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1 Nonprofit Management Research Panel Liquidity Pool Management for U.S. Colleges and Universities Gain a better understanding of your school s financial risks and the benefits of integrating the investment strategies of multiple asset pools. Investment management continues to grow in complexity for U.S. colleges and universities. External pressures like decreases in tuition revenues and donations, increased capital expenditures, as well as state funding for public institutions, continue to place a demand on short-term asset pools. In addition, many institutions report not feeling confident in their abilities to quantify risk across their multiple asset pools. By integrating different balance sheet strategies into one asset management approach, colleges and universities can gain a stronger understanding of their school s financial risks and potentially improve their return profile, while being better positioned for strategic decision-making that supports key financial goals. Colleges and universities have multiple asset pools to manage. Some are short-term balance sheet assets earmarked for operating expenses, or some might be more intermediate-term assets held for future use. Endowment assets, typically the result of fundraising activity, might be housed with an affiliated foundation or as a long-term asset pool on the balance sheet. Every institution has its own unique process to manage these multiple pools as demonstrated in the research in this paper. The goal is to manage this continuum of financial assets to compliment and support broader institutional objectives. A lot of higher education research focuses on investment management trends of the endowment pools, whose long horizons allow for greater risk. However, these strategies need to work in conjunction with other shorter- and intermediate-term pools in order to best support day-to-day operating needs and expenses, while mitigating unnecessary balance sheet risk. While each school is unique, there is value in examining common trends and best practices when it comes to the often complex task of managing these multiple pools of assets. seic.com/institutions

2 In an effort to identify and support key trends in short-term liquidity pool management, SEI s Nonprofit Management Research Panel completed a survey of 52 U.S. colleges and universities in October 2015, the results of which follow in this paper. The combined endowment and operating assets of the participating institutions ranged in size from $35 million to $4 billion. Private universities represented 67% of the participant population and public universities accounted for 33%. None of the institutions were current clients of SEI s Institutional Group. Figure 1: Participants by Title Figure 2: Participants by Asset Size 25% 15% 6% 8% 29% 4% 13% CFO CIO Finance Exec Director Accountant/Analyst Board/Committee Other 4% 6% 19% 19% 15% 13% 23% Under $25 million $26-$50 million $51-$100 million $101-$300 million $301-$500 million $501-$999 million More than $1 billion Operating Governance and Oversight Because schools differ in the way their various asset pools are structured and governed, our research panel sought to identify how the shorter-term investment pools are managed. More than 50% of participating public institutions and more than 60% of private schools have balance sheet and or/operating investments that are separate from the endowment assets, and managed in-house by finance staff. The remaining pools are generally managed by foundation staff, either as separate pools or combined with foundation assets. Two public universities reported that these asset pools are managed externally by their respective states. Figure 3: Management of Balance Sheet and/or Operating Investments Managed as a separate pool by university finance staff Managed as a separate pool from foundation assets by foundation staff Combined with foundation assets and managed by foundation staff All Public Private Not sure Other 0% 20% 40% 60% 80% Similarly, the resources used to assist with the investment management of liquidity/operating pool assets are more often than not internal. About 50% of private institutions and 35% of public institutions rely on their own internal staff. About one-third use consultants to assist with this task and slightly more than 10% use an outsourced investment provider. Those reporting other referred to board subcommittees and investment advisors as external resources. 2

3 Figure 4: Resources Used for the Investment Management of Balance Sheet and/or Operating Assets None internal resources All Public Private Investment consultant OCIO/discretionary provider Cash management specialist Other 0% 10% 20% 30% 40% 50% 60% Investment Management Trends and Challenges More than two-thirds of respondents have high confidence in their ability to manage these multiple asset pools holistically in accordance with their institution s broader financial goals. When it comes to risk management, however, nearly half (47%) said they are not very confident in their ability to quantify total financial risk across all asset pools. In addition, 58% report not frequently stress-testing portfolios for coverage or debt-issuance ratios. Figure 5: Investment Management Practices 1. Not confident 2. Somewhat confident 3. Very confident 4. Extremely confident We manage multiple asset pools holistically to align our institution s strategic and financial goals. We quantify total financial risk across all asset pools. We create optimal strategies for each pool that supports institution-level goals. Our asset allocation decisions incorporate key metrics like hurdle rate, credit rating and spending. We frequently stress-test portfolios from a coverage ratio/debt-issuance perspective. We effectively analyze the diversification of revenues and dependence on investment portfolios. 12% 21% 48% 19% 10% 37% 46% 8% 10% 31% 48% 12% 17% 21% 44% 17% 25% 33% 33% 8% 8% 31% 46% 15% 3

4 4 Asset allocation for these types of nonendowment balance sheet assets naturally tends to have a heavier allocation to shorter-term fixed income and cash (32%), followed by an additional 25% to fixed income in general. However, more than 25% are in global equities and an additional 11% are invested in alternative asset classes with less liquidity than traditional public equity and fixed income. The chart in Figure 6 gives the allocation breakdown of these operating pools. Case Study University Liquidity Pool Analysis 1 Separate from the survey, the following case study looks at how to think about asset allocation strategies of multiple nonendowment pools on a university balance sheet. Each pool has its own risk and return objective carefully outlined in the investment policy, as well as certain quality, duration and liquidity restrictions. There are new allocations being reviewed for each pool in order to achieve these objectives, and then the allocations are tested to show the impact across selected liabilities, debt coverage ratios, liquidity requirements, etc. The pools are combined for analysis purposes and stress-tested showing a worst case (5th percentile), best case (95th percentile) and median (50th percentile) outcomes using two portfolio allocations. The analysis shows how various portfolio returns at different probability levels impact financial metrics. Finally, the outcomes are compared to the school s internal budget and financial report projections to quantify how much deviation there is between good, average and bad market scenarios, and determine the comfort level around those risks. This is an important fiduciary exercise as it documents the acceptable levels of risk the institution is considering. A one-year analysis is shown but longer time periods can and should be stress-tested as well. 1 This case study describes the attributes of a specific client that SEI has determined is relevant based on objective criteria, including organizational goals, asset size and industry sector. Any discussion of specific asset allocations is intended to help the reader understand SEI s customized investment approach, and should not be regarded as a recommendation. Information concerning SEI s recommendations over the last year is available on request. Figure 6: Operating Pool Average Asset Allocation Domestic equities Fixed income International equities Alternatives Short-term/Cash Other Figure 7: Sample Allocations for Different Balance Sheet Asset Pools Asset Class 8% 11% 6% 18% Diversified Pool 25% Liquidity Pool 32% Cash Pool (based on average) Total Combined Target Portfolio US Managed Volatility 8 S&P 500 Index 10 6 US Small/Mid Cap Equity Index 3 2 US Small Cap Equity 3 2 World Equity Ex-US Emerging Markets Equity 4 3 Dynamic Asset Allocation 6 4 Total Equity US High Yield 4 3 Emerging Markets Debt 6 4 Core Fixed Income 10 5 Limited Duration Fixed Income 60 9 Short Term Corporate Fixed Income 40 6 Short Term Bond 31 6 Cash Total Fixed Income Multi-Asset 7 5 Total Inflation Hedge/Real Assets 7 5 Moderate Volatility Hedge 7 4 Private Real Estate 3 2 Structured Credit 6 4 Private Equity 4 3 Total Alternatives/Other Net Expected Return 7.50% 2.50% 2.10% 5.80% Standard Deviation 14.60% 2.90% 1.10% 9.80% Risk of Loss (5th percentile) % -2.30% 0.00% -9.10% Sharpe Ratio Source: SEI Capital Market Assumptions updated June Please see important disclosures at the back of the presentation. Net of Fees (Total Portfolio fees are SEI fees only excludes outside cash held). There is no assurance that the asset allocation set forth above was actually accepted and implemented by the client. Past performance is not indicative of future results. You should not assume that future recommendations will be as profitable or will equal the performance of past recommendations. The expected return, standard deviation and duration do not reflect actual investment results and are not guarantees of future results. This information reflects projections based on SEI s capital market assumptions. See the Important Information section for additional information.

5 This exercise allowed the University to analyze the potential impact of changing their allocation in their search to enhance returns within a risk-managed framework. Using our analysis, we were able to calculate the type of market loss that would cause the ratios to move to an unacceptable level. Figure 8 and 9: Investment Allocation Strategy Determined by Institutional Risk Tolerance in Financial Operations and Portfolio Scenarios 1 Year Financial Projections Prior to Transition Portfolio Proposed Portfolio School Projection Confidence Interval* Based on budget 95th 50th 5th 95th 50th 5th (Downside Risk Year 10) projections Expendable Net Assets (in millions) $ $ $ $ $ $ $ Operating Expense (in millions) $ $ $ $ $ $ $ Expendable Net Assets to Operating Expense Primary Reserve Score Expendable Net Assets (in millions) $ $ $ $ $ $ $ Debt (in millions) $ $ $ $ $ $ $ Expendable Net Assets to Debt Viability Score Change in Total Assets (in millions) $20.70 ($5.00) ($27.10) $21.30 ($4.70) ($26.90) ($1.60) Revenues, Operating and $ $ $ $ $ $ $ Nonoperating (in millions) Change in Total Assets to Revenues Net Income Score Senate Bill 6 Ratio Year Financial Projections Prior to Transition Portfolio Proposed Portfolio School Projection Confidence Interval* Based on budget 95th 50th 5th 95th 50th 5th (Downside Risk Year 10) projections Unrestricted Net Assets (in millions) $ $ $97.00 $ $ $97.30 $ Operating Expense (in millions) $ $ $ $ $ $ $ Unrestricted Financial Resources to Operating Expense Unrestricted Net Assets (in millions) $ $ $97.00 $ $ $97.30 $ Debt (in millions) $ $ $ $ $ $ $ Unrestricted Financial Resources to Debt Total Revenues (in millions) $ $ $ $ $ $ $ Less Capital Grants (in millions) ($2.00) ($2.00) ($2.00) ($2.00) ($2.00) ($2.00) ($2.00) Total Operating Revenues (in millions) $ $ $ $ $ $ $ Change in Unrestricted Net Assets ($3.90) ($3.40) ($2.90) ($3.90) ($3.40) ($2.90) ($3.20) Change in Unrestricted Net Position to Total Operating Revenue University Financial Resources per FTE Actual (Measure of Reserves) 19,778 18,435 17,271 19,810 18,451 17,287 17,998 * 95th percentile is the good scenario, 50th percentile is the average scenario and 5th percentile is the poor scenario. Source: SEI Capital Market Assumptions updated June Financial Statements for the University for June 30, Gross of Fees. There is no assurance that the recommendation set forth above was actually accepted and implemented by the client. Past performance is not indicative of future results. You should not assume that future recommendations will be as profitable or will equal the performance of past recommendations. The expected return, standard deviation and duration do not reflect actual investment results and are not guarantees of future results. This information reflects projections based on SEI s capital market assumptions. See the Important Information slides for additional information. 5

6 Public College and University Trends There are often particular state requirements for public colleges and universities that must be integrated into the investment analysis. In some cases, there s an additional governance level like a board of regents that is established at the state Capitol. The responses below indicate some of these restrictions by polling only public institutions. The majority have limitations on the allocation to illiquid alternative types of asset classes. More than one-third of the respondents have quality restrictions on their fixed-income holdings. Less than 20% need to hold a minimum stated amount in cash, and more than one-third have other forms of restrictions, such as 100% allocation to cash, investments in U.S. government securities only and other domestic-only holdings. Of those that have minimum cash requirements, almost 20% have to hold more than 40% of their allocation in cash. Presumably, this cash is for very short-term purposes, thus, the ultraconservative mandate. Figure 10: Public Institutions Respondents with State-mandated Balance Sheet Guidelines 60% 50% 40% 30% 20% 10% 0% Minimum allocation to cash Minimum allocation to high-quality fixed income Restrictions on illiquid asset classes Maximum duration Other Figure 11: Public Institutions Balance Sheet Cash Requirements 13% 19% 69% need 0 to 10% in cash need > 40% in cash said they have no cash requirements Diversification of revenue sources is also different for public institutions, which have some level of dependence on stateand/or federal-level financial support. As state and federal budgets are continually challenged, these sources of income have generally trended lower, requiring public schools to seek alternative means of income, such as from their investment pools. Care needs to be taken, however, on how much risk is embedded in the search for return, which can ultimately influence an institution s financial profile and cost of capital. Figure 12: Public Institutions Credit Ratings 14% 29% 57% Aaa Aa A When asked what potential factors can influence credit ratings, which ultimately can increase or decrease the cost of debt, most respondents ranked low endowment returns and decreases in fundraising as having little to no impact. Perhaps this is because of the differing governing bodies that control these two separate asset pools, and the lack of holistic risk analysis. Credit rating agencies do take into consideration both endowment and nonendowment assets as resources, which might not be as much a consideration for the fiduciaries of the endowment assets. Not surprisingly, nearly 75% felt that the inability of state funds to keep up with school expenses and tuition pressures had a high or very high impact on credit ratings. 6

7 Figure 13: Public Institutions Potential Impacts on Credit 1. No Impact 2. Marginal Impact 3. High Impact 4. Very High Impact Low endowment returns 54% 46% 0% 0% Decline in net tuition revenues 14% 36% 43% 7% State funding not keeping up with expenses Increased competition for sponsored-research dollars Pressure of underfunded pension and healthcare costs 13% 13% 67% 7% 21% 57% 21% 0% 36% 43% 14% 7% Decreases in gifts and donations 20% 73% 7% 0% Figure 14: Public Institutions Pension Accounting Changes 29% 29% 6% 35% High impact Little impact Not sure NA no pension Lastly, for those public universities with a defined benefit pension plan, new accounting standards issued this year (GASB-68) require that universities now report their pension liabilities on their balance sheets. Twenty-nine percent of survey participants said this new accounting requirement will have a high impact on their financial reporting, creating significant drag on the balance sheet. Only 6% thought there would be little impact. Most (35%) were not sure yet of the financial statement impact to the institution, although this development should become clearer in future years. Conclusion Different governance practices and different state regulations will have influence over the investment management of both the long-term endowment assets and the shorter- and intermediate-term asset pools. However, it s vital that these pools work together within these practices and requirements to support the broader objectives of the university or college. Identifying ways to strategically manage all of the investible assets on the balance sheet in order to understand these risk and liquidity considerations will ultimately enhance the financial management of the institution. For more information or a complimentary balance sheet analysis, please contact SEI at seiresearch@seic.com or

8 1 Freedom Valley Drive P.O. Box 1100 Oaks, PA seic.com/institutions NONPROFIT MANAGEMENT Research Panel The Nonprofit Management Research Panel, sponsored by SEI s Institutional Group, conducts industry research in an effort to provide members with current best practices and strategies for the investment management of nonprofit foundations and endowments. Important Information Information provided by SEI Investments Management Corporation, a wholly owned subsidiary of SEI Investments Company. This information is for educational purposes only. Not intended to be investment, legal and/or tax advice. Please consult your financial/tax advisor for more information. The probability distribution graphs and/or tables presented are meant to provide an overview of the range of possible outcomes for a given variable (e.g., returns, pension contributions, expense) for a given asset allocation. The probability distributions are generated using SEI s proprietary modeling tool and simulated capital market behavior. Capital market behavior is simulated for 1,000 possible scenarios based on expected performance of each asset class and reflecting current economic conditions. Capital market assumptions such as return, standard deviation and covariances are inputs into this process, combining with model parameters to create market scenarios. We use these 1,000 capital market scenarios to create 1,000 output scenarios for each variable being considered. A 90% confidence interval should be interpreted as 90% of the projected output variables, falling between the 5% and 95% results, based on SEI Capital Market Assumptions. This projection is hypothetical in nature, does not reflect actual investment results and is not a guarantee of future results. SIMC develops forward-looking, long-term capital market assumptions for risk, return, and correlations for a variety of global asset classes, interest rates, and inflation. These assumptions are created using a combination of historical analysis, current market environment assessment and by applying our own judgment. In certain cases, alpha and tracking error estimates for a particular asset class are also factored into the assumptions. We believe this approach is less biased than using pure historical data, which is often biased by a particular time period or event. The asset class assumptions are aggregated into a diversified portfolio, so that each portfolio can then be simulated through time using a Monte Carlo simulation approach. This approach enables us to develop scenarios across a wide variety of market environments so that we can educate our clients with regard to the potential impact of market variability over time. Ultimately, the value of these assumptions is not in their accuracy as point estimates, but in their ability to capture relevant relationships and changes in those relationships as a function of economic and market influences. The projections or other scenarios in this presentation are purely hypothetical and do not represent all possible outcomes. They do not reflect actual investment results and are not guarantees of future results. All opinions and estimates provided herein, including forecast of returns, reflect our judgment on the date of this report and are subject to change without notice. These opinions and analyses involve a number of assumptions which may not prove valid. The performance numbers are not necessarily indicative of the results you would obtain as a client of SIMC. We believe our approach enables our clients to make more informed decisions related to the selection of their investment strategies. For more information on how SIMC develops capital market assumptions, please refer to the SEI paper entitled, Executive Summary: Developing Capital Market Assumptions for Asset Allocation Modeling. If you would like more information on the actual assumptions used, you may request them from your SEI representative SEI (01/16)