I hope everyone is having a great spring so far. Here where I am in Arizona, it is already looking more like summer at this point. As we move through the 2nd quarter, I wanted to shift gears from a broad economy article, to a more strategy-driven one with a discussion on direct indexing. While this is not a new concept, investment personalization is something that comes up with clients and this is unlikely to change.
When I first heard this term, it seemed like an overly complicated way to try to replicate an index with some tax loss harvesting benefits. Mechanically, how it works is complicated. To build this process yourself, you would need knowledge of Python or similar programming language and be close to a data scientist. To direct index, you are hoping to minimize tracking error with an index, for example, the S&P 500 with anywhere from 80 to 250 stocks. Tracking error is the gold standard measurement of how a direct indexed portfolio is working since, after all, you are trying to match an index.
To match an index, you would want to take the investment amount, say $1 million, and buy the market-cap-weighted top 30 stocks in the S&P which would account for nearly half of the investment amount (or 50% of the S&P). Because the top of the S&P is exceptionally concentrated, it is more critical than ever to make sure you have this core if you want any chance of keeping tracking error low. This creates a ‘core’ that anchors the portfolio, allowing the remaining 50% to be the ‘optimization engine’ that harvests the tax losses. After, you would need to match the varied factors (value, momentum, size), beta (correlation to the market) and sector weighing to the remaining 470 stocks in the S&P. This involves complex math and correlation/covariance statistics that you would likely need a PhD or data scientist on staff to do.
As you can see, this gets extremely complicated very quickly if you want to keep tracking error within 1%, and it also makes trading difficult, especially in cases of concentrated positions, trade restrictions, ESG integration, etc. Luckily, TAMPs and investment platforms have caught up and have the technology to automate this for the advisor within a UMA structure. A few of the platforms we work with have this ability.
Why go through all the trouble? The primary goal is tax alpha. When you are replicating an index with leeway to sell individual securities (unlike if you were in an index fund), the tax-loss harvesting opportunities are substantial, especially in early years. Many constituents of an index will rise while others fall. Selling the ones that have fallen and inserting a highly correlated proxy to avoid wash sales while maintaining market exposure during the 30-day stay-out period, can generate tax alpha. It is worth noting that there are diminishing returns to this tax alpha (tax-alpha decay) as time passes due to the generally upward trend of markets—historically, of course. You can smooth out this tax alpha decay with additional contributions to the account, but there can still be diminishing returns in an upward market. Another reason for direct indexing is personalization. If a client has a concentrated position or an ESG concern where they would rather not buy the whole index, direct indexing could be a great tool in personalizing a client’s investment strategy.
