18 May noon study
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Last updated
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Olivinia Heritage Case Scenario
Olivinia is an oil-rich state in the country of Puerto Rinaldo, which uses the US dollar as its official currency of exchange. In 1981, the state’s legislature created the Olivinia Heritage Fund (OHF) to collect a portion of the state’s non-renewable resource revenue and invest it on behalf of future generations.
James Lafferty, the managing director of the fund, is one of the keynote speakers at the Global Wealth Creation Conference. He begins his presentation with a brief overview of OHF’s history (Exhibit 1).
Exhibit 1
An Overview of the Olivinia Heritage Fund
Phase 1 (1981–1991)
The fund was given an initial allocation of $1 billion by the state.
The fund was to receive 10% of all state revenues arising from taxes on oil and gas production and extraction.
The fund was given a 20-year accumulation period over which no distributions were allowed and the fund was forecasted to grow to $10 billion.
Income earned following the accumulation period was to be used to provide for public works and other public infrastructure within the state.
Investments were restricted to cash and investment-grade bonds.
During Phase 1, the most significant constraint on OHF’s asset allocation choices was the result of:
Solution
C is correct. During Phase 1, OHF was restricted to investing in only cash and high-grade debt instruments.
B is incorrect. The fund started at $1 billion and was worth $2.2 billion at the end of Phase 1; such a size is considered in the low range for a large institutional portfolio.
A is incorrect. During Phase 1, OHF had no liquidity constraints. No outflows were expected for 20 years, all income was to be reinvested, and the nature of the portfolio was quite liquid by regulation, consisting of cash and high-grade bonds.
Asset Allocation with Real-World Constraints Learning Outcome
Discuss asset size, liquidity needs, time horizon, and regulatory or other considerations as constraints on asset allocation
Phase 2 (1991–2001)
By 1991, after being in existence for 10 years, the fund value had grown to $2.2 billion.
At this time, transfers of state revenues from taxes on oil-related resources was halted and the government began to use income generated by the fund for direct economic development and social investment purposes.
In addition to cash and investment-grade bonds, the investment mandate for the fund was expanded to include investments in private and public companies, real estate, and infrastructure investments.
Management of cash and bond investments was performed in-house. For the higher-risk component of the portfolio, the fund hired external managers in an effort to increase return and correspondingly lower the incidence of negative performance.
These managers were hired or retained if they had outperformed other active managers in their sectors in at least the prior two years. The fund value at the end of this period was $6 billion.
During Phase 2, OHF’s external manager selection and retention policy was most likely susceptible to which of the following biases?
Solution
C is correct. Representative (or recency) bias is the tendency to overweight the importance of the most recent observations and information compared with information arising from a longer-term set of observations. For this situation, it implies that those managers who outperformed their peers in at least the prior two years would continue to do so in future periods.
B is incorrect. Loss aversion is a bias in which individuals tend to strongly prefer avoiding losses as opposed to achieving gains. The basis for hiring and retaining managers was not based on gains or losses but, rather, was performance relative to that of their peers.
A is incorrect. The endowment bias is the bias that individuals ascribe more value to things merely because they already own them.
Asset Allocation with Real-World Constraints Learning Outcome
Identify behavioral biases that arise in asset allocation and recommend methods to overcome them
Phase 3 (2001–2014)
Strong reform legislation related to the original intent of the fund was introduced in 2001.
It reinstated transfers of oil-related taxes to the fund, increasing them to 35% of oil- and gas-related state revenues.
In addition, the fund was mandated to have 50% in public equities through passive index funds and 10% in cash and investment-grade bonds.
The remainder was to be divided equally between high-yield bonds, real estate, private equity, and hedge funds and would continue to be managed externally.
All investments were to be made outside the country to avoid overheating the national economy.
Investments managed by individual external managers was limited to approximately $75 million.
A two-thirds majority in both the upper and lower legislative bodies was required to change any future legislation related to the fund.
By the end of this phase, the fund was worth $28 billion.
In Phase 3, the most likely change in the constraints facing OHF’s ability to undertake asset allocation arose from an increased need for:
Solution
A is correct. In Phase 3, 40% of assets were to be invested in high-yield bonds, real estate, private equity, and hedge funds, which were to be managed externally. Each external manager was limited to approximately $75 million of the fund’s assets. As indicated in the table below, the number of external managers required grew from about 30 to almost 150 by the end of the period. This would have placed substantial demands on the governance resources of the fund to allow for identification of suitable managers and to monitor their performance.
Start of Phase 3
End of Phase 3
Portfolio value
$6b
$28b
Externally managed funds @ 40%
$2.4b
$11.2b
Managers needed with $75m limit
32
149
B is incorrect. The liquidity requirements of the fund had not changed; tax revenues were again flowing into the fund, and the fund held substantial (10%) bond and cash investments.
C is incorrect. Phase 3 indicates no change in risk from Phase 2. In Phase 2, the fund was allowed to increase its risk exposure; it could invest in public equites, high-yield bonds, private equity, hedge funds, and real estate. In Phase 1, investments were very low risk—cash and high-grade bonds.
Asset Allocation with Real-World Constraints Learning Outcome
Discuss asset size, liquidity needs, time horizon, and regulatory or other considerations as constraints on asset allocation
Lafferty states that ever since the fund was given the authority to vary asset class policy weights from their strategic levels, it has actively engaged in tactical asset allocation (TAA) using a variety of proprietary short-term forecasting tools that have been developed in-house. He provides the data in Exhibits 2 and 3 to illustrate the results of one such shift in the fund’s asset allocation following a signal from its TAA model.
Exhibit 2
Example of a Short-Term Shift in Asset Allocation
Asset Class
Current Weights*
Policy Weights
TAA Weights
Period Returns
Investment-grade bonds
12%
15%
10%
1.75%
High-yield bonds
8
6.25
10
3.0
Public equity
63
60
65
7.0
Private equity
5
6.25
6.25
4.5
Real estate
6.25
6.25
2.5
–4.0
Infrastructure
6.25
6.25
6.25
2.5
Based on Exhibit 2, compared with the strategic asset allocation, the incremental return added to the fund through tactical asset allocation was closest to:
Solution
B is correct. By underweighting investment-grade bonds and real estate and overweighting public equity and high-yield bonds, the TAA strategy added 0.53% to the return of the fund, as shown below.
(1)
(2)
(3)
(4) = [(2) – (1)] × (3)
Policy Weights
TAA Weights
Period Returns (%)
Incremental Returns (%)
Investment-grade bonds
0.1500
0.1000
1.75
–0.0875
High-yield bonds
0.0625
0.1000
3.00
0.1125
Public equity
0.6000
0.6500
7.00
0.3500
Real estate
0.0625
0.0250
–4.00
0.1500
Total from TAA
0.8750
0.8750
0.5250
The following had no impact on the incremental return
Private equity
0.0625
0.0625
4.50
0.0000
Infrastructure
0.0625
0.0625
2.50
0.0000
0.1250
0.1250
0.0
A is incorrect. It compares current weights with the TAA weights.
Current Weights
TAA Weights
Period Returns (%)
Incremental Returns (%)
Investment-grade bonds
0.1200
0.1000
1.75
–0.0350
High-yield bonds
0.0800
0.1000
3.00
0.0600
Public equity
0.6300
0.6500
7.00
0.1400
Private equity
0.0450
0.0625
4.50
0.0788
Real estate
0.0625
0.0250
–4.00
0.1500
Infrastructure
0.0625
0.0625
2.50
0.0000
Total
1.0000
1.0000
0.3938
C is incorrect. It compares the current and target weights.
Policy Weights
Current Weights
Period Returns (%)
Incremental Returns (%)
Investment-grade bonds
0.1500
0.1200
1.75
–0.0525
High-yield bonds
0.0625
0.0800
3.00
0.0525
Public equity
0.6000
0.6300
7.00
0.2100
Private equity
0.0625
0.0450
4.50
–0.0788
Real estate
0.0625
0.0625
-4.00
0.0000
Infrastructure
0.0625
0.0625
2.50
0.0000
Total
1.0000
1.0000
0.1313
Asset Allocation with Real-World Constraints Learning Outcome
Discuss the use of short-term shifts in asset allocation
Exhibit 3
Efficient Frontier from Assets Utilized by OHF
The most appropriate conclusion that can be drawn from Exhibit 3 is that:
Solution
A is correct. The Sharpe ratio is the slope of the line drawn from the risk-free rate to a particular portfolio. The two portfolios of interest are the policy portfolio and the TAA portfolio because both are indicated as being efficient.
The diagram to the right indicates that the policy portfolio/risk-free combination has a higher slope than the TAA/risk-free combination.
Even though the TAA portfolio has a higher return than the policy portfolio, the additional return requires too much additional risk. In addition, the TAA portfolio may exceed management’s risk tolerance.
B is incorrect. Corner portfolios are efficient portfolios and represent a portfolio where an asset weight changes from zero to positive or positive to zero. No such behavior in weights is indicated for the current portfolio allocation in Exhibit 2. It is also an inefficient portfolio.
C is incorrect.
The Sharpe ratio is the slope of the line drawn from the risk-free rate to a particular portfolio.
The two portfolios of interest are the policy portfolio and the TAA portfolio because both are indicated as being efficient. The diagram to the right indicates that the policy portfolio/risk-free combination has a higher slope than the TAA/risk-free combination.
Asset Allocation with Real-World Constraints Learning Outcome
Discuss the use of short-term shifts in asset allocation
Phase 4 (2014–Present)
The fund’s management felt that the significant decline in oil prices since mid-2014 and lowered production levels were likely to persist through several business cycles, requiring a change in strategy to maintain the long-term objectives of the funds.
They sought government approval for lower withdrawals from the fund, higher equity exposure, and the flexibility to vary asset class policy weights by as much ±5% for each asset class from the static weights that had previously existed.
The government reaffirmed its commitment to the fund given in Phase 3, and legislative approval was received for these changes, including the ability to increase public equity exposure to 65% and reduce investment-grade bond exposure to as little as 7.5%.
Of the remaining authorized assets, no one asset class could have a weight in excess of 10%.
The changes that were allowed in OHF’s strategic asset allocation in Phase 4 are best classified as relating to changes in:
Solution
B is correct. In Phase 4, lower oil prices and reduced production would have resulted in substantial declines in cash inflows that management perceived to be long term in nature. To compensate for these changes, management sought both lower withdrawals and increased equity exposure to bolster its declining cash inflows.
A is incorrect. A change in constraints would arise from a change in time horizon, liquidity needs, asset size, or regulatory constraints, none of which are indicated in Phase 4.
C is incorrect. Although business conditions have changed because of lower oil prices and reduced production, there is no indication that the main goal of the fund has changed; indeed, the government reaffirmed its commitment to the fund given in the latter part of Phase 2.
Asset Allocation with Real-World Constraints Learning Outcome
Recommend and justify revisions to an asset allocation given change(s) in investment objectives and/or constraints