Remington Wealth Partners Case Scenario
Preston Remington is the managing partner of Remington Wealth Partners. The firm manages high-net-worth private client investment portfolios using various asset allocation strategies. Analyst Hannah Montgomery assists Remington.
Remington and Montgomery’s first meeting of the day is with a new client, Spencer Shipman, who recently won $900,000 in the lottery. Shipman wants to fund a comfortable retirement. Earning a return on his investment portfolio that outpaces inflation over the long term is critical to him. He plans to withdraw $54,000 from the lottery winnings investment portfolio in one year to help fund the purchase of a vacation home and states that it is important that he be able to withdraw the $54,000 without reducing the initial $900,000 principal. Montgomery suggests they use a risk-adjusted expected return approach in selecting one of the portfolios provided in Exhibit 1.
Exhibit 1
Investment Portfolio One-Year Projections
Return | Standard Deviation | |
Portfolio 1 | 10.50% | 20.0% |
Portfolio 2 | 9.00 | 13.0 |
Portfolio 3 | 7.75 | 10.0 |
All data are tax adjusted.
Remington and Montgomery discuss the importance of strategic asset allocation with Shipman. Remington states that the firm’s practice is to establish targeted asset allocations and a corridor around the target. Movements of the asset allocations outside the corridor trigger a rebalancing of the portfolio. Remington explains that for a given asset class, the higher the transaction costs and the higher the correlation with the rest of the portfolio, the wider the rebalancing corridor. Montgomery adds that the higher the volatility of the rest of the portfolio, excluding the asset class being considered, the wider the corridor.
Remington and Montgomery next meet with client Katherine Winfield. The firm had established Winfield’s current asset allocation on the basis of reverse optimization using the investable global market portfolio weights with further adjustments to reflect Winfield’s views on expected returns.
Remington and Montgomery discuss with Winfield some alternative asset allocation models that she may wish to consider, including resampled mean–variance optimization (resampling). Remington explains that resampling combines mean–variance optimization (MVO) with Monte Carlo simulation, leading to more diversified asset allocations. Montgomery comments that resampling, like other asset allocation models, is subject to criticisms, including that risker asset allocations tend to be under-diversified and the asset allocations inherit the estimation errors in the original inputs.
Montgomery inquires whether asset allocation models based on heuristics or other techniques might be of interest to Winfield and makes the following comments:
- The 60/40 stock/bond heuristic optimizes the growth benefits of equity and the risk reduction benefits of bonds.
- The Norway model is a variation of the endowment model that actively invests in publicly traded securities while giving consideration to environmental, social, and governance issues.
- The 1/N heuristic allocates assets equally across asset classes with regular rebalancing without regard to return, volatility, or correlation.
Finally, Remington and Montgomery discuss Isabelle Sebastian. During a recent conversation, Sebastian, a long-term client with a $2,900,000 investment portfolio, reminded Remington that she will soon turn age 65 and wants to update her investment goals as follows:
- Goal 1: Over the next 20 years, she needs to maintain her living expenditures, which are currently $120,000 per year (90% probability of success). Inflation is expected to average 2.5% annually over the time horizon, and withdrawals take place at the beginning of the year, starting immediately.
- Goal 2: In 10 years, she wants to donate $1,500,000 in nominal terms to a charitable foundation (85% probability of success).
Exhibit 2 provides the details of the two sub-portfolios, including Sebastian’s allocation to the sub-portfolios and the probabilities that they will exceed the expected minimum return.
Exhibit 2
Investment Sub-Portfolios & Minimum Expected Return for Success Rate
Sub-Portfolio | BY | CZ |
Expected return (%) | 5.70 | 7.10 |
Expected volatility (%) | 5.10 | 7.40 |
Current portfolio allocations (%) | 40 | 60 |
Probability (%) | Minimum Expected Return (%) | |
Time horizon: 10 years | ||
99 | 2.90 | 2.50 |
90 | 3.40 | 2.80 |
85 | 3.60 | 3.00 |
Time horizon: 20 years | ||
95 | 5.10 | 5.40 |
90 | 5.20 | 5.70 |
85 | 5.60 | 5.90 |
Assume 0% correlation between the time horizon portfolios.
Using Exhibit 2, which of the sub-portfolio allocations is most likely to meet both of Sebastian’s goals?
- The current sub-portfolio allocation
- A 43% allocation to sub-portfolio BY and a 57% allocation to sub-portfolio CZ
- A 37% allocation to sub-portfolio BY and a 63% allocation to sub-portfolio CZ
Solution
C is correct. Sebastian needs to adjust the sub-portfolio allocation to achieve her goals. By adjusting the allocations to 37% × $2,900,000 = $1,073,000in BY and 63% × $2,900,000 = $1,827,000in CZ, she will be able to achieve both of her goals based on the confidence intervals.
Goal 1: Sebastian needs to maintain her current living expenditure of $120,000 per year over 20 years with a 90% probability of success. Inflation is expected to average 2.5% annually over the time horizon.
Sub-portfolio CZ should be selected because it has a higher expected return (5.70%) at the 90% probability for the 20-year horizon. Although sub-portfolio CZ has an expected annual return of 7.10%, based on the 90% probability of success requirement, the discount factor is 5.70%.
Goal 1: k = 5.70%; g = 2.50%.
Determine the inflation-adjusted annual cash flow generated by sub-portfolio CZ:
$1,827,000×(0.057−0.025)[1−(1+0.0251+0.057)20](1.057)=$120,432.04>$120,000
Goal 2: Sebastian wants to contribute $1,500,000 to a charitable foundation in 10 years with an 85% probability of success.
Sub-portfolio BY should be selected because it has a higher expected return (3.60%) at the 85% probability for the 10-year horizon. Although sub-portfolio BY has an expected annual return of 5.70%, based on the 85% probability of success requirement, the discount factor is 3.60%.
Goal 2: k = 3.60%.
Determine the amount needed today in sub-portfolio BY:
$1,500,000(1+0.036)10=$1,053,158.42<$1,073,000
A is incorrect: 40% × $2,900,000 = $1,160,000 in BY, and 60% × $2,900,000 = $1,740,000 in CZ.
Goal 1: k= 5.70%; g = 2.50%.
Determine the inflation-adjusted annual cash flow generated by sub-portfolio CZ:
$1,740,000×(0.057−0.025)[1−(1+0.0251+0.057)20](1.057)=$114,697.18<$120,000
Goal 2: k = 3.60%.
Determine the amount needed today in sub-portfolio BY:
$1,500,000(1+0.036)10=$1,053,158.42<$1,160,000
Goal 1 is not realized because the inflation-adjusted annual payment is below $120,000.
Goal 2 is realized
B is incorrect: 43% × $2,900,000 = $1,247,000 in BY, and 57% × $2,900,000 = $1,653,000 in CZ.
Goal 1: k = 5.70%; g = 2.50%.
Determine the inflation-adjusted annual cash flow generated by sub-portfolio CZ:
$1,653,000×(0.057−0.025)[1−(1+0.0251+0.057)20](1.057)=$108,962.32<$120,000
Goal 2: k = 3.60%.
Determine the amount needed today in sub-portfolio BY:
$1,500,000(1+0.036)10=$1,053,158.42<$1,247,000
Goal 1 is not realized because the inflation-adjusted annual payment is below $120,000.
Goal 2 is realized.
Doubt: unable to understand the calculation and formula used in the solution for Goal 1. Please help
The detailed audio explanation has been provided by Sanjay Sir earlier. You need to search.
A shortcut to solving these type of sums is to simply use the TVM mode in calci, calculate the PV of each goal, add them up, see the total amount required and get the percentages to each PF.
For eg : Goal 1 : find out the PV of 120000/year for 20 years in bgn mode and using growth adjusted disc rate…do remember the rule of choosing the highest minimum adjusted return in the required probability of success row. Choose the PF ( By or Cz from there only)
Do the same thing for Goal 2.
PV of goal 1+ PV of goal 2 = total amount required.
PV of goal 1/total amount required = how much you need to allocate in which PF.
CAN YOU PLEASE SHARE THAT AUDIO I AM UNABLE TO FIND THAT