“Active” vs. “Passive” Management

Good Governance = Managing Alpha and Beta

www.mcubeit.com Dr. Arun Muralidhar

2

OPERS’ Investment Philosophy Asset allocation is the key determinant of

return

Ranges will be maintained through a disciplined rebalancing program

Diversification by and within asset class is the primary risk control element

Passive alternatives to actively managed portfolios are suitable investment strategies, especially in highly efficient markets

OPERS Statement of Investment Objectives and Policies, June 2003; pg 5

3

Key Conclusions Clients are separating “alpha” from “beta” - Too

much alpha focus, not enough on the beta

Not enough focus on the impact versus liabilities

All portfolio decisions (including “passive” rebalancing) impact returns and risks; Must make decisions in an informed manner

Evaluate every decision in context of portfolio

The Greater Fool Theory of Asset Management

4

The Pension Fund Balance Sheet

FutureFutureContributionsContributions

Current Current AssetsAssets

FutureFutureReturnsReturns

LIABILITIESLIABILITIES=

Funded ratio = assets/liabilitiesFunded ratio = assets/liabilities

+

+

=

5

Key to Success – Effective Key to Success – Effective DecisionsDecisions

Traditional Approach – Alpha from External Managers

New Approach – Add Alpha from Informed Decisions

Governance/Oversight of Decisions - Transparency

Measure Risk

Evaluate Performance

Outperform Benchmark

Determine Benchmark

Set Objectives

Annual Daily MonthlyMonthly

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Portfolio: Many Embedded Decisions

Total Portfolio/Liabilities

LCSCACWI EMG SC PassiveActive

Equity - 64%Cash - 0% Alternatives

13%Fixed Income - 23%

Non US - 24% Dom. - 40% Core HY Emerging PE - 4%RE - 9%

Benchmark Misfit Risk

Asset Allocation Decision

Manager Selection and Allocation

Important to manage/monitor each decision and understand individual

and aggregate contribution to risk/return

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Strategic

Passive = Simple calendar or range-based rebalancing

True passive (Dutch model): benchmark includes drift until range is met or calendar period is completed

OPERS Policy Gives Discretion = Tracking Error

+/- 3% range for most assets; 4% for Real Estate

When range hit, go either to range or target or in-between

The “Old” Active-Passive Framework

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Manager Level

Usually restricted to a tracking error budget

Usually with a single asset class focus

Optimize information ratio on active component

Developed a performance measure (M3) to show why this is incorrect (for single and multi-manager portfolios)

Could hire a negative IR manager!! Modigliani insight

Will not focus on today (See Appendix, pgs 24-27)

The “Old” Active-Passive Framework

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Passive Rebalancing: Can Be “Risky”*

OPERS Buy and Hold: 0.16% ann. Return; 1.09% tracking error; Worst drawdown = -2.15% over a multi-year period

+/-3% range for most assets; 4% for RE**

– Impact: 0.01% annualized for 0.21% risk

– Worst drawdown: much lower at –0.43% (multi-year period)

– No transactions cost: 0.02% ann. (1% turnover)

– On $ 60 bn = $90 mn/year impact, but lower risk!

– Does not capture Asset-Liability risk or impact

*Rebalancing was evaluated from 01/99 – 04/05. Only tested at the highest portfolio benchmark level. Proxied Lehman Universal with Lehman Composite and Custom Real Estate Index with NAREIT – data not provided by OPERS and hence can differ

from true results.

Transactions costs (one way) = 15 bps for equity; 10 bps for fixed income; 0.5% for alternatives

**Range-based rebalancing = if any asset drifts to the range limit, all assets are rebalanced to benchmark

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“Passive” Rebalancing: Not Cost/RiskNeutral

Buy and Hold

Rebalancing

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Informed Decisions within Ranges Portfolio rebalancing is an “active” decision

Pension funds experience cash flows – use them to structure fund appropriately

Asset class structuring also creates opportunity

Large cap vs. Small cap (+/-2%)

Core vs. HY vs. EMG (+/-2%)

EAFE vs. EMG vs. Small (+/-2%)

Can OPERS staff use discretion to create value?

Key is to have a robust, transparent, consistent process

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Improving the Quality of Decisions

Institute consistent evaluation and performance metrics

Test variety of rules to use for specific decisions

Many resources can be tapped Internal staff – have ideas that are unused

Research – lots of research on when asset classes do well

Leverage external managers/relationships – Verizon model

Transparency and process are key for good governance

“Prudence is Process”

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Portfolio: Focused on a Few Decisions

Total Portfolio/Liabilities

LCACWI EMG SC

Equity64%

Cash0%

Alternatives13%

Fixed Income 23%

Foreign24%

Domestic40%

Core HY

Chose a few decisions to make the point

Developed multiple rules to diversify the risk for each strategy

Asset Allocation - Equity vs FI

Foreign Equity - ACWI vs

EMG

Domestic Equity - LC vs

SC

Fixed Income - Core vs HY

Asset Allocation -

Equity vs Cash

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Informed Decisions/Managing Beta: Improved Risk/Return * At highest level of fund, keep return (relative to “Buy and

Hold”, lower risk relative to Rebalancing

Every decision is within passive rebalancing guidelines

Intelligent decisions contribute return at every level of fund – alpha can compound…

*All Decision regimes were evaluated from 01/99 – 04/05. Transactions costs were higher for sub-asset class level. Decision making frequency was monthly

Excess Annualized

ReturnTracking

ErrorInformation

RatioWorst

Drawdown Confidence

in SkillSuccess

Ratio

Asset Allocation level 0.16% 0.19% 0.81 -0.21% 98% 57%

Domestic Equity

Foreign Equity

Fixed Income

0.08% 0.19% 0.43 -0.35% 85% 55%

0.04% 0.12% 0.36 -0.21% 82% 53%

0.04% 0.07% 0.57 -0.18% 92% 56%

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Comparing Impact on Entire Fund

Excess Annualized

ReturnTracking

ErrorInformation

RatioWorst

Drawdown Confidence

in SkillSuccess

Ratio

Buy and Hold 0.16% 1.09% 0.14 -2.19% 69% 51%

Strict +/-3% Rebalancing 0.01% 0.21% 0.03 -0.43% 53% 42%

Informed Decisions 0.32% 0.21% 1.5 -0.18% 99% 64%

At total fund level, can get better return with lower risk

Drawdown at total fund level is also lower

Translates into meaningful dollars = $180 million/year!

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Managing Beta = “Alpha” ($) + Risk Management = Good Governance

Informed

Decisions

Rebalancing

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Informed Decisions can Lower A-L Risk

Annualized Growth in Surplus

Volatility of Ann. Growth in Surplus

Correlation of asset returns with Liabilities

Probability that Funded Ratio < 105%

(y/e 2006)

Max Drawdown of

Surplus

Static SAA -0.71% 6.7% 0.0683 35% -8.6%

Annual

Rebalancing-0.58% 6.85% 0.049 32% -8.59%

Informed Decisions 0.27% 6.99% 0.0114 20% -8.11%

Annualized Liability Return (Benchmark) = 8.2%Note: These results are indicative and were obtained from another fund using the “informed decision” approach ( 2001-2005)

Showed another client how informed decisions can lower asset-liability risks beyond standard rebalancing

Similar liability target to OPERS (8% annualized)

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Summary

Can use current Statement of Objectives to add meaningful value from “managing beta”

Being “active” within “passive” range = good governance

Can also control risk in a meaningful way

Cheaper source of excess return at total fund return (than any other “alpha” option)

Easy to adopt by leveraging external relationships

AppendixAppendix

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Where Should a Fund Take Risk?

12

-1

0

1

2

3

4

5

0 2 4 6 8 10

Tracking error

Exc

ess

retu

rn

Tracking error vs. excess returns (net of fees) US Equity Large Cap

US Fixed Income

High Yield

Non-US Equity EAFE

Non-US Equity EAFE- Japan LiteEmg Mkt Equities

Non-US FixedIncomeUS Equity Small Cap

US Equity Mid Cap

Non-US FixedIncome - Japan Lite

Source: Muralidhar (2001), Innovations in Pension Fund Management

Consistent with OPERS philosophy – WHO IS THE MUG?

From 12/87 to 12/97

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The Wilshire U.S. Large Cap Universe: Zero Average Alpha

Source: Muralidhar (2001), Innovations in Pension Fund Management

Tracking error

From 12/87 to 12/97

-6

-4

-2

0

2

4

6

0 2 4 6 8 10 12 14

Exc

ess

retu

rns

Average (Gross)

Average

Average (Net)

Can OPERS beat the pack with a dynamic manager strategy?

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Should managers be constrained? The case for conservative management

From 12/87 to 12/97

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

0 1 2 3 4 5 6

Info

rmat

ion

ratio

Tracking error

Wilshire U.S. Fixed Income Universe

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The Greater Fool Theory of Asset Management If average alpha is zero, must believe that another

sponsor is selecting a bad manager….

The average alpha in international came from a bet on Japan – will the future have another?

Does not negate the case for active management – need to be smart about “managing managers”

New performance measures give new insight on optimal portfolio construction (cash vs passive vs active)

24

Riskless asset

ReturnActive portfolio

Standard deviation of active portfolio

Benchmark

Standard deviationMarket risk

Active portfolio

An Evaluation of the M2 Measure

25

Riskless asset

Return

Benchmark

Standard deviationMarket risk

An Underperforming Manager Has a Higher Risk-Adjusted Return!!!

Information ratio is a bad measure of performance!!Information ratio is a bad measure of performance!!

MM22 provides allocation information between cash and active manager provides allocation information between cash and active manager

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The M3-adjustment – Normalize for Tracking Error

Fund Return(%)

Standarddeviation

(%) r(RAP)

(%)TE(basic)

(%)TE(RAP)

(%)r(CAP)

(%)

(1) (2) (3) (4) (6) (7) (8) (12)

F 5.50 0.00 0.00

B 17.09 13.27 1.00 17.09

1 33.24 27.57 0.71 18.85 20.45 10.14 18.43

2 25.63 24.93 0.77 16.21 17.02 9.04 17.43

3 25.04 25.02 0.73 15.86 17.74 9.68 17.41

4 24.08 21.33 0.80 17.06 13.34 8.38 17.65

5 21.95 21.75 0.59 15.53 17.52 11.97 17.68

6 21.90 13.84 0.84 21.21 7.76 7.57 19.26

7 21.61 14.37 0.83 20.37 8.13 7.74 18.91

8 20.89 23.06 0.79 14.36 15.07 8.69 16.70

9 20.77 14.00 0.89 19.97 6.53 6.32 18.83

10 20.56 14.79 0.92 19.00 5.74 5.24 18.43

Get a totally different ranking of external managersGet a totally different ranking of external managers

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M3 Gives Information on Allocation to Cash, Passive and Active Managers

Ranking UnadjustedSkill usingraw returns

M2 orSharpe

Skill usingM2 M3

Skill usingM3

Informationratio

(1) (2) (3) (4) (5) (6) (7) (8)

First 1 6 6 6 6 6 1

Second 2 9 7 9 7 7 6

Third 3 7 9 7 9 9 10

Fourth 4 10 10 10 1 1 9

Fifth 5 1 1 1 10 10 7

Sixth 6 4 4 4 5 5 4

Seventh 7 2 2 2 4 4 2

Eighth 8 3 3 3 2 2 3

Ninth 9 5 5 5 3 3 5

Tenth 10 8 8 8 8 8 8

Have shown that M3 is only measure consistent with skill-based rankingsHave shown that M3 is only measure consistent with skill-based rankings

Can use same technique for multi-manager portfoliosCan use same technique for multi-manager portfolios

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Caveats and Disclaimers Data was not provided by OPERS – we used our own and

hence actual analysis by OPERS will differ. Data was used to make a hypothetical study of the fund to show the impact of different investment options and was not meant to be an investment recommendation.

We have developed some intelligent allocation rules across various asset classes and sub-asset classes. These are purely research ideas, tapped from publicly available research, and there is no guarantee that they will generate performance in the future for OPERS.

We have attempted to use very onerous transaction costs assumptions to see if these ideas would still be beneficial. Again, OPERS’ own experience will differ because of the size of the fund and other institutional constraints.