Topic Five: Case Study: Asset Allocation at the Texas Teacher Retirement System

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1 Topic Five: Case Study: Asset Allocation at the Texas Teacher Retirement System

2 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 assetliability 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 5-1

3 Summary of Current Situation: June

4 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 5-3

5 Funding Framework #1: Fixed 6.4% Employee Rate; 10% Employer Max; 79% Non-Fixed Allocation Projected Range of Funded Ratio 170% 160% 150% 140% 130% 120% 110% Dark shaded area indicates the 50% probability zone, and light shaded area indicates the 90% probability zone. Median / trend line 112% 142% No real upward trend at the median 100% 90% 91% 80% 80% 70% 60% 50% 40% 30% 20% 72% 54% 64% 32% 67% 26% Shortfall risk expanding 10% 0% FY Funded 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% Contributions at the 16.4% max well over ½ of the time 5-4

6 Funding Framework #1: Distribution of Funded Ratio After 15 Years Severe shortfall risk after 15 years, with 41% chance of funded ratio below 60% Below 60% 60% to 80% 80% to 100% 100% to 115% Over 115% 41% 22% 15% 8% 14% 35% 30% 25% Cost After 15 Yrs. (% of pay) 20% 15% 10% 5% 0% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 110% 120% 130% 140% 150% 160% 170% 180% 190% 200% Final Funded Ratio 5-5

7 Revised Strategic Asset Allocation Process Traditional Approach New TRS Approach Determine Market Expectations for Return and Risk Survey Construct Expected Returns, Volatilities, and Correlations Robust Correlation Matrix Derive Efficient Frontier Optimize Analyze Efficient Frontier Analysis using VaR Non-normality Identify Optimal Asset Mix on Efficient Frontier Forecast Hedge Future Economic Conditions Insurance 5-6

8 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 10 Year Forecast Asset Class Mean Return Std Deviation Sharpe TRS EK TRS EK TRS EK US Large Cap 8.0% 7.5% 16.3% 15.9% US Small Cap 9.3% 7.7% 20.4% 18.7% EAFE Unhedged 8.3% 7.4% 18.4% 19.0% Emerging Markets 8.8% 7.3% 25.0% 27.3% Private Equity 10.0% 10.4% 28.5% 30.3% Cash 2.7% 0.6% 0.5% 0.0% US Aggregate 4.4% 4.7% 6.5% 6.6% US Treasurys -- Intermediate 3.0% 4.3% 6.3% 5.1% US Treasurys -- Long 3.7% 4.6% 9.5% 12.1% US Investment Grade Credit 5.1% 5.0% 7.8% 8.6% US High Yield 6.9% 6.2% 11.1% 11.6% WGBI ex US Unhedged 3.3% 3.9% 8.9% 12.5% Emerging Market Debt 9.3% 6.4% 14.7% 14.7% Bank Loans 5.4% 4.9% 6.6% 6.6% Hedge Funds - Non-Directional 6.5% 5.5% 7.7% 7.2% Hedge Funds - Directional 8.3% 7.5% 10.3% 13.5% Real Assets 5.3% 7.3% 8.5% 16.0% REITS 8.0% 6.5% 20.0% 19.0% Direct Real Estate 6.6% 6.4% 8.8% 11.6% Value Added Real Estate 7.3% 6.7% 13.0% 26.4% US TIPS 4.0% 4.2% 5.9% 5.8% Global Inflation Linked Bonds 3.6% 4.4% 6.8% 6.5% Gold -1.0% n/a 20.0% n/a n/a Commodities 4.9% 4.3% 21.3% 16.1% Ennis Knupp Correlation Matrix TRS Average Correlation Matrix Global Equity Stable Value Real Return Global Equity Stable Value Real Return Global Equity Global Equity Stable Value Stable Value Real Return Real Return

9 Optimization Based on Highest Probability of Achieving Target Return 4.50% o o Optimization routine solves for the asset allocation that has the highest probability of achieving target Tested results using bearish, base case and bullish forecasts Probability Density 4.00% 3.50% 3.00% 2.50% 2.00% 1.50% 1.00% 0.50% Target Return (8.0%) Portfolio Distribution (14% Stdev) Expected Return (9.5%) Probablility of AchievingTarget Return (54.27%) 0.00% -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% Return 5-8

10 Probability VaR Efficient Frontier 60% 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) Probability of Reaching Return Target 50% 40% 30% 20% 10% 8.0% Base Case - Simulated 9.7% Base Case - Simulated Current Portfolio 8% Target Portfolio 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. 9.7% Target Portfolio 0% 5.00% 10.00% 15.00% 20.00% 25.00% 30.00% 35.00% VaR (99%) (Higher is riskier) 5-9

11 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 Suggested Policy - Current Policy Pre 2007 Policy 8% Target Global Equity US 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 Value Cash 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 Return Real 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% 5-10

12 TRS vs. Peer Strategic Allocations: June

13 Asset Allocation Recommendations: June

14 Texas Teachers: Tactical Asset Allocation Program In 2007, TRS made a major commitment to increase its riskadjusted 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 basis points of the total TRS portfolio 5-13

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26 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 5-25

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29 Modifying the TAA Model: Stocks vs. Bonds

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37 TAA at TRS: Update 5-36

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42 Hedge Fund Replication: Review and Expansion 5-41

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