagers

w Alpha in bear markets; struggle in bull markets.

w Value managers exit early - miss market tops.

w Value managers enter too early - catch the tail end of downtrade. Capture Good Value Often Contrarian

- Caught by downgrades- Have to weather volatility


Value $andPrice

Time

Price

Intrinsic Value

ValueSell

Value Buy

ValueSell




Style &Credit Risk Momentum

w See safety through credit ratings.w Avoid securities with complex and volatile capital structures.w Emphasis on minimizing defaults.w Emphasis on ratings that are likely to be upgraded.w Use VAR/covariance analysis to avoid losses.

Enhanced Indexerw Match ratings weighting of index.w Tilt slightly towards momentum style.w Emphasize matching volatility to that of the index.

Valuew View credit risk vs. asset coverage.w Limited “new” information from agencies.w Credit exposure = % default probability x stressed asset recovery.w Emphasis on minimizing credit losses.w Asset coverage most important element. Secured bank debt First mortgage bonds Asset-backed transactions



CONCLUSION:

Manager Style w Market dynamics and innovation should exacerbate style differences. Increased illiquidity due to proliferation. Increased volatility due to non-independence of models.

w Disciplined managers should add alpha with either style.

w Alpha should be uncorrelated. Value managers outperform when the market is down. Momentum managers outperform in rising markets. Enhanced index managers have less clear patterns.

Which style has more risk/volatility?

Can more efficient portfolios be constructed?













37



Manager StyleIdentification Value

w Limited tracking to the benchmark.w Alpha negatively correlated to the benchmark.w Lower overall volatility.

Positives: Low volatility/high value added, negative correlation of alphaProblems: Alpha is volatile and lumpy

Enhanced Indexw Near perfect tracking to the benchmark.w Low alpha, but high information ratio.w Alpha uncorrelated to the benchmark.

Positives: Strong trackingProblems: Low alpha

Momentumw Moderate index tracking.w Alpha positively correlated to the benchmark.w Higher overall volatility.

Positives: Good alpha, decent trackingProblems: Higher overall volatility, postively correlated with the market



MANAGERSTATISTICS:

Total Return1995 - Q1 2005

monthly

w Value: Best risk/return on absolute basis.

w Value: Outperforms in down markets.

w Mixing value and momentum gives the best of both worlds. Low overall volatility/high returns. Still outperforms in down markets. Moderate alpha volatility. Moderate tracking to the index - not necessarily a good thing.

w Gross Performance: Subtracting fees would make the results more powerful.

Evaluated three leading managers* in categories - only those with long-term disciplines.

Correlationof Alpha to

Index

AlphaStandardDeviation

Correlationto

BenchmarkMomentum 40% 80 bps 98%

Value -32% 210 bps 84%

Enhanced -6% 55 bps 99%

50/50Value/Momentum

-17% 115 bps 95%

7.30%7.40%7.50%7.60%7.70%7.80%7.90%8.00%8.10%8.20%8.30%8.40%8.50%8.60%

3.6 3.7 3.8 3.9 4 4.1 4.2 4.3

Volatility (SD)

Retur

n

Value (8.43%, 3.72)

50/50 V/M (8.33%, 3.80)

Momentum (8.21%, 4.20)

Enhanced Index (7.96%, 3.83)

Lehman Aggregate (7.48%, 3.82)

Source: LehmanLive

*In MWAM's opinion, thehighlighted managers are

considered leading managersbecause they all have the

following attributes: They have managed assets

with a particular style forover 10 years. They have exhibited style

consistency through multiplemarket cycles. They have outperformed the

relevant indices over longperiods of time. They have all received

meaningful institutionalsponsorship of theirstrategies.



MANAGERSTATISTICS:

Low Duration1995 - Q1 2005

monthly

w Value: Style has the best absolute risk/return proposition.

w Value: Outperforms in down markets.

w Mixing value and momentum gives the best of both worlds. Low absolute volatility/high returns. Outperformance in down markets. Moderate alpha volatility. Good tracking.

w Gross Performance: Subtracting fees would make the results more powerful.

5.40%5.50%5.60%5.70%5.80%5.90%6.00%6.10%6.20%6.30%6.40%6.50%6.60%

1.5 1.6 1.7 1.8 1.9 2 2.1 2.2

Volatility (SD)

Retur

n

Correlationof Alpha to

Index

AlphaStandardDeviation

Correlationto

BenchmarkMomentum 20% 95 bps 88%

Value -50% 152 bps 60%

Enhanced -20% 40 bps 97%

50/50Value/Momentum

-25% 98 bps 83%

Value (6.51%, 1.67)

50/50 V/M (6.42%, 1.68)

Momentum (6.32%, 2.05)

Enhanced Index (6.05%, 1.60)

Merrill Lynch 1-3 UST (5.54%, 1.66)Source: LehmanLiveIn MWAM's opinion, the

highlighted managers areconsidered leading managers

because they all have thefollowing attributes:

They have managed assetswith a particular style forover 10 years. They have exhibited style

consistency through multiplemarket cycles. They have outperformed the

relevant indices over longperiods of time. They have all received

meaningful institutionalsponsorship of theirstrategies.



CONCLUSIONS:

Manager Stylew Changes in the market/technology are further differentiating managers.

w Absolute return oriented investors are best with a value style.

w Relative performance/tracking investors may want enhanced indexing, BUT: After fee, alpha may disappoint. Using a blend of value/momentum can dominate.

w Because alphas are uncorrelated: Using blends of managers with disciplined, distinct styles can dominate. Poor attributes of each style are easily offset.

Investors gain due to increased choice and diversification.













42



VALUE INU.S. FIXED INCOME

MARKETS?

Nominal& Real Rates

Still NotCompelling

5-Year Nominal and Real UST Rates

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

7.00%

8.00%

Jul-97

Nov-9

7

Mar-9

8

Jul-98

Nov-9

8

Mar-9

9

Jul-99

Nov-9

9

Mar-0

0

Jul-00

Nov-0

0

Mar-0

1

Jul-01

Nov-0

1

Mar-0

2

Jul-02

Nov-0

2

Mar-0

3

Jul-03

Nov-0

3

Mar-0

4

Jul-04

Nov-0

4

Mar-0

5

Yield

5 yr Nominal

5 yr Real

Source: Bloomberg




MARKETS?

MoreFlatteningto Come

Spread of 30-Yr UST and Fed Funds vs. 6-Month Annualized Core CPI

-2.00%

-1.00%

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

1/31

/199

0

9/28

/199

0

5/31

/199

1

1/31

/199

2

9/30

/199

2

5/31

/199

3

1/31

/199

4

9/30

/199

4

5/31

/199

5

1/31

/199

6

9/30

/199

6

5/30

/199

7

1/30

/199

8

9/30

/199

8

5/31

/199

9

1/31

/200

0

9/29

/200

0

5/31

/200

1

1/31

/200

2

9/30

/200

2

5/30

/200

3

1/30

/200

4

9/30

/200

4

Spread

-2.00%

-1.00%

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

CPI

Spread (left) CPI (right)

Source: Bloomberg




MARKETS?

CreditStill Not

Compelling

0%

20%

40%

60%

80%

100%

120%

140%

160%

180%

200%

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

0

20

40

60

80

100

120

Avg.

Price

< $60 $60 - $80 $80 - $95 $95+ Avg. Price 4/22

/200

5

Source: LehmanLive



I. Organizational Stability and Integrityw We’ve maintained near 100% retention of all portfolio management staff, senior management

team, and founding members.

w The firm was founded in 1996; the investment team has worked together since 1992.

w We are privately owned and employee controlled.

w We possess a large investment team with a relatively modest asset base.

II. Strong Record in Challenging Bond Marketsw MWAM’s disciplined value-oriented philosophy has led to consistently outperforming

benchmarks during monthly periods when benchmark performance is negative.

w Our modest asset base magnifies the effectiveness of issue selection and sector rotationstrategies.

w We focus on finding undervalued securities and respect the mean-reverting nature of fixedincome instruments.

w Our long-term value discipline allows us to consistently exploit ongoing pricing dislocations inthe marketplace.

III. Discipline and Patiencew The strength of our investment discipline and long-term orientation has proven to be

successful during periods of temporary, short-term emotional market behavior.

Firm Overview



Team History& Evolution

Source: MWAM, PIMCO

PIMCO AUM figures refer tothe entire firm; other AUMreferences relate solely to the

fixed income assets managed bythe MWAM Investment Team.

PIMCO Hotchkis & Wiley MWAM At Founding1990 - 1992 1992 - 1996 1996

MWAM TodayAs of 3-31-2005

$20 Billion AUM

Approx. 100Total Employees

29Investment Professionals

$200 mm - $2 BAUM

6Total Employees


$0 Billion AUM

7Total Employees


$13.7 Billion AUM

85Total Employees


w Coalescing during their tenure at PIMCO, the team of Tad Rivelle, Laird Landmann,and Steve Kane has remained together for over 13 years.

w At MWAM today, their investment team has expanded to 25 fixed incomeinvestment professionals.



InvestmentStrategiesSummary

Standard & Poor’s 500®

is a trademark ofThe McGraw-Hill Companies.

AlphaTrakSM is a service markof Metropolitan West

Asset Management, LLC.

*Absolute return strategies aremanaged by West Gate

Advisors, LLC, a wholly-ownedsubsidiary of MWAM.

Investment Products DescriptionCore Fixed Income Products

Ultra Short Active cash management.

Low Duration Relative value short duration management – Total return with low durations.

Intermediate Total return with intermediate durations.

Total Return Relative value core & core plus.

Long Duration High quality asset/liability matching strategies.

High Yield Management

High Yield Disciplined, credit intensive high yield process.

Portable Alpha – Enhanced Equity

AlphaTrakSMA combination of S&P 500® futures and/or swap contracts with a short-term bond portfolio that backs the futures contracts to achieveoutperformance of the S&P 500 index

Absolute Return*

Leveraged Mortgage Assets Emphasis on mortgage instruments to achieve absolute return.

Long-Short Credit Emphasis on credit instruments to achieve absolute return.

Strategic Income Emphasis on broad market instruments to achieve absolute return.

Real Return

TIPS TIPS and other inflation-hedging strategies

Total Firm Assetsas of 3-31-2005

$13.7 Billion



MWAMINVESTMENT TEAM:

PortfolioManagers

Tad Rivelle UCLA, M.B.A. Hotchkis and WileyChief Investment Officer USC, M.S. Mathematics PIMCO

Yale University, B.S. Physics

Laird Landmann University of Chicago, M.B.A. Hotchkis and WileyGeneralist Portfolio Manager Dartmouth College, B.A. Economics PIMCO

Stephen Kane, CFA University of Chicago, M.B.A. Hotchkis and WileyGeneralist Portfolio Manager UC Berkeley, B.S. Business PIMCO

David Lippman Hofstra University School of Law, J.D. Credit Suisse First Boston Specialist Portfolio Manager Drew University Donaldson, Lufkin & Jenrette

Mitchell Flack University of Chicago, M.B.A. Bear StearnsSpecialist Portfolio Manager - MBS UC Santa Barbara, B.A. Business Economics Bankers Trust

Brian Loo, CFA Carnegie Mellon University, M.S.I.A. Hotchkis and WileySpecialist Portfolio Manager - ABS UCLA, B.S. Math/Applied Sciences Trust Company of the West

Bryan Whalen, CFA Yale University, B.A. Economics Credit Suisse First BostonSpecialist Portfolio Manager - MBS/ABS Donaldson, Lufkin & Jenrette

Hahn Kang Columbia University, M.B.A. Lehman Brothers Specialist Portfolio Manager US Military Academy, West Point, B.S. U.S. Army

Mark Unferth University of Rochester, M.B.A. Credit Suisse First BostonSpecialist Portfolio Manager - High Yield UNC Chapel Hill, B.A. Economics Donaldson, Lufkin & Jenrette

Bret Barker Loyola Marymount University, B.A. Business Coast Asset ManagementSpecialist Portfolio Manager - Treasuries



MWAMINVESTMENT TEAM:

Analysts& Traders

Jamie Farnham UCLA, M.B.A. Primus Venture PartnersAnalyst - Credit Princeton University, A.B. Economics Merrill Lynch

Gino Nucci, CFA UCLA, M.B.A. Pacific Life Insurance Co.Analyst/Trader - Credit Yale University, B.A. Economics Volpe Brown Whelen and Co.

Aaron Unverzagt, CPA Marquette University, B.S. Accounting Fox Entertainment GroupAnalyst - Credit

Joel Shpall UCLA, B.A. Economics Wilshire AssociatesAnalyst - Credit

Sonya Lee The Wharton School at the University of Pennsylvania Lehman BrothersAnalyst - Credit B.S. Economics

Tammy Karp University of Arizona, B.S. Business Finance The Capital GroupTrader - Corporates

Jeannie Fong USC, B.S. Business Administration Pacific Income AdvisorsTrader - Money Markets

Marie Choi USC, M.B.A. PIMCOAnalyst - MBS/ABS, CDOs UC San Diego, B.S. Management Science

Patrick Ahn UCLA, M.B.A. City of BurbankAnalyst - MBS/ABS UCLA, B.S. Mechanical Engineering

Brian Rosenlund Brigham Young University, B.S. Sociology/B.A. Philosophy Clinger & Company, Inc.Analyst - MBS/ABS

Katherine Wu California State University, M.A.Psychology UCLAAnalyst - MBS/ABS

Stephen Burns, PhD UCLA, PhD Physics UCLAAnalyst - Risk University of Virginia, B.A. Physics/English

Marcos Gutierrez UCLA, B.S. Mathematics & Economics CalPERSAnalyst - Risk



Issue selection processes and tools illustrated throughout this presentation are samples, are not intended to be current and may be modified periodically. Suchcharts are not the only tools used by the investment teams, are extremely sophisticated, may not always produce the intended results and are not intended foruse by non-professionals.

Investment strategies may not achieve the desired results due to implementation lag, other timing factors, portfolio management decision-making, economicor market conditions or other unanticipated factors. The views and forecasts expressed in this material are as of May2005, are subject to change withoutnotice, may not come to pass and do not represent a recommendation or offer of any particular security, strategy, or investments.

Securities discussed are not recommendations and are presented as examples of issue selection or portfolio management processes. They have been picked forcomparison or illustration purposes only. No security presented within is either offered for sale or purchase. MWAM reserves the right to change itsinvestment perspective and outlook without notice as market conditions dictate.

While we have gathered this information from sources believed to be reliable, MWAM cannot guarantee the accuracy of the information provided.

This presentation contains material that is protected, individually and collectively, by copyright, trademark or other proprietary rights of Metropolitan WestAsset Management, LLC or others as indicated. CFA is a registered trademark of The CFA Institute.

This material is intended for use exclusively in direct presentations to potential institutional clients and/or their investment consultants and advisors. Not for reuse.

The Impact ofInnovation and Technologyon Bond Manager Style


May 2005 w Laird Landmann, Brian Cone

© 2005 Metropolitan West Asset Management LLC