Andrew G Haldane: The race to zero
Speech by Mr Andrew G Haldane, Executive Director, Financial Stability, of the Bank of
England, at the International Economic Association Sixteenth World Congress, Beijing,
8 July 2011.
* * *
1. Introduction
Stock prices can go down as well as up. Never in financial history has this adage been more
apt than on 6 May 2010. Then, the so-called "Flash Crash" sent shocks waves through global
equity markets. The Dow Jones experienced its largest ever intraday point fall, losing
$1 trillion of market value in the space of half an hour. History is full of such fat-tailed falls in
stocks. Was this just another to add to the list, perhaps compressed into a smaller time
window?
No. This one was different. For a time, equity prices of some of the world's biggest
companies were in freefall. They appeared to be in a race to zero. Peak to trough, Accenture
shares fell by over 99%, from $40 to $0.01. At precisely the same time, shares in Sotheby's
rose three thousand-fold, from $34 to $99,999.99. These tails were not just fatter and faster.
They wagged up as well as down.
The Flash Crash left market participants, regulators and academics agog. More than one
year on, they remain agog. There has been no shortage of potential explanations. These are
as varied as they are many: from fat fingers to fat tails; from block trades to blocked lines;
from high-speed traders to low-level abuse. From this mixed bag, only one clear explanation
emerges: that there is no clear explanation. To a first approximation, we remain unsure quite
what caused the Flash Crash or whether it could recur.1
That conclusion sits uneasily on the shoulders. Asset markets rely on accurate pricing of risk.
And financial regulation relies on an accurate reading of markets. Whether trading assets or
regulating exchanges, ignorance is rarely bliss. It is this uncertainty, rather than the Flash
Crash itself, which makes this an issue of potential systemic importance.
In many respects, this uncertainty should come as no surprise. Driven by a potent cocktail of
technology and regulation, trading in financial markets has evolved dramatically during the
course of this century. Platforms for trading equities have proliferated and fragmented. And
the speed limit for trading has gone through the roof. Technologists now believe the sky is
the limit.
This rapidly-changing topology of trading raises some big questions for risk management.
There are good reasons, theoretically and empirically, to believe that while this evolution in
trading may have brought benefits such as a reduction in transaction costs, it may also have
increased abnormalities in the distribution of risk and return in the financial system. Such
abnormalities hallmarked the Flash Crash. This paper considers some of the evidence on
these abnormalities and their impact on systemic risk.
Regulation has thin-sliced trading. And technology has thin-sliced time. Among traders, as
among stocks on 6 May, there is a race to zero. Yet it is unclear that this race will have a
winner. If it raises systemic risk, it is possible capital markets could be the loser. To avoid
that, a redesign of mechanisms for securing capital market stability may be needed.
2. The topology of trading
During the course of this century, financial market trading has undergone a transformation.
This has been driven in part by technology and in part by regulation. The key words are
structure and speed. Both the structure of the markets in which participants operate, and the
behaviour of those participants, has undergone a phase shift. That highly adaptive topology
of trading has made understanding markets a more hazardous science than a decade ago.
Chart 1 plots equity market capitalisation relative to nominal GDP in the United States,
Europe and Asia through this century. On the face of it, it paints a rather unexciting picture.
Equity market values relative to GDP in the US are roughly where they started the century. In
Asia there is evidence of some deepening of equity markets relative to the whole economy
but it is pretty modest.
Measures of equity market capitalisation to GDP have often been used as proxies for the
contribution of financial development to economic growth.2 These effects are typically found
to be significant. By that metric, the contribution of equity markets to economic growth in the
US, Europe and Asia has been static, at best mildly positive, during the course of this
century.
Yet that picture of apparent stasis in equity markets conceals a maelstrom of activity beneath
the surface. To see this, Chart 2 plots stock market turnover in the US, Europe and Asia over
the same period. It shows a dramatic rise, especially in the world's most mature equity
market, the United States. Equity market turnover in the US has risen nearly fourfold in the
space of a decade. Put differently, at the end of the second World War, the average
US share was held by the average investor for around four years. By the start of this century,
that had fallen to around eight months. And by 2008, it had fallen to around two months.
What explains this story? Regulation is part of it. Two important, and almost simultaneous,
regulatory developments on either side of the Atlantic changed fundamentally the trading
landscape: in the US, Regulation NMS (National Market System) in 2005; and in Europe,
MiFID (Markets in Financial Instruments Directive) in 2004. Though different in detail, these
regulatory initiatives had similar objectives: to boost competition and choice in financial
market trading by attracting new entrants.
Central exchanges for the trading of securities evolved from the coffee houses of
Amsterdam, London, New York and Paris in the 17th century. From those foundations
emerged the physical exchanges which dominated the period from the 18th right through to
the 20th centuries. Central trading exchanges maintained their near-monopoly status for
300 years. In the space of a few years, that monopoly has been decisively broken.
A diverse and distributed patchwork of exchanges and multilateral trading platforms has
emerged in its place. These offer investors a range of execution characteristics, such as
speed, cost and transparency, typically electronically. Equity market trading structures have
fragmented. This has gone furthest in the US, where trading is now split across more than
half a dozen exchanges, multilateral trading platforms and "dark pools" of anonymous trading
(Charts 3 and 4). Having accounted for around 80% of trading volume in NYSE-listed
securities in 2005, the trading share of the NYSE had fallen to around 24% by
February 2011.
A similar pattern is evident across Europe. In the UK, the market share of the London Stock
Exchange has fallen from two-thirds in June 2008 to less than one third today (Charts 5
and 6). The same pattern is found in Germany and France. In Asia, there is as yet less
fragmentation.
Accompanying this structural evolution in trading has been a technological revolution.
Electronic trading is not new. The first electronic exchange (NASDAQ) is already over forty
years old. But advances in computing power have shifted decisively the frontier of electronic,
and in particular algorithmic, trading over the past few years. That frontier is defined by
speed.
The average speed of order execution on the US NYSE has fallen from around 20 seconds a
decade ago to around one second today. These days, the lexicon of financial markets is
dominated by talk of High-Frequency Trading (HFT). It is not just talk. As recently as 2005,
HFT accounted for less than a fifth of US equity market turnover by volume. Today, it
accounts for between two-thirds and three-quarters.
The picture is similar, if less dramatic, in Europe. Since 2005, HFT has risen from a tiny
share to represent over 35% of the equity market. In Asia and in emerging markets, it is
growing fast from a lower base. What is true across countries is also true across markets.
HFT is assuming an ever-increasing role in debt and foreign exchange markets. In some
futures markets, it already accounts for almost half of turnover. In the space of a few years,
HFT has risen from relative obscurity to absolute hegemony, at least in some markets.
HFT itself is far from monolithic, comprising a range of strategies.3 Some involve high-speed
liquidity provision, which is akin to market-making. Others involve statistical arbitrage, using
trading algorithms to detect and exploit pricing anomalies between stocks or markets.
Because these anomalies tend to be eliminated quickly, HFT algorithms have to be highly
adaptive, not least to keep pace with the evolution of new algorithms. The half-life of an HFT
algorithm can often be measured in weeks.
One variant of these arbitrage strategies exploits pricing differences between common
securities quoted on competing trading platforms. For that reason, HFT firms tend to have
their tentacles spread across multiple trading venues, arbitraging tiny differences in price
(Chart 7). These strategies have grown up as a direct response to the fragmentation of
trading infrastructures. In other words, HFT is at least in part the (possibly unplanned)
progeny of regulators pursuing competitive ends.
The ascent of HFT goes a long way towards explaining the rise in equity market turnover in
the major equity markets and in particular the rise in number, and fall in the average size, of
trades executed. Put differently, the trading behaviour of HFT has contributed to the
downward fall in the average duration of stock holdings. HFT holding periods lie in a narrow
time range. The upper bound is perhaps around one day. The lower bound is a perpetual
downward motion machine, as computing capacity compresses the timeline for trading.
A decade ago, execution times on some electronic trading platforms dipped decisively below
the one second barrier. As recently as a few years ago, trade execution times reached "blink
speed" - as fast as the blink of an eye. At the time that seemed eye-watering, at around
300-400 milli-seconds or less than a third of a second. But more recently the speed limit has
shifted from milli-seconds to micro-seconds - millionths of a second. Several trading
platforms now offer trade execution measured in micro-seconds (Table 1).
As of today, the lower limit for trade execution appears to be around 10 micro-seconds. This
means it would in principle be possible to execute around 40,000 back-to-back trades in the
blink of an eye. If supermarkets ran HFT programmes, the average household could
complete its shopping for a lifetime in under a second. Imagine.
It is clear from these trends that trading technologists are involved in an arms race. And it is
far from over. The new trading frontier is nano-seconds - billionths of a second. And the
twinkle in technologists' (unblinking) eye is pico-seconds - trillionths of a second. HFT firms
talk of a "race to zero". This is the promised land of zero "latency" where trading converges
on its natural (Planck's) limit, the speed of light.4
The race to zero seems like a million miles from the European coffee shop conversations of
the 17th century and the noisy trading floors of the 18th, 19th and 20th centuries. The dawn
of electronic trading coincided with the dusk for floor trading. Physical proximity lost its allure.
As soon as computers processed faster than traders talked, the time was up for human
interactions on physical exchanges. Trading became virtual, distance a dinosaur.
Or so it seemed. Latterly, however, HFT is turning that logic on its head. The race to zero
has encouraged traders to eke out the last pico-second. And one way to do that is by limiting
physical distance. The shorter the cable to the matching engine of the trading exchange, the
faster the trade. Every 100 miles might add a milli-second to estimated execution times. For
HFT, that is the difference between the tortoise and the hare.
The recognition of that has led to a phenomenon known as "co-location". HFT firms have
begun to relocate their servers as close as physically possible to the trade-matching engine.
That allows them to eke a pico over their (non co-located) competitors. For a price, a number
of exchanges now offer co-located services, with a perimeter strictly enforced, including the
NYSE, Euronext and the London Stock Exchange.
This has added a new dimension to the "adverse selection" problem in economics - of
uninformed traders suffering at the hands of the informed. Being informed used to mean
being smarter than the average bear about the path of future fundamentals - profits, interest
rates, order flow and the like. Adverse selection risk meant someone having a better
informed view on these fundamentals.
Adverse selection risk today has taken on a different shape. In a high-speed, co-located
world, being informed means seeing and acting on market prices sooner than competitors.
Today, it pays to be faster than the average bear, not smarter. To be uninformed is to be
slow. These uninformed traders face a fundamental uncertainty: they may not be able to
observe the market price at which their trades will be executed. This is driving through the
rear-view mirror, stock-picking based on yesterday's prices.
Co-location speeds up the clock. But it has also had the effect of turning it back. Location
matters once more. The race to zero has become a physical, as well as a virtual, one.
Distance matters more than ever. The dinosaur has been resurrected, this time in high
definition. In some ways, it is the ultimate contradiction.
In sum, through this century changes in the structure of trading, and in the behaviour of
traders, have gone hand in hand. Liberalisation and innovation have delivered fragmentation
of structure and transformation of speed. Both structure and speed have experienced a
highvelocity revolution. So what impact has this race to zero had on market dynamics?
3. From microstructure to macrostructure
This is difficult detective work. But there are theoretical clues and empirical fingerprints. The
theoretical clues come from a literature that flourished after the stock market crash of 1987
- the so-called market microstructure literature.5 This departs from the notion of frictionless
trading and information-efficient prices. In its place it introduces frictions in the price-setting
process, which arise from characteristics of market participants (such as their trading speed)
and of the trading infrastructure (such as its degree of fragmentation).
Frictions in pricing arise from the process of matching buyers and sellers. Here, the role of
market-makers is key. The market-maker faces two types of problem. One is an
inventorymanagement problem - how much stock to hold and at what price to buy and sell. The
market-maker earns a bid-ask spread in return for solving this problem since they bear the
risk that their inventory loses value.6
Market-makers face a second, information-management problem. This arises from the
possibility of trading with someone better informed about true prices than themselves - an
adverse selection risk. Again, the market-maker earns a bid-ask spread to protect against
this informational risk.7
The bid-ask spread, then, is the market-makers' insurance premium. It provides protection
against risks from a depreciating or mis-priced inventory. As such, it also proxies the
"liquidity" of the market - that is, its ability to absorb buy and sell orders and execute them
without an impact on price. A wider bid-ask spread implies greater risk in the sense of the
market's ability to absorb volume without affecting prices.
This basic framework can be used to assess the impact of the changing trading topology on
systemic risk, moving from analysing market microstructure to market macrostructure. Take
the effects of fragmentation. That heightens competition among trading platforms, offering
differing execution options and wider access to participants. This would tend to attract
liquidity providers, including cross-market arbitraging HFT. As new liquidity-providers enter
the market, execution certainty and price efficiency might be expected to improve. Inventory
and information risk would thereby fall and, with it, bid-ask spreads.
Some of the early empirical fingerprints suggest just such a pattern. For example, Brogaard
(2010) analyses the effects of HFT on 26 NASDAQ-listed stocks. HFT is estimated to have
reduced the price impact of a 100-share trade by $0.022. For a 1000-share trade, the price
impact is reduced by $0.083. In other words, HFT boosts the market's absorptive capacity.
Consistent with that, Hendershott et al (2010) and Hasbrouck and Saar (2011) find evidence
of algorithmic trading and HFT having narrowed bid-ask spreads.
Chart 8 plots a measure of bid-ask spreads on UK equities over the past decade, normalising
them by a measure of market volatility to strip out volatility spikes. It confirms the pattern
from earlier studies. Bid-ask spreads have fallen by an order of magnitude since 2004, from
around 0.023 to 0.002 percentage points. On this metric, market liquidity and efficiency
appear to have improved. HFT has greased the wheels of modern finance.
But bid-ask spreads can sometimes conceal as much as they reveal. For example, by
normalising on volatility, Chart 8 air-brushes out what might be most interesting: normalising
volatility might normalise abnormality. It risks falling foul of what sociologists call
"normalisation of deviance" - that is, ignoring small changes which might later culminate in
an extreme event.8
So is there any evidence of increasing abnormality in market prices over the past few years?
9
Measures of market volatility and correlation are two plausible metrics. Chart 9 plots the
volatility of, and correlation between, components of the S&P 500 since 1990. In general, the
relationship between volatility and correlation is positive. Higher volatility increases the
degree of co-movement between stocks.
Now consider how this volatility/correlation nexus has changed. This can be seen from the
difference between the mass of blue dots (covering the period 1990 to 2004) and red dots
(covering the period 2005 to 2010) in Chart 9. Two things have happened since 2005,
coincident with the emergence of trading platform fragmentation and HFT.
First, both volatility and correlation have been somewhat higher. Volatility is around
10 percentage points higher than in the earlier sample, while correlation is around
8 percentage points higher. Second, the slope of the volatility / correlation curve is steeper.
Any rise in volatility now has a more pronounced cross-market effect than in the past.
Another way of making the same point is to plot measures of "excess correlation"
- measured market correlation in excess of volatility. This is currently at historic highs
(Chart 10).
Taken together, this evidence points towards market volatility being both higher and
propagating further than in the past. Intraday evidence on volatilities and correlations
appears to tell a broadly similar tale. Overnight and intraday correlations have risen in
tandem.10 And intra-day volatility has risen most in those markets open to HFT.11
Coincidence does not of course imply causality. Factors other than HFT may explain these
patterns. Event studies provide one way of untangling this knitting. Recent evidence from the
Flash Crash pinpoints the particular role played by HFT using transaction-level data. The
official report on the Flash Crash, while not blaming HFT firms for starting the cascade,
assigns them an important role in propagating it. For example, many HFT firms significantly
scaled back liquidity and overall HFT firms were net sellers of stock.12
Taken together, this evidence suggests something important. Far from solving the liquidity
problem in situations of stress, HFT firms appear to have added to it. And far from mitigating
market stress, HFT appears to have amplified it. HFT liquidity, evident in sharply lower
peacetime bid-ask spreads, may be illusory. In wartime, it disappears. This disappearing act,
and the resulting liquidity void, is widely believed to have amplified the price discontinuities
evident during the Flash Crash.13 HFT liquidity proved fickle under stress, as flood turned to
drought.
In some respects, this may sound like old news. For example, an evaporation of liquidity,
amplified by algorithmic trading, lay at the heart of the 1987 stock market crash. And it is also
well-known that stock prices exhibit non-normalities, with the distribution of asset price
changes fatter-tailed and more persistent than implied by the efficient markets hypothesis at
frequencies of years and months, perhaps weeks and days.14 But these abnormalities were
thought to disappear at higher frequencies, such as hours and minutes. Over shorter
intervals, efficient market pricing restored itself.
Recent studies point, however, to a changing pattern. Non-normal patterns in prices have
begun to appear at much higher frequencies. A recent study by Smith (2010) suggests that,
since around 2005, stock price returns have begun to exhibit fat-tailed persistence at
15 minute intervals. Given the timing, these non-normalities are attributed to the role of HFT
in financial markets.
The measure of stock price abnormality used by Smith is the so-called "Hurst" coefficient.15
The Hurst coefficient is named after English civil engineer H E Hurst. It was constructed by
plotting data on the irregular flooding patterns of the Nile delta over the period 622-1469 AD.
Hurst found that flooding exhibited a persistent pattern. Large floods were not only frequent,
but came in clumps. They had a long memory.
The Hurst coefficient summarises this behaviour in a single number. For example, a
measured Hurst equal to 0.5 is consistent with the random walk model familiar from efficient
markets theory. A Hurst coefficient above 0.5 implies fatter tails and longer memories. In his
study, Smith finds that the Hurst coefficient among a selection of stocks has risen steadily
above 0.5 since 2005. In other words, the advent of HFT has seen price dynamics mirror the
fat-tailed persistence of the Nile flood plains.
To illustrate, Chart 11 plots the path of three simulated price series with Hurst coefficients of
0.5, 0.7 and 0.9. A higher Hurst coefficient radically alters the probability of sharp
dislocations in prices. It also prolongs these dislocations. Prices become de-anchored and
drift; their variance grows over time and is unbounded. If this long-memory property of prices
is emerging at ever-higher frequencies, it might provide an important clue to how HFT affects
systemic risk.
4. A sketch model of market macrostructure
To see that, consider a sketch model of market-making. This builds on an analytical insight
which is already more than 40 years old. It owes to the late Benoit Mandelbrot,
FrenchAmerican mathematician and architect of fractal geometry. Mandelbrot found that a great
many real-world topologies exhibited a fractal pattern. By this he meant that the basic pattern
repeated itself, whatever the scale at which it was observed. They were "self-similar".
Selfsimilarity appears to be present throughout the physical world, from coastlines to
cauliflowers, from snowflakes to lightning bolts, from mountain ranges to river deltas.16
One of Mandelbrot's earliest applications of fractal geometry was to stock prices. In a 1967
paper, he argued that stock prices could best be understood by distinguishing between two
measuring rods: clock time and volume time.17 While empirical studies typically used the first
measuring rod (days, hours, seconds, milli-seconds), stock prices were better understood by
using the second.
Mandelbrot's explanation was relatively simple. If trading cannot occur within a given time
window, price movements can only reflect random pieces of news - economic, financial,
political. So, consistent with efficient market theory, price changes would be drawn from a
normal distribution with a fat middle and thin tails when measured in clock time. They were a
random walk.
But as soon as trading is possible within a period, this game changes. Strategic, interactive
behaviour among participants enters the equation. Volumes come and go. Traders enter and
exit. Algorithms die or adapt. Behaviour within that time interval may then no longer be
random noise. Rather trading volumes will exhibit persistence and fat tails. This will then be
mirrored in prices.18 So when measured in clock time, prices changes will have thinner
middles and fatter tails, just like a cauliflower, a coastline, or a cosmos.19
Subsequent studies have shown that this clock time / volume time distinction helps explain
equity price dynamics, especially at times of market stress. For example, Easley et al (2011)
show that the distribution of price changes during the Flash Crash was highly non-normal in
clock time, with fat tails and persistence. But in volume time, normal service - indeed,
nearnormality - resumed. This fractal lens can be used to explain why market liquidity can
evaporate in situations of market stress, amplifying small events across time, assets and
markets. Fractal geometry tells us that what might start off as a snowflake has the potential
to snowball.
(a) Behaviour of high frequency traders
HFT has had three key effects on markets. First, it has meant ever-larger volumes of trading
have been compressed into ever-smaller chunks of time. Second, it has meant strategic
behaviour among traders is occurring at ever-higher frequencies. Third, it is not just that the
speed of strategic interaction has changed but also its nature. Yesterday, interaction was
human-to-human. Today, it is machine-to-machine, algorithm-to-algorithm. For algorithms
with the lifespan of a ladybird, this makes for rapid evolutionary adaptation.
Cramming ever-larger volumes of strategic, adaptive trading into ever-smaller time intervals
would, following Mandelbrot, tend to increase abnormalities in prices when measured in
clock time. It will make for fatter, more persistent tails at ever-higher frequencies. That is
what we appear, increasingly, to find in financial market prices in practice, whether in
volatility and correlation or in fat tails and persistence.
This change in price dynamics will in turn influence market-making behaviour. Consider the
problem facing an HFT market-maker. They face inventory risk from market fluctuations and
information risk from adverse selection. Pricing these risks means forming a guess about the
future path of prices. The greater the potential range of future prices, the larger the insurance
premium they will demand.
The future price range (maximum - minimum) for a price series at some future date T, R(T),
can be written generically as:20
(1)
κ
where is a constant and is the standard deviation of the process up to time T. H is our
σT
old friend the Hurst coefficient, dredged up from the Nile; it can be thought to summarise the
degree of fat-tailedness and persistence in prices.
When the holding period is short (say T=1), as with HFT, the future price range is the
volatility of the series magnified by the Hurst coefficient. In other words, the fatter and more
persistent the tails (H), the greater the market risk and the wider the bid-ask spread of the
HFT.
This has implications for the dynamics of bid-ask spreads, and hence liquidity, among HFT
firms. During a market crash, the volatility of prices (σ) is likely to spike. From equation (1),
fractality heightens the risk-sensitivity of HFT bid-ask spreads to such a volatility event. In
other words, liquidity under stress is likely to prove less resilient. This is because one
extreme event, one flood or drought on the Nile, is more likely to be followed by a second, a
third and a fourth. Reorganising that greater risk, market makers' insurance premium will rise
accordingly.
This is the HFT inventory problem. But the information problem for HFT market-makers in
situations of stress is in many ways even more acute. Price dynamics are the fruits of trader
interaction or, more accurately, algorithmic interaction. These interactions will be close to
impossible for an individual trader to observe or understand. This algorithmic risk is not new.
In 2003, a US trading firm became insolvent in 16 seconds when an employee inadvertently
turned an algorithm on. It took the company 47 minutes to realise it had gone bust.21
Since then, things have stepped up several gears. For a 14-second period during the Flash
Crash, algorithmic interactions caused 27,000 contracts of the S&P 500 E-mini futures
contracts to change hands. Yet, in net terms, only 200 contracts were purchased. HFT
algorithms were automatically offloading contracts in a frenetic, and in net terms fruitless,
game of pass-the-parcel. The result was a magnification of the fat tail in stock prices due to
fire-sale forced machine selling.22
These algorithmic interactions, and the uncertainty they create, will magnify the effect on
spreads of a market event. Pricing becomes near-impossible and with it the making of
markets. During the Flash Crash, Accenture shares traded at 1 cent, and Sotheby's at
$99,999.99, because these were the lowest and highest quotes admissible by HFT
marketmakers consistent with fulfilling their obligations. Bid-ask spreads did not just widen, they
ballooned. Liquidity entered a void. That trades were executed at these "stub quotes"
demonstrated algorithms were running on autopilot with liquidity spent. Prices were not just
information inefficient; they were dislocated to the point where they had no information
content whatsoever.
(b) Behaviour of low frequency traders
If the story stopped here the ending would be an unhappy, but perhaps not critical, one. After
all, the Flash Crash was done and dusted within an hour. But the framework developed
suggests these effects need not, in general will not, be transient. To the contrary, these
effects might actually magnify. To see why, consider now the behaviour of low frequency
traders (LFT).
They face the same set of risks as the HFT market-maker - inventory and information risk.
But persistence and fat tails in short-term price movement amplify these risks. From equation
(1), the greater the holding period, T, the greater the potential dispersion in future prices.
Intuitively, with fat-tailed persistence, a large shock is both more likely and its effects will
linger longer. Slower trading increases the distance prices can travel once de-anchored.
Chart 12 illustrates this. The distribution of returns observed at a low frequency has far
greater variance and kurtosis than that at a higher frequency. For example, the variance of
prices faced by a LFT firm (trading at T=50) is around nine times greater than for a HFT firm
(trading at T=1), for H=0.9.23
Given these price dynamics, HFT aggravates the market-making problem for LFT firms by
magnifying the market risk they face.24 And it is not just that LFT firms are slower to execute.
In situations of stress, they may not even be able to see the prices at which they can trade.
Co-located traders may have executed many thousands of trades before LFT firms have
executed their own. LFT firms face intrinsic uncertainty about prices. When volumes and
price movements are large, LFT market-making is driving in the dark, stock-picking with a
safety-pin.
During the Flash Crash, many traders suffered just this problem. Message traffic resulted in
delays in disseminating quotes for over 1000 stocks. These delays lasted for up to
35 seconds. As a result, discrepancies emerged between the prices of common stocks
trading on different exchanges (Chart 13). Faced with such uncertainty, a number of market
participants paused or halted trading. The equilibrating force of long-term investors went
missing. Bargain-hunting shoppers simply had no price list.
The combined effects of these inventory and information problems is to widen the bid-ask
spreads LFT market-makers charge. Greater execution risk and uncertainty calls for a larger
insurance premium. This, too, may have an adverse feedback effect on financial market
pricing. That is because it is likely to render uncompetitive LFT firms relative to HFT firms
able to charge tighter spreads. Market-making will increasingly congregate around HFT firms
proffering these lower spreads.
If the way to make money is to make markets, and the way to market markets is to make
haste, the result is likely to be a race - an arms race to zero latency. Competitive forces will
generate incentives to break the speed barrier, as this is the passport to lower spreads which
is in turn the pa