Finance at the speed of light: Is faster trading always better?

Marius Zoican

20 September 2014



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 just 2.21 milliseconds.1 Light needs 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 (Lewis 2014) is a very well-known rendition of the latter view. How exactly does high-frequency trading affect markets? How can researchers and policymakers improve financial markets in the 21st century?

The benefits of algorithmic and fast trading

High-frequency traders 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 data on related assets. High-frequency traders 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, as documented by Riordan and Storkenmaier (2012). 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 minimises this delay, so the risk for a high-frequency market maker is lower than for a human one. Consequently, high-frequency traders are able to charge lower spreads. Trading costs are smaller for everybody (see Hendershott et al. 2011).

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 of finding 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 (see, e.g., Domowitz and Benn 1999 and Menkveld and Yueshen 2013). 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. Foucault et al. (2013) argue that speculator-type high-frequency traders trade on quickly processed information to take advantage of other participants’ delay in updating price quotes. A natural reaction of high-frequency 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? In Menkveld and Zoican (2014), my colleague Albert Menkveld and I analyse the impact a very fast exchange has on market liquidity.

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. Hagstromer and Norden (2013) document such order type specialisation. 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 to 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. First, exchange latency dropped from 2.5 to 0.25 milliseconds. Second, traders were allowed to collocate with the exchange’s servers. Following the upgrade, the adverse-selection cost and the spread on quotes submitted by high-frequency market makers increased substantially. The adverse-selection cost jumped more than five-fold, from 0.39 basis points to 2.50 basis points. The effective spread also increased by 32%. 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 rapid trading has less to do with better liquidity provision and more to do 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 maximise the benefits and eliminate the costs? For example, Budish et al. (2013) advocate replacing existing limit order markets with a frequent auctions setup. These are important questions for both academia and regulators.

Figure 1.

Note: This figure depicts the daily average adverse selection cost on price quotes of high frequency market makers (HFMs) surrounding the NASDAQ-OMX speed upgrade (INET) on 8 February 2010. The adverse selection cost and the effective spread are averaged across all stocks included in the OMX Nordic 40 index.


Budish, E, P Cramton, and J Shim (2013), “The high-frequency trading arms race: Frequent batch auctions as a market design response”, mimeo, University of Chicago. 

Domowitz, I and B Steil (1999), “Automation, trading costs, and the structure of the trading services industry”, Brookings-Wharton Papers on Financial Services. 

Foucault, T, J Hombert, and I Rosu (2013), “News trading and speed”, HEC Paris Research Paper 975/2013. 

Hagstromer, B and L Norden (2013), “The diversity of high-frequency traders”, Journal of Financial Markets, 16: 741–770.

Hendershott, T, C M Jones, and A J Menkveld (2011), “Does algorithmic trading improve liquidity?”, Journal of Finance, 66: 1–33.

Lewis, M (2014), Flash Boys, Allen Lane.

Menkveld, A J and B Z Yueshen (2013), “Middlemen Interaction and Market Quality”, mimeo, VU University Amsterdam. 

Menkveld, A J and M A Zoican (2014), “Need for speed? Exchange latency and liquidity”, mimeo, VU University Amsterdam. 

Riordan, R and A Storkenmaier (2012), “Latency, liquidity and price discovery”, Journal of Financial Markets, 15: 416–437.


1 Source: Bloomberg News -- “Wall Street Grabs NATO Towers in Traders Speed of Light Quest”, July 15, 2014.



Topics:  Financial markets

Tags:  high-frequency trading, algorithmic trading, technology, liquidity, spreads, price discovery, adverse selection, exchanges, competition-stability trade-off

Assistant Professor in Financial Economics, Université Paris-Dauphine