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Tackling Model Drift in a Tax-Aware Portfolio

Risk, Performance, and Reporting

By Daniel Poll  |  August 4, 2021

Constructing and running tax-efficient portfolios has been a growing need in the market over the last several years. With long-term appreciation of equity stock returns, increased scrutiny over capital gain tax legislation by the current U.S. administration, and a client-driven desire for customized products, the appetite to offer tax-aware solutions continues to expand. The necessity to offer these services is becoming industry standard. Furthermore, running tax-efficient strategies can also lead to the competitive advantage of tax alpha, which is defined as the additional value the portfolio can return by managing it in a tax-aware fashion. Managing tax-efficient strategies can ensure that taxes do not erode current and future portfolio returns.

Challenges of Managing Tax-Efficient Strategies

Historically these seemingly complex workflows have been either outsourced or inefficiently run in-house via outdated technologies and processes. Not only can outsourcing become costly but it can inhibit the ability to react quickly to tax-loss harvesting opportunities during periods of higher market volatility. Additionally, manually managing and integrating necessary inputs, such as portfolio lot level data, benchmarks, risk models, tax rates, and model portfolios, is no easy feat.

Tax-Efficient Portfolio Management Use Cases

  • Model Drift/Tax Drag: Minimizing the drift from a model portfolio while limiting tax gains to a fixed budget or minimizing tax costs within a specified guardrail
  • Creation of Tax Efficient Model Portfolios: Managing model portfolio changes with a tax-aware mindset
  • Tax-Loss Harvesting: Selling securities at a loss to offset current and future tax liability
  • Transition Management: Onboarding a new client while minimizing tax impact on existing portfolio
  • Management of Inflows/Outflows: Leveraging tax-efficient cash management
  • Direct/Custom Indexing: Replicating the performance of an index by owning the individual names while accounting for tax liability
  • Tax-Efficient Thematic Investing: Customizing portfolio tilts (e.g., ESG, smart beta) while maximizing tax alpha

In this article, we will focus on model drift to help answer the question: How can we limit current and future tax liability while closely tracking a model portfolio?

Model drift can be set up to use any model target portfolio, but for our use case, we created a systematic model portfolio by using a multi-factor ranking of U.S.-traded large cap stocks. We then defined real-life portfolio construction constraints: minimize active risk while maximizing alpha, maximum absolute weight of 8% per individual name, relative weight bounds at the sector level, and a maximum of 100 names. We then examined historical performance as well as the tax liability for this model portfolio from December 31, 2015, through December 31, 2020, setting a six-month rebalance and a maximum turnover of 25% (see Table 1). Note that we could have incorporated tax constraints into this model portfolio and analyzed tax alpha generated from a tax-efficient model portfolio, but instead we focused any tax rules to the model drift portion of this workflow.

Table 1: Model Portfolio Performance

Model Portfolio

$10,000,000 to $14,419,242 (44.2% total return over five years)

Prior to 2020, $901,000+ in net gains, leading to an overall tax liability of $253,125 that would further erode the 44.2% return

Note: 2020 produces $(578,000) in losses that can be used to offset future gains

Source: FactSet

Minimizing Net Tax Gains

Our goal is to create a new portfolio that closely tracks the model portfolio while minimizing net tax gains. Any negative “gains” can be harvested to offset any future gains. We modeled this using the following objectives and constraints:

  • Objective: Minimize net tax gains
  • Main Constraint: Maximum active risk (to model portfolio) of 25bps
  • Additional constraints: Min/max security and group weights to help further control the drift contributions

We evaluated this model drift strategy over the same five-year period as above (December 31, 2015, through December 31, 2020), using a quarterly rebalance. The quarterly rebalance provides tax-loss harvest opportunities while avoiding any potential wash-sale violations. To analyze tax alpha, we look at the performance of this model compared to the performance of the model portfolio (after taxes). This is shown in Table 2.

Table 2: Minimum Tax, Model Drift Performance

Model Drift Strategy

$10,000,000 to $14,467,861 (44.7% total return over five years)

Prior to 2020, $477,787 in net losses harvested, no tax liability

2020 produces $(749,000) in harvested losses

.25 active risk to model portfolio

Source: FactSet

By comparing the model portfolio to the model drift strategy, not only do we realize the return (and some) of the model portfolio at the “risk” of 25bps, but we do it without a tax bill; as well as capture a large pool of harvested losses to offset any future gains.

Managing Model Drift in Production

While the above provides historical validation for tax alpha under certain pre-set conditions, there is usually a more interactive decision-making process when it comes to applying the model drift workflow into production. Frontier analysis can be a very powerful tool to understand trade-offs between tax and risk.

For instance, if we can obtain an acceptable tax loss harvest while having smaller drift (18bps vs. 20bps vs. 23bps vs. 25bps), why take on the additional risk?

Table 3 below shows the same model drift strategy as of March 31, 2020, at different levels of maximum active risk. On this date, each potential portfolio creates a sizeable, realized net loss (tax loss harvest). With this information, the client can then determine their risk tolerance along with tax goals to determine which portfolio makes the most sense for them.

Table 3: Frontier Summary (March 31, 2020)

Active Risk

0.18

0.20

0.23

0.25

Tax Liability

$0

$0

$0

$0

Net Realized Short Term Gain/(Loss)

(78,137)

(83,019)

(87,534)

(90,495)

Net Realized Long-Term Gain/(Loss)

(163,245)

(193,742)

(233,097)

(252,298)

Net Total Realized Gain/(Loss)

$(241,382)

$(276,671)

$(310,630)

$(343,793)

Source: FactSet

Beyond frontier analysis, there are additional ways in which the model drift workflow can be further enhanced and customized:

  • Wash sale constraints
  • Almost long-term gain constraints (why sell names that are almost long term)
  • Individual name restrictions/constraints
  • Cash inflows/outflows

Conclusion

While generating tax alpha offers an additional opportunity to gain a competitive edge, incorporating tax decisions into portfolio construction and management decisions is becoming industry standard. Technology is allowing a workflow that historically has either been outsourced or managed inefficiently into the hands of those in the best position to make tax-efficient decisions in a customized manner.

For this analysis, both the model portfolio and the minimum tax portfolio were constructed using the Axioma Portfolio Optimizer on FactSet.

Rob Martin from FactSet and Walid Bandar and Edward Reis from Qontigo also contributed to this article.

The information contained in this article is not investment advice. FactSet does not endorse or recommend any investments and assumes no liability for any consequence relating directly or indirectly to any action or inaction taken based on the information contained in this article.

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Daniel Poll

VP, Regional Director, Specialty Sales, Quantitative and Risk Analytics

Mr. Daniel Poll is Vice President, Regional Director of Quantitative and Risk Analytics at FactSet for the Midwest and Western regions of the Americas. In this role, he oversees a team specializing in workflows and solutions for portfolio and quantitative analytics including, but not limited to, factor research, portfolio construction, optimization, performance, and risk and factor attribution. Prior to joining FactSet’s Quantitative and Risk team as a product specialist in 2009, he began his career as a consultant in FactSet's Chicago office in 2006, where he spent three years working with some of FactSet's largest buy-side clients across the Midwest. Mr. Poll earned a Bachelor of Science in finance from the Kelley School of Business at Indiana University.

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