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The following demos illustrate different aspects of the intelligence of our investing algorithm.

Direct Indexing can increase returns in taxable accounts

With Direct Indexing, instead of buying a fund that tracks a stock index, we buy the individual stocks. This strategy can increase after-tax returns through tax loss harvesting. Selected losses are harvested, and the proceeds are invested in a way that attempts to maintain similar overall portfolio exposure to the stock index.

More details here.

Direct Indexing returns increase with looser tracking

With Direct Indexing, there is a tradeoff between tracking a stock index closely and increasing returns through tax loss harvesting. Our backtest infrastructure, together with automatically generated web-based charts, allows us to simulate investing behavior over a multi-year period and investigate the results, which allows us to choose an optimal point in the tradeoff.

More details here.

Direct Indexing returns can use custom expectations of future external gains

Direct Indexing works best when there are other capital gains to offset. Instead of always assuming infinite external gains, which everyone we know of does, our software allows us specify a schedule of expected future gains. This can demonstrate a customized benefit of DI by tailoring the outcome to a client's situation.

More details here.

Applying tilts and filters to Direct Indexing can improve ESG scores

We can load data from multiple ESG data providers and apply custom tilts and filters. We can also specify custom scoring mechanisms - even to non-numeric ESG factors - to display charts and aggregate results about ESG performance.

More details here.

Targetting ESG scores directly further improves on tilts and filters

Telling the portfolio optimizer to address ESG scores directly is a better approach than tilting target weights based on ESG. Our software allows us to specify the tradeoff of desired ESG scores vs. other goals such as tracking error and tax efficiency. This enables tax-efficient migration into an ESG-friendly portfolio, by weighing tax realized upfront against improved ESG scores.

More details here.

Tax-sensitive rebalancing

We rebalance less closely to the target portfolio if it is not worth the upfront tax cost. We quantify and evaluate the tradeoff of tax efficiency vs. tracking accuracy, instead of using a simple rule such as "never sell gains" which would only consider the tax efficiency side of the tradeoff. For example:

More details here.

Improved risk reduction using a dynamic factor model

We use a sophisticated mathematical model to offset external unsellable holdings. This tracks the target allocation better than a simple approach.

More details here.

Considering external assets vs. ignoring them

We track the target portfolio better when we do not ignore external holdings.

More details here.

Tax loss harvesting activity scales with tax bracket; also, TLH can coexist with external holdings

The lower the account holder's tax bracket, the less the benefit from TLH will be. We consider the tax bracket when deciding how aggressively we should harvest losses, instead of treating TLH as an on/off feature and always doing the same actions when it is turned on.

Additionally, there is never a choice between using one feature or another; the system is perfectly capable of handling TLH in conjunction with tracking the target.

More details here.

Simple pairs-based tax-loss harvesting (TLH) can increase after-tax returns

After-tax returns are higher when we apply a simple ETF-pair-based tax loss harvesting strategy. We are not the only ones who can do this, but we have the ability to display an individualized benefit to clients based on their tax bracket, external holdings, etc.

More details here.

Tradeoff of misallocation vs. tax efficiency

If we start with many positions containing large embedded gains:

We consider this tradeoff intelligently.

The code that does this exists; the demo is coming soon.