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I’ve seen and heard many things about the investment industry that have made me cynical, but it still stings to hear the “valuations” that are deployed to woo retail investors into buying expensive assets. Nothing (the best thing I’ve heard is from a former colleague) who says that companies are overvalued based on price-to-earnings ratio (P/E), but earnings yield (E/P ) declared undervalued based on…they are the same thing!).
It is on this basis that analysts at leading investment bank Morgan Stanley MS recently declared that technology stocks are “attractively valued” based on the “price-to-innovation” ratio. After hearing this, my credence grew even more. Price-to-innovation is the ratio of a company’s revenue plus its price for research and development, and is designed to make companies with poor returns and large risky investment budgets look attractive.
As a rule of thumb, when the financial world starts devising new metrics, it’s time to worry. In the early 2000s, analysts introduced valuation ratios such as “price-to-clicks” for Internet stocks in an attempt to lend some ostensible legitimacy to bubble-level valuations. Of course, it all ended badly.
Introducing the idea of a “price-to-innovation” ratio into the market narrative should heighten fears of a stock market bubble, especially in artificial intelligence (AI)-driven stocks. The bubble is difficult to define, but according to a well-known legal opinion on pornography, “we know it when we see it.”
The components of a bubble are usually based on innovations (railroads, internet) pumped with cheap money that combine to significantly outperform a particular group of assets, leading to historically extreme valuation levels. Masu.
The core of the bubble is most likely a group of supercap tech stocks increasingly known as the Magnificent Seven, led by chip designer Nvidia. Using a very simple valuation metric, the price-to-earnings ratio, the European stock market trades at a price-to-earnings multiple of 1.3x, while the American market trades at a price-to-earnings multiple of 2.5x (large-cap tech stocks drive this up). ), MicrosoftMSFT is trading at 2.5x. Its sales ratio is 13x, while Nvidia’s is 33x (for comparison, UBS’s ratio is 1.7x and Siemens’ 1.3x).
There is a similar valuation bubble in the private market, with venture investors investing in AI-focused startups at very high multiples. The underlying rationale is that the revenue growth potential for these big tech companies is so promising that we want higher valuation multiples.
Many tech companies have reported earnings this past week, and while most of these tech companies have healthy earnings, revenue growth has not been impressive, leading to optimism about the future impact of AI. This suggests that there are many
In that context, if AI becomes the organizing logic for the next bubble (over the past 50 years we have seen asset bubbles in gold, Japan, Asia, the internet, housing, China, to name a few)? We need a boost, or “petit cou de whiskey” (as New York Fed Chairman Benjamin Strong put it in 1927…imagine what happened next).
In general, financial conditions have been very accommodative since the global financial crisis, leading to asset inflation rather than consumer inflation. This trend has recently changed as an explosion in inflation has caused interest rates to rise sharply. Despite this, the credit markets are working very well (another bubble?). Expectations are rising that central banks could lower interest rates as inflation declines, potentially adding further fuel to the AI bubble hypothesis.
In the pre-quantitative easing era, there was a legitimate debate about whether central banks were aiding and abetting the formation of asset bubbles. At the time, the conventional wisdom was that bubbles were difficult to identify, and even if they were identified, it would be difficult for central banks to deflate them. The difference today is that few central bankers worry about this risk, and instead talk about the “wealth effect” of asset prices.
The reason they’re more cautious here is that asset bubbles usually destroy wealth, always transferring it from poor investors to wealthier investors (rich people buy early, poor investors buy late), and the economy as a whole. It distorts investment over a period of time, and when it collapses, the fallout can be costly (witness Japan’s lost decade).Bubbles often leave behind useful infrastructure – railroads in the late 19sth You get 2000’s internet/communications infrastructure, but at a higher price.
In my opinion, we are not yet in a full-blown AI bubble. So far, it belongs to a small number of companies, and it is very unusual for those companies to occupy a large part of the stock market. BCA Research calculates that the top 10 largest companies in the United States control 75% of the stock market, something that only happened in 2000 and 1929.
For the AI bubble to grow into mania (Charles Kindleberger’s Mania, Panic, and Crash remains the best analysis of the bubble), companies in sectors that will be positively impacted by AI (healthcare , life sciences, financial data-centric companies, etc.) should be captured by the AI story and see their stock prices rise accordingly. In the same sense, AI mania will also need to spread to countries in Europe, Japan, and perhaps China.
Pay attention to other indicators as well. When taxi drivers start talking about error correction in quantum computing, we’re definitely going to be in an AI bubble.
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