financial risk management prof. jeff (yuqing) shen, uc berkeley section 4: risk management for hedge...

Financial Risk Management

Prof. Jeff (YuQing) Shen, UC Berkeley

Section 4: Risk Management for Hedge Funds



2

The BIG Picture

• Theory

•Efficient Frontier

•Asset/Liability Framework

•Alpha/Beta risk budgeting

• Practice

•Pension Crisis

•GM Pension Plan

•Yale Endowment

• Theory

•VaR

•Stochastic behavior of asset

returns

•Worst case scenario

• Practice

•Investment bank VaR

•Bear Stearns

• The Theory

•Active Portfolio Management

•Quantitative equity models

•Hedge fund investment

• The Practice

•Hedge fund industry

•Aug 2007 crisis for quant

Investment BankInstitutional Investor Hedge Fund



Active risk

3

Exp

ecte

d a

lph

a %

Risk-controlled Active Traditional Active

The Risk Budget — How much risk?

Index Fund

Active risk %



Asset mix with in US equity

4



Active management trend in international equity

5



Active management in US bond

6



Lots of managers

7



8

The BIG Picture

• Theory




• Practice

•Pension Crisis

•GM Pension Plan

•Yale Endowment

• Theory

•VaR


returns


• Practice


•Bear Stearns

• The Theory




• The Practice






9

Active return: alpha

Benchmark portfolio of stock and bond with weights wstock and wbond, where wbond = 1 - wstock

The tactical bets are betstock and betbond, with betstock = -betbond.

Return for benchmark = wstock*Rstock + wbond*Rbond

Return for tactical allocation portfolio = (wstock + betstock) *Rstock + (wbond + betbond) *Rbond

Alpha = Return for tactical allocation portfolio - Return for benchmark

=betstock (Rstock - Rbond)



10

Historical Tracking error

Historical Tracking error calculation:

2

1 1

1

1

1

T

t

T

tttt TT

TE

Tracking error annualization:

tTETE year ain periods ofnumber

Issues:– Assume time independence of alpha



11

Information Ratio

Information ratio calculation:

Information ratio annualization:

Error Tracking

AlphaRation Informatio

Error Tracking dAnnualilze

Alpha dAnnualilzeRation Informatio dAnnualilze



12

Typical IR in the industry

Percentile IR

90 1.00

75 0.50

50 -

25 (0.50)

10 (1.00)



13

The fundamental law of active management

IR: Information Ratio

IC: Information Coefficient; skill of return forecasting; correlation (forecast, realization)

BR: Breadth; number of independent bets per year

IR IC B R *



14

Example: What’s expected IR?

Case 1: Stock picker, 200 stocks, IC=0.02, quarterly rebalancing

Case 2: Market Timer, IC=0.10, 4 bets per year



15

Example: What’s expected IR?

Case 1: Stock picker, 200 stocks, IC=0.02, quarterly rebalancing

Case 2: Market Timer, IC=0.10, 4 bets per year

IC = 0 .0 2 , B R = 2 0 0 * 4 = 8 0 0

IR = 0 .0 2 * 8 0 0 0 5 7.

IC = 0 .1 0 , B R = 4

IR = 0 .1 0 * 4 0 2.



16

Implications of the fundamental law of active management

Be good

Given some skill, bet as often as possible

Diversify bets

Don’t market time



17

It’s hard to distinguish skill from luck

We have seen a top quartile manager has an IR of 0.5. How long must we observe such a manager to measure the IR with 95% confidence?

We want the t-statistics, the ratio of the IR to its standard error, to exceed 2

But requiring so long a time series is quite problematic. Things change!



18

The BIG Picture

• Theory




• Practice

•Pension Crisis

•GM Pension Plan

•Yale Endowment

• Theory

•VaR


returns


• Practice


•Bear Stearns

• The Theory




• The Practice






Defining Risk as Standard Deviation

That’s what Harry Markowitz used.

It has several important and useful properties:– Symmetric– Well-understood statistical properties.– Machinery exists for aggregation from asset to portfolio.

– Predictable

2 TP P P h V h



Issues

Magellan FundJ anuary 1973 - September 1994

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

30.00%

35.00%

40.00%

-25% -20% -15% -10% -5% 0% 5% 10% 15% 20%

Return

Non-normal distributions– Fat tails (kurtosis)– Skewness

Other choices:– Semivariance– Shortfall probability– Value at Risk



Why Risk Models?

Let’s look at how we aggregate portfolio risk:

For N assets, we require N(N+1)/2 parameters. If N=1,000, we need to estimate 500,500 parameters.

That is the challenge.

2

2 2 2 2 2 21 1 2 2

1 2 12 1 3 13 1 1,2 2 2

TP P

N N

N N N N

h h h

h h h h h h

h V h



Problems with historical risk

Let’s think about the number of parameters.

We need to estimate N(N+1)/2 parameters, and we have NT observations. We require at a minimum, 2 observations per parameter estimate. (How can we estimate a variance from 1 number?)

Hence, NTN(N+1), or T>N. This causes problems when we are looking at monthly returns for 1,000 assets.– Technical version: unless T>N, we will estimate a singular covariance

matrix. What does that mean, mathematically? Intuitively?

And even if we had 1,000 months of data (or more), we know that assets and markets change over time. We also regularly observe new assets.



Single Factor Model Sharpe’s Market Model

– This predated CAPM. It isn’t the same thing.

Decompose returns into market and residual components.

Postulate that residual components are uncorrelated.

2

21

22

2

0 0

0

0

mkt

Tmkt

N

r

r β θ

V β β Ω

Ω



Sharpe’s Market Model

Solves the number of parameters problem:– For N assets, it requires 2N+1 parameters.– N betas, N residual risks, 1 market volatility.

Problem with this model: it doesn’t capture observed correlation structure in the market. Are Exxon and Chevron correlated only through their market exposure?

Advantage: simplicity makes this useful for back-of-the-envelope calculations.



Factor Models of Risk

These are extensions of Sharpe’s approach, designed to capture real market issues.

Separate returns into common factor and specific (idiosyncratic) pieces. We choose K factors, where K<<N. The covariance matrix is then:

r X b u

T V X F X Δ



Choosing the Factors

Art not science.

Three general approaches:– Fundamental factors (BARRA, Northfield)

Industries and investment themes.– Macroeconomic factors (Salomon RAM, BIRR model)

Industrial productivity, inflation, interest rates, oil prices…– Statistical factors (Quantal, Northfield)

Use statistical factor modeling, principal components analysis…

These three approaches involve different estimation issues, and exhibit different levels of effectiveness.



Fundamental Models

Typically about 60 factors for a major equity market.

Calculate factor exposures, X, from fundamental data.– Industry membership.– Risk index exposures (e.g. value based on B/P)

Run monthly cross-sectional GLS regressions to estimate factor returns.

Use N observations to estimate K factor returns.

11 1 T Tb X Δ X X Δ r



Aside: Factor Portfolios

Our extensive use of fundamental models makes it worth understanding factor portfolios in more detail.

We estimate factor returns as:

This equation has the form:

Each estimated factor return, bj, is a weighted sum of asset returns. We can interpret those weights as the asset weights in a factor portfolio, or factor-mimicking portfolio.

11 1 T Tb X Δ X X Δ r

T b H r



Factor Portfolios

The columns of H contain the portfolio weights, with one column for each factor.

The GLS estimation approach guarantees that factor portfolio-j has:– Unit exposure to factor-j– Zero exposure to all other factors.– Minimum risk.

Industry factor portfolios are typically fully invested, with long and short positions.

Risk index factor portfolios are typically net zero investments, with positions in every stock.



Macroeconomic Models

Typically about 9 factors for a major equity market.

Calculate the change (or shock) in each macrovariable each month.

Estimate exposure to such shocks, stock by stock, using time-series data.

This approach requires NK parameter estimates.

1

K

n k nk nk

r t b t X t



Statistical Models

Start with only returns data.

Use statistical analysis to determine number and identity of most important factors.– While this approach sounds completely objective, it involves many subjective decisions, e.g.

choosing portfolios to build an initial historical covariance matrix.

This approach implicitly assumes that factor exposures are constant over estimation period.

Factors change from month to month.



Performance and Uses* Fundamental models

– In general, best risk forecasting out-of-sample, but dependent on choosing the right factors.

– Intuitive factors also useful for performance attribution and alpha forecasting.

Macroeconomic models:– Poor at risk forecasting.– Direct macroeconomic connections can be useful for alpha forecasting.

Statistical models:– Best in-sample forecasts. Will outperform fundamental models with poorly chosen

factors.– Misses factors whose exposures change over time (especially momentum).– Not useful for performance attribution. Difficult to use for alpha forecasting.

*See Gregory Connor, “The Three Types of Factor Models: A Comparison of their Explanatory Power.” Financial Analysts Journal, May-June 1995, pp. 42-46.



Covariance Matrix Estimation

For fundamental and macroeconomic models (and even to some extent for statistical models), we still need to estimate the covariance matrix, given the factor return history.

We also need to estimate the specific (idiosyncratic) risk matrix.



Example: BARRA Approach*

Want best forecast of portfolio risk over the next several months. (Horizon will depend on use.)

Given that risk varies over time, we would like to overweight more recent observations.

At the same time, we have the parameter estimation challenge: we want T>>K. Lowering the weight on historical observations effectively lowers T.

BARRA US Equity approach combines exponential weighting, GARCH modeling of market volatility, and a special specific risk model.

*This does not exactly describe current BARRA approach.



BARRA US Covariance Matrix

Historical monthly data back to 1973. Between 65 and 70 factors.

Step 1: Use exponential smoothing to estimate factor covariance matrix:

Step 2: Build a GARCH model for market volatility.

1

1

1ˆ 1

1

T

i i j jt

ij T

t

r t r r t r Exp T tT

T Exp T t



Market Volatility Model

Apply GARCH model to market volatility (i.e. 1 factor):

2

2 2 2

, ~ 0,

1 1

mktr t t t N t

t t t



GARCH Model Intuition

We can rewrite this model by defining:

Then the GARCH model becomes:

In this format, we can see that is the long-term (unconditional) volatility, measures mean reversion in volatility, and measures how current shocks impact volatility.

2,1

2 2 2 2 2 21 1 1t t t t



Step 3: Specific Risk Model

Our monthly factor return estimations also estimate specific returns:

We could calculate historical specific risk. Instead we estimate the following model:

We build models for S and v.– What are the estimation issues around this?

r X b u

2

2

1

1

1

n n

N

nn

u t S t v t

S t u tN



Step 4: Combining these Steps

The challenge is to adjust the factor and/or specific risk matrices so that we can match the GARCH market volatility forecast.

BARRA does this by rescaling the systematic component of the factor covariance matrix.

2 T T Tmkt mkt mkt mkt mkt mkt mkts h V h x F x h Δ h



Testing Risk Forecasts

Given r(t) and (t), how can we test whether (t) is a good risk forecast?

Bias test:– Convert returns to standardized outcomes:

– The bias statistic is the sample standard deviation of these outcomes:

– If the bias>1, we have under-predicted risk, and vice versa.

r tx t

t

| 1,bias StDev x t t T



Testing Risk Forecasts

Statistical significance: Remember that for normally distributed random numbers:

The bias test estimates whether we are accurate on average. We can apply it to total, residual, common factor, and specific risk.

We can use further tests to see if our forecasts are above average when realized risk is above average, etc.

2

SET



Total and Active Risk

Given the covariance matrix, we can estimate total and active risk:

We can also estimate the correlation of returns from two portfolios:

2

2

T T TP P P P P P P

T T TP PA PA PA PA PA PA

h V h x F x h Δ h

h V h x F x h Δ h

,TA B

A BA B

Corr r r

h V h



What could go wrong with the equity risk models?



44

The BIG Picture

• Theory




• Practice

•Pension Crisis

•GM Pension Plan

•Yale Endowment

• Theory

•VaR


returns


• Practice


•Bear Stearns

• The Theory




• The Practice






45

Hedge Fund

Introduction

Demand

Supply

Issues

Outlook



Hedge Fund Industry Questions

What are the pros and cons of hedge fund investment style?

What’s the future outlook for the hedge fund industry given the demand and supply landscape?

46



47

What is a Hedge Fund?

The term “hedge fund” is not defined or used in the federal securities laws

– Today the term refers to a variety of pooled investment vehicles loosely regulated and not registered under federal securities laws as public corporations, investment companies, or broker-dealers

– Hedge funds differ substantially in their investment objectives, use of different financial instruments, exposure to various markets and risk and return objectives

– Other generally accepted attributes include: ability to use leverage ability to use short sales to mitigate or increase risk performance based compensation schedule

Relative ValueRelative Value

Hedge Fund Strategy Classifications

Opportunistic/Opportunistic/MacroMacro

Merger Arbitrage/Merger Arbitrage/Event DrivenEvent Driven

Long/Short EquitiesLong/Short Equities Distressed SecuritiesDistressed Securities Short SellingShort Selling

47



48

Hedge fund industry AUM



49

Performance



50

The Advantage of Long-Short Investing

Long-short approach gives hedge funds a greater opportunity set to benefit from alpha

In most scenarios, long-short managers are able to reduce risk and increase expected return potential, relative to long-only managers

Efficient Frontier Scenario 1: Efficient Frontier Scenario 1: SPX, NDX Returns > 0SPX, NDX Returns > 0

-10

0

10

20

30

40

50

0 20 40 60 80Expected Risk

Exp

ect

ed

Re

turn

Long-Short Port Fully Invested1 Long Only Portfolio

Note: Borrowing and lending costs are assumed to be zero in these examples.1. Assumes the long-short portfolio is fully invested at all points, with leverage only to the degree needed to remain fully invested.Source: Morgan Stanley Quantitative Strategies.

Efficient Frontier Scenario 2: Efficient Frontier Scenario 2: SPX, NDX Returns < 0SPX, NDX Returns < 0

-50

-40

-30

-20

-10

0

10

0 20 40 60 80 100Expected Risk

Exp

ect

ed

Ret

urn



51

Other Advantages

Unconstrained by benchmark tracking considerations

Fewer regulatory hurdles

Organizational focus

Compensation similar to holding a real cal option



52

Hedge Fund

Introduction

Demand

Supply

Issues

Outlook



53

Demand to increase



Demand profile of hedge funds

54



Institutional demand for hedge fund

55



Secular trends on AUM

56



57

Hedge Fund

Introduction

Demand

Supply

Issues

Outlook



58

Supply side dynamics

Low barrier to entry ($30-50K in legal, accounting and audit expense to start)

High incentive due to significant upside potential

Prime-brokers and fund-of-funds facilitate the distribution channel

Large institutional managers venture into hedge fund space as well



59

Fee structure

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

7.00%

-10% -5% 0% 5% 10% 15% 20%

Return of fund

Fee

Note: Assume 2% base management fee, 20% performance fee and 0% hurdle rate



Hedge fund return dispersion

60



New landscape of hedge funds

61



Alignment

62



63

Hedge Fund

Introduction

Demand

Supply

Issues

Outlook



64

Persistence



65

Transparency



66

Scale and performance



67

Is hedge fund truly alpha or beta?

Source: Bridgewater Associates



68

A worrying picture




69

Doing the same thing?




70

Hedge Fund

Introduction

Demand

Supply

Issues

Outlook



71

Outlook #1: the squeeze on traditional long-only management



72

Outlook #2: Institutional quality hedge fund is the future



73

Outlook #3: Pure alpha is the future

Alpha correlation to

– US Equity

– International equity

– Fixed income

– Commodity

– Other major Beta risk premium

…should be ZERO



74

The BIG Picture

• Theory




• Practice

•Pension Crisis

•GM Pension Plan

•Yale Endowment

• Theory

•VaR


returns


• Practice


•Bear Stearns

• The Theory




• The Practice






Quant equity models

1. Value and Momentum model

2. Earning Surprise model


Prof. Jeff (YuQing) Shen, UC Berkeley76

1. Value & Momentum Screen

Relative value and momentum equally weighted to calculate composite rank

Price Momentum

Earnings Momentum

Composite Rank

Relative Value (50%)

PriceAcceleration

orP/CF Model P/S Model

Momentum (50%)

Source: Smith Barney.



Value & Momentum Model

Cumulative Performance of the Cumulative Performance of the Top-Over-Bottom DecilesTop-Over-Bottom Deciles

Monthly Return Spread from the Monthly Return Spread from the Top-Over-Bottom DecilesTop-Over-Bottom Deciles

Source: Smith Barney. Source: Smith Barney.

0

1

2

3

4

5

6

7

Most attractive

Least attractive

Universe

Launch of the Model

Modify Model due to IFRS

-12

-8

-4

0

4

8

12

16

20

%

Monthly Return SpreadSix Month Moving Average



STOCK RANKING

Company Name

Sector

Price (€)

Composite Rank

Relative Value Rank

Momentum Rank

SB Analyst Rating

IBES Mean Rec.

This Month’s Rank

Last Month’s Rank

ING GROEP Diversified Financials 22.93 1 1 1 1M 2.1 1 1 FORTIS Diversified Financials 21.80 1 1 2 2M 2.8 1 1 SUEZ Utilities 20.72 1 3 1 2H 2.4 1 2 ASSICURAZIONI GENERALI Insurance 24.79 1 1 3 2M 2.8 1 1 AXA Insurance 20.18 1 1 3 1M 2.3 1 1 BBVA Banks 12.57 1 2 2 2M 2.8 1 4 HBOS GROUP Banks 11.95 1 2 3 1M 2.2 1 2 SWISS REINSURANCE CO Insurance 54.04 1 1 4 2H 2.1 1 4 CREDIT SUISSE Diversified Financials 32.91 1 3 2 2H 2.4 1 3 BASF Materials 55.01 1 5 1 2.1 1 3 BARCLAYS Banks 7.99 1 1 4 3M 3.1 1 1 ALLIANZ Insurance 97.00 2 2 4 2H 2.1 2 3 BNP PARIBAS Banks 54.25 2 2 4 1M 2.1 2 2 ABN AMRO HOLDING Banks 19.14 2 2 5 2M 2.8 2 2 AVIVA Insurance 9.16 2 1 6 2M 2.7 2 1 SHELL T & T Energy 6.99 2 5 2 2L 3.0 2 2 TESCO Food & Staples Retailing 4.60 2 5 2 1M 2.2 2 3 LLOYDS TSB GROUP Banks 6.87 2 6 2 2M 3.2 2 3 DEUTSCHE BANK NAMEN Diversified Financials 66.84 3 1 6 2.3 3 3 BSCH BCO SANTANDER CENTR

Banks 9.26 3 3 6 2M 2.7 3 2 UBS NAMEN Diversified Financials 64.98 3 4 4 1M 1.8 3 4 DEUTSCHE TELEKOM Telecommunication

Services 15.37 3 4 5 1M 2.0 3 5

ENI Energy 20.34 4 5 4 2M 2.4 4 4 ROYAL BANK OF SCOTLAND Banks 24.27 4 4 5 1M 2.0 4 9 SOCIETE GENERALE Banks 79.35 4 7 2 1M 2.4 4 1 ROYAL DUTCH PETROLEUM CO

Energy 46.70 4 5 5 2L 2.8 4 4 BT GROUP Telecom Services 3.02 5 2 8 1M 3.4 5 3 E. ON Utilities 66.21 5 5 5 1M 2.0 5 4 NOKIA CORP Tech Hardware & Equip 11.81 5 7 4 3H 2.6 5 6 DAIMLERCHRYSLER Auto & Components 34.19 5 1 10 3.0 5 5 TELEFONICA Telecom Services 13.25 6 7 4 1M 2.2 6 6 CARREFOUR Food & Staples Retailing 40.66 6 3 8 3M 2.7 6 5 ANGLO AMERICAN (GB) Materials 18.42 7 7 6 2H 3.2 7 9 PHILIPS ELECTRS (KON.) Cons Durables & Apparel 20.53 7 6 6 2.1 7 4 TOTAL Energy 182.70 7 6 7 1L 1.9 7 9 BP Energy 8.17 7 7 6 1L 2.2 7 9 GLAXOSMITHKLINE Pharma & Biotechnology 17.53 8 6 8 2L 2.6 8 6 TELECOM ITALIA ORD Telecom Services 2.91 8 6 8 1M 2.6 8 8 VODAFONE GROUP Telecom Services 2.05 9 8 7 3H 2.1 9 6 ASTRAZENECA Pharma & Biotechnology 30.38 9 5 9 2M 2.8 9 8 NESTLE Food Beverage & Tobacco 208.60 9 6 9 1M 2.2 9 10 HSBC HOLDINGS (GB) Banks 12.19 9 7 8 3M 3.2 9 1 NOVARTIS Pharma & Biotechnology 35.61 9 7 9 2L 2.3 9 9 DIAGEO Food Beverage & Tobacco 11.06 9 7 9 2L 2.9 9 8 ROCHE HOLDING GENUSS Pharma & Biotechnology 82.60 9 6 10 1M 2.0 9 9 SIEMENS Capital Goods 60.77 10 8 8 2.3 10 9 ERICSSON (LM) B Tech Hardware & Equip 2.17 10 6 10 2H 2.6 10 9 LOREAL Household & Personal

Products 60.20 10 10 8 2M 2.8 10 10

UNILEVER NV CERT Food Beverage & Tobacco 52.00 10 9 10 1M 3.2 10 10 SAP STAMM Software & Services 122.61 10 10 10 2H 2.3 10 10 The prices in the table above are taken from the close of 4 April 2005. Sources: STOXX, IBES, Factset and Smith Barney.



2. Earnings Surprise Model

Analysts on average tend to overestimate earnings

Average Analysts’ Forecast Errors Average Analysts’ Forecast Errors (1990 – 03) (1990 – 03)

Average Analysts’ Forecast Errors for One to 11 Average Analysts’ Forecast Errors for One to 11 months Prior to Announcement (1990 – 03)months Prior to Announcement (1990 – 03)

-20%

-15%

-10%

-5%

0%

5%

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Analysts Pessimistic

Analysts Optimistic

-40%

-35%

-30%

-25%

-20%

-15%

-10%

-5%

0%

-11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1

Large-Caps Small-Caps

Sources: IBES, MSCI and Smith Barney. Sources: IBES, MSCI and Smith Barney.



Price Impact of Earning Surprises

We find an asymmetric effect of surprises to share prices

The effect is consistent across time but seems more pronounced in periods of slower economic growth

Average Market Relative Return Around the Earnings Announcement Period for SUE Quintiles, 1990-04

-10%

-8%

-6%

-4%

-2%

0%

2%

4%

6%

-65 -55 -45 -35 -25 -15 -5 5 15 25 35 45 55 65

Days Around Earnings Announcement

Quintile 5(PositiveSurprises)

Quintile 4

Quintile 3

Quintile 2

Quintile 1(NegativeSurprises)

Sources: IBES, MSCI and Smith Barney.

Cumulative Market Relative Performance for Positive and Negative Surprises 65-Days Around the Announcement Period

-20

-15

-10

-5

0

5

10

15

199019911992199319941995199619971998199920002001200220032004

Negative Surprises Positive Surprises Sources: IBES, MSCI and Smith Barney.



Can We Predict Earnings Surprises?

Likely Drivers of Earnings SurprisesLikely Drivers of Earnings Surprises


Prof. Jeff (YuQing) Shen, UC Berkeley 82

Earnings Surprise Model

Estimated Factor Sensitivities (1990 – 03)Estimated Factor Sensitivities (1990 – 03)

Univariate OLS Model Multivariate OLS Model Logit Regression Model

(Intercept) -1.26* -2.45*Return on Equity 0.21* 0.14* 0.25*Earnings Revisions FY1 0.16* 0.12* 0.25*Price Momentum (1-year t stat) 0.16* 0.10* 0.18*Market Capitalisation 0.07* 0.02* 0.05*Previous Surprise 0.34* 0.21* 0.43*Direction of Next EPS Forecast 0.63* 0.14* 0.24*



Prof. Jeff (YuQing) Shen, UC Berkeley 83

Predictive Accuracy of theEarnings Surprise Model We observe a monotonic increase in the actual SUE score as we We observe a monotonic increase in the actual SUE score as we

move from negative to positive predicted SUE quintilemove from negative to positive predicted SUE quintile

-1.50

-1.00

-0.50

0.00

0.50

1.00

1.50

Large Negative Small Negative No Surprise Small Positive Large Positive

Predicted Surprise Quintile

Aver

age

Real

ised

SUE

Sco

re





Pan-European Earnings Surprise ForecastsPan-European Earnings Surprise Forecasts

Pan-European Earnings Surprise Forecasts

0

20

40

60

80

100

120

140

Large Negative Small Negative No Surprise Small Positive Large Positive

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

16.0%

18.0%

No. of Stocks (LHS) % of Total Market Cap (RHS)





Earnings Surprise Forecasts by Industry

Number of Stocks Reporting

% of Sector’s Market Cap

Number of Large Positive SUE

Number of Small Positive SUE

Number In Line

Number of Small Negative SUE

Number of Large Negative SUE

Automobiles & Components 6 46.0% 0 2 0 4 0 Banks 29 16.6% 14 4 4 5 2 Capital Goods 29 39.0% 16 5 6 1 1 Commercial Services & Supplies 4 5.9% 1 0 1 2 0 Consumer Durables & Apparel 16 45.2% 6 3 5 1 1 Diversified Financials 12 17.6% 3 1 3 2 3 Energy 14 60.6% 7 3 3 1 0 Food & Staples Retailing 7 54.7% 4 1 0 1 1 Food Beverage & Tobacco 7 10.6% 4 2 0 1 0 Health Care Equipment & Services 6 15.1% 4 1 0 1 0 Hotels Restaurants & Leisure 5 9.6% 4 0 1 0 0 Household & Personal Products 3 35.5% 3 0 0 0 0 Insurance 13 16.1% 3 4 2 3 1 Materials 21 23.4% 9 0 5 3 4 Media 17 10.3% 4 2 5 2 4 Pharmaceuticals & Biotechnology 13 39.3% 3 2 3 3 2 Real Estate 12 19.9% 5 3 2 1 1 Retailing 16 36.3% 5 3 2 4 2 Semiconductors & Equipment 6 100.0% 0 1 1 1 3 Software & Services 9 53.3% 6 0 1 0 2 Technology Hardware & Equipment 13 84.5% 6 0 3 1 3 Telecommunication Services 5 4.9% 1 0 0 3 1 Transportation 8 16.1% 4 0 2 2 0 Utilities 6 18.8% 3 0 1 1 1

Source: Smith Barney analysis.



The BIG Picture

• Theory




• Practice

•Pension Crisis

•GM Pension Plan

•Yale Endowment

• Theory

•VaR


returns


• Practice


•Bear Stearns

• The Theory




• The Practice






The backdrop



Fund flow

88



Fund flow

89



Investment landscape before the crisis

90



Factor volatility and correlation

91



Investment landscape

Managers both in the long-only but mainly in the HF space, which had been promising double-digit returns to their clients, had to do something. Over the last few years it was quite apparent that three things were happening:

Long-only portfolios became more concentrated and with higher active weights

A large number of 130/30 quant products had been launched

Market neutral hedge funds increased their leverage taking advantage of the very low cost of financing Even the risk disciplined quant investment community had to react to be able to compete with long/short equity managers and survive in a period of very low systematic volatility. But the increase in AUM of Market Neutral Hedge Funds together with multiple levels of leverage applied to their positions gave them significant firing power. adding leverage to their portfolios

92



Leverage for Market Neutral Hedge funds

93



Aug 6-Aug 10, 2007



Aug 6 – Aug 10 2007

95



Valuation factor daily return

96



Quality factor daily return

97



Market sentiment factor daily return

98



Aug 6-Aug 10, 2007

99



A tail event

100



Market context from Aug 7-Aug 10

101



Monthly perspective

102



Hedge fund performance in Aug 2007

103



Return dispersion

104



An experiment

10 out of the 15 largest quantitative managers agreed to give Lehman quant equity research team the rankings coming out of their Core models for 2006 Q1, 2006 Q2 and 2006 Q3 – Top quintile ranked stocks in S&P 500; Bottom quintile ranked stocks in S&P 500 – Top quintile ranked stocks in R2000; Bottom quintile ranked stocks in R2000

We picked a small sample –want homogeneity. These are pure quants. True quants Focus solely on top and bottom quintiles in order to maximize agreement, plus this is where the models take their largest positions

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Doing the same things?

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Doing the same things?

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Quant equity investment landscape

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Alpha or Beta

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The BIG Picture

• Theory




• Practice

•Pension Crisis

•GM Pension Plan

•Yale Endowment

• Theory

•VaR


returns


• Practice


•Bear Stearns

• The Theory




• The Practice




financial risk management prof. jeff (yuqing) shen, uc berkeley section 4: risk management for hedge...

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