Topic Five:

Case Study: Asset Allocation at the Texas Teacher Retirement System

Case Study: Asset Allocation at Texas Teacher Retirement System

Background: The Teacher Retirement System of Texas (TRS) is a public defined-benefit pension fund dedicated to delivering retirement benefits and related services for more than 1,300,000 public education employees and their annuitants in the state of Texas. It currently has approximately USD 135 billion of assets under management.

Investment Problem: The Board of Trustees at TRS faces a typical “asset-liability” management problem in that they must invest so as to simultaneously satisfy the income needs of current retirees and beneficiaries as well as provide sufficient asset growth to provide for future funding needs. The system is currently underfunded relative to actuarial liabilities, largely due to the fact that contributions from the state legislature have not kept pace with needs.

Portfolio Optimization Application (Spring 2003): Mean-variance optimization approach across multiple asset classes, including U.S. equity, non-U.S. equity, fixed-income, private equity, hedge funds, and real estate. (Summer 2009 & 2014): Mean-VaR optimization within economic “asset silos”

Miscellaneous Issues: - AON Hewitt Associates in the main economic consultant to the TRS Board - TRS is required by state law to revisit strategic allocation process every 3-5 years

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Summary of Current Situation: June 2014

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Overview of Allocation Update Process

Several factors impact the ability of TRS to provide benefits to participants: Contributions to trust (employee and State) Level of benefits Return on invested assets

Significant capital market declines of 2008 have negatively impacted pension

funding levels

While asset allocation can be changed to influence the expected return going forward, investment decisions alone cannot close the unfunded liability Some change in contribution level or benefits may be necessary to improve funded

status Investment policy decisions must be integrated with the overall funding policy for the

System.

An asset allocation that is prudent and consistent with an aggressive pursuit of the equity risk premium (i.e. a relatively high allocation to return-driven assets, such as the current 79%) creates the best opportunities to control both long term total cost and shortfall risk. Marginal tweaks to the current target asset allocation may result in improvements in

the risk/return tradeoff without significant change to the absolute level of risk in the portfolio

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Funding Framework #1: Fixed 6.4% Employee Rate; 10% Employer Max; 79% Non-Fixed Allocation

FY 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024Funded ratio percentile values:

5% 91% 70% 60% 54% 48% 46% 42% 39% 36% 33% 32% 30% 29% 28% 26% 26%25% 91% 73% 68% 65% 61% 56% 54% 53% 51% 51% 50% 50% 49% 49% 47% 46%50% 91% 75% 74% 72% 66% 62% 62% 62% 63% 63% 64% 65% 65% 65% 67% 67%75% 91% 78% 80% 76% 71% 69% 71% 73% 76% 78% 79% 80% 83% 85% 88% 91%95% 91% 81% 82% 80% 78% 79% 85% 92% 98% 105% 112% 119% 127% 130% 136% 142%

Total cost rate distribution:20%+ 0% 0% 0%

16.4% to 20% 85% 68% 60%13% to 16.4% 14% 19% 15%10% to 13% 0% 14% 25%

Less than 10% 0% 0% 0%

Projected Range of Funded Ratio

80%

112%

142%

72%64% 67%

54%

32%26%

91%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

110%

120%

130%

140%

150%

160%

170%

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024

Dark shaded area indicates the 50% probability zone, and light shaded area indicates the 90% probability zone.

Median / trend line

No real upward trend at the median

Shortfall risk expanding

Contributions at the 16.4% max well over ½ of the time

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Funding Framework #1: Distribution of Funded Ratio After 15 Years

Below 60% 60% to 80% 80% to 100% 100% to 115% Over 115%

41% 22% 15% 8% 14%

0%

5%

10%

15%

20%

25%

30%

35%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 110% 120% 130% 140% 150% 160% 170% 180% 190% 200%

Final Funded Ratio

Cos

t Afte

r 15

Yrs.

(% o

f pay

)

Severe shortfall risk after 15 years, with 41% chance of funded ratio below 60%

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Revised Strategic Asset Allocation Process

Traditional Approach New TRS Approach

Survey

•Expected Returns, Volatilities, and Correlations

Construct •Robust Correlation Matrix

Optimize •Efficient Frontier Analysis using VaR

Analyze •Non-normality

Forecast • Future Economic Conditions

Hedge • Insurance

Determine

•Market Expectations for Return and Risk

Derive •Efficient Frontier

Identify

•Optimal Asset Mix on Efficient Frontier

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Strategic Asset Allocation 2012 Assumptions – TRS and Aon Hewitt

o TRS Higher: • EM Equity • Credit • REITS and

value-added real estate.

o TRS Lower: • Treasury • TIPS • Private Equity

Ennis Knupp Correlation Matrix TRS Average Correlation Matrix

Glo

bal E

quity

Stab

le V

alue

Real

Ret

urn

Glo

bal E

quity

Stab

le V

alue

Real

Ret

urn

Global Equity 1.00 0.13 0.59 Global Equity 1.00 0.17 0.39Stable Value 0.13 1.00 0.33 Stable Value 0.17 1.00 0.48Real Return 0.59 0.33 1.00 Real Return 0.39 0.48 1.00

10 Year ForecastAsset Class Mean Return Std Deviation Sharpe

TRS EK TRS EK TRS EKUS Large Cap 8.0% 7.5% 16.3% 15.9% 0.32 0.43US Small Cap 9.3% 7.7% 20.4% 18.7% 0.32 0.38EAFE Unhedged 8.3% 7.4% 18.4% 19.0% 0.30 0.36Emerging Markets 8.8% 7.3% 25.0% 27.3% 0.24 0.25Private Equity 10.0% 10.4% 28.5% 30.3% 0.25 0.32Cash 2.7% 0.6% 0.5% 0.0% 0.00 0.00US Aggregate 4.4% 4.7% 6.5% 6.6% 0.26 0.62US Treasurys -- Intermediate 3.0% 4.3% 6.3% 5.1% 0.04 0.72US Treasurys -- Long 3.7% 4.6% 9.5% 12.1% 0.10 0.33US Investment Grade Credit 5.1% 5.0% 7.8% 8.6% 0.30 0.52US High Yield 6.9% 6.2% 11.1% 11.6% 0.37 0.48WGBI ex US Unhedged 3.3% 3.9% 8.9% 12.5% 0.06 0.26Emerging Market Debt 9.3% 6.4% 14.7% 14.7% 0.44 0.40Bank Loans 5.4% 4.9% 6.6% 6.6% 0.39 0.65Hedge Funds - Non-Directional 6.5% 5.5% 7.7% 7.2% 0.49 0.67Hedge Funds - Directional 8.3% 7.5% 10.3% 13.5% 0.54 0.51Real Assets 5.3% 7.3% 8.5% 16.0% 0.30 0.42REITS 8.0% 6.5% 20.0% 19.0% 0.26 0.31Direct Real Estate 6.6% 6.4% 8.8% 11.6% 0.44 0.50Value Added Real Estate 7.3% 6.7% 13.0% 26.4% 0.35 0.23US TIPS 4.0% 4.2% 5.9% 5.8% 0.20 0.63Global Inflation Linked Bonds 3.6% 4.4% 6.8% 6.5% 0.12 0.59Gold -1.0% n/a 20.0% n/a -0.19 n/aCommodities 4.9% 4.3% 21.3% 16.1% 0.10 0.23

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Optimization Based on Highest Probability of Achieving Target Return

o Optimization

routine solves for the asset allocation that has the highest probability of achieving target

o Tested results using bearish, base case and bullish forecasts

Portfolio Distribution (14% Stdev)

0.00%

0.50%

1.00%

1.50%

2.00%

2.50%

3.00%

3.50%

4.00%

4.50%

-32.

5%

-29.

7%

-26.

9%

-24.

1%

-21.

3%

-18.

5%

-15.

7%

-12.

9%

-10.

1%

-7.3

%

-4.5

%

-1.7

%

1.1%

3.9%

6.7%

9.5%

12.3

%

15.1

%

17.9

%

20.7

%

23.5

%

26.3

%

29.1

%

31.9

%

34.7

%

37.5

%

40.3

%

43.1

%

45.9

%

48.7

%

51.5

%

Prob

abili

ty D

ensit

y

Return

Target Return(8.0%)

Expected Return(9.5%)

Probablility of Achieving Target Return(54.27%)

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Probability – VaR Efficient Frontier

o While the 9.65%

target portfolio has a slightly higher probability of success, the 8% target portfolio has lower tail risk

o The critical difference between the 8% and 9.65% target portfolio is a 10% allocation to credit versus equities (i.e. 50/30/20 vs. 60/20/20)

8.0% Base Case - Simulated

9.7% Base Case - Simulated

9.7% Target Portfolio8% Target Portfolio

Current Portfolio

0%

10%

20%

30%

40%

50%

60%

5.00% 10.00% 15.00% 20.00% 25.00% 30.00% 35.00%

Prob

abili

ty o

f Rea

chin

g Re

turn

Tar

get

VaR (99%) (Higher is riskier)

Moving from the ideal 8% portfolio to the ideal 9.65%portfolio increases the chances of meeting the 9.65% target by 0.9% (47.0% to 47.9%) and increases the VaR by 5.9% (from 20.9% to 26.8%). The biggest difference between the two portfolios is the ideal 9.65% portfolio has 10% overweight to equity and underweight to credit relative to the ideal 8% portfolio.

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TRS Long-Term Investment Plans

o The new policy introduces credit into the allocation at the expense of Treasury exposure

o Overall Real Return exposure remains unchanged

Current Policy Pre 2007 PolicyGlobal EquityUS Large Cap 20% 36% 4%

US Small Cap 5% 11% 9%

EAFE Unhedged 15% 12% 8%

Emerging Markets 10% 1% 9%

Private Equity 10% 4% 16%

Total Global Equity 60% 64% 46%

Stable ValueCash 1% 1% 1%

US Aggregate 0% 28% 0%

US Treasurys -- Intermediate 0% 0% 0%

US Treasurys -- Long 15% 0% 0%

US Investment Grade Credit 0% 0% 0%

US High Yield 0% 2% 2%

WGBI ex US Unhedged 0% 0% 0%

Bank Loans 0% 0% 0%

Emerging Market Debt 0% 0% 32%

Hedge Funds 4% 2% 0%

Total Stable Value 20% 33% 35%

Real ReturnReal Assets 5% 0% 0%

REITS 0% 0% 9%

Direct Real Estate 5% 3% 1%

Value Added Real Estate 5% 0% 5%

US TIPS 0% 0% 0%

Global Inflation Linked Bonds 5% 0% 0%

Gold 0% 0% 0%

Commodities 0% 0% 3%

Total Real Return 20% 3% 18%

Total Fund 100% 100% 99%

Suggested Policy - 8% Target

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TRS vs. Peer Strategic Allocations: June 2014

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Asset Allocation Recommendations: June 2014

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Texas Teachers: Tactical Asset Allocation Program

In 2007, TRS made a major commitment to increase its risk-adjusted returns (i.e., alpha) throughout its entire organizational structure - This involved (i) allocating more capital to non-traditional asset classes

(e.g., private equity, hedge funds), and (ii) using more external managers

In connection with these organizational changes, TRS also launched a systematic effort to produce alpha through tactical asset allocation strategies - A new Portfolio Strategy & Execution team was formed and the

TAA Team is located within that segment of the Investment Management Division

- The TAA Team operates as an “overlay” entity that has responsibility for roughly 50-100 basis points of the total TRS portfolio

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TRS Strategic Partner Network (SPN)

In July 2008, TRS made its first major initiative to use public market, external management - Four firms were selected and given the mandate to manage

customized, risk-controlled portfolios across all of the asset classes in the TRS investable universe

- Each firm was funded with USD 1 billion

The objectives of the program were - Access managers with proven track record of producing alpha - Create useful proprietary research through focused SPN project

collaboration - Maximize the full breadth of services and talent offered by the

selected external managers

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Modifying the TAA Model: Stocks vs. Bonds 3.0

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TAA at TRS: Update

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Hedge Fund Replication: Review and Expansion

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