Direct indexing > Diversify > White paper
White paper: Diversification indexing
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Earlier this year, we launched our Long short direct indexing product, a strategy that allows investors to hold a diversified portfolio, gain broad-based market exposure, express their factor views (such as value, quality, or momentum), and systematically harvest tax losses to offset taxable gains.
However, this framework is not well suited for investors whose wealth is concentrated in one or a few highly appreciated positions and who lack additional liquidity to seed a long short direct indexing portfolio for loss harvesting and diversification. Selling these positions outright would trigger substantial tax liabilities, while holding them leaves investors exposed to elevated idiosyncratic and factor risks.
To address this, we’re introducing a new product: Diversification indexing—a solution designed specifically for investors seeking to gradually diversify concentrated positions with minimal tax liability.
What is Diversification indexing?
Diversification indexing employs a 140/40 leveraged long short portfolio, the same portfolio structure detailed in our Long short direct indexing white paper, to build a complementary portfolio around an investor’s existing concentrated holdings. Instead of selling appreciated assets, we construct a customized overlay that offsets concentrated risks and introduces diversified market and factor exposures.
While the leverage structure is similar to our Long short direct indexing product, the objectives of portfolio construction and tax-loss harvesting are fundamentally different. The long short product is designed to express factor views in a tax-efficient way to enhance after-tax returns. In contrast, Diversification indexing focuses on reducing concentration and factor risk while maintaining tax efficiency. Its optimization and tax-aware trading algorithms are designed to:
- Manage factor exposures and concentration risk by aligning the portfolio’s asset and factor profile with that of a benchmark index.
- Minimize tax liability by offsetting realized gains from selling concentrated positions with losses generated from overlay trades which ensures the diversification process remains as tax-neutral as possible.
Case study: Diversifying a concentrated AAPL position
To illustrate, consider a simulated investor who begins 2015 with a 100% position in Apple (AAPL) valued at $1 million, with a 25% cost basis, meaning the position had already appreciated fourfold prior to the start of the simulation. Over the following ten years (2015-2025), we design and manage a diversification overlay that gradually transitions portfolio risk toward a broad equity benchmark (Russell 1000) with minimal tax liability. The simulation assumes a 0.6% annual management fee and an additional 0.5% financing cost.
In this simulation, the diversification overlay gradually transitions the portfolio from a fully concentrated AAPL position toward a diversified benchmark exposure. The table below summarizes key performance and risk metrics over the ten-year period.
| Diversification indexing | Concentrated portfolio (AAPL) | Benchmark (Russell 1000) | |
| Annualized return (net of fee) | 15.64% | 23.76% | 12.81% |
| Annualized volatility | 17.93% | 26.23% | 15.12% |
| Tracking error forecast at end of year 10 | 1.27% | 19.50% | 0% |
The overlay strategy liquidates 95.42% of the concentrated AAPL position, reducing the position weight from 100% to 9.95% and lowering the forecasted annualized tracking error from 19.50% to 1.27%. During the process, the portfolio preserves much of AAPL’s strong performance, achieving a 15.64% annual return (net of fees) compared with 23.76% for AAPL and 12.81% for the Russell 1000 Index, while maintaining lower volatility than holding AAPL alone—18.58% realized volatility versus 26.23% for AAPL and 15.12% for the Russell 1000.
The following figures illustrate the diversification timeline of the simulation, showing both the remaining number of AAPL shares in the portfolio and the point-in-time forecasted annual tracking error. It’s important to note that tracking error does not necessarily decrease monotonically over time, as it also depends on the forecasted volatility of the concentrated stock. For instance, the simulation shows an uptick in forecasted tracking error in 2021, driven by the heightened market volatility during the COVID period. Overall, the strategy is able to bring the portfolio’s projected tracking error below 4% in 7.8 years.
Let’s examine the inner workings of the diversification overlay, specifically how it allows us to manage the portfolio’s active risk without selling the concentrated position and incurring a tax liability. The overlay achieves this by controlling factor exposures. The figure below compares the excess factor exposures of three portfolios: the initial concentrated 100% AAPL position, the AAPL position with the overlay applied at the start of the simulation, and the portfolio’s risk profile at the end of year 10. This comparison highlights how the overlay systematically reduces concentrated factor exposures over time while aligning the portfolio more closely with the benchmark’s risk characteristics.
Some bars may be too small to appear visibly above, but they are still represented in the data.
How long does diversification take?
Next, we examine how long it typically takes to diversify out of a concentrated position. In the AAPL case study, the strategy reduces projected tracking error below 4% in 7.8 years. This example reflects a 25% cost basis and a period of strong relative performance, both of which shape the diversification timeline. In general, positions with higher cost bases, meaning they have appreciated less, tend to diversify more quickly because there is less unrealized gain to realize during the process. Likewise, if the concentrated stock underperforms the benchmark, diversification tends to occur sooner since gains are smaller or losses can be harvested more readily. To understand how these dynamics generalize beyond AAPL, we extend the analysis to a broader set of stocks and cost-basis assumptions.
To study this relationship, we select 56 stocks from the Russell 1000 universe across different return quantiles and run a series of simulations assuming initial cost bases of 0%, 25%, 50%, and 75% of their initial value. We categorize stocks based on their historical returns over the 10-year diversification window (2015–2025): the top 25% are classified as strong performers, the middle 50% as average performers, and the bottom 25% as weak performers. For each scenario, we then track how long it takes for Diversification indexing to bring the portfolio’s projected tracking error below 4%, which we treat as the point at which the concentrated position is effectively diversified.
The table below summarizes the average number of years needed to diversify. When the cost basis is high, the strategy is able to diversify in a little over a year, largely independent of the stock’s specific performance. However, for lower cost-basis positions—those that have appreciated more—the stock’s relative performance plays a much more significant role in the diversification timeline. For example, in the 25% cost-basis case, it takes an average of 7.6 years to diversify strong-performing stocks, while underperforming stocks diversify more quickly, averaging 5.4 years.
| Cost basis | Strong performance | Average performance | Weak performance |
| 75% | 1.2 | 1.1 | 1.1 |
| 50% | 4.7 | 3.8 | 2.9 |
| 25% | 7.6 | 6.5 | 5.4 |
| 0% | 9.2 | 8.4 | 8 |
Conclusion
Diversification indexing represents the next evolution of direct indexing—one designed to help a broader range of investors. Through tax-aware and factor-aware optimization, investors can manage risk and diversify concentrated positions without triggering unnecessary taxes.
Get started
Frec is currently onboarding customers to Frec Diversify. If you are interested, schedule a demo with our team, or enter the waitlist here.
All results in this white paper are hypothetical, do not reflect actual investment results, and are not a guarantee of future results.
This white paper describes implementation and performance details of a diversification indexing approach similar to that used on the Frec platform at the time of writing (November 2025) details may differ from implementation used in the product now and in the future. This paper may be amended at any time to reflect new findings, improve readability, or correct inaccuracies.
This white paper is for information purposes only and is not intended as tax advice or a trade recommendation. Clients should consult with their personal tax advisers regarding the tax consequences of investing with Frec and engaging in these tax strategies, based on their particular circumstances. Clients and their personal tax advisors are responsible for how the transactions conducted in an account are reported to the IRS or any other taxing authority on the investor’s personal tax returns. Frec assumes no responsibility for tax consequences to any investor of any transaction.
The effectiveness of Frec’s tax-loss harvesting strategy to reduce the tax liability of the client will depend on the client’s entire tax and investment profile, including purchases and dispositions in a client’s (or client’s spouse’s) accounts outside of Frec, the type of investments (e.g., taxable or nontaxable) or holding period (e.g., short-term or long-term. The performance of the new securities purchased through the tax-loss harvesting service may be better or or worse than the performance of the securities that are sold for tax-loss harvesting purposes.
