As we talked about in Monday’s feature (click here), there are plenty of correlations between today’s market and that of the late 1990s. Other than valuations being elevated as they were in the late 90s, we’ve also seen an extremely strong run from the cap-weighted indices relative to their equal-weighted counterparts, particularly in the large cap space. Over the last three years, the S&P 500 Index (SPX) has beaten the S&P 500 Equal Weight Index (SPXEWI) by more than 40% on a cumulative return basis for the first time since the 1999/2000 market. Unfortunately, our data set only goes back to 1990, so it’s easy to get infatuated with mapping over to the tech bubble era. While we do see similar levels of outperformance by the SPX against the SPXEWI on a relatively long-term basis, we first saw the three-year performance spread hit 40% in January 1999. There was still another 15 months until the cap-weight began to abate.

One of the great features of the updated charts on NDW is being able to select a specific period on a chart. Instead of looking at the SPX in the late 1990s, let’s see if there’s any key differences between January 1996 – January 1999 period (the first point in which SPX outperformed the SPXEWI by 40% in the tech bubble era) vs the last three years when looking at price action for the SPXEWI. Before digging into the charts, the performance of the SPXEWI over each of those periods is quite a bit different. The SPXEWI from 1/26/1996 – 1/26/1999 was 62.50% (the SPX was up 101.46%) while over the last three years the index was up a more modest 35.44% (the SPX was up 76.64%). There is less of a big run today versus the late 1990s.

When looking at the charts from today versus the late 1990s, the stronger performance in the early years of the 90s three-year lookback is evident. There is one interesting similarity between today and the late 90s, a quick and violent sell-off in the later years of our lookback periods. In the 1990s it was Long Term Capital Management blowing up due to Russia’s default on its debt and recently it was the Tariff Tantrum that led to the market selloffs. However, following both selloffs the market was able to recover to make new ATHs not too long thereafter. As it stands today, the magnitude of outperformance by the SPX against the SPXEWI is a concern on longer time frames, but it does not mean collapse is imminent. While the SPXEWI is lagging on a relative basis, the absolute picture is fairly strong which doesn’t give us much to be overly concerned about. One key point to keep in mind is that the SPXEWI didn’t start to show cracks until mid-1999 following a blow-off top move which was a harbinger of what was to happen early in the next year. Until we start to see absolute technical evidence of weakness, the trend remains upwards.

Momentum strategies aren’t afraid to trade. Those familiar with momentum models are no stranger to turnover, but that isn’t to say your clients understand why (or how) they see a perceived excess of trades in their accounts. Some may like to see the activity, while others may question why you feel the need to constantly rotate positions, especially if they don’t completely understand the idea of relative strength investing. Regardless of your client’s position on trading, there is one concept all of us do understand: taxes. Around the office, we like to say there are only three things certain in life: death, taxes, and the daily report being published by the end of the day. Of course, we all want to avoid paying Uncle Sam, but clients often think that more trades always equal a higher tax bill. But is that true?

Today’s feature will unveil a tool the analyst team designed to help answer that question. The idea of “tax/trade efficiency” is simple: how good is my model at realizing long-term gains, which receive more favorable tax treatment? Conversationally, we frequently discuss momentum’s tendencies to cut losers off quickly and let winners run. This, in theory, would create favorable tax treatment (this trading pattern should generate short-term losses and long-term gains) but until now there hasn’t been a way to “prove it”. The trade efficiency tool allows us to quantify this idea. Before going further, we want to stop and provide a general disclaimer: The provided score should not be construed as tax advice, but rather as a general guide as to a model’s tendencies when it comes to overall trading patterns over time.

Released on 10/30, a model’s popout will have another tab available, detailing a model’s trade efficiency. The biggest point to note is the model’s “trade efficiency score” (highlighted below) which should be interpreted as a percentage. In short, the score represents the total proportion of gains that were classified as long-term compared to the total number of gains realized over the course of the backtest. Higher score = models with more “efficient” trading tendencies. The yearly breakdown going into the score is under the “cumulative trading efficiency” tab, breaking down each calendar year into more detail. Years that realized losses (or were still carrying forward losses from previous years) did not generate a score. You can adjust the lookback period by adjusting the start/end date (highlighted below). This will adjust the total score and will help create a more personalized view. A practical use case would be to adjust the timeframe to when you started following the model, or focus on the last 10 years, etc. Remember, realized gains typically will not align perfectly with a models performance over the year… since this tab is only focused on realized gains, not inclusive of unrealized gains that impact overall model performance but have no impact on the taxes your client would hypothetically pay.

We also included a hypothetical tax liability tab (date range fully adjustable) that allows you to get a peak behind the curtain of what a tax bill might have looked like following said model based on unique long & short term tax rates, carryforward allowances, and starting investments. Again, don’t use this as explicit advice, but more so of a guideline for what you might have experienced. We also included the effective tax rate as an additional data point.

For those of you less interested in the year by year analysis or hypothetical tax liabilities over time, the cumulative trade efficiency table gives some high level details of overall trade patterns, including the total percentage of trades in each tax bucket. As you would expect, the majority of trades for our example (Large Cap Core) are short term losses as the model rotates away from weakness, followed next by the long-term gain bucket, where most of the historical gains come from (+120% on average). We also include a historical trade distribution that allows you to X-ray into any trade over time and see its unique holding period and % gain or loss.

A few closing points. This score is best used as a secondary indicator. Tax inefficient models can still perform quite well, but are perhaps best suited in qualified accounts to reduce tax drag. A general rule of thumb, models that give positions more room to run will typically be more efficient (pre-made matrix models generally hold a higher score than FSM models due to more lenient sell thresholds). Use the score as another tool in the toolbox. Many of you will have clients that have asked (or complained) about the number of trades in their account. Use this tax score to help back up the claims that momentum strategies are more efficient than they may appear.

Click here for the whitepaper. For further questions, email miles.clark@nasdaq.com.

Each week the analysts at NDW review and comment on all major asset classes in the global markets. Shown below is the summary or snapshot of the primary technical indicators we follow for multiple areas. Should there be changes mid-week we will certainly bring these to your attention via the report.

Remember, these are technical comments only. Just as you must be aware of fundamental data for the stocks we recommend based on technical criteria in the report, so too must you be aware of important data regarding delivery, market moving government releases, and other factors that may influence commodity pricing. We try to limit our technical comments to the most actively traded contracts in advance of delivery, but some contracts trade actively right up to delivery while others taper off well in advance. Be sure you check your dates before trading these contracts. For questions regarding this section or additional coverage of commodities email james.west@nasdaq.com.

Data represented in the table below is through 10/28/25:

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