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Portfolio construction · Case study

Tax-aware multi-account asset location

Optimizing asset placement across taxable, tax-deferred, and tax-exempt accounts under household-level allocation constraints.
Organization
Fidelity Investments
Context
Lead, Financial Solutions, Quant Strategy
Period
2024 – 2026
Methods
Convex optimization · Stochastic simulation · Monte Carlo
Tools
Python · Gurobi · CVXOPT

12 bps/yr

annualized reduction in tax leakage

Model result, relative to baseline

30-year

Monte Carlo validation horizon

Validation

≈3.5%

more after-tax wealth after 30 years

Illustrative compounding · 4% annual return

Note The formulation is a stylized version of the problem class. It does not describe Fidelity's production model, parameters, or data.

01Problem

Assets are held across taxable, tax-deferred, and tax-exempt accounts with different account-level tax treatment, subject to household-level allocation constraints on stocks, bonds, and cash. The decision variable is placement: where each asset class is held.

It is a coupled allocation problem: moving bonds into one account forces an offsetting move elsewhere to satisfy the household constraint. And tax drag is path-dependent, driven by realized returns, rebalancing, and withdrawal timing, so placements cannot be ranked by a single-period calculation.

02Formulation

A stylized, deterministic allocation subproblem. Let xa,k≥0x_{a,k} \ge 0 be the dollars of asset class aa held in account kk:

min⁡x≥0  ∑a∈A∑k∈Kτa,k xa,k\min_{x \ge 0} \; \sum_{a \in \mathcal{A}} \sum_{k \in \mathcal{K}} \tau_{a,k}\, x_{a,k}
(1)
s.t.  ∑axa,k=Bk∀k∑kxa,k=πa∑kBk∀a\text{s.t.}\; \begin{aligned} \textstyle\sum_{a} x_{a,k} &= B_k \quad \forall k \\ \textstyle\sum_{k} x_{a,k} &= \pi_a \textstyle\sum_{k} B_k \quad \forall a \end{aligned}
(2)
A\mathcal{A}
Asset classes: stocks, bonds, cash
K\mathcal{K}
Household accounts, each with a tax treatment
BkB_k
Balance of account kk
πa\pi_a
Household target weight of asset class aa
τa,k\tau_{a,k}
Expected tax drag per dollar of asset aa in account kk

The first constraint fills each account; the second holds the household allocation, and it is the one that couples the accounts. The problem is linear in this form and remains convex under standard extensions such as penalized rather than hard allocation targets.

03Method

Asset-location workflowInputs feed a linear or convex optimizer that produces an asset placement. The placement and a baseline are both evaluated by 30-year Monte Carlo simulation. The optimized placement reduces annualized tax leakage by 12 basis points a year.01 · INPUTSAccounts and targetsBalances, tax treatmentTarget mix, market assumptions02 · OPTIMIZELP / convex programMinimize expected tax dragGurobi · CVXOPT03 · OUTPUTAsset placementAssets by accountHousehold target mix metREFERENCEBaseline placementComparison policy04 · EVALUATEMonte Carlo simulation30-year horizonTaxes tracked along each path05 · RESULT12 bps/yrAnnualized reductionin tax leakage
Deterministic allocation optimization, followed by stochastic evaluation against the baseline placement policy over 30-year paths.
  1. Inputs. Account balances and tax treatment, household target allocation, capital-market assumptions.
  2. Optimization. Solve (1)–(2) for the asset placement.
  3. Stochastic evaluation. Simulate the optimized placement and the baseline placement policy over 30-year Monte Carlo paths.
  4. Comparison. Annualized tax leakage, optimized placement versus baseline placement policy.

04Delivery

  • Formulated and implemented in Python; solved with Gurobi and CVXOPT.
  • Stochastic simulation for the 30-year evaluation.
  • Architected and deployed at Fidelity while leading a four-person quant team.

05Results

12 bps/yr

annualized reduction in tax leakage

Model result · relative to baseline

30-year

Monte Carlo validation horizon

Validation

≈3.5%

more after-tax wealth after 30 years

Illustrative compounding · 4% annual return

The 12 bps/yr is the model result: annualized tax leakage relative to the baseline placement policy, from stochastic evaluation over a 30-year Monte Carlo horizon. The ≈3.5% is not a simulation output; it is the compounding implication of a persistent 12 bps annual reduction at a 4% annual return:

(1.04121.04)30−1  ≈  3.52%\left(\frac{1.0412}{1.04}\right)^{30} - 1 \;\approx\; 3.52\%

Because asset location changes where assets are held rather than what is held, the improvement comes without changing the household's allocation or market exposure.

06Limitations

  • Capital-market assumptions. Results are conditional on the assumed return and risk inputs.
  • Tax rules. Tax treatment is assumed to persist as modeled over the full horizon.
  • Contributions and withdrawals. Cash-flow timing is an assumption, not an observation.
  • Rebalancing. Results depend on the assumed rebalancing policy.
  • Account mix. The benefit scales with how differently the household's accounts are taxed; a household with one account type has no location decision.
  • Projection uncertainty. A 30-year projection is a distribution, not a forecast.