When the market crash becomes systemic: the analysis of the November global downturn

In November 2024, the financial world witnessed a rare event: a global, simultaneous, and coordinated market crash, as if guided by a single invisible hand. It was not just a sector correction but a systemic resonance that affected stocks, cryptocurrencies, and even safe-haven assets like gold indiscriminately. The U.S. stock market plummeted, Hong Kong suffered significant losses, Chinese stocks crashed, Bitcoin fell below $86,000, and even gold prices continued their decline. But what truly triggered this dramatic and widespread market collapse?

Chain Reaction: How the Crash Spread from Fed Data

If one were to assign primary responsibility, the finger would inevitably point to the Federal Reserve. For two months, the market had been buoyed by expectations of a rate cut in December—a narrative fueling confidence in risky assets and supporting the rally. However, unexpectedly, several Fed officials suddenly changed tone, adopting a surprisingly more aggressive and “hawkish” stance.

The central bank officials emphasized that inflation was falling more slowly than expected, the labor market remained resilient, and the Fed “does not rule out further tightening” if necessary. This was the message the market did not want to hear: no rate cuts in December. CME FedWatch data precisely confirmed the speed of this psychological reversal. A month earlier, the probability of a cut was estimated at 93.7%; suddenly, it collapsed to 42.9%. This shift instantly transformed sentiment from euphoria to fear, pushing both the stock and crypto markets from exuberant celebration straight into intensive care.

NVIDIA and the Limits of Good News: When Fundamentals Aren’t Enough

After expectations about rates dissolved, market focus shifted to NVIDIA, the company that had driven the entire bullish run in the tech and AI sectors. NVIDIA reported a Q3 earnings result that exceeded expectations—a news that normally would have ignited the tech sector. Yet, something unusual and revealing happened: the stock’s reaction was the opposite of what was expected. NVIDIA’s share price quickly turned red, suffering a significant drop.

This episode reveals a fundamental truth of markets: when good news fails to push prices higher, it signals the strongest possible bearish indicator. In an environment where tech stocks are already severely overvalued, positive news merely provides a golden opportunity for long-position holders to realize profits and exit. This dynamic catalyzed a broader sell-off.

Michael Burry, the well-known NVIDIA short-seller, seized the moment to intensify his criticisms, highlighting how the complex web of “circular financing” among NVIDIA, OpenAI, Microsoft, Oracle, and other AI giants conceals a disheartening reality: the final demand is ridiculously low, with almost all clients receiving financing from their suppliers. Burry had already issued numerous warnings about an AI bubble, comparing it to the dot-com bubble of the early 2000s—a parallel many in the market had begun to take seriously.

Nine Catalysts Behind the Collapse: The True Nature of the Crash

Goldman Sachs’ analysis team conducted an in-depth investigation to understand the true drivers of the market crash, identifying nine factors operating simultaneously. First, NVIDIA’s rally simply ran out of steam; despite strong Q3 results, the stock did not sustain its upward momentum—a sign that the market had already priced in all available good news. “When real good news doesn’t elicit a reaction, it’s usually a bad sign,” Goldman commented.

Second, Federal Reserve Governor Lisa Cook publicly raised alarming concerns about the vulnerability of asset valuations in the private credit sector, emphasizing how its complex interconnection with the financial system could pose a systemic risk. This warning immediately triggered widespread worries and widened overnight credit market spreads.

Employment data, although the September non-farm payroll report was solid, did not provide enough clarity to guide the Fed’s December rate decisions, leaving the market in prolonged uncertainty.

An interesting phenomenon was Bitcoin’s fall below the psychological threshold of $90,000, which preceded the stock market crash, triggering a broader sell-off of risky assets. This demonstrated that risk sentiment transmission started from the most risky areas of the financial ecosystem and then spread to the rest of the markets.

Commodity Trading Advisor (CTA) funds, previously in extremely long positions, began systematic and accelerated selling when the market broke short-term technical levels, further increasing downward pressure. Simultaneously, short positions re-entered the market, exploiting the reversal to push prices even lower.

The poor performance of major Asian tech stocks like SK Hynix and SoftBank offered no positive support externally, depriving the U.S. stock market of a potential ally. Even more critically, Goldman Sachs’ data revealed a dramatic deterioration in liquidity: bid-ask spreads of key S&P 500 stocks fell well below the annual average, creating a “liquidity drought” where even modest sales caused extreme oscillations.

Finally, ETF trading volume as a percentage of total market volume soared to record levels, indicating that trading was increasingly driven by macro outlooks and passive funds rather than individual fundamentals, amplifying the overall bearish trend.

Fragile Market Structure: Automation, ETFs, and Liquidity Chains

A deeper analysis of this market crash reveals a concerning picture of the underlying structure of modern financial markets. Global liquidity is extremely fragile. With “Tech + AI” becoming the preferred sector for global funds, any small rotation point can trigger an uncontrollable chain reaction.

The growing number of automated quantitative trading strategies, index funds, and passive investment instruments that characterize the modern market infrastructure have fundamentally altered price dynamics. The more automated trading strategies increase, the higher the likelihood of forming a “one-way flight,” where buy and sell orders amplify each other exponentially. This structure creates a multiplier effect that turns ordinary corrections into market crashes.

The November crash was thus not a sudden “Black Swan” event but rather an inevitable consequence of excessive automation combined with massive capital crowding in the same sectors and assets. It is a structural, not cyclical, collapse.

Bitcoin as an Indicator: Cryptocurrencies Enter Global Pricing

A fascinating phenomenon was the pioneering role of cryptocurrencies in this global crash. Bitcoin fell from a October high of $126,000, wiping out all 2025 gains and briefly dropping below $90,000, marking a 9% decline from the year’s start. Ethereum dropped below $2,800. In 24 hours, over 245,000 liquidations resulted in a total loss of $930 million.

For the first time in market history, cryptocurrencies were no longer marginal, isolated assets: they became the true thermometer of global risk assets, the canary in the risk sentiment mine. Bitcoin and Ethereum led emotional market reactions, with their movements now influencing the psychology of traditional asset investors. This integration into the global pricing chain marks a significant structural shift: cryptocurrencies are no longer peripheral but have entered the core of the risk signaling system.

Is the Bull Market Really Over? The Recalibration Continues

Many investors wonder if this crash marked the end of the bull market. To answer properly, it’s worth considering Ray Dalio’s perspective, founder of Bridgewater Associates, one of the world’s largest investment funds. Dalio noted that while AI investments are indeed pushing the market toward bubble-like extremes, investors should not rush to panic sell their positions.

According to Dalio’s analysis, the current situation is not entirely comparable to the peaks of the 1999 or 1929 bubbles. For his indicators, the U.S. market is currently at about 80% of those extreme bubble levels. “What I want to emphasize is that before a bubble bursts, many things can still go higher,” Dalio said, suggesting that the AI investment cycle will not end immediately.

Available analysis indicates that the November crash actually marks the beginning of a high-volatility phase, where the market needs significant time to recalibrate expectations around “growth and interest rates.” The era of “irrational rallies” driven solely by expectations has indeed ended, but this does not mean the end of the AI investment cycle. The market is simply shifting from a dynamic driven by optimistic expectations to one more focused on profit realization. This process affects both U.S. markets and Chinese A-shares.

Since cryptocurrencies, as risky assets with the highest leverage and weakest liquidity in this downturn cycle, experienced the sharpest declines, they are historically also the first to rebound when sentiment begins to improve. As of today (March 2026), BTC is around $66,740, with a -0.33% change in the last 24 hours, while ETH stands at $1,970, up 0.16%, reflecting a gradual stabilization after the dramatic market crash of 2024. The market continues its recalibration process, not toward new irrational highs but toward a more conscious and sustainable valuation of underlying value.