Taxable clients and prospects of Epsilon often ask about the taxable nature of our investment strategies and the viability of adding systematic tax loss harvesting to them.  Before we dive into that, let’s discuss why taxes may be more of a hot-button topic now than in prior years.  To begin with, as investors have gravitated more towards passive investment vehicles (namely ETFs), the focus on tax-sensitivity in the active vs passive debate has intensified.

Because ETFs accommodate flows through creation units rather than through underlying share transactions, the impact of said flows (e.g., a redemption) don’t necessitate taxable events for unit holders, which is an advantage for the long-term investor.  Compare this to a mutual fund where fellow shareholder behavior can impact your tax bill!

To take it a step further, passive ETFs are rules based products that track indices.  Most of these indices (and predominantly all of the assets) are market-capitalization weighted, which presents inherent advantages from a rebalancing (and thus tax sensitivity) standpoint.  Finally, there are a deluge of indices that are highly correlated if not veritably pari passu with one another (e.g., different flavors of emerging markets exposure), which opens the door for tax loss harvesting through simultaneous replacement while avoiding a 30 day wash sale.  This is done by switching between these instruments after an investment becomes an unrealized loss, which allows for a legal tax loss harvest.  There has been quite a bit of debate regarding the explicit language of the rule, but by all accounts, this has been an accepted form of tax management in the financial advisory space for some time.

The result is an explosion in the demand for tax-loss harvesting products for taxable clients (especially HNW/FO/MFO).  The most visible versions of course are those provided by robo-advisors, which systematically tax loss harvest and are available for retail.  In reading these firms’ whitepapers, they have back tested in the order of ~100bps of annual “tax-harvest” alpha to pro-forma asset allocation model portfolios.  Of course, the key to this alpha is long-term holding and its ability to exploit the compounded effect of deferred tax payment.

Passive investing offers an easy conduit to construct tax loss harvesting in the form of replacing individual asset classes (usually expressed through ETFs or pooled investment funds) with highly correlated / similar investments.  Active strategies pose far more challenges in this regard.

The Active Manager’s Dilemma

Many investors are simply not sensitive towards tax impact in their trading strategies by virtue of the nature of their assets.  For example, 72% of hedge fund industry’s assets were in offshore funds as of 2010, which cater to different tax laws or even non-taxable assets (Liang, 2011).  Of the $5trn of assets in 401k structures, more than 60% of these assets are held in open-ended mutual funds (see ICI chart below).  Funds managing said assets may have less of a mandate to manage to tax sensitivity.

Even if their assets are taxable, active managers have far less latitude to systematically tax loss harvest. This is because replacing betas has no impact on generating alpha.  Alphas by definition are idiosyncratic, and thus can’t be replaced by correlated assets.  It almost makes the active fund manager’s life easier to focus on non-taxable money and be done with the headache!  The question remains: is there a way to actively manage assets but also systematically tax optimize?

The most common (but hardly systematic) approach is year-end tax loss selling.  Generations of research, the most relevant of which focuses on the period post the Tax Reform Act of 1986, have studied the impact of fund managers dumping year-end losers in the waning days of the year/fiscal year to avoid carry-forward losses.  The effect of which is to harvest the time-value of the tax loss, rather than holding it into the next taxable period.  The impact of which has been measured through changes in seasonality for negative returning securities (see suggested reading below).

This is the simplest form of tax loss harvesting, butit  is confined to a narrow window of action depending upon the investment vehicle.  The newer generation of robo tax-loss harvesting is done continuously throughout the year.  To do so with an active strategy requires a different approach to idea selection.  It requires an opportunity cost framework with a focus on quantifiable risk/return to make the process viable.

To unpack this, if each of an active manager’s security selections are based upon some form of expected return (e.g., a Bayesian framework of discounts, probabilities, and expected outcomes), then any taxable event (e.g., selling said security down the line) can factor in the ultimate net-of-tax return within its expected return framework.  For discretionary strategies, this is a real challenge because it may force upon the investor a degree of precision that is anathema to his or her strategy.  Is every trade, trim, add, or exit measured on an opportunity cost framework with enough accuracy to factor in taxable gains?

For systematic strategies, expected returns are baseline rates that underwrite investment strategies.  They help anchor strategies on scientific approaches and help neutralize behavioral bias.  The practical implementation of tax consideration in a security’s risk/reward is a function of changing a prioris rather than human behavior.  Incorporating tax impact can be done with rules-based mathematical substitution.

Of course, taxes are not homogenous and depend on each investor’s unique situation.  Epsilon believes in working closely with its clients to help them optimize their investments’ tax footprint.  If we can close the gap between their economic gain and their ultimate taxable gain, it may not show up on our performance reporting, but it ultimately drives more value and alignment to our clients.

Suggested Reading:

Taxes and Investment Choices. Spatt, Damon, 2012

A November Effect: Revisiting the Tax-Loss Selling Hypothesis. Bhabra, Dhillion, and Ramirez, 1999

Tax Loss Selling: Evidence from December Stock Returns and Share Shifts. Rozeff, 1986

Windows-Dressing, Tax-Loss Selling, and Momentum Profit Seasonality. Sias, 2006

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