Fixglobal.com Global Trading Q4, 2018 Issue #68

Q4 2018 • Issue #68

Fixed Income TCA: A Competitive Differentiator Laurent Albert, Global Head of Execution, Natixis Asset Management Finance EASTSPRING INVESTMENTS, JM ASSET MANAGEMENT LTD, JP MORGAN AM, NASDAQ, NORTHERN TRUST CAPITAL MARKETS, VOYA INVESTMENT MANAGEMENT, CANTOR FITZGERALD ALSO INSIDE :

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GlobalTrading’s Editorial Think Tank Dear Readers, One sign of the evolution of electronic trading is the way we continue to refine the concepts which have served us successfully for years. In a maturing industry, we recognize that the original solutions we devised were built for problems that existed years before.

We build, test, refine and rebuild.

Bill Hebert Co-Chair, Global Member Services Committee, FIX Trading Community

Carlos Oliveira Brandes Investment Partners

Greg Lee Barclays

This process is clearly visible in the way traders respond to new demands for regulatory compliance, market microstructure, technology developments and increased availability and techniques for managing data.

In this issue of GlobalTrading, we look at the way buy-side desks are leveraging regulatory requirements to certify best execution as a platform to improve TCA for fixed income. We also explore the way algorithms are built and how this can be done in newer and better (and importantly, faster and cheaper) ways, while still meeting a growing range of situational trading needs.

There are additional contributions looking at the nature of data science, how venues can get help with regulatory reporting and why we still can’t forget the fundamentals of trading, even as we apply increasing levels of automation.

As we all continue to drive the industry forward, we look to you, our readers, to continue to educate one another through initiatives like GlobalTrading.

Thank you for reading,

Emma Quinn AB

Michael Corcoran ITG

Bill Hebert Co-Chair, Global Member Services Committee, FIX Trading Community

Kathryn Zhao Cantor Fitzgerald

GlobalTrading Publisher Markets Media

Managing Editor Will Haskins

Sales & Marketing Yulia Kuksina

Design & Production Manager Ian Harling

Design & Layout Goldie Lee

Photographer Damien Grenon Fabrice Vallon

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CONTENTS 7

FOCAL POINT

7 Fixed Income TCA: A Competitive Differentiator - Laurent Albert, Natixis Asset Management Finance

INSIGHT

12 Data Science And The Trading Desk - Terry Flanagan, Markets Media Featuring: - Todd Lopez, UBS - Curt Engler, JPM AM - Enrico Cacciatore, Voya IM 16 Building the Next Generation of Algos - Kathryn Zhao, Cantor Fitzgerald 19 The Weakest Link: Balancing Algorithmic Expertise And Fundamental Understanding In Trading - Max Rybinski, JM Asset Management Ltd

16

19

OPINION

AMERICAS

23 TCA: “Is This Good or Bad?” - Jason Lam, Deutsche Bank

37 Buy-Side Multi-Asset Trading: Challenges And Opportunities - A GlobalTrading Roundtable Discussion

27 Best Execution And The “Electronification” Of High Touch Trading - Michael Mollemans, Pavilion Global Markets

40 Big Data: Navigating The Hype Of AI And Machine Learning - David Firmin, Instinet INDUSTRY

30 Outsourcing Can Help ATSs Jump Regulatory Reporting Hurdles - Paul Roland, Banks & Brokers, Nasdaq 32 Northern Trust Gains As Buy-Side Outsources Trading - Shanny Basar, Markets Media Featuring: - Guy Gibson, Northern Trust Capital Markets ASIA 35 How Technology Is Making Derivatives More Transparent And Accessible - Sanjay Awasthi, Eastspring Investments

42 FIX Trading Community Members MY CITY

44 Paris - Laurent Albert, Natixis Asset Management Finance

23

HIGHLIGHTS “The challenge is to frame this data in an engaging way for fixed income portfolio managers. Whereas equity portfolio managers are well-accustomed to TCA, their fixed income peers are less used to it.” P.7 Laurent Albert, Global Head of Execution, Natixis Asset Management Finance

“We should not try to apply AI/ML in every situation simply because it’s the current buzzword. In many cases, traditional mathematical/statistical models are still the best overall solution.” P.16

Kathryn Zhao, Global Head of Electronic Trading, Cantor Fitzgerald

“Only by blending the skills and technical understanding of manual traders with the programming and quantitative abilities of modern algorithmic strategies can today’s traders provide the best returns for their clients and their firms.” P.19

Max Rybinski, Head of Proprietary Trading, JM Asset Management

“Traditional slippage metrics only provide the magnitude of slippage and not insight to where this compared to others or the market. An effective measure must then include a component of peer group analysis.” P.23 Jason Lam, Director - Head of APAC Electronic Equities, Quantitative Analytics & Consulting, Deutsche Bank

“Linking the buy-side order management systems to RFQ systems and markets electronically makes trading less time-consuming, seamless and free of obvious manual errors.” P.35 Sanjay Awasthi, Head of Dealing, Eastspring Investments

FOCAL POINT | 7

Fixed Income TCA: A Competitive Differentiator

By Laurent Albert, Global Head of Execution, Natixis Asset Management Finance

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8 | FOCAL POINT

Building out TCA reporting for fixed income improves execution quality, raising at the same time the level of service for end investors.

Unlike traditional asset management trading desks, we serve our internal wealth management and asset management clients while also offering our execution expertise to external portfolio managers. As such, we believe we owe it to them to have the metrics onhand. This is the reason why we have extended our transaction cost analysis (TCA) reporting from our equities trades, which we have been doing for years, to our fixed income trading desk.

“This was a real opportunity to build a truly detailed report that would increase our ability to demonstrate value. It was also a chance to improve our best selection process. We realized that once we had a robust TCA solution, we could then integrate it into our pre-trade systems on the dealing desk.”

We were looking for a pragmatic approach when searching for TCA support for fixed income. We quickly decided to choose an independent player and outsource these services. We are currently working with an established equity TCA provider, but wanted an integral player for the fixed income market.

We received proposals from six or seven different candidates and selected one for their methodology and user-friendly metrics.

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This was a real opportunity to build a truly detailed report that would increase our ability to demonstrate value. It was also a chance to improve our best selection process. We realized that once we had a robust TCA solution, we could then integrate it into our pre-trade systems on the dealing desk. This now means we can provide additional pre-trade information to help the trader select the best channel for execution, and improving selection means best execution.

Defining the challenge The first challenge to creating a TCA system for fixed income is to define it, as this is a new approach in the fixed income world. Given the complexity of the fixed income markets compared to equities, we needed to find a very simple solution for measuring this very complex asset class. The simple metric for analyzing our best execution in fixed income is to compare our execution price with a composite bid and offer. It is calculated as a mix between dealer streaming prices and a selection of executing prices drawn from a specified time frame. This allows us to compare our traded price with the composite price.

There are several analysis possibilities, once that data becomes available. We can assess the contribution of each bank by assets, or in a more managerial approach, we can use our TCA data to assess execution quality by each individual trader or instrument.

The first step was to assemble a huge amount of data and structure it before sending it to our provider.

Our mandate was to work on the data to deliver transaction competencies. For two months now, we have been sharing this report with our clients and the feedback has been very positive. They are surprised and curious about the results since it’s a new approach in fixed income. On average, if you can provide between 4-7 basis points in added value in government bonds over the last six months, this is acknowledged to be reasonable performance.

However, if we can factually demonstrate a capacity to deliver 5 basis points of added value, this fact is important to clients given the percentage of transactions that are done at midpoint. In high yield or emerging market bonds, the TCA will provide interesting details about the basis point improvement, especially when it is much larger. This gives us the

FOCAL POINT | 9

“The challenge is to frame this data in an engaging way for fixed income portfolio managers. Whereas equity portfolio managers are wellaccustomed to TCA, their fixed income peers are less used to it.�

ability to rank by basis points our capacity to add value across fixed income instruments in simple metrics.

The challenge is to frame this data in an engaging way for fixed income portfolio managers. Whereas equity portfolio managers are well-accustomed to TCA, their fixed income peers are less used to it.

Laurent Albert, Global Head of Execution, Natixis Asset Management Finance

Better benchmarks We started with a pursuit of simple metrics based on the composite number, but that was merely the initial stage of TCA for fixed income, and our report keeps improving as we receive more feedback from our clients. A later stage could be to compare our execution quality with a global peer group benchmark across all fixed income assets. This would obviously make it more challenging for us, but our final clients will appreciate the capability, transparency policy and quality of our execution team compared to those of our peers.

means that we have global coverage, excluding swaps and credit default swaps, but that will likely come soon. Each third-party provider has limits to their methodology, but we work with our partner to improve the quality and detail of the reporting across asset classes and to increase comparability between instruments. Some providers deliver no comparative data when the instrument is illiquid, but others interpolate and offer data drawn from a wider timeline.

For our fixed income TCA reporting, we currently cover a vast number of instruments, such as Treasury bills, asset-backed securities, high yield bonds, government bonds, non-rated credit, etc, which

While our investment in TCA has already yielded results for clients in the months following the launch, and we will continue both refining and expanding our reporting, there is always room for further

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10 | FOCAL POINT

“If tomorrow the fixed income industry came together and developed some type of an EMS (Execution Management System) to merge trading venues and systems across the same screen, it would immediately increase our ability to capture liquidity for our clients.”

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improvement. For example, if tomorrow the fixed income industry came together and developed some type of an EMS (Execution Management System) to merge trading venues and systems across the same screen, it would immediately increase our ability to capture liquidity for our clients. Key players would need to integrate their information, but if such an EMS existed, it would significantly improve the quality of our execution. This would allow us to use algorithms as we do for equities and seamlessly connect to multiple venues.

INSIGHT | 11

Data Science And The Trading Desk

By Terry Flanagan, Managing Editor, Markets Media

Francis Bacon, René Descartes and Isaac Newton were among pioneers who advanced the idea of making conclusions based on observation and evidence, rather than just reasoning.

Sell-side trading desks utilizing data isn’t new. What is new is the level of sophistication of buy-side investment managers, who need to see evidence that a methodology works. Brokers need to show, not just tell.

Centuries later, institutional brokers are incorporating tenets of the scientific method into their own pursuits of buying and selling blocks of equity.

“Our clients are becoming increasingly sophisticated in how they measure results and are pushing us harder to optimize our capabilities to solve their specific use cases,” Lopez said. “They require empirical evidence that taking a particular approach will result in lower implementation costs of trading.”

The nutshell premise is that data and proof walk, conjecture talks. This is especially the case in a rapidly evolving market with a multitude of promising — but untested — trading options.

“At UBS in the Americas our view is that the equity ecosystem continues to evolve and become increasingly complex in terms of new order types, new venues and new sources of liquidity,” said Todd Lopez, Head of Americas Cash Equities at investment bank UBS. “There continues to be more competition and diversity in liquidity sources. To effectively navigate this environment we need to understand in forensic detail when and how to access these sources and leverage new order types.”

‘Significant Differentiator’ “A broker’s client base is diverse and each buy-side customer may have varying order flow and therefore different liquidity needs,” said Curt Engler, Head of Equity Trading, Americas, at J.P. Morgan Asset Management. “The ability to test varying theories and quantify the results, especially client- specific needs, should be a significant differentiator for algorithmic providers.”

In the early 17th century, Galileo Galilei used the scientific method to contradict the long-accepted

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12 | INSIGHT

“[Our clients] require empirical evidence that taking a particular approach will result in lower implementation costs of trading.”

Aristotelian notion that the rate at which objects fall is proportional to their weight. He did this by dropping two balls of different weights onto ramps, which slowed speeds and enabled more precise time measurement. When the balls reached the ground at the same time, the theory that objects fall with the same acceleration regardless of mass was proven.

In 2018, trading desks are out to prove or disprove their own theories, in complex, high-speed electronic markets rather than backyards. UBS is doing so via a framework which is designed to improve algorithmic performance by allowing for controlled experimentation with different trading hypotheses.

There are plenty of new developments for trading desks to work with. For instance, recently exchange operator Nasdaq launched Midpoint Extended Life Order, which is meant to unite counterparties with longer-term investment horizons. Conceptually, the order type is attractive, as large institutions with buy-and-hold clientele prefer to trade with each other rather than with market participants who make their money moving in and out of markets quickly.

UBS ran an internal pilot program to determine if M-ELO lives up to its promise. “We collect a statistically significant number of observations, which helps us understand where this new order type may or may not make sense,” Lopez said. “We can then work with clients and use this data to further optimize their execution process.”

Another market dynamic whose impact needs to be tested is the rise of electronic liquidity providers. Years ago many institutional buy-side participants were wary of trading with ELPs given their roots in high-frequency trading strategies, but that has subsided as the proprietary traders moved into the mainstream and are now major liquidity providers in many markets.

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Todd Lopez, Head of Americas Cash Equities, UBS

ELPs represent one counterparty option for UBS and its clients, in addition to other liquidity sources such as UBS PIN, within the UBS ATS in the Americas, to help maximize crossing with UBS retail and institutional flow. The firm is taking a fresh look at ELPs on a top-down basis, i.e. assessing the trade-execution efficiency of the entire ‘parent’ order, in addition to the individual ‘child’ orders that emanate from the parent.

“The ability to source liquidity bilaterally, which in the US has primarily been through ELPs, has been expanding,” Lopez said. “That presents a potential opportunity in the effort to reduce implementation costs for clients and our traders.”

“This is something we have been focused on; trying to quantify what if any benefit is realized at both the child and parent order level by incorporating these liquidity sources into the execution process,” Lopez added.

“Ultimately we want to prove that this does actually reduce the slippage from clients’ primary objective function.”

Rigour Needed Institutional investment managers, who have a direct economic interest in the rigour of the sell side’s testing, are watching closely.

“As the buy side focuses more on higher level optimization of trading performance at the strategy and urgency level, we are leaning more on the sell side

INSIGHT | 13

Curt Engler, Head of Equity Trading, Americas, J.P. Morgan Asset Management

Enrico Cacciatore, Senior Quantitative Trader and Head of Market Structure & Analytics, Voya Investment Management

“The ability to test varying theories and quantify the results, especially client-specific needs, should be a significant differentiator for algorithmic providers.”

Cacciatore is formalizing a committee, comprised of both buy side and sell side market participants, that will work to normalize child-order reason codes. The aim is to add transparency around intention of broker routing, and also better evaluate liquidity sourcing for best execution.

to provide optimization around implementation of strategy in the marketplace, especially around access to liquidity,” said Enrico Cacciatore, Senior Quantitative Trader and Head of Market Structure & Analytics at Voya Investment Management.

“To really understand and evaluate quality of liquidity, we need to understand intention, cost expectation, time decay, and potential for adverse selection,” Cacciatore explained. “If we are only getting large block prints in conditional venues during adverse situations, we need to be able to quantitatively evaluate what would potentially have been the outcome if we decided to not interact with the block liquidity and instead trade more passively over a longer duration.”

“Understanding the reasons for a route and a fill are important. We intend to release new functionality to let clients know when they interacted with conditional liquidity in UBS ATS,” said Lopez.

“Having controlled, structured efforts to gather data and assess the effectiveness of new order types or other configurations is the right approach,” J.P. Morgan’s Engler said. “Only by collecting data over a significant number of observations can we better configure our routing decisions, whether on child orders or assessing broker algorithms, on an apples-toapples basis.”

Sponsored by

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14 | INSIGHT

Building The Next Generation Of Algos

By Kathryn Zhao, Global Head of Electronic Trading, Cantor Fitzgerald While many buy-side firms trade on a suite of existing algorithms, is it time for a new breed of algo? Kathryn Zhao, Global Head of Electronic Trading at Cantor Fitzgerald, sat down with GlobalTrading to discuss the roadmap to building a new generation of algos.

Are algos commoditized?

Trading in today’s complex marketplace requires advanced technology solutions that are performant, robust and flexible. Plug-and-play algorithms used to be enough to satisfy the needs of large institutions who engaged in electronic trading; however, today it is a different story. Utilization of algorithmic trading is growing and so are the customization demands of the buy-side. Investments in analytics raise the bar for algo performance, while also varying the nature of a successful trading outcome. Modern, competitive electronic trading offerings are required to deliver

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customized solutions tailored to each client’s specific trading preferences, and that fits our business model perfectly.

A helpful analogy is that of Precision Medicine, a medical model, where healthcare is customized according to each patient’s specific characteristics. By targeting the context of each patient’s DNA structure and tailoring a medical treatment, doctors can achieve the best healthcare outcome with the fewest side effects. Likewise, the next generation of algos must target each client’s trading DNA and optimize trading performance for each client. This is the exact definition of our Precision Algo Platform.

With growing sophistication of quantitative models and ever increasing electronic solution variety, buy-side firms face a unique challenge: how to choose a product that is straightforward to use and yet easy

INSIGHT | 15

“We should not try to apply AI/ML in every situation simply because it’s the current buzzword. In many cases, traditional mathematical/ statistical models are still the best overall solution.” to customize. We kept these considerations in our focus when we designed our algorithmic trading platform.

Our Precision Algo framework is modular and easily customizable, with speed of turnaround in mind, to help traders reduce their order trading flow and take advantage of price and liquidity conditions as they occur.

Replicate or blaze a new trail?

Our Precision Algo Platform was designed from scratch, with latest technologies, armed by experienced electronic trading professionals, devoted to pure meritocracy and achieving the best results for our clients.

When we began the process of developing a new suite of next generation algos nine months ago, we had a late-mover advantage. The electronic trading technology landscape has experienced dramatic changes over the past 20 years. Innovations in hardware, networking, and software have had an immense impact on current state of the art technology. To truly stand out from the rest of the competition, an electronic trading product must be performant from latency and throughput point of view and resilient to failure without compromising capabilities and quality of execution. We took and will

Kathryn Zhao, Global Head of Electronic Trading, Cantor Fitzgerald

continue to take a craftsman-like approach to building and continually improving our software. Our platform is designed with performance in mind.

For example, we use cutting-edge open-source libraries for inter-process communication and execution framework throughout the entire Client Gateway / Algo Engine / Exchange Gateway environment. We also use these highly efficient data structures for event sourcing to support full determinism in analysis and debugging. We pay a tremendous amount of attention to reliability and failover to eliminate the possibility of an outage and to minimize client impact if an outage does occur.

Additionally, we rely on a comprehensive suite of automated testing and simulated execution tools to validate and guarantee the quality of our product.

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16 | INSIGHT

With regard to quantitative research, we choose to use AI/ML only where there is a clear case for adding value. There are certainly cases when AI/MLpowered models give the best results. However, in multiple cases traditional statistical models still represent an overall better solution.

Clients’ key considerations and when to involve them?

The electronic trading business is highly competitive.

In my view, state of the art technology and sophisticated quantitative research enable differentiated product. Execution quality, customization, access to liquidity and system stability are differentiators of a competitive low-touch offering.

“With modular approach, software components don’t have to change much to meet new requirements and different modules can be combined to create a new strategy, rather than designing from scratch each time the buy-side trader identifies a new trading pattern.”

Traders demand better performance and predictability of execution results, all without sacrificing the ability to source liquidity, transparency and control of their executions. With the launch of our Precision Algo Platform, we offer our clients a fully customizable algo suite optimized to each client’s trading DNA.

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It is critical to engage clients actively at every stage of the process to ensure transparency and understanding of the full capabilities of the product, and to cater for clients’ current and future requirements. Underlying algos, such as VWAP / TWAP / POV, are the building blocks for “algo of algos”. As our Precision Algo Platform is modular and flexible, the majority of customizations are easy to achieve without having to directly alter the code base.

Extended into other asset classes The flexibility incorporated in a modular algo framework should enable the next generation of algos to be adapted to new asset classes and market structures without a full rebuild. Most of the code base, for example, the Allocation and Order Optimization framework, the Macro-Trader (Scheduler) and the Micro-Trader (analytics-driven, quantitative model-based order placement logic) are common components of any electronic trading algorithm and can be shared across different asset classes.

The essential differences between asset classes are market data and their microstructure characteristics.

Specialized quantitative models may need to be developed and calibrated to deliver better performing algorithms. Unique aggregators are also required to deliver a unified and normalized interface to both market data and market access.

As we roll out our next generation, cross-asset electronic trading product, we hope to help shape the industry standard for algo development.

INSIGHT | 17

The Weakest Link: Balancing Algorithmic Expertise And Fundamental Understanding In Trading

By Max Rybinski, Head of Proprietary Trading, JM Asset Management Ltd. As electronic trading extends further from institutional into retail trading, how well do today’s traders know the building blocks of the tools they employ?

Electronic trading rules Hong Kong’s trading, just as it does around most of the developed world. The popularization of online discount brokers, targeted social media advertisements, sophisticated platforms, along with faster data connectivity, have also created a swell of retail trader confidence in electronic trading tools.

Unfortunately, many of these retail traders will lose most, if not all, of their money in a very short period.

Easy access to trading technology is to blame. It has become very affordable, accessible and complex trading strategies have very simple execution options that only require a click of a mouse.

Algos without fundamentals Our new reality is that most retail traders here in Hong Kong specialize in using electronic trading tools, but can barely scratch the surface when trying to understand the underlying strategy. Therefore, when the market conditions are in flux, traders should know how the strategy will behave and have the skills to adjust accordingly.

In the institutional trading space, the current generation of traders has all come up after the mass adoption of electronic trading, which raises the question whether they may also be overly reliant on pre-built electronic execution strategies.

Having started my career on manual trading desks, working with traders with a wide range of successful trading strategies, and now developing algorithmic strategies to execute in the market, I have seen the benefits of each approach. More importantly, I have seen the skills that each approach requires and fosters among the traders that employ it.

I do not intend to demean the quality of the trading talent in Hong Kong, but if the majority of traders merely supervise a fully automated electronic system, are they familiar enough with the fundamentals of developing a trading strategy? By no fault of their own, the industry has shifted to a fully electronic system and developing the understanding of the fundamental underpinnings of algorithmic strategies has not been sufficiently addressed.

There are core fundamental skills that appear to be receding from the industry. If these are reduced, will it inhibit a trader’s ability to understand and service their

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18 | INSIGHT

“In the institutional trading space, the current generation of traders has all come up after the mass adoption of electronic trading, which raises the question whether they may also be overly reliant on pre-built electronic execution strategies.” clients’ needs? Are we confident enough to ensure best execution to an algorithm while not fully understanding the client or portfolio manager’s fundamental intentions, even including HFT firms and market makers?

Max Rybinski, Head of Proprietary Trading, JM Asset Management Ltd.

Strategy Construction Most traders will understand dynamics around spotting and sourcing liquidity in the market, but they must also be familiar with the tenets of financial strength (Altman Z, Beneish M and Piotroski F Score), value metrics and sector weight. For example, traders need to track the sector performance to correctly weigh order sizes for each trade they need to execute.

movements, which are especially visible using a price bar instead of a traditional time bar. Finding the harmonic range helps a trader determine realistic targets and stops. Typically, a trader would attempt to enter in a new range on the early pivot and target two-thirds of the remaining move. The stop may be placed slightly outside of the range at the point of range violation.

Besides these fundamental investment concepts, traders should also have a deeper understanding of the technical analysis that their algorithmic strategies are built on, to help them determine realistic targets, safety stops and also validate momentum.

Knowing the position of your trend is also another basic, yet overlooked aspect. A trend is simply a general direction, which the price is developing or changing.

Traders can confirm the trend on the second point and attempt to enter on the second higher low (long entries) or lower high (short entries) and on each consecutive point thereafter. Trend analysis is important because certain algorithms can overreact in choppy markets, making inopportune entries, and

A good example of a skill manual traders know well, but not all electronic traders know is harmonic ranges. Each instrument and its timeframe display habitual Fundamental Analysis

Technical Analysis

Focuses on economic forces

Focuses on past and current market action

Analyses the cause of the event

Analyses the effect of the event

Attempts to forecast the price change

Attempts to forecast and time the price change

Relies on the current price of the asset being analysed

Relies on the current price of the asset being analysed

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INSIGHT | 19

(Notice in the image above that both the red and green lines are the same length in each swing) traders must understand the trend in the signals the algorithm is processing.

make the oscillator more sensitive or lagging depending on the desired behaviour of the strategy.

This naturally brings us to momentum. Momentum oscillators are another favourite tool of manual traders, which are calculated from the price movement. Some complex momentum calculations will only attempt to

These indicators are only useful to help traders gain confidence to execute the trade. A momentum indicator is a small but useful validation to help a trader time their trade entry.

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20 | INSIGHT

“Trend analysis is important because certain algorithms can overreact in choppy markets, making inopportune entries, and traders must understand the trend in the signals the algorithm is processing.”

Fully Automated and Semi-automated At our firm, we believe in mixing the best of electronic execution and human insight. Below is my trading chart, including our proprietary banding and momentum oscillator tools. We use these to identify the changing trends and price speed when entering trades. Using simplified tools allows our traders to focus primarily on trade management, position sizing and market speed.

Fully automated systems are useful for arbitrage and marketing making strategies, but require a large budget for infrastructure and development. Semi-automated systems have a much smaller budget, but require trader discretion to execute a trade efficiently in the chosen direction. Both systems provide distinct advantages,

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however, experience has shown that a semi-automated system in the hands of a talented trader will outperform purely automated strategies. A semiautomated trading tool paired with a talented trader brings the best of technology and human talent that will always outperform each element trading on their own.

The image above is the semi-automated tool that we use, which includes the sophisticated institutional metrics and options that professional traders desire, but it has been further simplified so the trader only needs to focus on the direction they want to enter.

Once the trader confirms the trend direction, the tool will attempt to enter at the next most advantageous price range and handle all the position management.

Each member of our trading team specializes in different areas of analysis, but all agree that the basic trading foundations maximize our performance. Only by blending the skills and technical understanding of manual traders with the programming and quantitative abilities of modern algorithmic strategies can today’s traders provide the best returns for their clients and their firms.

OPINION | 21

TCA: “Is This Good or Bad?”

By Jason Lam, Director - Head of APAC Electronic Equities Quantitative Analytics & Consulting, Deutsche Bank

When measuring transaction costs, a prevalent practice today is to reference price benchmarks that are relevant to the specific algorithm. For example, referencing the order interval market VWAP (VolumeWeighted-Average-Price) for VWAP strategy orders, arrival price for IS (Implementation Shortfall) orders, etc. The price benchmark slippage can then be computed by comparing the order average price to the reference price. This methodology is widely accepted and almost universally applied within the industry.

For over a decade, this has been the de facto standard to examine algo performance, to evaluate whether best execution has been achieved, and to compare performance across strategies or brokers for each of the trading objectives. However, when presented with results drawn upon these traditional metrics, do we often find ourselves asking the question “is this good or bad”? Surely, if this methodology is not effective in answering this, we must continue to seek a better solution!

Figure 1

Why traditional benchmark slippages do not work well?

Figure 1 presents typical results of performance slippages. These measures are inherently noisy and sometimes misleading. Directional market moves and stock specific volatility tend to skew these measures significantly, where higher volatility likely yields higher deviation from benchmark. For example, if an IS order managed to beat the arrival benchmark by 2 bps (basis points), most would be overjoyed with this performance but “is this good or bad”? What if the majority of market trades were executed at even better prices? The optimism would quickly turn into disappointment. To counter these

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22 | OPINION

“Traditional slippage metrics only provide the magnitude of slippage and not insight to where this compared to others or the market. An effective measure must then include a component of peer group analysis.” issues, the industry has continued to innovate new techniques to separate noise from signal – from normalizing slippage numbers in relative terms by representing them in spread instead of bps, to applying adjustments based on pre- and/or post- trade models.

These models evolve over time and have gotten more complex and sophisticated. New factors were added to these models, including beta, stock specific volatility, liquidity conditions, impact estimates, etc. Furthermore, many agency brokers (Deutsche Bank included) have their own proprietary models, which makes it difficult for clients to compare adjusted-performance numbers across brokers.

Many clients find themselves having to apply adjustments themselves to reduce noise in these metrics. More importantly, how do we actually evaluate the effectiveness of these adjustments?

If an objective method existed to measure this, should we not directly apply it to measure transaction cost? Absolute EBEX is one answer.

Absolute EBEX Traditional slippage metrics only provide the magnitude of slippage and not insight to where this compared to others or the market. An effective measure must then include a component of peer group

analysis. Absolute EBEX is an absolute performance measure proposed by the EDHEC Business School in France as part of the EBEX (EDHEC Best Execution) framework1 for TCA (Transaction Cost Analysis). It quantifies the quality of execution with a simple score between 0 and 1, the higher the better. Absolute EBEX can be understood as the fraction of market volume traded at or worse than the order average price.

Suppose half of market volume traded at prices better than the order average price, the order would achieve a score of 0.5. We find this analysis intuitive and simple to implement, only requiring final order average price of individual orders and market trade data.

Applications of EBEX EBEX provides a standardised framework to assess the quality of execution across orders aggregated at any level. It is also generally free of noise typically found in traditional price benchmark slippage. With these advantages, an effective systematic review process may be introduced to evaluate best execution, which is key to achieve the best possible results through agency algorithms. To monitor for changes in execution performance per strategy and market, one could simply observe the distribution and aggregated absolute EBEX scores. The stability of aggregated absolute EBEX score allows for the introduction of an expected performance band by defining an appropriate lower and upper bound based on acceptable tolerance level, such as a multi-month rolling average of 35 and 65 percentile respectively. In addition, absolute EBEX as an absolute measure allows for a definitive minimum threshold defined by a constant, such as 0.3.

In figure 2, we present the absolute EBEX score over a sample set of VWAP orders. The round dot represents aggregated score over each month. The vertical line represents the distribution of EBEX scores between 35 to 65 percentiles. The shaded zone represents the acceptable performance band, which is a rolling 2-month average of 35 and 65 percentile respectively.

Also, note the stability of aggregated EBEX score and its distribution month over month, as compared to figure 1. Both figure 1 and figure 2 are based on the same underlying order set. The EBEX analysis identified a drop in performance in October but was significantly less pronounced than what traditional slippage shown. Indeed, a deeper investigation found higher volatility in October having skewed traditional TCA.

1 Further details can be found in the following paper to ESMA: https://www.esma.europa.eu/file/7457/download?token=Z6cr7EN1

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OPINION | 23

Figure 2

EBEX Variants Alongside absolute EBEX, EDHEC also introduced directional EBEX as part of the EBEX framework.

The goal of directional EBEX is to evaluate whether the correct time horizon had been chosen to trade the order by comparing NBBEX (Number of Before-Better Executions) and NABEX (Number of After-Better Execution), which can be understood as the analogous of absolute EBEX within the order interval, and the period between order end time and market close respectively.

For schedule-based algorithms, such as VWAP and TWAP (Time-Weighted Average Price), clients may prefer to define the trading horizon by specifying the start and the end time of the order. In such cases, we find it more appropriate to use NBBEX over absolute EBEX to evaluate performance only within the order interval.

For shorter duration orders, where orders are more likely to be a larger part of interval volume (IV), we

Figure 3

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24 | OPINION

“EBEX provides a standardised framework to assess the quality of execution across orders aggregated at any level. It is also generally free of noise typically found in traditional price benchmark slippage.” propose a minor adjustment to exclude own order flow. Consider 2 Buy orders in figure 3. Each order having only 2 executions, and the price of the latter and larger execution of each order being the only difference between the otherwise identical orders. The size of the bubble represents the relative trade size.

The color of the bubble represents order execution and market trades in dark blue and light blue respectively.

One would prefer the performance of order B to order A because the bulk of its executions were done at the low prices of the order interval as opposed to the near-high. However, the NBBEX score does not reflect this, yielding a score of 0.89 and 0.11 for order A and B respectively, which is the opposite of our preference.

Order A scored well under NBBEX because the latter execution itself already accounts for 87% of interval market volume and traded at a price worse than the order average price. NBBEX therefore has included its own execution as the fraction of market volume having done at or worse than its order average price.

By excluding own order execution, the modified NBBEX yields 0.15 and 0.85 respectively. We compute this by excluding own order executions at or worse than the order average price from market volume within the same price range, and dividing it with the orderinterval market volume. Modified NBBEX converges with the original as the Interval Volume percentage of the order decreases. The implementation of modified NBBEX only comes at the extra cost of having to identify the executed volume of individual orders executed below the order average price but this information should be readily available.

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Jason Lam, Director - Head of APAC Electronic Equities Quantitative Analytics & Consulting, Deutsche Bank

Conclusion EBEX provides a standardised TCA framework to evaluate the quality of order executions aggregated across any level. Deutsche Bank has included EBEX as part of our formal Best Execution review process in APAC. The definition of EBEX is intuitive and easy to implement. It is a form of peer group analysis by considering all trades in the market, and generally free of noise typically present in traditional price benchmark slippage. It’s an absolute measure based on a simple score between 0 and 1, where clear objectives can be defined, such as a minimal performance threshold. It is also a versatile measure without having to define relevant and specific reference benchmarks to measure each algorithm. We believe EBEX to be an effective measure and should be included in the suite of standard TCA metrics.

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Best Execution And The “Electronification” Of High Touch Trading

By Michael Mollemans, Head of Sales Trading Asia-Pacific, Pavilion Global Markets

Best execution is an evolving process, and so it is incumbent upon us to anticipate the trend in service expectations.

The unbundling of research brought about by the Markets in Financial Instruments Directive (MiFID II) put the cost of execution into the limelight with many buy-side traders responding by increasing their use of low cost, “low touch” algorithms (algos). Best execution policy disclosures also brought quantitative performance and qualitative service factors into focus. As best execution regulation and service expectations evolve, so too do the traditional “low touch” and “high touch” roles, with the road ahead leading to an electronification of “high touch” client service partnerships.

Liquidity issues in certain names, and on certain days, are always a source of frustration for buy-side traders.

Markets are dynamic and liquidity in small- and midcap names can be here today, gone tomorrow, and a few illiquid names in a “basket” or “program” can destroy overall performance numbers. A sales trader’s ability to combine expert advice on liquidity-seeking algo parameter settings with a breadth of counterparty relationships gained through experience makes all the difference when aiming to achieve the highest possible ranking in a client’s execution performance scorecard. Buy-side traders are increasingly taking

a multi-factor approach when evaluating brokers against their peers, with value-added service and other qualitative measures weighted highly next to execution quality. A “high touch” approach, in an otherwise “low touch” algo business, not only helps achieve a superior weighted average performance result, but also helps move up in the performance scorecard ranks.

Block crosses can help enhance trading performance but can also damage client trust if crosses are sourced without consent, or if the client’s unique qualitative parameters and constraints are not properly followed.

Sales traders certainly need to interact with electronic crossing venues selectively while seeking to utilise the breadth of counterparty relationships. When crossing stock at a price, however, there is no substitute for skilled technical analysis of stock trends to estimate the periodicity of alpha so as to minimise the chance that stock prices trend into you only after executing your block at a relatively unfavourable price. Timing contribution of executing a block at a price, and the opportunity cost of not executing a block, must be carefully considered. Information leakage costs must also be considered when reaching out to counterparty relationships. Block executions provide an opportunity to minimise impact but if unnecessary information leakage is created they can also hurt overall performance.

Automated “smart IOI” (Indications of Interest)

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26 | OPINION

“The trade lifecycle doesn’t end at execution, it ends at settlement. Smooth settlement, especially with difficult emerging market ID matching requirements and amended settlement terms, requires constant communication...”

Michael Mollemans, Head of Sales Trading Asia-Pacific, Pavilion Global Markets

processes, geared to targeting known holders of a name, can be very helpful when trying to minimise leakage.

Reliability of execution across a robust trading platform that can sustain any type of system failure is often cited by the buy-side as a key qualitative factor when it comes to making broker routing decisions. Availability of backup algo networks is valued because the last thing clients want to hear an hour into the trading session is “trade away please.” Market data issues, network outages, database failures, algo engine issues, etc. can happen, but the ability to fail over to a parallel backup algo infrastructure makes all the difference when it comes to providing reliability of execution.

A sales trader’s ability to provide clients with quick and detailed communication about the nature of a system failure, and an informed estimate on when systems will return to normal, is always appreciated.

The trade lifecycle doesn’t end at execution, it ends at settlement. Smooth settlement, especially with difficult emerging market ID matching requirements and amended settlement terms, requires constant communication between traders and settlements teams, which can be a challenge if these teams are separated in different buildings or cities.

Trading and settlements teams also need to rely on experience to help anticipate issues and become problem solvers, when needed, to assure clients get a consistently reliable settlement experience.

Analytics are at the front-and-centre of the trading relationship as buy-side traders aim to maximise alpha through smarter execution strategies. Sell-side traders are expected to have deeper discussions with clients around transaction cost analytics (TCA) and

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so data visualisation tools are being used more and more to assist communication with clients across a growing number of data points. TCA based primarily on performance measurements versus “VWAP” or “arrival” benchmarks can be misleading when best execution regulation requires the consideration of both quantitative and qualitative factors across the entire trade lifecycle. MiFID II regulation and the regulatory technical standard (RTS 28) requires Best Execution Policy annual reports to be disclosed publicly for the first time ahead of the April 30th 2018 deadline. However, regulators have made it clear that next year, and in the years to follow, best execution policy reports are expected to become increasingly specific and transparent about how venue and counterparty routing decisions are made.

Pre-trade reports set strategy selection in motion.

Then, real-time market data feed into technical trend analytics designed to help sales traders add alpha by capitalising on directional opportunities with the help of adjusted algo parameters and curve tilts. Real-time analytics are designed to be actionable but their value is largely determined by a sales trader’s ability to interpret the signals and take action on opportunities in spread capture, timing contribution, opportunity cost, etc.

Venue analysis is a great tool to drive informed discussions with buy-side traders around performance at the venue level and help the sell-side to act in the best interest of clients by removing hard-coded venue biases. Venue analysis also sheds light on the venues that provide the most opportunity for earning spread capture and speed of execution, with the

OPINION | 27

lowest reversion cost. Guided conversations with clients about venue performance provides added depth of understanding of their performance goals, which can help sales traders fine-tune dynamic venue prioritisation and smart order routing strategies across various market conditions and levels of trade urgency.

“Algos and automated processes do not produce best execution by themselves; rather, they are tools and efficiencies that allow sales traders to allocate more time to the qualitative value-added side of trading services...”

Monitoring and reviewing of execution quality requires constant communication between buy-side and sellside traders and yields a valuable feedback loop used to help produce performance results with improvements to algo parameter settings and customised client service partnerships. Distribution of weighted average trading performance is increasingly being looked at by the buy-side, and tightening of the distribution of performance is becoming a goal in itself. Clearly, from a risk point of view, buy-side traders don’t want longtail, “all over the map,” performance outcomes.

To be successful at delivering consistently above average, low standard deviation, trading performance to clients, sales traders need to bring automated “low touch” tools and “high touch” market experience together when navigating through the latest market-moving news announcements, sentiment changes and directional trend shifts.

Algo engines are often being equipped with news factor data but they cannot yet compare with an experienced trader’s ability to take news on complex economic and geopolitical events and translate it into alpha producing opportunities for clients.

logic and smart order routing prioritisation settings require informed communication between buy-side and sell-side traders. Email blasts with technical update jargon and marketing brochures offering limited detail are not enough. Sales traders are expected to help clients understand what is happening, or changing, “under the hood” by thoughtfully and efficiently translating the proprietary technical language. The overall goal is to help clients choose the best algo parameter settings and smart order router preferences to get the best possible performance result.

Execution consultancy services are needed more than ever as the range of algo strategies, alternative venues, and smart order routing preferencing options widen.

Sales traders are expected to assess new technologies, like artificial intelligence or machine learning, and advise clients on where quantifiable, statistically significant, results are being seen, or to what extent it is just marketing. Sales traders are expected to stay updated on market structure changes and advise clients on how to best position themselves to take advantage of change.

All in all, the sell-side service model that is best positioned for the anticipated change in best execution regulation and service expectations is one that brings the key components of “low touch” and “high touch” roles together seamlessly into one combined approach, which is the aim of the Pavilion Global Markets value proposition. Algos and automated processes do not produce best execution by themselves; rather, they are tools and efficiencies that allow sales traders to allocate more time to the qualitative value-added side of trading services. Access to liquidity is a primary concern and so sell-side traders are expected to regularly assess all new and alternative electronic venues. At the same time, they must source liquidity from a myriad of counterparty relationships gained through experience. Constant monitoring, review, and communication with clients around pre-trade, real-time and post-trade analytics will not only support efforts to produce superior weighted average performance results, but will also help tighten the standard deviation of performance outcomes over time.

Transparency is often cited by the buy-side as one of the main qualitative factors considered when making broker routing decisions. Transparency of order routing

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28 | OPINION

Outsourcing Can Help ATSs Jump Regulatory Reporting Hurdles

By Paul Roland, Global Head of Markets & Services, Banks & Brokers, Nasdaq Alternative Trading Systems (ATSs) in the US already spend a significant amount of resources on regulatory reporting – both internally and on outside legal counsel. On July 18, 2018, the Securities and Exchange Commission (SEC) amended Regulation ATS to enhance transparency and oversight of ATSs. It did so by introducing a new filing, the Form ATS-N, which will increase the regulatory reporting burden and absorb even more resources.

However, ATSs can jump this reporting hurdle more easily by outsourcing the operation of their platform to an expert in running marketplaces.

The equities markets have evolved substantially since Regulation ATS became effective in 2000. For starters, there are now many more ATSs, and these platforms are a significant source of liquidity in National Market System (NMS) stocks. According to the SEC, ATSs now account for about 11.4% of the total dollar value traded in NMS stocks1. FINRA estimates that more than 30% of the total NMS volume of shares traded occurs over the counter,2 and 54.7 billion shares3 were traded on ATSs in the second quarter of 2018 alone.

Moreover, NMS Stock ATSs have been a source of innovation within the US equities markets. They have become more complex and sophisticated, and some platforms now offer features similar to registered national securities exchanges, such as The Nasdaq Stock Market, which are required to be more transparent in their activities. With the SEC’s recent public focus on competition4 , ATS operators will likely play a key role in shaping innovation and market structure.

To this end, the SEC has introduced a new Form ATS-N to enhance transparency and oversight of the platforms that trade stocks listed on national securities exchanges. Existing NMS Stock ATSs will be required to file a Form ATS-N no earlier than January 7, 2019 and no later than February 8, 2019. As of January 7, 2019, an entity seeking to operate as an NMS Stock ATS will be required to file a Form ATS-N. The disclosures on Form ATS5 are relatively minimal compared to what will need to be disclosed on Form ATS-N in the future. In many ways, the standards of

1 See https://www.sec.gov/news/press-release/2018-136 2 See http://www.finra.org/newsroom/2014/finra-makes-dark-pool-data-available-free-investing-public 3 See https://www.finra.org/industry/otc/ats-transparency-data-quarterly-statistics 4 See https://www.sec.gov/news/speech/speech-jackson-101118) 5 See https://www.sec.gov/about/forms/formats.pdf

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“With the SEC’s recent public focus on competition, ATS operators will likely play a key role in shaping innovation and market structure” disclosure on the new form are ‘exchange like’. That is, the amount and level of description will look more similar to an exchange’s rule book than in years past, and similar to a MiFID II disclosure. The effort and expertise to complete the initial operation report on Form ATS-N, along with the hours preparing a cessation of operations report, are not trivial. The requirements on the 20-page Form ATS-N are extensive.6 The SEC estimates that an NMS Stock ATS will spend about 127.4 hours completing the form, about nine hours preparing each amendment to Form ATS-N, and about two hours preparing a notice of cessation.

The disclosures on Form ATS-N have been designed to inform market participants about how the ATS operates. They include the order types and market data used on the ATS, fees, the ATS’s execution and priority procedures, and any procedures to segment orders on the ATS. Market participants will also be able to assess potential conflicts of interest and risks of information leakage arising from the ATS-related activities of the ATS’s broker-dealer operator and its affiliates.

Whenever an ATS makes any change to the operation of the platform, including types of securities traded, or the types of subscribers, they must update their Form ATS-N, as they do now on Form ATS. If the number of rule filings that stock exchanges make annually is any comparison, the reporting burden is enormous: exchanges propose dozens of rule changes per year.

Paul Roland, Global Head of Markets & Services, Banks & Brokers, Nasdaq sheets, and have left them searching for ways to reduce costs in order to achieve capital efficiency. Some conclude that they cannot meet these demands entirely alone, and it makes sense to investigate outsourcing the operation of their trading platform, including regulatory support, as an alternative to doing it in-house.

Ultimately, outsourcing enables ATSs to concentrate their efforts on enhancing core competencies aimed at adding value for clients and generating revenue.

Broker-dealers should look for an outsourcer that can support their platform holistically. A partner should be able to provide support services related to operations, compliance, surveillance, supervisory, recordkeeping and reporting obligations.

Nasdaq actively invests in technology and services for bank and broker venues from design and build, to hosting, and throughout all operations. Currently powering 100+ of the world’s market infrastructure organisations, including exchanges, clearinghouses, central securities depositories and regulators, in over 50 countries with end-to-end, mission-critical technology solutions, Nasdaq effectively manages outsourced venues with exchange-grade expectations, both from a technology and regulatory perspective.

Where Experience Meets Outsourcing To compete in today’s environment, ATSs need to acquire and maintain advanced technology and retain internal and external compliance experts. These costs have put enormous pressure on broker-dealers’ balance 6 See https://www.lw.com/admin/Upload/Documents/Alert%202359%20(Form%20ATS-N%20link)/FormATSN.pdf

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30 | OPINION

Northern Trust Gains As Buy-Side Outsources Trading

By Shanny Basar, Markets Media Northern Trust has signed 23 fund managers for its outsourced trading capability and expects strong growth for the offering next year as asset managers face pressure on margins.

at Northern Trust Capital Markets, told Markets Media that these factors were combining to drive down profitability on the buy side.

The US asset manager and custodian bank has relaunched Integrated Trading Solutions to provide trade execution, middle office, matching, settlement and regulatory reporting for the buy side.

Gibson said: “In January last year we decided that we could differentiate ourself in a MiFID II environment through our role as a custodian bank and helping clients outsource their trading and middle office. As a custodian bank, we do not have the conflicts of investment banks.”

Costs have been increasing for fund managers due to increased regulation and there is also pressure on fees as flows switch from active to passive products. For example, MiFID II came into force in the European Union this year and introduced new trade reporting mandates, stronger best execution requirements and unbundled research payments from trading commissions.

One way of reducing costs for buy-side clients is through Integrated Trading Solutions using Swift technology in middle office trade processing. A client can trade at 11 am and settlement messages can be matched at the custodian within an hour. As a result, the risk of failed trades falls to virtually zero.

Guy Gibson, head of institutional brokerage, for Europe, Middle East, Africa (Emea) and Asia-Pacific

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Integrated Trading Solutions can also access all liquidity across 33 venues and 88 global markets, without clients having to set up their own connections.

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“Wealth managers and asset managers will increasingly seek to automate or outsource everything in the middle and back office in order to reduce costs” The team has six traders on the European equities desk for both program and cash trading and two traders for fixed income. There are also eight sales traders.

“We do not interact with high-frequency traders,” Gibson added. “We rotate our executing brokers on performance, each month the worst performing broker is stood down.”

Before onboarding a client, Northern Trust can analyse how much cost can be decreased based on how that individual fund trades. The Integrated Trading Solutions team then meets clients every month to review transaction costs for each trade and discuss how execution can be improved.

“We use artificial intelligence to review execution quality and find that subtle changes can make significant improvements,” said Gibson. “For example, in reviewing the data and re-running the trades under different footprint scenarios, improved outcomes can be highlighted for future trade instructions.”

Growth He continued that Integrated Trading Solutions was relaunched in September this year. As a result, Northern Trust Capital Markets had a 135% year-on-year growth in trading volumes into the third quarter of this year.

“In the first quarter of this year we traded $50bn in equities, which is a 100% increase on the same quarter last year,” he added.

Integrated Trading Solutions currently outsources for equities and bonds globally but Gibson said the firm plans to expand the service to derivatives in Europe, Middle East and Africa in the first quarter of next year.

He added there has been an increase in clients since September as market volatility has increased.

“We expect 2019 to be a strong growth year globally,”

said Gibson. “We also expect new competition to enter the space as they see our success. I see this is very helpful for the development of this space within the asset management community.”

Wealth managers and asset managers will increasingly seek to automate or outsource everything in the middle and back office in order to reduce costs, according to a report from PwC last year. The study, Asset & Wealth Management Revolution: Embracing Exponential Change, said large regional and global wealth managers and global asset managers will either automate their inhouse operations or outsource to a third-party provider.

“Smaller firms will outsource to the big asset servicing firms or even to global utilities that could emerge for functions such as know your client, transfer agency, trade processing and risk and tax reporting,” added PwC. “Automating the middle and back offices will also improve data analytics, providing a lot more information about what’s going on in the business.

Product, deal and client profitability will become known, as well as manageable key metrics.”

Gibson said: “There will a lot more alignment in capital markets over the next two to three years. Clients are also increasingly looking to outsource foreign exchange trading.”

MiFID II unbundling Gibson joined Northern Trust in 2016 when the firm acquired Aviate Global, a UK-based institutional equity brokerage. The business was integrated into Northern Trust’s existing brokerage.

Aviate provides research, a business which is being transformed by the unbundling requirements of MiFID II. The EU regulation requires fund managers to either pay for research themselves or set up a research payment account, where the budget has been agreed with the client. Most asset managers have opted to absorb research costs, which has led to a drop in research budgets.

To help adapt to MiFID II, Northern Trust hired Glenn Poulter for its brokerage business in May. Poulter was previously head of European cash equities at Citi and chief executive for equities at Icap.

Gibson said: “Glenn Poulter estimated, in 2015, that research revenues would drop 70% under MiFID II hard landing.”

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“Regardless of where this approach to transparent alignment is applied, we see bundling, and other forms of entrenched opacity (e.g., fee schedules for private-equity managers) as its enemy” He continued that the first half of this year has been extremely difficult for research providers but there is light at the end of the tunnel. “Winners are emerging and fund managers are beginning to pay good money for the right analyst,” said Gibson.

Guy Gibson, Head of Institutional Brokerage, for EMEA and Asia-Pacific at Northern Trust Capital Markets

Neil Scarth, principal at Frost Consulting, has been building a research spending database/ benchmarking product – FrostDB. He found that managers using client money to pay for research spend more far more, as much as 100 times more, as fund managers who pay for research themselves.

However, he argues that cutting spending on research may be a false economy if returns then fall.

said: “Emerging lessons from MiFID II suggest that the use of disclosure mechanisms, such as research budgets, could more adequately foster transparent alignment than can be achieved by strictly forcing asset managers to internalize more of their costs.”

Scarth told Markets Media: “Asset owners may pay between three and five basis points, for instance, but the difference in return between the first and fourth quartile of funds can be thousands of basis points over time, depending upon the strategy.”

The study described how such budgets may be used to support deeper relational contracts – a shared process by which institutional asset owners and their external asset managers can achieve more transparent alignment and both achieve better returns.

He added that the discussion between investors and research providers is evolving as the performance implications of cutting research budgets may become clearer over time.

“The blueprint we describe could easily be transported to other areas of interaction between investors and managers, such as managers’ spending on technology or data services,” said the paper. “Still, regardless of where this approach to transparent alignment is applied, we see bundling, and other forms of entrenched opacity (e.g., fee schedules for private-equity managers) as its enemy.”

“Asset owners and fund managers both want the fund to do well in generating returns,” added Scarth.

“So thoughtful asset owners may prefer to agree a research budget in order to give the managers the tools they need to run the strategy, particularly given the large spread between research costs and returns.”

In a paper with Stanford University in June, Scarth argued that more asset managers should set up MiFID II research accounts with an agreed budget. The paper

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How Technology Is Making Derivatives More Transparent And Accessible

By Sanjay Awasthi, Head of Dealing, Eastspring Investments Increased adoption of OTC derivative technology is giving buy-side traders better pricing and reduced operational risk. More will only help.

For the purpose of this article, I shall segregate the derivative markets into exchange-traded and Overthe-Counter (OTC) derivatives. For exchange-traded derivatives, including futures and options contracts, electronic trading is relatively straightforward and the markets can be accessed electronically via electronic messaging protocols and broker algorithms.

These OTC markets still work on the basis of Request for Quotes (RFQ), which require traders to ask a few counterparties, typically banks, for quotes and trade on the best price available. The traditional approach involves calling a broker or sending messages in chat rooms, however, there are a few inherent limitations to this method.

What is more interesting is the increased acceptance and adoption of technology to trade OTC across foreign exchange (FX) and fixed income derivatives.

These are much larger markets and traditionally, there have been issues around transparency and pricing.

First, it limits the trader’s ability to reach out to multiple counterparties and consequently, a buy-side trader may not get the best possible price. Second, it is time-consuming, which is increasingly relevant as many trading desks reallocate headcount away from high-touch trading and toward quantitative roles.

Third, this process is unnecessarily prone to errors as manual entry of trading particulars invites the risk of fat finger order mis-entry.

Historically, financial derivative contracts were OTC and over time, as they were standardised and gained wider acceptability, we began to see them trading on exchanges in the listed space. However, FX and fixed income derivatives continue to trade on OTC markets.

The OTC FX and fixed income derivative markets have been around for a long time and operate in a particular manner for various reasons, however, the time it takes to explain is beyond scope of this article.

The electronic RFQ platforms, be they Bloomberg,

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34 | ASIA

“Linking the buy-side order management systems to RFQ systems and markets electronically makes trading less time-consuming, seamless and free of obvious manual errors.”

Tradeweb, FX connect, etc., facilitate ease of trading without significantly changing the way these OTC markets operate. As a result, we are increasingly seeing new technology being implemented into Request for Quotes in the OTC derivative space.

The obvious advantages to a buy-side trading desk are as follows: • One is able to immediately request more counterparties for pricing. This increases the chances of getting better pricing for client mandates.

• Linking the buy-side order management systems to RFQ systems and markets electronically makes trading less time-consuming, seamless and free of obvious manual errors.

• The quotes are stored electronically and are available for assessment of trading quality on a tangible basis. Electronic record assists TCA on pricing and counterparty reviews which adds a quantitative and an objective dimension to the whole process.

• Across most jurisdictions, there are increasing regulatory and compliance requirements around trading OTC derivatives. RFQ systems bring transparency in terms of electronic audit trail, controls and reporting which in turn significantly enhances compliance and regulatory oversight.

• The RFQ systems bring in an STP element to the OTC market which significantly reduces operational risk.

Most institutional investors have concerns around transparency in OTC derivative markets and this, in

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Sanjay Awasthi, Head of Dealing, Eastspring Investments

turn, limits their use of these products. RFQ systems which bring in increased transparency makes one more inclined to use OTC derivatives to achieve their portfolio objectives.

RFQ systems even facilitate ease of accessing quotes listed derivative like options, especially longer-dated relatively illiquid options.

Another area where technology will have a significant impact is in post-trade operations around these OTC contracts. These contracts being bilateral are governed under cumbersome ISDA contracts which increases operational complexity and risk. Evolution of technology which can facilitate ‘smart contracts’ as a protocol to digitally facilitate, so as to electronically record all contract conditions and performance can completely transform the OTC derivative space.

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Buy-Side Multi-Asset Trading: Challenges And Opportunities

By Terry Flanagan, Managing Editor, Markets Media

For the buy side, multi-asset trading offers the promise of efficiencies, cost savings, and closer alignment with an increasingly interconnected global marketplace.

But how far do large investment firms wish to go to amalgamate legacy siloed trading desks? What is the blueprint? And most critically, what are the risks and rewards?

Those questions were among topics explored at a September buy-side roundtable in Boston, hosted by the GlobalTrading Journal and sponsored by TradingScreen.

As the buy-side universe is expansive, with an abundance of investment models and strategies, each journey toward multi-asset trading is unique. But there is one common denominator that’s vital in driving the process forward: the underlying technology.

“All of the consolidation and activity in the tradingtechnology space highlights the need for integrated workflows and increased automation across asset

classes,” said Varghese Thomas, Chief Operating and Strategy Officer at TradingScreen. “Leveraging established and trusted technology partners are necessary to help bridge the gap between current solutions, fintech platforms as well as new emerging asset classes.”

At a TradingScreen-sponsored roundtable in New York in June, participants noted that the buy side was shifting to buying technology rather than building technology; interoperability of systems and ‘big data’ management were core challenges; and automation of processes, while a key ingredient in the evolution of a buy-side trading desk, has its limits.

The Boston discussion addressed the latter point.

“The reality is that not everything in multi-asset trading can be done programmatically — there will still be a need for human oversight,” said Joseph Bacchi, Head of Multi-Asset Trading and Investment Operations at Acadian Asset Management. “There isn’t one venue, one strategy, one platform or one relationship. That’s the foundation of multi-asset trading.”

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36 | AMERICAS

Market Recon Some senior traders and technologists who attended the roundtable event said they were there to gather market intelligence, i.e. assess industry trends and vet what an individual firm is doing versus what the broader marketplace is doing. In multi-asset trading in particular, there is ample opportunity to learn from earlier movers.

“Firms are at varying stages in the migration to a multi-asset class environment – some are just starting, some are fully multi-asset,” said Rob Hegarty, Managing Partner at Hegarty Group and roundtable moderator. “Others prefer to keep equity separate from fixed income and currency, at least for now.”

As drivers of the migration to multi-asset trading, Hegarty cited increasing cost pressures; opportunities for collaboration; the availability and ubiquity of data; the rise of quant resulting in cross-asset strategies; advancements in available technologies such as Execution Management Systems; and new asset classes such as cryptocurrency.

One roundtable participant noted that consolidation of trading technology across asset classes is a lengthy process, and there is the question of whether

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“All of the consolidation and activity in the tradingtechnology space highlights the need for integrated workflows and increased automation across asset classes,” said Varghese Thomas, Chief Operating and Strategy Officer at TradingScreen.” technological products are mature enough to support multiple trading scenarios.

This person said one approach to technology is to leverage vendor platforms to handle ‘plain vanilla’ tasks, which frees up traders to concentrate on higher-value, more strategic workflow.

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“The reality is that not everything in multi-asset trading can be done programmatically — there will still be a need for human oversight,” said Joseph Bacchi, Head of MultiAsset Trading and Investment Operations at Acadian Asset Management. “There isn’t one venue, one strategy, one platform or one relationship.

That’s the foundation of multiasset trading. ” Another participant agreed. Amid the evolution of high-touch and low-touch trading, “you need deeplevel experts to deal with the more difficult order flow,” this person said. “There’s lots of easy-to-do stuff on every desk. The challenge for the team is how to siphon that out.”

One topic of interest was the recent flurry of mergers and acquisitions in the vendor space — State Street acquiring Charles River, SS&C acquiring Eze Software, and Ion acquiring Fidessa. The deals are in line with the overall theme of consolidation and reduction of technology footprint in the front and middle office.

“The buy side appears to be strongly in favor of the (M&A) strategy, as it should help them reduce costs,” Hegarty said.

Joseph Bacchi, Head of Multi-Asset Trading and Investment Operations, Acadian Asset Management

so, how a firm can integrate it into a multi-asset trading framework.

One trading technologist said he believes cryptocurrency is for real, and his firm has set up a committee to study the asset class. “It rides some of the same rails we have today, but there are some new ones,” this person said. “Security is a huge deal and custodial aspects are monumental.”

“Once it gets up to speed you’ll see a lot of similarities to currency trading, and ETFs will be involved,” the participant continued. “But there are scary pieces and you really have to be very cognizant of how it works.”

Another person noted there are many new trading platforms, primarily in fixed income and cryptocurrency, but some of these are “pop-ups” that disappear as quickly as they appear. “There doesn’t seem to be much staying power,” this person said.

‘Who will be the winners? We can try to pick one but then we may find out later that the liquidity isn’t there.”

Investment managers are closely watching the emergence of cryptocurrencies. Roundtable participants weren’t so much interested in whether the price of bitcoin will rise or fall, but they are very interested in whether crypto has staying power, and if

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38 | AMERICAS

Big Data: Navigating The Hype Of AI And Machine Learning

By David Firmin, MD and Head of Global Trading Research, Instinet

With all the big talk about big data these days, the financial services industry must make better sense of machine learning and artificial intelligence (AI) applications.

This is an element we believe all constituencies should actively evaluate, define, and build into their programs.

Definitions vary widely and discussions are often vague, so it can be hard to determine what is real and what is spin. Without consistency in what these terms mean, it can be a challenge understanding how these advances in processing power have changed the way algo engines work or enhanced the tools that deliver analytics and insights.

Volume Volume is a function of the depth and breadth of data and their sources. The term “alternative data” means non-traditional sources that can now be applied to quantitative analysis. We see many more sources, as well as larger quantities of data, and possibly also greater frequency and/or lower-latency real-time increments of data— all of which combine to dramatically increase the overall volume of data that has to be captured, stored, processed, and analysed.

What are the criteria that define big data? Is there a standard definition?

The 3Vs: Volume, Velocity, and Variety, are often used to differentiate simple data from big data. Any big data project would factor in these criteria, but we would include a 4th V: Value. Value refers to the quality of the data, as well as the quality of its return on investment (ROI) relative to how it is being used.

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THE “4Vs”

Velocity With new sources of data such as social media, machine data, and mobile applications streaming into the ecosystem in real time, support for high velocity extends to not only how swiftly data is captured and collected,

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“It’s important to be mindful that big data isn’t a virtue unto itself. Its value lies in its effective application to a specific problem or model.” but all the way through the process until the application of that data drives a resulting business action. The time horizons between data capture and results have been massively compressed, especially for industries with business models that critically depend upon low-latency capabilities and capacity, such as financial services.

Variety Data variety refers to the many sources and types of data being consumed, both structured and unstructured.

• Structured data: This includes the typical market or tick data and transaction reference data that traders or quants have contended with for years. These datasets have predetermined formats that are designed to fit into systems analysed easily.

• Unstructured data: Examples of unstructured data include social, sentimental, and voice data. You can find drastic variations between these data points, and they will need to be constructed into a machinereadable format for analysis (becoming structured data). Trade emails, voice, and IM data are good examples of what is captured for compliance and risk analysis.

Value The final V—Value—is by far the most important. It characterises the potential ROI and strategic impact of big data on your day-to-day business activities or organisation. As we think about the Value factor, the first order of business is to assess the quality of your data. If the content you are collecting is not trustworthy or clean, the entire process is corrupted. More isn’t necessarily more if you cannot be assured that the data being collected is going to add value. By the same token, if the way in which you are applying the data is not well considered, i.e., if you are not “asking the right questions of that data,” then you will not extract benefit from the process. The age-old story of “garbage in,

David Firmin, MD and Head of Global Trading Research, Instinet

garbage out” certainly applies to the process of big data management, but we can add a new maxim, as well: bad question, bad result.

It’s important to be mindful that big data isn’t a virtue unto itself. Its value lies in its effective application to a specific problem or model.

Why doesn’t everyone use big data analysis?

A tall order.

Big data digitises the sheer volume of information that is being produced globally and synthesises that information to deliver benefits, improve efficiencies, or advance an organisation’s goals. That’s a rather tall order.

To do this, an enterprise must first be able to develop strategies, operations, and the right resources to plan and manage the logistics of all this information.

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40 | AMERICAS

Requires enterprise-wide change.

There are several imperatives: • A cultural change needs to happen. Traditional financial firms are not set up to take advantage of the data that’s available today. They must adopt new mindsets and skill sets in order to realise the benefits that new technology can bring.

• You must have a modern data platform in place to support your big data strategy across the enterprise.

Some financial institutions lack the systems and technologies to integrate siloed data and model data to produce insights that they can incorporate into their operations.

• Traders and analysts need to be comfortable and effective in applying new techniques. Workflow needs to change, along with their tools and strategies.

Committing to these imperatives is not easy.

It requires a change in your business model that aligns the organisational structure, your processes, and technology to create a robust, secure, and scalable data management infrastructure. It also requires having uniquely talented people—not only data scientists, but IT and business people—who know how to pursue the 4Vs and ask the right questions. This mix of talent can be difficult to find, especially when so much of this technology is still new.

Not quick. Not cheap. No guarantees.

The upfront investment of time and resources can be a challenge to firms of all types. Depending upon the nature, size, and mission of a firm, committing to a major, long-term investment such as a big data project can be hard to sell to executive management and boards, since quantifying the tangible benefits and understanding the timeline for reaping the ROI can require a leap of faith.

Analytics has always been important to trading. How has big data analysis changed the way data is used in financial services?

Since Instinet launched electronic trading in 1969, technology has been playing an ever-expanding role in the financial sector. Big data is a significant factor in the most recent rapid evolution of electronification. It is pushing the industry to new heights and across functions such as idea generation, analytics, execution, risk management, regulatory compliance, marketing, and client relationship management.

Big data technology has enabled the storage and analysis of data sets not possible before. Many firms

GLOBALTRADING | Q4 • 2018

are putting greater emphasis on new data management platforms that enable them to integrate and deliver data and analytics in real time, rather than using numerous separated analytics engines. Using the latest technologies, data infrastructure, and processing methods to harvest greater intelligence from increasingly higher volumes of data is becoming a competitive necessity.

The existence of big data makes the following more possible: • Real-time responsiveness. Incorporating low-latency stimuli from alternative sources into existing strategies and the behaviour of live orders.

• Heuristic capabilities. Going beyond static models.

• Machine learning. Combining advanced computational analysis and simple automation.

• Artificial intelligence. Handing over decision-making discretion to the platform.

What is the relationship between big data, machine learning, and artificial intelligence?

Big data is the core fuel that drives technologies like machine learning and AI. These technologies are dependent upon the advanced computational capabilities and characteristics (the 4Vs) of the underlying data.

Machine learning is a way of distilling patterns and/or achieving automation that is genuinely heuristic.

Instead of writing millions of lines of code with complex rules to perform a task, you can develop technology that can look at a lot of data, recognise patterns, and learn from the data. “Learning” requires feeding huge amounts of data to the algorithms and allowing them to adjust and improve. While machine learning may be considered an evolution or extension of known statistical methods, it requires new data logistics and analytical skill in order to derive signals that are relevant to the investment process, and drive conclusions or actions in a way that delivers against the goals with precision and consistency.

Artificial intelligence (AI) is an attempt to build machines that can perform tasks that are characteristic of human intelligence. This includes understanding language, recognising objects and sounds, learning, and problem-solving. AI in financial services puts greater discretion over decision making into the technology versus the human operators. This means that AI offerings are replacing certain aspects of

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“Traditional financial firms are not set up to take advantage of the data that’s available today. They must adopt new mindsets and skill sets in order to realise the benefits that new technology can bring.” human labour or effort, empowering technology to perform these explicit tasks with a degree of predetermined control.

human insights and judgment to drive it forward—it allows you to use automation to recognise patterns, or remove bias, in a way that is faster than what a human could do.

Using artificial intelligence means you are asking the technology to make decisions on your behalf. This level of discretion is something that must be designed and weighed carefully. It’s analogous to the difference between using a GPS guidance tool versus a selfdriving car.

At the end of the day, machine learning and other data processing technologies should not seek to replace the experience, judgment, and insight of the human trader, but rather they should amplify his/her capabilities and complement his/her intuition.

Don’t try to replace all human involvement.

Businesses need to be mindful of where to apply machine learning and other new data processing technologies. Machine learning is the first step in the integration of big data analytics with automation. This is where technology utilises big data to learn and respond or adapt. Machine learning still allows for

Q4 • 2018 | GLOBALTRADING

EMEA Trading Conference 2019 14 March 2019 | Old Billingsgate, London Firmly established as Europe's largest one-day trading conference and championed by the industry, the EMEA Trading Conference will return on 14th March 2019. The event will feature a four stream agenda developed by members of the EMEA trading community and will bring together 1000+ senior industry participants for an insightful day of education, regulatory updates and endless networking opportunities.

With thanks to our sponsors —

“FIX Trading Conferences are unmissable events for the European trading community delivering high -quality content, outstanding attendance, and a relaxed atmosphere enabling constructive talks. It’s the perfect opportunity to stay close to your clients, meet new ones, and promote your brand” Marketing Manager, Euronext

To view full event information and for details on how to register, please visit:

www.fixtrading.org/emea2019

Created by the industry, for the industry

42 | FIX TRADING COMMUNITY MEMBERS

FIX Trading Community Members *Premier Global Members marked in bold

360T Asia Pacific 42 Consulting Pte Ltd Actuare AFME- Association for Financial Markets in Europe Alcova AM Algomi AllianceBernstein American Century Investments Ancoa Software Appsbroker Fintech Aquis Exchange ASIC Association of International Wealth Management of India Australian Securities Exchange AXA Investments Managers Ltd B2BITS EPAM Systems Company Baillie Gifford Banca IMI SpA Banco BTG Pactual S.A. Banco Itau S.A Bank of America Merrill Lynch Barclays Barings Baymarkets AB Beijing RootNet Technology Co., Ltd. Berenberg Bank BlackRock, Inc. Blitz Trading Bloomberg L.P. Bloomberg Tradebook BlueBay Asset Management BM&F BOVESPA BNP Paribas Bolsa de Valores de Colombia Bolsas y Mercados Españoles (BME) Brandes Investment Partners LP Brook Path Partners, Inc. BSO Network BT Global Services BVI Cameron Edge Cantor Fitzgerald Capital Group Companies, Inc. Cboe Global Markets Cedar Rock Capital CFETS Charles River Development Chi-X Global Inc Chronicle Software Cinnober Financial Technology AB Circle Citi CL&B Capital Management

Clearing Corporation of India Ltd CLSA Limited CME Group Colonial First State Global Asset Management Colt Technology Services Commonwealth Bank of Australia Connamara Systems LLC Cowen Corvil Credit Suisse Crown Jewels Consultants Ltd Daiwa SB Investments Daiwa Securities Group Inc. Danske Bank DATAROAD DataArt Dealogic Delta Capita Deutsche Bank Deutsche Boerse Group Dimensional Fund Advisors Drebbel DTCC DXC Technology Eastspring Investments (Singapore) Limited EBS BrokerTec EDMA Europe Egypt For Information Dissemination Emagine Consulting Esprow Pte. Ltd. ETLogic Ltd Etrading Software Ltd Eurex EuroCCP Euronext Paris SA European Venues & Intermediaries Association (EVIA) EuroTLX Exactpro Systems Exane BNP Paribas Eze Software Group EZX Inc. FactSet Federated Investors FIA (Futures Industry Association) Fidelity Management & Research Co Fidelity International Fidessa Group Financial Information Forum First Boston Group FISD Fiserv FIS Global FIX4wards Fix8 Market Tech FIX Flyer LLC FIXSOL FlexTrade FpML Franklin Templeton Investments

Premier Global Members

GLOBALTRADING | Q4 • 2018

Gamma Three Trading, LLC GATElab GETCO Asia GMO Goldman Sachs GTT GreySpark H2O Asset Management Haitong International Securities HCL Technologies Hilltop Securities HM Publishing Hong Kong Exchanges & Clearing Limited Hong Kong Investment Funds Association (HKIFA) HSBC HSBC Global Asset Management ICMA (International Capital Markets Association) IG Group Ignis Asset Management Incisus Capital Partners Indata Recon LLC Indian Association of Alternative Investment Funds Informagi AB Infoware Infront AS ING Bank Instinet InstrumentiX Intercontinental Exchange (ICE) ITG Ipreo IPC Systems IRESS

ISITC ISO Itiviti Janus Henderson Investors Jefferies J.P. Morgan JP Morgan Investment Management Jordan & Jordan KB Tech KCG Holdings Kotak Securities Kx Systems LCH Linedata Liquidnet LiquidMetrix LIST Group Lloyds Banking Group London Stock Exchange Group M&G MACD Macquarie Securities MAE - Mercado Abierto Electronico S.A. Mansukh Securities and Finance Ltd MarketAxess

FIX TRADING COMMUNITY MEMBERS | 43

Marshall Wace Asset Management Mawer Investment Management MDSL Metamako MFS Investment Management Mizuho Securities Mongol Securities Exchange (MSX) Morgan Stanley Investment Management Morgan Stanley MTS SpA MUREX Nasdaq Nasdaq Nordic National Physical Laboratory Newton Investments NEX Group Nikko Asset Management Nomura Asset Management Nomura Nordic Growth Market (NGM) Norges Bank Investment Management Northern Trust Global Investments Ltd OCBC Securities Private Ltd. OMERS OMG (Object Management Group) Omniex On Budget and Time Ltd Ontario Teachers’ Pension Plan Board Onix Solutions [OnixS] Options Clearing Corporation Options Technology Ltd Orbis Investment Management Limited Oslo Bors ASA OTC Exchange Pantor Engineering AB Peresys (IRESS) Perpetual Motion Research PIMCO Pioneer Investments Portware Primary E Trading Principal Global Investors Putnam Investments QuantHouse Quantitative Brokers Quendon Consulting R Shriver Associates Rabobank International Rapid Addition Raptor Trading Systems, Inc. RBC Capital Markets RBC Global Asset Management Research Exchange Santander Global Banking & Markets SASLA (South African Securities Lending Association) Schroders Sensiple Shanghai Stock Exchange Shield Finance Compliance SimCorp

Singapore Exchange SIX Swiss Exchange Skandinaviska Enskilda Banken AB Sloane Robinson smartTradeTechnologies Societe Generale Softsolutions! Srl Southeastern Asset Mgmt Spectracom SS&C Technologies Standard Chartered Bank Standard Life Investments State Street Global Advisors State Street Bank & Trust Sumitomo Mitsui Trust Bank Swedbank Robur Fonder AB SWIFT Systemware Innovation Corporation (SWI) Taiwan Stock Exchange Tata Consultancy Services Technistock Telstra Global The Continuum Partners The Investment Association The London Metal Exchange The Nigerian Stock Exchange The Realization Group The Technancial Company The Vanguard Group Thomson Reuters Tokyo Stock Exchange TORA Torstone Technology Tower Research Capital India PVT Ltd TP ICAP TradeHeader, S.L. Tradeweb Trading Technologies TradingScreen Tradition Traiana (ICAP) Transaction Network Services (TNS) TransFICC Trax Turquoise TWIST UBS ULLINK UniCredit Vela Velocimetrics VOEB Vontobel Warsaw Stock Exchange Wellington Management Company Winterflood Securities XBRL XLP Capital Xetra (Deutsche Börse) XTRD Zeopard Consulting

New Member FIX Trading Community wishes to welcome the following companies to its growing worldwide membership.

For more information, please visit: www.fixtrading.org

Big XYT

big-xyt.com

Exactpro Systems exactpro.com

Gibraltar Stock Exchange www.gsx.gi

GTT

www.gtt.net

Stifel Europe

www.stifel.com

Torstone Technology

www.torstonetechnology.com

Tradeflow AB

www.tradeflow.se

Premier Global Members

Q4 • 2018 | GLOBALTRADING

44 | LAST WORD

My City

Paris

By Laurent Albert, Global Head of Execution, Natixis Asset Management Finance

Best thing about your city?

Definitely the architecture and its many restaurants. Paris is a multicultural city with a wonderful diversity of neighborhoods ranging from colorful Montmartre to the historical “Quartier Latin” without forgetting the authentic Chinatown of Paris, there is always something new to discover.

Worst thing about your city?

People use to say the taxi but I guarantee you this is not true. With the emergence of ride hailing services like Uber and private drivers, the competition has had a beneficial effect!

Getting to work?

I use public transport because it is for me the fastest and most efficient way. Paris is also a city very well served by transport.

GLOBALTRADING | Q4 • 2018

Best place to stay when visiting?

The 6th arrondissement (Boulevard St Germain) is a neighborhood very popular with Parisians and tourists alike.

You are next to the Seine river, and can combine shopping and sightseeing, visit Luxembourg gardens on foot and walk along the river to the Louvre Museum.

Where to take guests to dinner?

Bistros and restaurants are everywhere, The train bleu with its majestic décor and is with lots of great addresses and culinary located close to our offices ! Or the Georges restaurant on the roof of the George Pompidou refences.

Museum for its magnificent night view of Paris.

Best tourist site?

Eiffel Tower, Louvre Museum, simply Relaxed spot with family or friend?

UNIQUE. I would also recommend the Hôtel Costes In a neo-baroque setting by streets and alleys of the “Quartier Latin” Jacques Garcia, here you are in a unique patio, surrounded by small lounges and more for a truly authentic experience.to the Royal Greenhouses of Laeken is also very intimate spaces that transition to a lively nice. atmosphere at night!

View from your desk?

We look out onto the Austerlitz train station that provides us with the ability to escape the Paris hustle and bustle if need be! Our setting also provides us with great views of the city, especially on a clear day.