Responding to concerns about an increasingly electronic stock exchange, Finance Professor Terry Hendershott studied algorithmic trading and found that computer-driven trading based on algorithmic formulas does, in fact, improve the market’s liquidity. Furthermore, this kind of high-speed trading allows stock prices to become more “efficient” or reflective of true supply and demand in the market, he found.
Hendershott’s research includes these key findings:
- Algorithmic trading narrows the spread between the stock’s bid and ask price.
- It reduces adverse selection, which occurs when buyers and sellers make decisions based on a different set of information and results in both kinds of investors acting adversely based on the known, rather than the accurate, level of risk.
- It reduces trade-related price discovery, meaning activity more truly reflects actual supply and demand.
The research data showed no evidence that algorithmic trading causes instability or volatility in prices.
Hendershott spent two years gathering data at the New York Stock Exchange. In 2003, the exchange implemented a change in trading practices, speeding up how fast data was delivered to market participants. The upgrade and increased algorithmic trading were introduced across stocks over time, allowing later affected stocks to act as the research’s control group.
Paper: Does Algorithmic Trading Improve Liquidity?
Forthcoming in the Journal of Finance