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A Cinnober white paper
Measuring market quality
Lars-Ivar Sellberg, Cinnober Financial Technology AB
Fredrik Henrikson, Scila AB
11 October 2011
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New trading patterns demand more
from market quality analysis
Today’s equities markets are increasingly fragmented, and the competition for liquidity among
trading venues is fierce. At the same time, algorithmic trading contributes to a growing share of
trading volumes and marketplaces earnings, both in equities and other instruments.
The potential negative effects of certain types of algorithmic trading are widely discussed in media, among
market participants, regulators and other stakeholders.
Does it increase market volatility, amplify short-term
market reactions and in the long run cause traditional
investors to pull out from these markets? This is still
a rather new phenomenon and there are still a lot of
unanswered questions.
This document does not go into the details of the various types of algorithms that are deployed by traders.
However, for any market stakeholder, including the
trading venue itself, it is vital to understand the quality
of the market and how it is impacted by algorithmic
trading. It is also clear that this demands more sophisticated analysis and tools today than most have been
used to.
Market liquidity is often measured using the following
three criteria (Bervas, 2006):
•
The tightness of the bid-ask spread. The bid-ask
spread gives an indication of the cost of immediate reversal of a position of standard amount.
•
Market depth. Market depth is the amount of volume one can buy or sell without moving the best
price (i.e. without slippage).
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•
Market resilience. Market resilience measures the
speed prices revert to equilibrium price after a
large order has been executed temporarily moving
the spread.
When measuring market quality it is important to look
at all three aspects above. For example, a simple spread
measure consisting of best bid/ask price does not say
much about the liquidity available at a given trading
venue. Traditional order rate measurements alone
don’t reveal the extent to which the orders contribute
to liquidity in reality.
One of the key functional areas of Scila Surveillance – a
market surveillance system – is its detailed analysis
of market quality. This is not market surveillance in its
traditional sense, but still of outmost importance to
improve understanding of the market. The two main
areas covered by this paper are:
•
Measuring liquidity
•
Understanding where algorithmic trading occurs
and what impact is has
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Measuring liquidity
As competition among trading venues has increased,
so has the number of methods devised to attract
liquidity. Examples include:
•
To fine-tune the parameters that define the trading venue’s market model, such as the level of
market transparency, counterparty information,
tick-sizes, trading method etc.
•
Trading tariffs schemas have grown increasingly
complex in order to attract liquidity. Besides the
actual price list, tariff schemas include more subtle
measures such as giving certain order types low
latency links or other advantages to selected
liquidity providers.
•
Marketing activities
•
Equity participation schemes
While there are many parameters to adjust, the actual
impact from these adjustments is hard to predict since
it varies among different markets and situations. If the
impact from different activities isn’t measured properly, market operators will end up changing parameters
without understanding their true effects.
In order to get an accurate picture of the market quality
of a marketplace, it is necessary to examine a number
of different aspects in more detail:
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•
Liquidity measures where the spread is adjusted
for a fictively traded volume, e.g. showing the cost
of trading 100, 1,000, 10,000 units, etc. This is especially important in fragmented markets where
spreads have tightened while volumes on the best
price levels have become thinner. This renders
standard top-of-the-book spread measurements
more or less useless.
•
Liquidity measures as described above, also adjusted for time.
•
Replenishment times after liquidity events – not
to be confused with events based on informed
decisions. That is, if a large order that temporarily
moves the spread is executed, how long does it
take for market participants to insert new volumes
that re-establish the original spread? This gives a
measurement of the amount of latent liquidity in
the market.
•
Understanding the liquidity replenishment time
might also constitute an important factor when
determining optimal lengths of circuit breakerinitiated auctions.
•
Average order lifetime. The contribution to liquidity by passive orders with a lifespan measured in
milliseconds can be questioned, or at least needs
to be evaluated in a different way than orders with
longer life spans.
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Understanding the impact of algorithmic trading
The previous section described ways to measure
liquidity in a market. This section introduces means
to understand how algorithmic trading contributes to
liquidity. Scila Surveillance utilizes several methods to
detect the occurrence of algorithmic trading in order to
understand its impact on the market.
Algorithmic trading systems utilize highly sophisticated
computer programs to analyze market data based on
advanced mathematic models to generate trading signals. The system can be designed to make own trading
decisions or to just optimize execution of already made
decisions, or both.
The term algorithmic trading covers a wide range of
trading strategies, some well known while others for
natural reasons are non-public and carefully guarded
secrets.
Many types of algorithmic trading are beneficial and
add to liquidity, but there are also harmful variants that
even reduce liquidity. An example might be the presence of “snipers” in a price driven market that in the
long run might drive off market makers by systematically taking advantage of weaknesses in their applications.
Identifying which order books are likely to attract
algorithmic trading
First, the trading venue needs to understand which
order books are potential candidates for hosting algorithmic trading. Two methods for this are:
•
Measuring predictability. Not all order books are
suitable for algorithmic trading. Alpha-creating
algorithms1 aim at consistently generating income
over a large number of trades. To achieve this, the
system tries to predict future prices, for example
with the help of technical analyse s. An order book
with a completely random price process is therefore of little interest.
To determine if the prices for a given security appear
to be random, or seem to have a level of predictability,
Scila Surveillance uses a method developed by Lo and
MacKinlay (1988).
While a price process may appear random when seen
over a longer time span, this might not be the case
over shorter time spans. A strategy that uses a short
position-holding time period may thus find predictability in a security, while a strategy with a longer positionholding time period finds it to be random.
Algorithmic trading can be divided into two main categories: Alpha-preserving and Alpha-creating. Alpha-preserving algorithms are used
when an investor has – through fundamental analysis or in some other manner – reached a trading decision and is about to execute it. Typically alpha-preserving algorithms deal with reducing market impact, thus minimizing slippage when executing the chosen trading strategy.
A trivial example is breaking up a large order into smaller chunks executed over time. Alpha-creating algorithms, on the other hand,
1
include a diverse set of algorithms that by themselves try to create alpha.
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In Scila Surveillance, randomness can therefore be
sought out at several time intervals to determine
which securities a certain type of algorithmic trader is
most likely to trade in.
•
Measuring information asymmetry. Asymmetric
information in a market leads to adverse selection and the possibility for more informed market
participants to make money at the expense of less
informed participants.
Informed traders possess private information that isn’t
available to the general public and enables predictions
of future price movements and spotting of market
inefficiencies. This information could, for example,
come from superior forecasts by advanced algorithms
or superior news sources and can have a significant
impact on the market.
There are several ways to detect information asymmetry in markets. Scila Surveillance uses a method based
on the measurement of the Probability of Informed
Trading that was developed by Easley et al. (1996).
Identifying algorithmic trading
Once it has been established that algorithmic trading
is likely to occur in an order book, the next step is to
identify the parts of the order flow that come from
algorithmic trading systems. Some methods used in
Scila Surveillance for doing this are:
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•
Measuring Order Aggressiveness. It can be assumed that alpha-creating algorithmic traders
possess superior information. It is also the case
that in the presence of more than one informed
trader, the informed traders are expected to trade
more aggressively, according to Back, Cao and
Willard (2000). Thus algorithmic trading can be
detected and categorized by measuring how often
a trader is on the aggressive side of trades.
•
Measuring position-keeping. Analyzing size, sign
(long/short) and the duration of positions can be
used to both detect and categorize certain types
of algorithmic trading.
•
Detecting order flow “anomalies”. A way to
detect informed order flow is to analyze it from a
technical perspective, not directly related to the
algorithms used by the trader. One example is the
habit of some algorithmic trading systems to send
in multiple cancels for the same order, in order to
reduce impact from the inherent jitter (variation in
latency) of the marketplace system.
A similar example is when the algorithmic trading
system submits multiple orders all marked as
“possible duplicate”.
These types of behavior are less likely to be implemented in standard systems used for the routing of
retail, uninformed order flows and therefore work as
identifiers of informed flows.
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Summary
There is no one-size-fits-all solution for how to measure market quality. As trading patterns
change and trading techniques become increasingly sophisticated, the tools used to
analyze market quality must also be upgraded. Only then can the trading venue and
other stakeholders better understand their market.
Various types of algorithmic traders are today a growing and important client group for
trading venues. At the same time, they are questioned by many stakeholders in the
industry due to the potential negative effects they have on the market. It is therefore
critical for any trading venue in today’s highly competitive markets to learn more
about the order flows from these traders, and their impact on the market.
For a more thorough discussion around some of the algorithms used in Scila Surveillance,
see Fredrik Henrikson’s “Characteristics of high-frequency trading” at:
http://www.scila.se/HFT-thesis.pdf
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References
Aldridge, I. (2009). High Frequency Trading: A Practical
Guide to Algorithmic Strategies and Trading Systems.
Wiley Trading, John Wiley & Sons.
Back, K., Cao, C. H., & Willard, G. A. (2000). Imperfect
Competition among Informed Traders. The Journal of
Finance, 5, 2117-2155.
Bervas, A. (2006). Market Liquidity and Its Incorporation into Risk Management. Financial Stability Review
8, 63-79.
Easley, D. K. (1996). Liquidity, Information, and Infrequently Traded Stocks. Journal of Finance, 1405-1436.
Lo, A. W., & MacKinlay, A. C. (1988). Stock Market Prices
Do Not Follow Random Walks: Evidence From a Simple
Specification Test. Review of Financial Studies, 1, 41-66.
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