Data Management for New Trading Opportunities - Sybase

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Data Management for New Trading
Opportunities
September 2010
An industry briefing prepared
by A-Team Group for

Data Management for New Trading Opportunities
Introduction
As high-frequency and quantitative trading techniques mature, trading firms are finding it
harder to make money. Market practitioners are recognizing that speed of market access
alone is no longer sufficient to stay ahead of the pack, as low-latency connectivity enters
the mainstream.
The emphasis is returning to the quality of the trading model. In order to gain an edge,
innovators are using more sophisticated techniques to improve the effectiveness of their
trading models. These techniques range from analysis of unstructured data to generate
an assessment of market sentiment, to the use of emerging machine-readable news
services.
In addition to a focus on the quality of the trading model, firms are using new capabilities
to identify cross-asset opportunities: across linked instruments, linked execution
venues and linked geographies. And they’re applying many of the techniques used in
listed markets like equities, futures and options to over-the-counter markets. The market
has already witnessed firms deploying event-driven and algorithmic models for foreign
exchange trading, and fixed income and derivatives capabilities are emerging.
Key to success in all of these new trading approaches is a sophisticated data management
strategy, as firms are forced to draw upon new data sources to drive their models
and trading applications. These new sources range widely in type: from traditional but
unstructured data, such as text-based company reports, to machine-readable news
(MRN) and unfamiliar data sets used in OTC markets, like synthetic pricing and valuations
models.
This briefing looks at how trading firms are using innovative techniques to take trading to
the next level, and the data management challenges that they need to overcome to be
successful.
An industry briefing prepared by A-TEAM GROUP for Sybase, Inc., an SAP company 2

Data Management for New Trading Opportunities
Current Landscape and Trends
Over the past 18 months, high-frequency trading (HTF) has emerged as a major contributor
to trading volumes both in U.S. equity and listed derivatives markets and, increasingly,
in European markets. The ubiquity of low-latency connectivity and high-performance
trading platforms— often offered by prime brokers to hedge funds and a new generation
of independent proprietary trading outfits—has meant that players small and large have
been able to move swiftly in and out of increasingly fast markets. This, and the emergence
of new types of liquidity providers, like Citadel and Getco, has boosted the proportion
of trading volumes attributed to HFT.
High-frequency trading
essentially involves
the use of high-speed
market access to quickly
take advantage of market
opportunities as they
arise. This often means
scheduling quantitative
models to generate
many small trades, each yielding relatively small incremental returns. Many high-frequency
traders employ statistical arbitrage models, which allow them to identify and execute
on pricing discrepancies between individual securities, sectors or markets.
“As the high-frequency trading segment
enters maturity, emphasis has returned to
the quality of the trading strategy. . ”
Such has been the success of the high-frequency trading community that new entrants
continue to adopt its techniques, aided by the broad availability of the kinds of trading
platforms needed to participate. As a result, some estimate that high-frequency trading
now accounts for as much as 70% to 80% of total equities volumes in the U.S., with Europe
at more than 30% and growing fast as emerging execution venues offer new arbitrage
opportunities. Many expect the developing Asian markets to follow much the same
path.
As the high-frequency trading segment enters maturity, though, emphasis has returned to
the quality of the trading strategy as alpha seekers attempt to wring profit out of a more
competitive and complex trading environment. Hedge funds, prop traders and other users
of quantitative and algorithmic trading models are now looking elsewhere to find an edge.
Increasingly, that involves drawing upon a wider range of data inputs to drive their trading
strategies. Traders are looking beyond traditional data sources— the price data feeds
from the major exchanges and aggregators/redistributors — to find new factors potentially
affecting the value of financial instruments.
Many of these new sources of data are unstructured. That is to say, data is available in
a raw format only, and is not subject to the usual data cleansing, formatting and other
processes typically offered according to a commercial supplier’s data model.
These new data sources are wide-ranging in character. They include raw, text-based
company financial data, web-based news services, social media sites and even feeds
of closed-caption TV coverage. Even where the data is more structured—such as is the
case among the growing number of machine-readable news services—trading firms are
using the new sources in unique ways to gain an edge. By feeding a machine-readable
news feed into a trading algorithm, for example, many firms are coming up with their own
assessments of market sentiment, based on their own reading of news content.
An industry briefing prepared by A-TEAM GROUP for Sybase, Inc., an SAP company 3

Data Management for New Trading Opportunities
Whatever its potential, this explosion of useful data sources is posing major headaches
for data managers charged with helping analysts and traders identify and exploit market
opportunities. The challenges of combining traditional structured data sources with new
and disparate unstructured ones are manifold.
Many would agree the challenge of managing today’s high-speed direct exchange feeds
alone is a significant one. Adding demands for incorporating unstructured data from a
wide variety of sources is adding to the data management challenge facing CIOs and
their staffs.
New data management technologies, though, are presenting financial institutions with opportunities
to harness this data deluge. The ability to embrace unstructured data is helping
firms build sentiment analysis, trade signaling and stock-screening applications that
can take into account text-based information such as company reports and, increasingly,
machine-readable news services.
At the same time, firms are looking to expand the use of techniques they’ve learned from
listed securities—equities, and related futures and options—to other, over-the-counter asset
classes. Particular beneficiaries of this trend are expected to be players in the foreign
exchange and fixed income markets.
New Client Applications
As they strive to differentiate their execution strategies from those of their rivals, trading
firms are drawing upon new techniques and new sources of information. Armed with
access to a broader range of data, they are able to devise new ways of looking at the
marketplace, with a view to giving themselves a unique perspective on the trade decision-making
process.
The adoption of technologies from other industries—most notably from military surveillance,
which processes vast amounts of data—is allowing firms to process information
from some unlikely sources. While many are using familiar unstructured text-based data
like company reports, regulatory filings and the like, others are looking further afield.
Practitioners report the use of sources like websites, social media and even closed-caption
TV feeds.
The latter may represent the obscure end of the data spectrum, to say the least. But
recent months have witnessed the emergence of several asset-based social media sites,
which have grabbed the attention of traders in their respective markets and are being
factored into the broader trade decision-making process. Another area that has seen
perhaps the most significant advancement is in the use of machine-readable news, which
is increasingly behind an array of new decision-support application types.
The analysis of these types of data can create high yields in applications like sentiment
measurement. Here, investment analysts and traders seek to gauge the market’s feeling
about a stock, pair of stocks, sector or index, based on a view of relevant parameters,
rather than on simple price or index values or straight news coverage.
A number of decision-support applications providers, like RavenPack and Alphacet, have
developed models for assessing the impact of market events on a given investment target.
Their applications can be used to measure sentiment by, for example, analyzing the
volume of ‘positive’ vs. ‘negative’ news items on a given stock. The resulting sentiment
An industry briefing prepared by A-TEAM GROUP for Sybase, Inc., an SAP company 4

Data Management for New Trading Opportunities
measure can be fed into trading algorithms often running within complex event processing
(CEP) frameworks, and executed as per the sponsoring broker’s assessment of the
situation.
There are many variants on the use of machine-readable news. Practitioners are using
MRN to drive traditional models, like directional trading signals and other trading algorithms,
and for back-testing their trading strategies. But they’re also using such services,
available from suppliers like Dow Jones, Reuters and Market News International, to drive
stock-screening applications. And more innovative developers are developing custom
applications that, for example, can predict the impact on specific stocks or sectors of
potentially volatile trading days, based on trend analysis.
Indeed, volatility is a recurring theme among trading model developers, particularly as
the days of exploiting volatility per se may be drawing to a close. The widely watched VIX
Volatility Index calculated by the Chicago Board Options Exchange has entered what appears
to be a period of lower values.
This could prove significant, as VIX is regarded as a measure of the market’s expectations
of future volatility. Lower VIX index values typically mean that volatility—the lifeblood
of the high-frequency trader—is heading lower. Should that be the case, it will become
increasingly important to wring maximum leverage out of any volatility-based applications.
As a result, traders are watching, and are programming their algorithms to watch
for any indications of volatility so that they can take full advantage of the opportunities it
presents.
Here, the proliferation of content through social media like Twitter is becoming increasingly
important. By monitoring ‘tweets’, traders’ algorithms are able to pick up on high
volumes of activity of information around a given target stock much earlier than through
traditional news outlets. In a world where milliseconds count, social media can offer traders
minutes’ and even tens of minutes’ worth of advantage over competitors relying on
traditional information sources.
Other emerging decision-support applications include chart pattern recognition and
actionable investment research. Chart pattern recognition tools typically draw upon a
wide variety of unstructured ‘primary’ information sources, including other analytical
applications, broker and other research, and internal back-testing systems to evaluate
how a given security behaves over time versus investment targets. Actionable investment
research, meanwhile, attempts to automate the interpretation of technical, fundamental
and other analytics to generate trading ideas.
To harness the potential of these techniques, players are turning to emerging technologies
such as complex event processing (CEP), which offer the ability to manage and process
vast quantities off data in real time. Being able to respond quickly to market events and
opportunities is a major step forward for financial institutions, which only a decade ago
may have had to rely on overnight (or worse!) batch processing to run the kind of complex
analytics CEP can handle in real time today.
CEP engines are increasingly used by financial institutions to gather intelligence from
events in real time that may be related to specific market conditions or emerging trading
patterns. CEP can allow firms to develop sophisticated tools for defining the processing
and analysis of events, notifications of which may arrive in any variety of message formats.
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Data Management for New Trading Opportunities
An event-driven architecture based on CEP can deliver the resources to assimilate, assemble,
interrelate and analyse incoming messages, creating new high-level messages
that can be applied to analytical applications. As such, these CEP engines correlate data
from multiple sources, and from them derive richer and more complete data sets that can
be analysed more effectively, often resulting in faster decision-making.
The CEP-based infrastructure can be used to identify trading patterns – or individual
events that are exceptions to these patterns – again offering users the opportunity to
respond quickly. As part of the overall picture, firms gain a more comprehensive view on
impacts of market events, backed up by summaries, statistics and trend analysis. Finally,
as a byproduct, the financial institutions is able to create a historical database of standardised
data that can be used to underpin future analytical applications.
One financial institution that recently took advantage of modern CEP-based technologies
to extend sophisticated analysis to its trading operation is London-based Mitsubishi UFJ
Securities International (MUSI). Starting with a blank slate, the firm – part of the Mitsubishi
UFJ Financial Group – had a rare opportunity to develop a trading infrastructure for its
Rates Trading Group that could capture and process tick data and combine it with historically
rich market data.
Taking into account internal IT expertise, MUSI’s system requirements included: fast capture
of all tick data; predicting price movements and identifying arbitrage opportunities;
processing real-time market data; storing massive amounts of data and retrieving this
information rapidly; and quickly analyzing historical trends and relationships.
MUSI embarked on an evaluation of a variety of existing technology products, benchmarking
them to determine how well they met key criteria, including:
• Fast data retrieval
• Rapid implementation
• Capture of real-time tick data
• Efficient storage
• Leveraging existing IT skill sets
• Maintenance and availability of technical support personnel.
Following the evaluation, the firm chose Sybase’s CEP solution Aleri Streaming Platform
combined with the Sybase RAP – The Trading Edition to support decision processes and
allow its 50 rates traders to trade more profitably.
Sybase RAP – Trading Edition is a unique consolidated analytics platform designed to
manage high-volume real-time data feeds as well as massive historical and market data
sets. It offers access to huge repositories of leaf-level market data, and can store additional
data vital to capital markets analytics. The result is greater speed and more efficient
data compression, which is combined with an in-memory database for the capture of
real-time streaming data.
Designed for real-time analysis of streaming data, Sybase Aleri Streaming Platform is a
high-performance CEP engine that can absorb and process high-volume data streams,
such as market data, in real time, applying custom logic to normalize and analyze the
data.
An industry briefing prepared by A-TEAM GROUP for Sybase, Inc., an SAP company 6

Data Management for New Trading Opportunities
For MUSI, the combination offered a number of important benefits. Because Sybase
RAP – The Trading Edition writes to disk frequently, if memory capacity is reached there
is no data loss and the database can pick up where it left off, avoiding the disastrous
scenario of losing a whole day’s data. Performance improved massively, allowing traders
to process and manipulate market data instantaneously rather than waiting minutes for
updates. And the platform’s single, cohesive design allowed traders to develop their own
models and spreadsheets in a centralized system, allowing sharing and collaboration.
Since implementation, MUSI has doubled tthe number of currencies it tracks, allowing a
richer view into market opportunities – without having to add more storage. With Sybase
RAP – The Trading Edition, it has also increased its data retention fourfold, from an initial
estimate of six months to two years. With richer data stores bearing no overhead costs,
MUSI has a solution that enables it to manage its business today and identify new opportunities
into the future.
Migration to Adjacent Markets
Market practitioners are also seeking to apply the expertise they’ve garnered in the equities
markets to other, adjacent markets. This involves both extending the informationintensive
equities analysis to linked markets – where listed corporations may have also
issued other types of financial security – and migrating those techniques to wholly new
areas, such as over-the-counter securities.
Nowhere has the migration occurred more swiftly than in foreign exchange. This shift has
been driven by cross-border trading, particularly in Europe, where the fragmentation of
markets under the 2007 EU Markets in Financial Instruments Directive (MiFID) encouraged
the multiple listing of stocks in multiple currencies. This exacerbated the existing
need to add a currency hedge to cross-border transactions, and encouraged a market for
FX speculation.
The vast number of instruments – stock, warrants, bonds, futures, other derivatives – now
being issued by a single corporation is forcing investors to consider a broader range of
asset classes. It’s no longer enough to trade the stock in isolation; it’s becoming imperative
to consider the impact on the stock of the behaviour of various other securities
issued by the same corporation. This in itself greatly expands the amount of data required
to create a comprehensive view of a particular security.
As well as keeping track of listed instrument values, practitioners also need to understand
how valuations for illiquid unlisted, or over-the-counter, issues are formed. And, possibly
most challenging of all, they need to be sure they have a comprehensive view of the
spectrum of securities – listed or otherwise – that a target corporation has issued, and to
keep that view up to date.
These and other factors originally pushed firms into the use of high-performance trading
techniques in listed futures and options. Indeed, many of the major prime brokers now
offer similar kinds of algorithms and other trading tools for listed derivatives as they do
for plain-vanilla equities. The adoption of fast execution platforms by futures and options
exchanges added to the potential. Now, a similar migration is occurring in cash bond
markets, as interdealer brokers, broker-to-dealer platforms and other bond execution
systems shift to higher-performance systems.
Other cross-asset activity is more sophisticated. Practitioners report the use of arbitrage
techniques between specific equity securities and specific commodities, for example,
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Data Management for New Trading Opportunities
based on the beliefs of a particular portfolio manager or analyst and incorporated into that
manager’s trading strategy.
In all cases, financial institutions are seeking to apply the real-time pre-trade analysis they
use for equity markets, to other asset classes. As electronic trading moves into over-thecounter
markets like foreign exchange and fixed income, firms are adopting many of the
latency, algorithmic trading and analysis techniques from their equities businesses. This
phenomenon is expected to take off over the next few years as OTC markets create the
technology conditions necessary for model-driven and high-frequency trading.
Data Analysis and Management
Challenges for Market Practitioners
Mainstream market data, and the ‘standard’ analytical applications it drives, have become
commoditised. With speed’s importance as a differentiator waning, access to similar trading
models driven by the same data sources makes it difficult or impossible for trading
firms to compete for alpha or executions, or differentiate the services they offer to clients.
As we’ve seen, this trend is forcing the business side to turn to new data sources, particularly
in listed markets. This has driven the growing popularity of certain types of fundamental
data, text-based information and new capabilities like machine-readable news. In
all of these instances, trading firms believe they can create an edge through innovative
use of new data sources.
Machine-readable news is a case in point. MRN is being used for a host of different purposes:
algorithmic execution, portfolio rebalance, real-time risk management and market
abuse detection. It holds the promise of allowing a trader to react to an event in near real
time, rather than wait for days for analysts to publish their assessment of a market or
company-specific event.
One way MRN is being used to improve pre-trade decision-making is in sentiment
measurement. This builds on the belief that news analytics when combined with standard
measures like implied volatility can provide better estimates of future volatility than
implied volatility models alone. Specifically, MRN can be used to help traders adapt their
models – which have traditionally looked to the past to predict future trends – essentially
to respond to the current market situation.
Through the analysis of MRN services, advanced sentiment measurement applications
can determine whether news coverage is generally negative, positive or neutral for a given
stock, industry, segment or geopolitical entity. While this may not be a new use of news –
traders have manually assessed incoming news services for sentiment since their inception
– the automation that MRN makes possible can allow models to respond much more
rapidly. Some estimates reckon it can take 20 minutes for the market to settle on a new
equilibrium price for a stock in response to a market event using manual techniques.
Automated MRN services can also help understand what the quantifiable trend actually
is. For example, in bull markets there are generally twice as many positive stories about
any given company than negative ones. But when stocks are falling, research has shown
there are almost equal amounts of positive and negative news; automated analysis of
MRN feeds can lend credibility to such an assessment under market conditions humans
may find difficult.
MRN is being used in a number of nuanced ways. It can be used to differentiate a model’s
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Data Management for New Trading Opportunities
response to ‘expected news’ – a planned earnings announcement, perhaps, or the latest
employment figures – from unexpected events – a CEO departing or a class-action lawsuit.
Whatever the topic or event, MRN can be tapped to trigger a range of investment actions
based on predetermined ‘what-ifs’ developed by the trader or investment manager.
Indeed, unscheduled news can be based around ‘noise’ in the marketplace about a particular
stock or sector. This can be monitored, assigned a positive or negative sentiment,
and used to drive a trading model or algorithm that identifies opportunities based on such
information. A trading algorithm could be programmed to assign a higher weighting to
negative sentiment than to positive sentiment, for example, based on the trading strategy.
As data managers grapple with the integration of the burgeoning number of MRN services
available into their internal clients’ trading models, their trading operations are looking
beyond these relatively structured data sets to new unstructured sources, such as those
we’ve already discussed.
The pressure is on to be able to automatically process and analyse any kind of information,
not just that sourced from professional news organisations or research analysts,
which come conveniently tagged with quantifiers of sentiment, for example. And increasingly,
trading algorithms and other models will be required to incorporate unstructured
data – from a quarterly financial statement, to the ‘noise’ generated by the blogosphere.
This has implications.
For one thing, the amount of data that needs to be managed by the application will rise
significantly. For another, the complexity of data will grow, based on three factors: a)
speed of incoming data, b) increasing volumes, both in terms of numbers of messages
and the quantity of data per message, and c) complexity of analysis.
Today’s trading algorithms are relatively simple, with many comparing one instrument to
another (IBM relative to Microsoft, say) or to a basket of stocks, a segment or the market
as a whole. But algorithms that once compared one stock to another will expand to take
into account trends and correlations, based on more – and more-complex – data, much of
it unstructured.
Meeting the New Data Management
Challenge
The drivers outlined above make it highly likely that financial institutions will be required to
consume more data going forward and from a broader range of suppliers in a multitude of
different formats.
What are the major considerations for a data management strategy when handling this
wide array of potentially unstructured data
Take the handling of text-based intelligence like news, for example. Tagging and filtering
on the source (supplier) side is one thing, but in order to rapidly take advantage of news
events within a trading system several key considerations must be taken into account by
the data manager.
Before an automated trading application can consume news, news has to be defined. Any
application must be supported by a data management infrastructure that can differentiate
between true ‘news’ – an event happening in real time – and information contained in a
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Data Management for New Trading Opportunities
news format – like a magazine article – that may not be news at all.
Making this distinction, and generally assessing the relevance of real news as it arrives, is
no mean feat. It may require the financial institution to define a kind of hierarchy of event
types that are important to the trading philosophy, execution styles and operational capabilities
in place.
We’ve already identified the challenges of handling official, scheduled news vs. unofficial,
unscheduled or breaking news. While MRN services can help in this respect, there remains
the data management issue of integration with existing internal and external news
sources to create and support the kind of comprehensive and consistent data model
required for complex analysis. Integration may extend to incoming real-time market data
services, reference data repositories and internal databases of holdings, clients and risk
parameters.
Drilling down further, data managers may be required to identify the context for an incoming
news story. A negative story may in fact be ‘positive’ in the sense that market expectations
were worse. Better-than-expected bad news can spell an opportunity, and allow a
financial institution to differentiate by offering its unique ‘take’ on the situation.
Understanding the context of this opportunity is another data challenge. Is it possible to
understand whether the opportunity is itself a reaction to news or other incoming data
event Or is it a result of a firm’s own analysis, allowing a trader to pre-empt the market
More intelligence can be gleaned from market conventions and rules. For example, US
exchanges routinely report whether an order was initiated as a buy or a sell. This can be
useful to a trading model that, say, is seeking to establish whether a security is trending
up or down in markets where such data is not available.
And let’s not forget the language issue. It’s true that many financial information services
are available in English. But as practitioners cast their nets more widely, highly targeted
foreign language services will become increasingly important. Natural Language Processing
can help. But it’s important not to understate the ‘training’ it requires, and the ongoing
resource required to keep it up to date.
In their 2009 book, The New Data Imperative, Sybase’s Dr. Raj Nathan, Irfan Khan and
Sinan Baskan outline 10 core infrastructure needs that must be fulfilled by financial institutions
seeking to address the broad and varied analytical requirements of their trading
operations:
1. A multithreaded asynchronous playback engine that can precisely replicate the
original timing between market events or accelerate and decelerate the data stream
across multiple instruments simultaneously. Data playback should also be able to
be paused/resumed and even reversed to step back in time for strategy debugging
purposes.
2. Being configurable to receive inbound order messages, match orders according
to user-defined matching logic and trading rules (trading sessions, order types,
validation, prioritisation logic) and deliver partial executions, fills, cancels, cancel/
replaces or unfilled open orders.
3. Support for complex market data messaging structures, such as full order
books, object-based messaging, client-side caching and time-series data.
4.
Ability to aggregate multiple data sources into a centralised book or montage
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Data Management for New Trading Opportunities
while maintaining the ability to treat each data source differently with respect to its
equality, timeliness and distribution latency harmonics.
5. Scalability across grids/clusters to quickly test computationally intensive, highfrequency,
high-volume trading strategies against massive high-frequency datasets.
6.
Ability to combine playback/simulation across multiple assets simultaneously.
7. Granular control over the playback speed of historical data (i.e., playback at
actual speed vs. multiples of real-time speed).
8. A means of collecting real-time data live, as well as the ability to integrate/import
historical market data (either collected internally or obtained from a third party).
9. Ability to process historical data of different quality and granularity levels, from
fully transparent (complete history of market depth and trades) to very coarse (best
bids and offers or trades only).
10. Capacity to run trading simulations in either live mode with real-time market
data or historical mode with previously stored data.
The list is useful as it helps make the case for a shared data management platform that
can be extended from the trading environment to cover post-trade activities like transaction
cost analysis, performance attribution and risk analysis. Ultimately, such a platform
could be used to apply a broad range of data sets – real-time market data, forecast estimates,
fundamental data, transaction data, product data, emails, historical market data,
customer data, index data, reference data and more – to the entire trading lifecycle, from
pre-trade analysis through to back-office clearing and settlement.
Indeed, the Sybase authors’ vision suggests: “When a continuous data flow is established
through the organisation from internal and external data sources to a shared data services
platform and access to data flow along the path is enabled on a request and real-time
basis, all users can have a single view of the state of the markets.”
How Sybase Can Help
Sybase Complex Event Processing (CEP) enables rapid application development for
quickly building and deploying new applications that derive insight from streaming event
data, enabling instant response to changing conditions. Sybase CEP technology users
report that building event-driven data analysis with Sybase’s CEP tools cuts development
time and effort by anywhere from 67% to 85%.
It represents a third option to the perennial ‘buy vs. build’ debate, by providing the flexibility
and fit of a custom solution at the lower cost and reduced implementation time of
an off-the-shelf solution.
Sybase RAP – Trading Edition is a unique consolidated analytics platform designed to
manage high-volume real-time data feeds as well as massive historical and market data
sets. It offers access to huge repositories of leaf-level market data, and can store additional
data vital to capital markets analytics. The result is greater speed and more efficient
data compression, which is combined with an in-memory database for the capture of
real-time streaming data.
Designed for real-time analysis of streaming data, Sybase Aleri Streaming Platform is a
high-performance CEP engine that can absorb and process high-volume data streams,
such as market data, in real time, applying custom logic to normalize and analyze the data.
An industry briefing prepared by A-TEAM GROUP for Sybase, Inc., an SAP company 11

Data Management for New Trading Opportunities
About Sybase
Sybase, Inc., an SAP company, offers highly secure,
high-availability software solutions that meet the
trading industry’s needs for intensive transaction
processing, advanced analytics and modeling. These
solutions capitalize on cutting-edge technological
advances, such as real-time risk analysis (Sybase
RAP - The Trading Edition) and complex-event
processing (Sybase CEP), to allow financial services
firms to gain a competitive edge in capital markets.
Today, Sybase customers include a number of the
world’s largest financial institutions. And every day,
tens of millions of Wall Street trades run on Sybase
technology. For more information, visit: http://www.
sybase.com/capitalmarkets
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