How A Flexible Optimizer Can Lower Taxes

Strong gains in the stock market mean more capital gains and more focus on how to minimize the taxes paid on them. At the same time, the rise of robo-advisors and other technology geared to help the average investor has created an enhanced need to efficiently manage a large number of portfolios.

You can systematically control the tax consequences associated with rebalancing your portfolio by incorporating tax-aware optimization techniques. A flexible optimizer can support a wide-array of tax-related limits on the trading that results from a rebalancing. For example, a tax-aware portfolio manager may want to:

  • Prevent short term gains from being realized
  • Allow gains to be taken only if offsetting losses are realized
  • Set a hard limit on the net taxable gains realized
  • Require a set amount of tax losses to be harvested
  • Prevent tax lots that are almost a year old from being sold and incurring a short-term gain
  • Prevent specific lots from being liquidated
  • Prevent wash sales

Rebalancing a tax-aware portfolio usually involves making tradeoffs between the tax implications of trading with other non-tax-related characteristics, such as risk and return.  Because of discrete breakpoints, rate changes, and gain/loss offsetting rules in the tax code, tax-aware optimization is inherently difficult. Tax-related costs can exhibit unexpected jumps as portfolio turnover increases, making it difficult for the portfolio manager to determine the appropriate level of turnover.

An optimizer with flexible frontier capabilities that can present a range of options illustrating the tradeoff between realized tax costs and other portfolio characteristics is extremely valuable for making these tradeoffs. For example, for a portfolio that is replicating a model, viewing a frontier of net tax gains vs. tracking error relative to the model portfolio can provide immediate insight into the most efficient outlay of tax costs to keep the portfolio in line. Similarly, for an actively managed portfolio, a frontier that trades tax costs off against expected portfolio return can provide the needed insight. An optimizer API can be used to automate the modeling, rebalancing, and frontier processes described above in order to manage large numbers of tax-aware portfolios. Encoding the human judgement used to automatically select the best point on a frontier for all cases may not be practical, but rules can be created to handle cases where the tradeoff is easy-to-quantify and to flag more complex cases where review by a human decision-maker will enhance the quality of the final decision.  

Optimization is a key tool for providing superior management of tax-aware portfolios. A flexible optimizer with tax-aware capabilities, like Axioma’s, can enhance management decision-making processes by automatically generating a range of options that satisfy all the portfolio manager’s tax-related requirements. This enables the portfolio manager to dedicate his time to making high-value decisions involving harder-to-quantify trade.

If you’d like to read more on this topic, we’ve also recently published a case study on the tax-aware features in Axioma Optimizer.

Pamela Vance

As Managing Director, Applications Development, Pam Vance is responsible for future development of Axioma’s software products. Pam acts as a liaison between clients and the development team, to ensure that the direction of product development aligns with market demands. Before joining Product Development, Pam ran Axioma’s Client Services team.