The outperformance from the names at the top of the S&P 500 Index is an industry-wide talking point. The hefty concentration of a few names in the primary market benchmark leaves investors wary from a risk standpoint, but many must juggle the need to keep up with the market or else face losing clients. With that said, it’s great to have some numbers and talking points in mind when these conversations inevitably come up. While historical comparisons are not perfect, they can help contextualize current market movements that feel unprecedented, just like the cap-weight dominance does today. However, we have seen this before if we compare the S&P 100 Index (OEX) against the S&P 500 Equal Weight Index (SPXEWI). On a rolling three-year return basis, the OEX has outperformed the SPXEWI by more than 50% which seems otherworldly. This gets more extreme if the S&P 500 Top 10 Index was used, but the limited history makes the OEX a better proxy for mega-cap names. Nonetheless, we can see in the image below that the only comparable period is the late 1990s.

The first thing that comes to mind when people hear that it’s just like 1999 is the tech bubble bursting soon after. While that is true, it’s difficult to make calls with such limited assumptions. One thing to keep in mind is that the first time OEX had outperformed SPXEWI by more than 50% in a three-year period was January 1999. It took another 13 months before the peak value of 73.34% was hit in March 2000. While things seem crazy right now, they can get crazier. In any case, what’s important is having a process that can deal with either the continuation of the current trend or the emergence of a new one. Until we see evidence of a new trend, it’s against our process to make changes on what should or could happen in the future. At the same time, understanding the lay of the land with respect to market environment can help with conversations with clients and preparedness for when that environment inevitably changes.

We have a new update to our Fund Score Whitepaper, which can be accessed here. The paper can also be accessed under the Media & Education page through the Whitepapers link. Highlights from the paper are included below. As always, please reach out to us with any questions.

Over the years at Nasdaq Dorsey Wright, we have created many innovative technical indicators based on momentum. One of our most popular indicators is the “Fund Score” rating we apply to each mutual fund/ ETF. The rating ranges from 0.0 (lowest strength) to 6.0 (highest strength) with values of 3.0 or higher generally regarded as investable.

The purpose of this study was to determine how effective fund scores are from a portfolio management perspective. If we buy simulated portfolios of funds with high scores, do we outperform an equal weighted basket including all funds? Alternatively, what happens if we buy simulated portfolios of low attribute funds? We found that simulated portfolios with high scoring funds had a strong propensity to outperform, with the largest outperformance reserved for the highest scores (5.00+), while low fund score simulated portfolios had a marked tendency to underperform.

Fund Score Study

To determine how fund scores work in a simulated portfolio management context we first calculated fund scores for all open end mutual funds (excluding alternate share classes and money market funds) from 1992-2024. Next, we constructed six different equal weighted simulated portfolios (one for each integer range fund score rating). The 5.0 - 6.0 fund score simulated portfolio, for example, would own every fund with a score between five and six, while the 4.0 - 5.0 simulated portfolio would own every fund with a score between four and five, etc. Finally, we reconstituted and rebalanced these simulated portfolios at the end of every month to account for changes in the fund scores over time. The graph below shows the cumulative performance of these simulated portfolios along with the performance of an equal weighted index we calculated comprised of every open end mutual fund (EQW).

Buying a simulated portfolio of funds with a score between five and six outperformed the average fund by a large margin (10.94%/year vs. 6.42%/year) in this simulated study the simulated portfolios that buy fund scores between four and five also outperformed the average fund (8.59%/year vs. 6.42%/year). Low score simulated portfolios, on the other hand, tended to underperform the equal weighted simulated portfolio. Scores between 0 and 1, in fact, posted a negative yearly return on average (-0.05%/year).

From a risk perspective, max drawdowns, standard deviations, and Sharpe ratios also improved as you move from the low fund score simulated portfolios to the high fund score simulated portfolios. In fact, even though the five-six fund score simulated portfolio outperformed the average fund by 4.52%/year, it did so with only 312 basis points of

additional risk as measured by its standard deviation. In turn, this allowed it to have a 61% higher Sharpe ratio. Yearly returns were also very positively skewed. The worst relative yearly performance to the equal weighted simulated portfolio was -10.23% in 2011 while the best relative yearly performance to the equal weighted simulated portfolio was 32.22% in 1999. The five-six fund score simulated portfolio also outperformed the average fund 86.70% of the time on a rolling three-year basis.

That may all be interesting, but a client cannot hold every 5.0-6.0 fund score fund in his/her portfolio. To do so, they would have to hold, on average, around 476 funds. How does this behave for a more manageable portfolio of 20 funds? To test this, we created a simulated portfolio of 20 funds with a score between 5.0 and 6.0 at random every month over the test period (1992-2024). We then ran that simulation 100 times to see how often it outperformed.

The chart below shows the number of random simulated portfolios at each CAGR level (grouped into 0.5% increments). As expected, most simulated portfolios exhibited returns in or around the average of the “Buy All 5.0-6.0 Fund Scores” simulated portfolio (10.94%/year). What’s interesting though is that every random simulated portfolio beat the equal weighted portfolio, and 100% of them even managed to beat it by 3%/year.

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 09/23/2025:

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