How high-frequency trading is changing the market of the 21st century.
Few activities embraced the computer age so actively as trading. Loud and hectic pits have been progressively replaced by silent computer server rooms. Transactions are no less dynamic for it, however. A London-based trader can buy stocks in Frankfurt within 2.21 milliseconds. Light needs only 2.12 milliseconds to travel the same distance. Welcome to the age of algorithmic and high-frequency trading!
There is a very active ongoing debate around high-frequency trading. Supporters claim that in the few years since algorithmic trading took off, market liquidity and exchange competition improved. Critics point to aggressive high-frequency trading strategies that generate losses for human investors. Michael Lewis’ book “Flash Boys”, a champion of the latter view, is a very well-known example. How exactly does high-frequency trading affect markets? How can researchers and policymakers improve financial markets in the twenty-first century?
The benefits of algorithmic and fast trading
High-frequency traders (HFTs) have a comparative advantage in providing liquidity as market-makers. Market-makers are suppliers of “quotes’’: a bid price, at which they are ready to buy an asset, and an ask price, at which they are ready to sell it. The difference between the two is referred to as the spread, and represents the profit of the market-maker.
Why are high-frequency traders better at making markets? Their computer algorithms monitor in real time all information relevant to the traded asset: news headlines, demand and supply changes, or related assets data. HFTs are able to incorporate this wealth of information into their price quotes faster than anyone else. Two advantages follow directly. First, price discovery improves: Price quotes accurately reflect all available information with minimal lag. Second, a savvy trader could have exploited the delay between news and price updates to earn a profit at the market-maker’s expense. Fast trading minimizes this delay: The risk for an HFT market-maker is lower than for a human one. Consequently, HFTs are able to charge lower spreads. Trading costs are smaller for everybody.
Algorithmic traders also promote competition between exchanges. In the past, assets were only traded at a single exchange. It made sense: having all potential buyers and sellers in one place increases the likelihood to find counterparties. The exchange had a natural monopoly and the power to set large fees. Algorithmic trading made it easier to automatically search for counterparties across multiple exchanges. Computer traders can take a position from a seller in one market and offload it to a buyer on a different one. There is no need for everybody to trade in the same place anymore. Under renewed competitive pressure, exchanges decrease trading fees.
The costs of algorithmic and fast trading
High-frequency traders are not always market makers, however. They can be, for instance, speculators. Speculator-type HFTs trade on quickly-processed information to take advantage of other participants’ delay in updating price quotes. A natural reaction of HFT market-makers is to become even faster to close the gap. A socially costly arms’ race ensues, with all high-frequency traders aiming to marginally increase speed.
Are faster exchanges always good?
Trading venues like the New York Stock Exchange or Nasdaq-OMX strive to provide low latency services to their clients. Today, a trade can be processed in just a few microseconds. To what extent is this trend beneficial to the quality of markets?
A starting point to answer this question is to acknowledge the empirical evidence showing high-frequency traders behave both as market-makers and as speculators. A speed improvement of a few microseconds directly affects high-frequency traders, irrespective of their strategy. Since human reaction time is hundreds of milliseconds, it does not directly affect human traders.
In a faster market, high-frequency market makers can update their quotes faster on new information. At the same time, high-frequency speculators are also faster to react on news. The market increasingly becomes a zero-sum game between these two types, with human traders being crowded out. Consequently, in low-latency markets, fast market makers are more likely to meet fast speculators. Whenever it happens, market makers are on the losing side; they are adversely selected. To compensate for the additional risk, market makers need to raise spreads.
We test the mechanism empirically, using a 2010 Nasdaq-OMX speed upgrade in three Nordic countries – Sweden, Denmark, and Finland. Following the upgrade, adverse-selection cost and spread on quotes submitted by high-frequency market makers increased substantially. In this case, a faster exchange led to lower liquidity.
A market design challenge
High-frequency trading has complex effects on market quality. Overall, evidence shows that the transition to the computer age improved market liquidity. However, after some point, increasingly faster trading has less to do with better liquidity provision and more with the interactions between high-frequency traders themselves. The social benefits of prices reflecting information one microsecond earlier are most likely not so important. Being one microsecond faster than a competitor just might tip the balance. How fast should trading be exactly? Can we design different markets that maximize the benefits and eliminate the costs? These are important questions for both academia and regulators.
The full paper on this topic authored by Marius Zoican and Albert Menkveld can be downloaded at: http://ssrn.com/abstract=2442690.