When global markets come to a halt: the November crash explained

In November 2025, a wave of synchronized selling swept through the world’s major markets, turning an ordinary trading day into an episode reminiscent of the most severe market crises. This market crash was not isolated to a single sector or region: it was a systemic reaction that caught many observers by surprise. But what truly triggered this simultaneous fall of seemingly uncorrelated assets? The November 2025 event served as a crucial lesson on how increasingly interconnected global markets—driven by algorithms and automated strategies—could implode following a panic transmission logic that no one had precisely foreseen.

The Numbers of the Global Disaster

The market crash left clear marks across all fronts. In the United States, the Nasdaq 100 retreated nearly 5% from intraday highs, ending the session down 2.4%, with a retracement from the October 29 peak reaching 7.9%. Nvidia, the processor giant that had led the tech rally, initially recovered over 5%, then completely reversed course and closed in the red, evaporating $2 trillion in market capitalization overnight.

Across the Pacific, Asian markets faced similar pressures. The Hong Kong Hang Seng index fell 2.3%, while the Shanghai Composite dropped below 3,900 points, with a loss close to 2%. Even more dramatic was the performance of the traditionally volatile crypto sector. Bitcoin fell below $86,000 (from $126,000 in October), Ethereum plunged below $2,800, and in just 24 hours, over 245,000 traders were liquidated for a total of $930 million. Even gold, long considered a safe haven against market panic, did not withstand the turbulence, dropping 0.5% and oscillating around $4,000 per ounce.

The Federal Reserve Changes Course: The End of Rate Cut Hopes

Where did this sudden reversal originate? The primary responsibility lies with the Federal Reserve. In the two months prior to the crash, markets remained firmly anchored to expectations of a rate cut in December. But the sudden shift in tone from Fed officials erased those hopes.

In a series of public statements, Fed governors adopted a surprisingly hawkish tone. They emphasized that inflation was declining slowly; the labor market remained strong; and if necessary, “further tightening of monetary policy” was not ruled out. The implicit message was clear: “Forget December’s rate cut; your optimism is misplaced.”

Data from CME FedWatch, the barometer of market consensus on future rates, captured this shift vividly. One month before, the probability of a rate cut was at 93.7%; at the moment of the crash, it had plummeted to 42.9%. This dramatic turnaround transformed market sentiment from celebration to intensive care within hours. Risk assets, both equities and cryptocurrencies, were simultaneously overwhelmed.

Nvidia: When Good News No Longer Suffices

After the surge in expectations for rate cuts, market focus turned to a single company: Nvidia. The firm had announced Q3 earnings exceeding expectations—a piece of news that should have ignited a strong rally in the tech sector. Instead, in a swift reversal revealing the fragility of the rally, Nvidia quickly turned red and plummeted.

This phenomenon—good news failing to sustain prices—is the most powerful bearish signal a market can emit. During the overvaluation cycle of the tech sector, when positive results no longer push prices higher, they instead become a pretext for a sell-off. Nvidia was no exception: traders who had built large long positions began to take profits.

Famous short seller Michael Burry seized the moment to intensify his criticisms. He highlighted the complex circular financing system among Nvidia, OpenAI, Microsoft, Oracle, and other major AI players: “The actual final demand is ridiculously low, with almost all clients being financed by Nvidia dealers.” Burry had repeatedly warned of a potential bubble in the AI sector, drawing parallels with the dot-com bubble burst of 1999-2000.

John Flood, a Goldman Sachs partner, told clients unequivocally that “a single catalyst is not enough to explain such a sharp reversal.” According to his analysis, market sentiment was deeply wounded. Investors had shifted into pure defensive mode, obsessively protecting gains and worrying about underlying risks.

Nine Factors Triggering the Simultaneous Sell-Off

Goldman Sachs’ trading team summarized the elements fueling the stock market collapse:

Nvidia’s rally has run out of steam. Despite positive results, the stock failed to maintain upward momentum. “When real good news doesn’t elicit a positive reaction, it’s usually a bad sign,” Goldman commented, suggesting that the market had already priced in the positive factors.

Growing concerns over private credit. Federal Reserve governor Lisa Cook publicly warned about potential vulnerabilities in the private credit sector and its complex interconnection with the financial system. The alarm triggered market fears and widened credit spreads.

Uncertain employment data. The September non-farm payroll report was solid but did not provide enough clarity to guide the Fed’s December decision, leaving uncertainties about future rate paths unresolved.

Crypto contagion transmission. Bitcoin dropping below $90,000 triggered a broader sell-off of risky assets. The crypto decline even preceded the stock market crash, indicating that emotional contagion originated from the most speculative areas.

Acceleration of CTA selling. Commodity Trading Advisors (CTAs) were positioned extremely long. When the market broke technical thresholds, their systematic selling accelerated, amplifying downward pressure.

Return of the bears. The reversal reignited short positions, pushing prices further down.

Weakness in foreign markets. Poor performance of Asian tech sectors (SK Hynix, SoftBank) failed to provide external support to the US stock market.

Liquidity drought. The bid-ask spreads of major S&P 500 stocks contracted significantly below the annual average, impairing the market’s ability to absorb large sell orders. Small sales caused wide fluctuations.

Macro operations dominate. ETF flow volumes as a percentage of total volume reached highs, indicating that trading was increasingly driven by macroeconomic outlooks and passive funds rather than individual fundamentals.

How Automated Trading Amplified the Fall

A particularly significant element of this crisis was the role played by automation. Global liquidity proved to be extraordinarily fragile. With “Tech + AI” becoming the crowded sector among funds worldwide, even minor turning points could trigger a devastating cascade reaction.

The growing prevalence of quantitative trading strategies, ETFs, and passive funds had profoundly altered market structure. The more algorithms and automated strategies were active, the easier it was to form a “one-way rush”—all algorithms selling simultaneously, amplifying the decline. The November market crash was essentially a manifestation of this structural vulnerability.

An intriguing aspect was that the crash was led by Bitcoin. For the first time in recent history, cryptocurrencies truly entered the global asset pricing chain. Bitcoin and Ethereum were no longer marginal assets; they had become the thermometer of global risk sentiment, at the heart of market emotion rather than on the fringes.

Has the Bear Market Truly Arrived?

Legendary Bridgewater founder Ray Dalio offered a tempered perspective. Although AI investments were pushing the market toward a bubble, investors should not rush to liquidate positions. In his view, the current situation was not yet comparable to the bubble peaks of 1999 or 1929. According to his monitoring indicators, the US market was currently around 80% of those extreme levels.

“What I want to emphasize,” Dalio said, “is that many things can still go higher before a bubble bursts.”

The November crash was not an abrupt “black swan” event but rather a collective correction after months of unmet expectations. However, this correction exposed critical structural issues: liquidity fragility, fund overcrowding in a few sectors, and the amplifying effect of automated trading.

Based on this analysis, the market was not truly entering a prolonged bear phase but rather a period of high volatility where assets needed to recalibrate growth and interest rate expectations. The AI investment cycle was not ending immediately; however, the era of “irrational rallies” was over. The market was transitioning from a sentiment-driven dynamic to one of profit realization.

This shift applied to both the US stock market and Chinese equities. Cryptocurrency, being the riskiest asset that fell first with the highest leverage and weakest liquidity in this crash cycle, experienced the sharpest decline—from $126,000 to $86,000 for Bitcoin, down to the last levels of $70,600 recorded in March 2026. Ironically, however, assets that fall first often rebound first when sentiment stabilizes, as happened this time too.

The November 2025 market crash will remain a crucial reference point: not so much as the start of a lasting winter but as the moment when the global market finally understood that not all gains can continue forever, and that the very structure of modern markets—with their reliance on fragile liquidity and widespread automation—deserves careful scrutiny.