Algorithmic trading, often referred to as algo-trading, uses computer programs to execute trades based on pre-set instructions. These programs can process large volumes of data and execute orders within milliseconds. By doing so, they can enhance market liquidity, as they frequently place both buy and sell orders, narrowing bid-ask spreads. Increased liquidity benefits market participants by making it easier to transact at stable prices. For example, in equity markets, algorithms can continuously adjust orders in response to price movements, ensuring a constant flow of activity. This consistent presence can reduce volatility during normal trading conditions, creating a smoother trading environment for both large and small participants.

While algorithmic trading can improve liquidity, it may also create challenges under certain market conditions. In highly automated environments, many algorithms are programmed to withdraw or adjust orders when volatility spikes. This can lead to sudden drops in liquidity during market stress. For instance, during flash crashes, liquidity can evaporate rapidly because algorithms collectively pull back, widening bid-ask spreads and amplifying price swings. Furthermore, the high-speed nature of these systems can cause abrupt market movements that are difficult for human traders to respond to. Therefore, although algorithmic trading generally supports liquidity in stable conditions, it can also contribute to its sudden deterioration when markets become turbulent.