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Article Listing | Search Articles | More Articles in  | More Articles by Medy Agami magami@celent.

What If Real Time Pre-Trade Price Discovery Became a Reality?

by Medy Agami magami@celent. - 03/05/2012
 
"Executive Summary
The US corporate bonds market is significantly large but relatively illiquid.
The Financial Industry Regulatory Authority (FINRA) shows
issuance levels estimated to be $813.1 billion, with the total market
value of outstanding corpor"
 

Despite this, daily trading volume stands
at around US$16.4 billion, indicating relatively low secondary trading
activities. With such a significantly large yet relatively illiquid market,
this creates the necessity for greater transparency.
Pricing US corporate bonds has historically been dominated by fragmented
data sources, retail bond trading platforms, and broker quotes
via both runs and messages and over the phone to clients. Currently,
sell side and buy side price aggregators are largely based on either
traded prices (post-trade data) that are at least 15 minutes old or based
on indicative pre-trade prices that are largely broker-dealer centric.
The quotes are neither independent nor streamed live, and certainly
not real time.
In this research, we profile an innovation by Benchmark Solutions,
which is bringing the US corporate bond market to a pre-trade transparency
era. The main analogy that went into building this platform
was making the system generate the closest possible pricing to what
can be done by human traders. The firm’s Market Calibrated Framework’s
model is unique in being a dynamic, continuously self-learning
system combined with complex event processing capabilities.
With the advent of this potentially disruptive pre-trade, real time pricing
technology, Celent anticipates several trajectories that introducing
such a platform can take in terms of bond trading frequency, turnover,
and transparency:
.. Buy-side fixed income trading desks and firms can exploit this
technology to help traders get smarter by aggregating various
pricing sources on a trader’s desktop in one concise view and
informing the trader of what is happening in the CDS market, and
what has happened overnight.

.. Sell side market-making functions can utilize this technology as
an automated front office pricing tool which can complement (or
even displace in the long term) trader responsibilities for marketmaking
and price/quote generation.
.. Middle and back office employing the pricing data as primary or
secondary valuation sources, for the purpose of risk management,
IPV, model validation, and valuation functions.
.. Trading venues or platforms integrating Benchmark's technologies
in conjunction with, platforms’ own pricing engines.
.. Lastly, regulators or regulatory authorities leveraging such a tool
to aid in monitoring and surveillance activities in order to preserve
orderly markets.
Overall, Celent anticipates technologies such as what Benchmark
Solutions is providing to spur further growth and possibly enhance
secondary trading liquidity in the markets it is applied to. Depending
on how the technology is positioned, buy side firms could
further reduce reliance on sell side-centric prices/quotes. Based on
anecdotal evidence, first mover disruptive technologies could
attract liquidity, as early adopters come in to utilize new pre-trade
pricing technologies to identify arbitrage and untapped trends to
exploit market inefficiencies and gain profit. Ultimately, institutional
investors can benefit because they get more efficient pricing,
and in turn tighter spreads. Beyond corporate bond and credit markets,
this new technology can be applied to other fixed income
markets (such as Municipal bonds), as well as OTC derivatives.
In the longer-term, as with any collection of potentially disruptive
technologies, the full impact could be difficult to call. Events and
adoption rates could take a prolonged time before the technology
has significant effect on established companies, usually due to the
power and/or apathy of incumbent firms. It may even be difficult to
recognize in its current incarnation or form. Hence, our advice is to
watch this space, and watch it closely.

Introduction
Most corporate bonds trade in the over-the-counter (OTC) market. Market
participants are increasingly using electronic transaction systems
to assist in the trade execution process. Some bonds trade in the centralized
environments of the New York Stock Exchange (NYSE) and
American Stock Exchange (AMEX), but bond trading volumes on
exchanges are small. The OTC market is much larger than the
exchange markets, and the vast majority of bond transactions, even
those involving exchange-listed issues, take place in the OTC market.
That being said, there is no exact determinate for what bonds trade at
or a limit for what a dealer can ask for a bond. This is precisely the
conundrum for regulators; they see two different buyers get different
prices, so they try to come up with ways to change that.
Without access to fair, independent pricing to guide pre-trade analysis,
corporate bond investors are left to make multimillion dollar investment
decisions based upon sparse observations of current market
conditions. Academic research shows that this OTC market structure
has not appreciably changed in decades. Other research addresses the
role increased transparency might play, and although there are competing
theories, some confirm that increased transparency leads to
more liquidity and lower transaction costs.
The US corporate bonds market is significantly large but insignificantly
liquid. The Financial Industry Regulatory Authority (FINRA) shows
daily trading volume estimated at US$16.4 billion. Issuance for 2011 (up
to Q3) was an estimated $813.1 billion. The total market value of outstanding
corporate bonds in the United States was approximately $7.7
trillion as of Q3 2011.
Figure 1 shows the average daily trading volumes as they compare to
the outstanding volumes. The figures are not even remotely close to
each other, showing how illiquid this market really is. Another evident
factor that can be derived from this chart is that as more and more corporate
bonds are issued, trading volumes tend to remain unchanged.

Before the introduction of Transaction Reporting and Compliance
Engine (TRACE), it was common for US corporate bond investors to rely
on the previous day’s closing prices from one of the end-of-day (EOD)
pricing services to make the present day’s trading decisions. TRACE has
provided a mechanism for current information to be introduced in the
decision process. TRACE has significantly improved transparency of
this market that was traded in the dark, for the most part. However,
TRACE reports transactions for the limited subset of universe that
trades, but this information is subject to a delay of up to 15 minutes;
therefore TRACE is a post-trade transparency provider.
In this report, Celent examines a new technology in which a price discovery
engine generates prices for every TRACE-eligible bond every 10
seconds, bringing the US corporate bond market into a new era of pretrade
transparency. We will begin with an overview of the current market
structure as it relates to market size, pricing methods,
transparency, and liquidity trends. Next, Celent will describe the new
pre-trade pricing technology, what it is, and how it works. Celent will
then draw a potential perspective of market change that this technology
will bring to the industry, as well as how market segments can
adapt to such a new technology. We conclude with a discussion of how
this technology can be implemented into other capital market asset
classes.
Figure 1: Daily Trading Volumes Vs. Outstanding Volumes
Source: FINRA
US Corporate Bonds
16.4
11.8
18.9
16.7 16.9 16.8 16.3
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
2004 2005 2006 2007 2008 2009 2010
Outstanding Volume ($bn)
0
5
10
15
20
Daily Trading Volume ($bn)
Outstanding Volume ($Bn) Daily Trading Volume ($Bn)

US Corporate Bond Market Overview
Market Structure and Size
The US corporate bond market is predominantly an over-the-counter
market, with broker-dealers being the primary market-makers through
voice and electronic broking. A small percentage of this market is
transacted through exchanges, NYSE and AMEX, but most trades taking
place on exchange platforms are retail size (less than 100 bonds;
$100,000).
The US bond market remains the largest in the world by a considerable
margin, accounting for approximately 50% of global debt outstanding.
Corporate debt outstanding currently stands at US$7.7 trillion, or 21%
of all US fixed income securities by outstanding notional. Corporate
bonds, however, remain one of the most illiquid segments of the US
bond market. As of 3Q2011, average daily traded volume was just
US$16.4 billion, representing a little more than 2% of all fixed income
activity.
What Determines Liquidity and Pricing
The most important determinants of bond pricing and trading are:
1. Issue Size and Age. Time since the bond was issued. Trading
volume declines substantially as bonds become seasoned and
are absorbed into less active portfolios.
2. Activity in Issuer’s Stock. Bonds of companies with publicly
traded equity are more likely to trade than those with private
equity, whereas public companies with more active stocks have
more traded bonds. Furthermore, bonds with companies with
publicly traded equity expect stock activity and bond liquidity
to be positively related.
3. Credit Risk. Uncertainty concerning value is likely to be higher
for lower credit quality issues. Speculation about changes in the
bond’s credit quality induces more trading.
4. Interest Rate Risk. Differences in investors’ forecasts result in
more speculative trading in the highest duration issues. The
price of fixed income instruments is directly affected by
changes in interest rates, so there is more active trading when
interest rates are higher.

5. Price Volatility. Trading volume is positively affected by return
shocks, because price volatility reflects differences in investors’
opinions.
6. Macroeconomic Conditions. Financial market conditions influence
bond trading as investors optimize and rebalance their
portfolios in light of new information.
7. Embedded Options. This implied insurance is expected to
reduce the interest rate effect on bond prices, hence reducing
price-induced trading.
Evolution of US Corporate Bond Market
Well-functioning security markets provide investors with liquidity.
However, the terms “liquidity” and “transparency” are broad and somewhat
elusive concepts, used to describe multiple properties of trading
in security markets.
Industry Transparency Initiatives
Regulatory bodies such as the Securities and Exchange Commission
(SEC) and the International Organization of Securities Commissions
(IOSCO) have long based their actions on the belief that greater transparency
in the credit markets will increase efficiency, fairness, and
quality. IOSCO, for example, believes that regulation should promote
transparency. For years, the TRACE regulators in the credit markets
took a “buyer beware” approach to corporate bond trading (essentially,
play at your own risk). While claims of outrageous dealer markups
were investigated, and in some cases led to fines and bad PR, investors
were left more or less at the mercy of brokers.
On January 23, 2001, the SEC approved the first major transparency initiative
in the OTC secondary corporate bond markets. A year-and-ahalf
later, after considerable industry input and negotiation, the
National Association of Securities Dealers (NASD), now known as the
Financial Industry Regulatory Authority (FINRA), launched the first
phase of a three-part initiative mandating that all broker-dealers
report the prices of corporate bond trades to its TRACE.
The pre-TRACE bond market was fragmented as an opaque market.
Previously, since there was no central quotation system, it may have
been possible to complete a sizable bond purchase with a single phone
call to a dealer that held sufficient quantities of the bond in inventory.

Currently, specified details of trades in virtually all US investmentgrade
and high-yield corporate bonds must be reported to TRACE
within 15 minutes of their actual occurrence. These trade details are
then disseminated to market participants via the FINRA website or via
subscription from third party vendors.
TRACE was intended to increase confidence in financial markets and
institutions by ensuring that bond prices are fair and efficient for all
market participants. The price transparency that TRACE brings is
meant to prevent bond dealers from charging mark-ups that might
otherwise be excessive. In principle the intent was to bring fairness to
the market, but trading cost was reduced as a byproduct.
However, the post-TRACE environment still involves communications
with multiple dealers, and delays as dealers search for counterparties.
The initial response to TRACE was that market-makers carried less
product, and it became more difficult to locate bonds for purchase in
the post-TRACE environment. Bond dealers either reduced expenditures
for research regarding bond valuation or stopped providing the
research to customers, instead using it for proprietary trading. The easiest
way to cut expenses in the wake of lower bid-ask spreads was to
reduce the number of analysts on the payroll. Some bond dealers (for
example, Citibank) no longer provided external research on the corporate
bond market. Additionally, the strategic decision to focus on
opportunities on the derivatives side led to personnel movements and
reduced research efforts.
Towards Real Time Pricing?
Bearing in mind that not all transparency is equal, there is a colossal
difference between the impact of pre-trade and post-trade transparency
on liquidity in the OTC market. In a world of abundant and rapid
communication, where price transparency and electronic exchanges
exist for almost every other capital market asset class, it is disquieting
that no such comprehensive source of reference prices exists for a
market as important as US corporate bonds.
Today, although not compiled into one platform, there are many
sources of price transparency publicly available including Bloomberg,
Market Axcess, and broker quotes via both “runs” and messages and
over the phone to clients including institutional investors and retail
stockbrokers. Keeping in mind that corporate bonds operate on a
request for quotes basis and are an illiquid market, the amount of pretrade
price transparency is in proportion to individual corporate bonds’
liquidity and trading frequency.

In an increasingly electronic and transparent market, margins will
continue to be compressed and it will be difficult for brokers, buy side
firms, and investors to build and maintain pre-trade pricing systems
on their own unless there is a sufficient scale to justify an in-house
investment and, more importantly, vast financial and technological
expertise. One possible solution would be for a third party platform to
provide pre-trade pricing and transparency, with the aim of solving the
issue for an entire market.

What Would a Pre-Trade Price
Discovery Mechanism Look
Like?
On a higher level, a price engine streaming prices for US corporate
bonds in real time would reflect new information in values across the
yield curve as the engine is updated. Automating a trader’s train of
thought when pricing US corporate bonds, looking at various factors
that affect bond prices such as US Treasury interest rates, trades in
bonds from the same issuer, credit ratings, industry pricing trends, and
transaction flows. Going into more depth, the engine would require a
network with a node for every factor, TRACE-eligible bond, and CDS. All
these factors would be connected by dependence to the bonds, somewhat
related to correlation, defined as the notion of how one thing
influences another.
In the formal mathematical definition, this network would be a complex
dynamic system. It obtains millions of pieces of data throughout
the day, and every piece of data that comes in influences and propagates
the system on the basis of the dependence network, keeping in
mind that data could come in simultaneously or asynchronously. The
engine in turn would converge with every piece of input information to
give a price for every bond covered by the engine. In regards to reference
data, the large amount of noise would require much effort spent
on separation which would be done by using event statistical techniques
to deal with signal to noise ratio and problems.
Algorithm and Functionality
The engine acts as an observer, monitoring and drawing inference
upon all information that affects the credit market and bond prices. At
that point the engine incorporates all the different information to
come up with a price. In other words, the information workflow traders
do at their desks is modeled automatically by the system.
There are numerous approaches for utilizing parameters that affect
the credit market and bond prices; every trader has a slightly different
way of pricing bonds, and they favor and prioritize some factors over
others, and that’s why there is a market where some traders are very
successful and some aren’t. That being said, there is no systematic
workflow definition of how traders draw inference; everyone has a different
way of inferring the consequence of moving different pieces of
information. The main problem is to find the hidden relationships that
are contingent, and decide which factors are more important.

In a way this system would replicate the algorithm for equity market
pricing engines, constantly learning and fine-tuning models exactly
the same way a trader thinks and reteaches himself to adapt to market
changes. The model used in the engine would continuously be tested
to make sure that it is up to speed with the current market posture.
So how would prices stack up with current sources of market inputs?
Once the engine is bought up next to an actual trader’s pricing, specifically
during volatile times, as a side-by-side analysis the system would
price the bonds exactly the same way the trader does, but with a feature
that is beyond any trader’s capability: evaluating all available
observations.
The system’s algorithm would be designed to understand a particular
bond’s maturity, rating, and liquidity level of the issue. Additionally it
would define the bid-ask spread. The system would also be designed to
understand and interpret the data like a trader would in determining
an actual bid-ask spread; consequently the bid-ask spread becomes a
variable that is based upon different dynamics. The system would be
incapable of capturing every single trade, nor would it attempt to do so;
instead it predicts, with an affective bid-ask spread, the price point of
the next institutional, in particular interdealer, trade.

Case Study of Benchmark Solutions:
Implementation of a Pre-Trade
Pricing Technology
With more than US$40 million invested in human capital and technology
to date, Benchmark Solutions has created The Market Calibrated
Framework engine to provide the US corporate bonds market a fully
independent, streaming, market-driven pricing. Not to be mistaken
with other market data vendors that aggregate prices supplied by large
dealers, Benchmark’s proprietary technology carefully analyzes the relevance
of 12 million market inputs per day to generate unbiased prices
with 120 million market outputs, updated every 10 seconds, offering
real time access to market-driven pre-trade pricing intelligence. In
essence, its algorithms think like an experienced trader by emulating
the process traders go through when pricing bonds, with the ability to
synthesize millions of market inputs dynamically.
The solution shows how bonds and credit default swaps are performing
now, with 10-second updates, the ability to view bond and CDS
relationships side by side, and analytics and drivers behind every price
Figure 2: Benchmark Algorithm
Source: Benchmark Solutions

movement, to provide the platform user a comprehensive overview of
real time credit market activity in a single screen, taking out stale trade
data, delays, time-consuming legwork, or guestimation.
The US credit markets are highly interconnected; sharp movements in
one bond’s price have a ripple effect on other bonds from the same
issuer, bonds from other issuers in the same industry, and so on. Similarly,
changes in volatility and shifts in the markets’ attitudes around
credit risk, as reflected in CDS quotes, have broad implications for
prices across the entire asset class. Through estimation of effects
exhibited in the available market data, Benchmark’s The Market Calibrated
Framework is able to construct a more complete picture of the
market, providing a credible foundation for establishing the next price
for each instrument. Adjustments are made to account for not only
changes in observed levels of yields, credit spreads, and volatility, but
also to account for mismatches in the timing of each observation.
How Does It Work?
Benchmark’s Quantitative Research team states that the term “model”
is used ubiquitously in quantitative finance. The Benchmark Framework
is not just a set of closed-form formulae or a multifactor model
that takes a set of inputs and deterministically computes a result (e.g.,
a standard option or interest rate pricing model). It is not just a Monte
Carlo simulation model that computes a distribution of scenarios and
then statistically analyzes this distribution for an approximate result.
Both these types of models are used internally, but they do not describe
the Framework’s fundamental dynamics. It is, in fact, better understood
as a complex dynamic or statistical learning system, one that in
this case generates market-calibrated prices every 10 seconds for every
TRACE-eligible bond and associated CDS curve.
Market Calibrated Framework Use Case
Imagine a bond trader sitting at his desk throughout the day as he
incorporates a myriad of data points. For the bonds he trades, every
time a new piece of data comes to the market he evaluates its impact.
Consider, for example, a trader in Ford bonds. If the Treasury curve
shifts, he will shift his view of entire Ford curve. If a specific five-year
Ford bond moves, he may shift his view of the other Ford bonds. If the
Ford CDS curve moves, he may alter his view of the Ford bond curve. If
a GM issue moves, he may decide that a corresponding Ford issue
changes since they are in the same industry. In a sense, he is an information
processing system for the small universe of Ford bonds he
trades.

Analogous to this trader, the Benchmark Market Calibrated Framework
can be thought of as a network in which each point in the network represents
a bond, CDS, or other factor that might have influence. The
points are connected by a “dependence relationship” in that a change
to one point can influence another. These points can also be the traditional
models of quantitative finance noted above or other applicable
data. As data comes into the system, the consequences are propagated
throughout the network. Since each day upwards of 12 million data
points are incorporated, and more than 120 million intermediate data
are calculated, many “waves” of propagation can be operating simultaneously
as the system settles on prices every 10 seconds. Alternatively,
the Framework can also be characterized as a statistical learning system
which dynamically finds “hidden” prices for every point. In
particular, these prices are predictive in that they are found upwards of
15 minutes before any corroborating TRACE print is available.
This kind of model is known formally as a complex dynamic system. It
is fundamentally non-deterministic and incorporates advanced statistical
techniques for dealing with noise, inference, and dampening. The
hard technical problem is, for every point in the network, to converge
to a price that is consistent with all observed data, and that is exactly
what Benchmark claims the Framework does in the form of the BMark
real time price.
The action of the Framework is illustrated in Figure 3, a chart of the
Ford 6.5’s of ’18 from September 20 to October 20 of 2011, where the
magenta line is the BMark Real-time price, the purple band is the
BMark bid-ask spread (both output by the Framework), and the colored
dots are various types of trades used by the Framework as one type of
the input data.
Figure 3: Ford 6.5’s of ’18
Source: Benchmark Calibrated Framework

Prices started to fall around the middle of September as concern
increased that troubles in Europe would lead to another global economic
slowdown and possibly a double-dip recession in the US. This
sell off was reflected in Ford’s five-year CDS spreads, which widened
25% from mid-September through October 4, peaking at 570bp at the
end of that day. At the end of October 4, the bond was yielding 6.04.
The first trade on October 5 at 8:50 a.m. was reported at 98.5, a 74bp
jump in yield, and the next trade at 9:01 a.m. was at 100.0, another
46bp jump. However, by 10 a.m. news of the a possible ratings upgrade
reached the market, the cost of CDS protection started to ease, and
prices for the bond started to recover.
A Ford bond trader sitting at his desk would have seen all this information
as it came in and incorporated it into his pricing. A caller asking
for a price at 9 a.m., one minute before the trade printed at 9:01 at
100.0, would presumably have been quoted a bid-ask spread around
100.0. Analogously, the Framework incorporates millions of such data
points and gives predictive prices for all TRACE-eligible bonds and
associated CDS curves.

Market Impact
Currently, sell side and buy side price aggregators are largely based
on either traded prices (post-trade data) that are at least 15 minutes
old or based on indicative pre-trade prices that are largely brokerdealer
centric. The quotes are neither independent nor streamed
live, and certainly not real time.
With the advent of a potentially disruptive pre-trade, real time pricing
technology, Celent anticipates several trajectories that
introducing such a platform can take in terms of bond trading frequency,
turnover, and transparency.
.. Buy side firms: for price discovery and analysis (to validate and/
or arbitrage against broker quotes). For example, buy side fixed
income trading desks and firms can leverage this technology to
help traders get smarter by aggregating various pricing sources
on a trader’s desktop in one concise view and informing the
trader of what is happening in the CDS market, and what is
happening overnight (instead of the trader having to go to five
or six different sources).
.. When proved credible, such platforms could become the norm
for buy side firms when pricing US corporate bonds, so that
when they approach a dealer with a trade they can refer to the
Market Calibrated Framework price as a benchmark price; hence
the firm’s name Benchmark Solutions.
.. Sell side market-making functions: as an automated front
office pricing tool which can complement (or displace in the
long term?) trader responsibilities for market-making and price/
quote generation. These technologies could also aid the trader
in identifying market dynamics and veiled trends, whether
affirming his manual price estimation, or challenging his predictions,
to alert him of untapped pre-trade information sources
that he failed to utilize. In other words the system provides a
reality check.
Additionally, firms could consolidate this technology with their
own platforms to gain an advantage by feeding the pricing
information generated by this engine into algos or black boxes.

.. Middle and back office price validation and marking: as primary
or secondary valuation sources, risk management, IPV, model validation,
and valuation functions. For instance, this technology can
become an industry standard for risk managers when setting order
boundaries with traders.
.. Another potential application could be to use this as a risk control
system by setting guard rails using the prices generated. We also
foresee risk managers wanting to utilize this technology more
than traders, taking firms out of the dark in these trillion-dollar
markets. The technology also uncovers a lot of hidden correlations
in risk movements, providing risk managers with more market
intelligence and allowing portfolio managers to hedge more risk. A
key feature of the Market Calibrated Framework is the Attribution
screen, which breaks down a movement of a bond into five components,
so a risk manager can understand the inter-relationship
between those components and other instruments owned.
.. Trading venues or platforms: to be used as, or in conjunction with,
platforms’ own pricing engines. We raise a few possibilities here.
Could this be integrated into interdealer bond trading platforms?
Or institutional fixed income trading applications (i.e., Pershing
Bond Central)? Or even exchanges (i.e., NYSE Bonds)? That would
be a matter of identifying the opportunity cost; who is willing to
pay to integrate it? Which platform to choose, and what features
can be fine-tuned or added? How long would it take? However, as
these vendors look to differentiate themselves from competitors,
introducing this as a pre-trade filter can be a viable option.
.. Regulators or regulatory authorities: to aid in monitoring and
surveillance activities in order to preserve orderly markets.
Overall, aggregating and structuring data in one place in such a clear
fashion can benefit counterparties in making more efficient markets.
Firms that are watching how the equity market has evolved are at the
most strategic position, since they have seen such developments put
them ahead. Next in line are firms that could leverage this technology
to increase their market share (after the algorithms/data behind this
technology proves reliable).
Depending on how the technology is positioned, buy side firms could
further reduce reliance on sell side-centric prices/quotes. Based on
anecdotal evidence, first mover disruptive technologies could attract
liquidity, as early adopters come in to utilize new pre-trade pricing
technologies to identify arbitrage and untapped trends to exploit market
inefficiencies and gain profit. Ultimately, institutional investors can
benefit because they get more efficient pricing, and in turn tighter
spreads.

Application of the New Technology Beyond
Corporate Bond and Credit Markets?
.. Fixed income markets. From a wider market standpoint,
what does this mean for fixed income in terms of risk? This
engine proposes a day-over-day price change into mechanisms
that will unlock tremendous abilities for much better
managed risk. This technology and the model behind it can
be applied to address some of the liquidity and transparency
issues in other markets, such as Municipal Bonds.
.. OTC derivatives markets. The same initiatives that are
addressed by this new technology in the fixed income market
are being heavily debated in the derivatives market
between market players and regulators as to what is going to
happen with swap execution facilities (SEFs) and pricing.
Legislative bodies in the US and Europe are moving to
increase regulation of the OTC derivatives market. These
global financial reform initiatives seek to achieve three key
objectives: increase transparency; improve market efficiency;
and reduce systemic risk.
The most immediate asset class that is adjacent to the fixed
income market is the OTC derivative markets.For now, the
idea of a price discovery engine for the fixed market is that it
is easier to apply, scrutinize, and identify shortcomings,
compared to applying similar procedures to the OTC derivatives
market. However, in the long-run, the potential for
applying this technology for bilateral/illiquid markets stands
as an interesting prospect.

Conclusion
Pricing US corporate bonds has been dominated by fragmented sources
including Bloomberg, Tradeweb, Market Axcess, the NYSE retail bond
trading platforms, and broker quotes via both runs and messages and
over the phone to clients. The amount of available pre-trade price
transparency is in proportion to the individual corporate bonds’ liquidity
and trading frequency. Therefore, pre-trade price transparency and
liquidity/trading frequency are highly interrelated. Today there are
numerous fixed income post-trade and end-of-day analytics platforms,
but pre-trade and post-trade transparency are not equal, so why is
there no unbiased benchmark pre-trade price discovery engine for US
corporate bonds?
Regulators, technology vendors, and dealers commonly make an
excuse that the market is too fragmented/illiquid for pre-trade transparency
to do much good. Furthermore, because dealers are the main
market-makers to this illiquid market, they don’t want their trades
published in real time or as pre-trade information. On the contrary,
with pre-trade transparency comes much good in terms of more market
transparency, liquidity, increased trading frequency, and, as a
byproduct, a visually mathematical way to hedge fixed income risk.
From our perspective, this innovation by Benchmark Solutions has
brought today’s US corporate bond market to a pre-trade transparency
era. The main analogy that went into building this platform was how
to make the system generate the closest possible pricing to what is
humanly possible by traders. The Market Calibrated Framework’s
model is unique in being a continuously self-learning complex
dynamic system combined with complex event processing capabilities.
No buy side firm would spend the time, money, or human capital to
develop such a product, but Benchmark Solutions has ambitions and is
trying to fix the problem for the whole market rather than just one
market participant. For now, no other technology vendor has
attempted to evolve into this space, leaving Benchmark with a first
mover’s advantage. Even if a firm were to start today, we expect it
would take at least two years for a competing technology to emerge.
Benchmark’s clients attest that the firm is quick to incorporate suggestions
into the platform, have a good first mover advantage, and expect
no short-term competitors to emerge which adds more value to Benchmark’s
unique model.

From Celent’s perspective, as with any collection of potentially disruptive
technologies, the full impact could be difficult to call. Events and
adoption rates could take a prolonged time before the technology has
significant effect on established companies, usually due to the power
and/or apathy of incumbent firms. It may even be difficult to recognize
in its current incarnation or form. Hence, our advice is to watch this
space and watch it closely.

Leveraging Celent’s Expertise
If you found this report valuable, you might consider engaging with
Celent for custom analysis and research. Our collective experience and
the knowledge we gained while working on this report can help you
streamline the creation, refinement, or execution of your strategies.
Support for Financial Institutions
Typical projects we support related to post-trade transparency include:
Vendor short listing and selection. We perform discovery specific to
you and your business to better understand your unique needs. We
then create and administer a custom RFI to selected vendors to assist
you in making rapid and accurate vendor choices.
Business practice evaluations. We spend time evaluating your business
processes. Based on our knowledge of the market, we identify
potential process or technology constraints and provide clear insights
that will help you implement industry best practices.
IT and business strategy creation. We collect perspectives from your
executive team, your front line business and IT staff, and your customers.
We then analyze your current position, institutional capabilities,
and technology against your goals. If necessary, we help you reformulate
your technology and business plans to address short-term and
long-term needs.
Support for Vendors
We provide services that help you refine your product and service
offerings. Examples include:
Product and service strategy evaluation. We help you assess your market
position in terms of functionality, technology, and services. Our
strategy workshops will help you target the right customers and map
your offerings to their needs.
Market messaging and collateral review. Based on our extensive experience
with your potential clients, we assess your marketing and sales
materials—including your website and any collateral.

Dodd-Frank and EMIR Derivatives Reforms: Impact on Derivatives Pricing,
Valuation and Technology Expenditures
November 2011
Fixed Income Trading in the US: Barriers to Automation
October 2011
Electronic Bond Trading: Reaching the Tipping Point
April 2008
TRACE: Overview and Implications for an Evolving Credit Market
May 2006
 
 
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