QQQ ETF: No Bubble on Productivity Rise, But Likely Downside (NASDAQ: QQQ) (2024)

Invesco QQQ Trust ETF (NASDAQ:QQQ) was trading around $437 at the time of writing, having gained more than 40% in the past year alone, driven mainly by artificial intelligence.Market participantsseems divided aboutif there is a techniquebubblewith some references to events in 1999-2000, where volatility suffered severely, as shown in the chart below.

It is noteworthy that there was an increase in productivity in the previous period. This thesis aims to show that this is likely to be repeated this time based on the potential of generative AI, meaning we are not dealing with a technology bubble.

But given the uncertainty caused by inflation remaining above 3%, along with the stock market's fatal pull on interest rates, amid inflated expectations for artificial intelligence, there are near-term downside risks.

The disadvantageRisks due to expectations for interest rate cuts and some AI hype

Firstly, referring to my last thesis on QQQ last March, I had called that from the marketirrational behaviorespecially as technology stocks advanced despite high interest rates, while persistent inflation made it unlikely that the Fed would cut spending anytime soon. Monetary policy has remained unchanged, but my Hold position turned out to be wrong, and the Invesco ETF is up 40% since then, thanks to investors' love for the AI ​​story.

While the LLMs or Large Language Models used by Generative AI are certainly innovative in this regard, they inheritprejudiceproblem because, after all, it is people who train them. There are also inaccuracies. Improvements have been made with the latest versionversionof ChatGPT, but depending on how it is used, it can also give rise toroyaltyproblems, as evidenced by the New York Times lawsuit against OpenAI for using millions of its articles to train its algorithms.

Looking across the industry, there are traditional AI variants such as ML (machine language) and AGI (artificial general intelligence) that have proven useful in a wide range of use cases, from demand forecasting, customer service automation, inventory optimization and targeted marketing. These older technologies accounted for approx91%of AI use last year and has been around for years, and is seeing renewed demand thanks to the ChatGPT eye-opener. Interestingly, it doesn't require the latest fancy GPUsdemonstratedfrom the Chinese Baidu (BEGIN) uses older chips for its Ernie bot.

This is whyNvidia(NASDAQ:NVDA) InSupermicro(NASDAQ:SMCI) that sell the hardware or the actual building blocks of AI may not necessarily see sustained demand and their future revenue growthexpectationscan be exaggerated. So, after their stocks rise as per the table below, there is a higher chance of volatility in case of adverse market conditions.

On the other hand, some others from QQQcompaniesjust as the AI ​​infrastructure providers and enablers (as shown below) have not performed well. Here names like Amazon (NASDAQ:AMZN) and Microsoft (NASDAQ:MSFT) (who, by the way, is also a software receiver at Copilot) with their enormous cloud platforms I think without Cisco (NASDAQ:CSCO), which is an important factor for AIdiffusein the company's data centers.

Next come the software companies that are improving their products to use AI to generate more revenue, such as MongoDB (NASDAQ:MDB) and Adobe (NASDAQ:ADBE). But once the product is AI-transformed, we should not expect sales to increase automatically. Some investors learned this the hard way when Adobe's stock plummeted17%after lower sales and revenue expectations. At the same time, the Firefly Gen AI imaging tool received some criticismsetbackdue to inaccuracies. Alphabet (GOOG) also experienced similar issues with Gemini AI.

Identifying a Bottom Caused by a 17% Downside

Now, market expectations about the AI ​​potential for some stocks are being combined with higher-than-expected inflationvanFebruary raised doubts about the Federal Reserve's ability to cut interest ratesJune. That's why, despite all the AI ​​frenzy, when it comes to spending money, whether it's building intelligent infrastructures, training algorithms, or developing software tools, CIOs are taking a more cautious approach before committing to new technology partners.Gartner. This may be because capital costs remain relatively high.

Thus, monetary policy is a key ingredient that determines market performance in general and technology stocks in particular due to their higher valuations. Therefore, QQQ trades at a price-to-earnings ratio of26.75xof26,5 %about the broader market or the SPDR S&P 500 ETF Trust (GIVEN), which itself comprises almost 30% of IT stocks. To identify a low point, I look at QQQ's historical performance during the Dot Com bubble that burst in March 2000, as shown below.

Note that the 67% downtrend (from about $120 to $40) spanned about a year. Immediately after the breakout there was a 33% downtrend from the $80 level, but this was temporary and the Invesco ETF was around $100 for some time, which equates to a 17% downtrend, which by the way is about the same decline that Adobe has suffered.

Therefore, since the Nasdaq 100 tracking ETF consists of57,64 %of IT stocks, and is currently trading around $437, a downside of 17% translates to a target of $362. This is relatively mild compared to the 67% bubble burst, reasoning that macroeconomic conditions did notdeterioratedas expected in 2024, and GDP should still grow, albeit at a slower pace than in 2023. Also, overall IT spendingexpectedto get better this year.

Moreover, there is a productivity increase in the long term.

Expect an AI-driven productivity boost

If you think about it, the current situation is somewhat analogous to the period 1991-1995, when economic growth was slow and interest rates were at 5.5% (September 1991). However, things changed dramatically in the second half of the decade, when consumption, expenditure and wage growth accelerated, all driven by aproductivityincrease.

Looking deeper, this was made possible by new technologies such as computers, off-the-shelf software and the Internet, all of which gained rapid acceptance thanks to a tight labor market as companies were forced to do more with fewer workers. Such a high level of innovation doubled productivity to an average of 2.5% in the period 1995-2000, compared to just 1.5% in the first half of the decade, or an increase of 67%. It should be mentioned that part of this increase was also due to the third era of industrial globalization1989.

At the time, IT investment as a percentage of GDP grew from 3% of GDP in the early 1990s to4,9%by the end of 2000. Comparing US technology, this rose to around 8% in 2022ExpendituremetBNP. By 2024, companies must use itpurelyon IT than last year, because in the 1990s more investments are being made in cloud computing and generative artificial intelligence than in general computing and software.

Notably, unlike older variants of AI such as machine learning, generative AI can understand natural languages ​​(or the languages ​​we commonly speak) and potentially automate60% to 70%of employees' time compared to 50% previously, according to McKinsey analysts, who further add that productivity gains ultimately depend on the speed of adoption and when Gen AI is combined with other technologies, for example by embedding it into existing software. Furthermore, unlike the 1990s when companies had to purchase IT, today companies can rent services by opting for cloud-based subscriptions, which not only reduces barriers to entry but can also accelerate time to market. Using ChatGPT requires virtually no training, but it does require some practice to generate reports, unlike older versions of the technology that required programming skills.

Once again, according to McKinsey, productivity growth could be for the United States, which along with other developed countries has one of the fastest adoption rates of artificial intelligence.3,6%. This is comparable to the average of1,6%in the period 2019-2023 according to the Bureau of Labor Statistics, representing a 125% increase in productivity.

It is significant that a productivity rate of 3.6% is higher than inflation3,2%This implies that AI-led gains in industrial production could offset the combined effects of deglobalization (on manufacturing) and supply chain overhead.

By the way, one of the most important factors that can determine the development of the stock market today is interest rates, and many have recorded price cuts. However, monetary policy will ultimately depend on the entire supply-side inflation paradigm, and to be realistic, we live in a different world today, a world characterized by deglobalization. The trend toward greater localization is exemplified by the CHIPS Act and the Inflation Reduction Act, both of which encourage the consumption of Made in America, but this will result in higher prices than Made in China. At the same time, the continuation of war in Europe, a major conflict in the Middle East and tensions in the Red Sea all contribute to transport costs.

As we enter a period of low growth, persistent inflation and skyrocketing government debt, increasing productivity may therefore be the answer. Continue in the same way as in1990'ernethis can lead to an increase in consumption and investments.

Disadvantage Probably, but no bubble has burst

In such a scenario, the Nasdaq could reach new highs, but for this to happen, production would have to rise enough to offset the effects of rising wages across all sectors. Companies like AT&T (T) is alManufactureit is happening, but it will take some time for it to spread through the economy, most likely by 2025, judging by550.000H100 GPUs that Nvidia sold in 2023. Moreover1,5-2 millionis expected to be sold this year and will cost approx4-9 monthsfor software companies to produce a stable version from the product conception phase.

In conclusion, this thesis argues for a downward trend, possibly around 17%, caused by the fact that interest rates remain higher for longer, together with the hype built up around the AI ​​potential in some stocks. Therefore, price-to-earnings ratios for most stocks, with the exception of Super Micro, are below or slightly above their five-year averages, as are Taiwan Semiconductor Manufacturing.TSM) as shown in the table below. This shows that not all technology valuations have reached bubble status, but a double-digit downtrend can be caused by a knee-jerk reaction when a sell-off from one stock spills over into others.

Still, generation AI should deliver real productivity gains in the long term, especially important in a period of lower economic growth. As such, I don't foresee a bubble that could wipe out 67% of QQQ's value, as was the case in 2000. Instead, the bubble could suffer a downside with the 17% figure being more than half. of approximately 2000.40%the decline suffered from the peak in November 2021 to the trough in December 2022 due to an aggressive rate hike.

Chetan Woodun