It’s important to note here that not all algorithmic trading is HFT. The former encompasses all forms of trading using pre-programmed systems. HFT involves a large amount of trades in a short period of time, with the aim of making profits by quickly buying and selling securities at a small price differential.
Some researchers say that HFT firms provide a market-making function because of the large number of orders they enter into the trading system. Researchers also say that these firms perform the role of arbitrageurs. Both of these roles enhance liquidity and price discovery. Before HFT came into being, trading desks performed these roles with the help of human traders. With HFT, these same roles are being performed at a much faster pace.
So is the additional speed detrimental to the markets? And will clamping down on speed help in any way? At a recent emerging markets finance conference in Mumbai, Pradeep Yadav, W. Ross Johnston Chair in Finance at the University of Oklahoma, pointed out that while research on HFT is still in nascent stages, most of the studies based on empirical evidence suggest that HFT improves market quality. For instance, Joel Hasbrouck of New York University and Gideon Saar of Cornell University, after studying NASDAQ’s trading data in 2007-08, came to the conclusion that low-latency trading improves traditional market quality measures such as short-term volatility, spreads and displayed depth in the limit order book. They define low-latency trading activity as strategies that respond to market events within milliseconds.