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AI trading in worst-case market crashes

To understand AI trading in the context of severe market downturns, let’s first look at what makes a “worst-case” crash. History has seen its share of devastating market routs, from the Great Depression of the 1930s to the Global Financial Crisis of 2008-2009. In these extreme “black swan” events, asset prices plummet across the board in a rapid deleveraging as panicked investors rush for the exits. Volatility spikes to extraordinary levels, and liquidity evaporates, making it difficult to exit positions without suffering massive losses. Correlations between generally uncorrelated assets suddenly snap to 1, eliminating the benefits of diversification. It’s the ultimate stress test for any investment strategy or trading system.

So, how might AI fare in such a treacherous environment? On the one hand, machine learning models are trained on historical market data, including past crises, so they should be prepared for such eventualities. AI processes vast amounts of data and spots warning signs that humans might miss. For example, an AI algorithm may detect subtle changes in market microstructure, such as decreased liquidity or widening bid-ask spreads, that often precede significant sell-offs. By picking up on these signals, AI systems could move faster than human traders to reduce risk and preserve capital.

AI trading systems are emotionless and not susceptible to the fear and irrationality that often take hold of human investors during market panics. People are prone to behavioural biases like loss aversion and herd mentality, leading to poor decision-making when markets are freeing. However, an AI model will stoically stick to its programmed rules and strategies without succumbing to panic or impulsive moves.

However, AI has some significant limitations and vulnerabilities when trading worst-case crashes. By their nature, machine learning models learn from and make predictions based on past data. However, a worst-case crash is a rare, unexpected outlier that likely differs from anything in the historical data used to train the AI. Most models are based on assumptions about statistical patterns and relationships between variables in the market. A severe enough crash could completely shatter those assumptions, rendering the model’s predictions unreliable.

The lack of high-quality, real-world training data is a crucial constraint for AI systems handling black swan events. It’s difficult for an algorithm to prepare for a scenario it has never encountered. Some models use simulated data to train for extreme crashes, but a simulation is only as good as the assumptions that go into it. If many significant AI-driven funds use similar models and risk limits, they could all start selling simultaneously after some critical threshold is breached, exacerbating a market rout. The speed and efficiency that is a strength of AI in regular times could become a liability if it means selling at a furious pace into an illiquid, plunging market. By some estimates, 80% of daily trading volume in the stock market is now machine-driven. A disorderly unwind of crowded AI-powered strategies could be highly destabilizing.

A worst-case crash is as much a test of behavioural finance as a quantitative strategy. An AI algorithm cannot understand and respond to market sentiment, narratives, and the raw human emotions driving a panic the way a seasoned trader can. The best human investors rely on wisdom, instinct and even gut feel, honed by experience, to navigate choppy market waters. They can recognize when overriding the model is time because conditions have fundamentally changed. A quantum ai canada only rigidly follows its pre-defined instructions.