“active” vs. “passive” management good governance = managing alpha and beta dr. arun...
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“Active” vs. “Passive” Management
Good Governance = Managing Alpha and Beta
www.mcubeit.com Dr. Arun Muralidhar
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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
23
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)
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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
26
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.
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