April was a fantastic month for equity markets. The Invesco QQQ Trust (QQQ) had its best calendar month since October 2002 with a gain of 15.69%. The rally comes following four negative months over the prior six months for QQQ. While it was an uneasy few months for the tech-heavy index, it gave the index time to build up enough energy for a major push higher. The combination of high volatility heading into April, multi-month consolidation, and earnings season was the perfect recipe for a rally if enough fear came out of the market. The feat is more spectacular given the mature nature of the Nasdaq-100 Index as compared to the other six times (1999-2002) it was able to gain +15% in a calendar month. The table below shows the frequency of monthly calendar returns with April breaking a tie between positive and negative 15% months. However, the return distribution is still negatively skewed as the average monthly return is less than the median monthly return for QQQ. On the bright side, the average and median returns are 1.07% and 1.45%, respectively, and nearly half of the calendar months since March 1999 have had a return of 2% or greater. In fact, the 2%-3% bucket is the most frequent monthly return for QQQ with a count of 28 months.

April’s historic return was clearly helped by timing as the market bottomed out on the last day of March. On a rolling 30-day basis, there have been many more +15% periods, even when excluding clusters. QQQ has been able to put together a handful of +15% 30-day periods just this decade whereas it happened only twice in the 2010s. For investors, there are natural concerns that arise following such a strong run in a short time. What goes up, must come down, right? Well, that’s not always the case. We see average and median returns over the next 30 days tend to also be strong at 1.83% and 2.41%, respectively. The next 30-day returns have been even better looking at data just since 2010 with average and median returns north of 3%. There are of course a few negative instances in the data set, but overall, these positive outlier periods tend to be a good sign over the next 30 days.

Note- this is the second edition of a larger study detailing how you can answer several frequently asked questions your clients might have when considering the broader momentum factor. Click here to read part 1, addressing the idea that momentum investing is “streaky” in comparison to other factors. Next Wednesday, we will address the final FAQ, the idea that momentum is tax inefficient. We will also include all three pieces together to make it easy to reference going forwards. With any questions/concerns, email miles.clark@nasdaq.com

Like any investment process, trend following is not without its share of ups and downs. Over time, buying winners and cutting losers can foster a strong point‑to‑point return stream, but the inter‑period experience of momentum investing can be difficult to stomach as natural rotation occurs and trends change. Time and time again, shifts in leadership create uncomfortable situations that often run counter to our natural “gut” instincts, causing even the most loyal trend follower to consider deviating from the rules. These untimely breakdowns in systematic rule‑following allow emotion to creep in at precisely the wrong moment, potentially erasing years’ worth of discipline- either psychologically, monetarily, or quite often, both.

With that in mind, we have taken the opportunity to break down several of the major roadblocks and common arguments that inevitably arise for trend followers. Our hope is that by better understanding the typical pitfalls of momentum investing, we can more appropriately contextualize returns over time.

Major arguments commonly raised against the momentum factor typically include:

1. Trend following is “streaky” and, as a result, does not belong in the average portfolio.
2. Trend following often “chases winners” inefficiently, leading to frequent whipsaws.
3. Frequent rotation driven by changing market leadership creates material tax inefficiencies.

Buying High, Selling Higher?

Momentum typically doesn’t adhere to the classic adage, “buy low, sell high”. Instead, the factor targets securities that have already established strong relative strength, essentially aiming to “buy high, sell higher” based on attractive performance metrics. This methodology sits at odds with other widely followed factors- a stock can reasonably become “too expensive” for a value investor or produce an “insufficient growth trajectory” for a growth investor. Since a stock can almost never “outprice” itself to the upside for a momentum strategy, a common critique is the factor’s perceived tendency to chase names that have already exhausted their upside, otherwise “buying high and selling low”.

To evaluate how significant this concern may be, we examined the trade distribution of the NDW Large Cap Core model, which seeks to hold high relative strength securities within the top 500 stocks by market cap over time. Since the start of 1992, the model has continuously rotated to RS winners, trading (if needed) on a monthly basis. Before proceeding, it is worth noting an important statistical caveat: equity returns are inherently non‑normal. Upside returns are theoretically unbounded, while downside outcomes are limited to a maximum loss of 100%. Despite this asymmetry, a trade distribution remains useful for visualizing how frequently the model captures extreme positive outcomes.

Across the full dataset, eight trades produced returns in excess of 400% before exiting the model- effectively placing them more than three standard deviations above the average trade outcome. As of August 2025, these eight observations represent 0.73% of the 1,090 total trades executed since 1992. Under a normally distributed framework, only 0.135% of observations would be expected to fall beyond three standard deviations above the mean. Put differently, the model captures extreme right-tail outcomes at a substantially higher rate than what would be considered statistically “normal.”

While the histogram illustrates the frequency of these outcomes, it fails to depict their importance to a model’s return stream over time. The single largest observed trade generated a return just shy of 2,775%, a magnitude that is difficult to contextualize using a standard distribution alone. Across all trades, the average return is 22.29%. Removing only the eight aforementioned right‑tail winners reduces that average nearly in half, to 14.57%. While still attractive, this sharp decline indicates that a small number of exceptional winners account for a disproportionate share of total returns.

This dynamic becomes even clearer when examining median outcomes. Unlike the mean, the median dampens the influence of extreme outliers. On this basis, the median trade return falls to –1.17%, implying that more than half of all trades are actually modest losers. This suggests that the model’s return profile is positively skewed: a handful of very large winners are the featured drivers of long-term returns, while the majority of trades reflect routine turnover as the model systematically removes positions that fail to demonstrate sustained leadership.

All this to say, this evidence suggests that constant rotation of “buying high” is not necessarily a liability for momentum strategies, but rather the defining feature that drives long‑term results. To realize a return on the order of ~2,800%, a security would almost certainly have violated the selection criteria of even the most flexible value or growth frameworks along the way. While trend following may involve periods of whipsaw as it searches for these comparatively exceptional winners, its systematic ability to remain invested in “golden ticket” securities longer than other approaches is ultimately the hallmark of the factor over time.

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 05/05/2026:

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