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GlobalTrading’s Editorial Think Tank Dear Readers, Undoubtedly, new technologies are changing trading behaviour and expectations. Sellside firms and third-party service providers have been in the vanguard of this evolution, but buy-side firms are being forced to catch up to meet regulatory demands and also the challenge posed by industry disruptors.

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

Carlos Oliveira Brandes Investment Partners

Greg Lee Barclays

Emma Quinn AB

Michael Corcoran ITG

The buy-side is responding in different ways, according BNY Mellon’s Nicholas Greenland. Some believe there are great opportunities within big data and machine learning, others have decided that they have limited relevance to their business models, and there are those who struggle to understand how they can best use them. But, there is a shared core belief. Alternative datasets and the new technologies can offer value by addressing not only trading and investment management needs, but must satisfy their clients’ interests. Yet, as Ambrose Tan at Aberdeen Standard Investments points out, diverse global regulatory regimes and contrasting trading procedures in different asset classes can restrict the effectiveness of new technologies in the trading process. Moreover, custom, inertia and the difficulties in enforcing change in dynamic markets mean differences in asset class trading practices are likely to remain entrenched for some time. On the other hand, a common policy shared among regulators might facilitate a more confident deployment of the latest technologies, notably artificial intelligence. Regulatory inconsistency compounds the difficulties firms face, as they decide how to embrace new technologies successfully. There isn’t a standard model with an unambiguous record of success – partly because it’s too early to make an accurate assessment, and partly because many buy-side firms have historically relied on the sellside for technologies and systems, for instance their trading algorithms. Meanwhile those algorithms are becoming complex. There is a major change underway within the trading industry as the focus shifts towards a more sophisticated and advanced quantitative and scientific execution logic, argue Macquarie’s Stuart Baden Powell and Hong Kong University’s Professor Dan Li in their article. Although, the value of human agency is diminishing as automated processes supersede an individual’s experience skill and intuition, humans retain a role that is skilled in a different way, namely an ability to understand and interpret complex technology. Best Regards,

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

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CONTENTS 7

FOCAL POINT

7 Limitations On The Use Of Artificial Intelligence - Ambrose Tan, Aberdeen Standard Investments

INSIGHT

11 Buy Side + FinTech + Big Data = ? - Nicholas Greenland, BNY Mellon Investment Management 14 VWAP Trap: Volatility And The Perils Of Strategy Selection - Erin Stanton, ITG

14

OPINION 21 Fixed Income Trading: Big Data Boost - Carl James, Pictet Asset Management 24 The Itiviti Perspective On Current Trends In Quality Assurance - Vaibhav Shukla, Itiviti 27 Adapting To Constant Change - Neil Bond, Ardevora Asset Management 30 ESG: Can Hedge Funds Save Our Oceans? - Phillippe Burke, Apache Capital

27

ASIA 38 Transforming Trading With Machine Learning - Lee Bray, J.P. Morgan Asset Management AMERICAS 40 Electronic Trading In The Brazilian Markets - Dr. Christian J. Zimmer and Leandro Pereira, Itaú Asset Management INDUSTRY 45 In Memory Of Tim Healy - FIX Trading Community

EUROPE 17 Widening The Net To Devise Sophisticated Trading Algorithms - Stuart Baden Powell, Macquarie and Professor Dan Li, Hong Kong University

46 FIX Trading Community Members 34 MiFID II’s Unintended Consequences - Gianluca Minieri, Amundi

MY CITY 48 Singapore - Ambrose Tan, Aberdeen Standard Investments


HIGHLIGHTS “Regulators are wary of buy-side firms that rely on in-house developed AI systems for trading and investment decisions, rather than a process featuring directly accountable individuals. Ultimately, humans, not machines, must be held accountable.” P.7 Ambrose Tan, Head of Dealing - Asia Pacific, Aberdeen Standard Investments

“The main options currently being discussed include adopting crossasset platforms from an existing firm, an amalgamation of best of breed platforms, or leveraging container-like technology to create one’s own ‘trading app store’.” P.11 Nicholas Greenland, Managing Director, Global Head of Broker/Dealer Relations, BNY Mellon Investment Management

“A crucial advantage from a multi-disciplinary approach is the ability to develop more complex algorithms and strategies to enhance trade execution performance, and differentiate a firm’s capability from the overcrowded and commonplace.“ P.17 Stuart Baden Powell, Head of Asia Electronic Product, Macquarie, and Professor Dan Li of Hong Kong University

“Automated trading of liquid, benchmark bonds is evolving, with increasingly reliable and systematic recalibrations of constituent bond issues taking place. One benefit is that more bandwidth is created for traders to deploy their skills and exploit their networks to concentrate on illiquid or esoteric bond issues.” P.21 Carl James, Global Head of Fixed Income Trading, Pictet Asset Management

“Capping dark pools might have led to suppressing rather than encouraging liquidity formation, with the risk of impacting negatively on long-term institutional investors, the very firms that are meant to be protected.” P.34 Gianluca Minieri, Deputy Global Head of Trading, Amundi


FOCAL POINT | 7

Limitations On The Use Of Artificial Intelligence By Ambrose Tan, Head of Dealing - Asia Pacific, Aberdeen Standard Investments

Q2 • 2018 | GLOBALTRADING


8 | FOCAL POINT

The successful application of the latest technologies to the trade execution process requires clear and consistent rules and standards. Diverse global regulatory regimes and contrasting trading procedures in different asset classes restrict the effectiveness of new technologies in the trading process. Custom, inertia and the difficulties in enforcing change in dynamic markets mean differences in asset class trading practices are likely to remain entrenched for some time. On the other hand, a common policy shared among regulators might facilitate a more confident deployment of the latest technologies, notably artificial intelligence (AI). The application of AI can offer buy-side dealing desks undoubted benefits, notably by reducing the time it typically takes for a human trader to gather and analyse information required to facilitate best execution. AI can access data faster, and then recognise trading patterns in order to provide the basis for a better informed trade decision. In future, it is feasible that dealing desks may have AI specialists working alongside human traders.

“AI cannot make recommendations based on inherently incomplete material, subjective instructions and constraints.” However, currently, it has fundamental limitations: AI is only able to access and evaluate the data that is available, recognise patterns and factors within preestablished parameters, and therefore cannot make recommendations based on inherently incomplete material, subjective instructions and constraints. AI can suggest a “most likely” outcome, not predict a definitive result.

GLOBALTRADING | Q2 • 2018

On the face of it, a statistically accurate assessment and decision forecast might seem attractive. But, both regulators and clients could feel justifiably uncomfortable. Regulators, especially in Europe following the introduction of Markets in Financial Instruments Directive (MiFID) II this year, might conclude that if a platform’s software is provided by a sell-side firm, and it promotes a decision-making process based on AI, then it could be an inducement to trade, which would potentially hinder best execution and not work in the interest of clients’. The MiFID II requirement for buy-side firms to provide LEI numbers designating discrete, individual accounts tightens the scrutiny even further, but promotes greater trade transparency and reporting. It is a fine line between enticement and inducement, and the advertisement of a buy or sell signal might fall on the side of the latter. Yet, the unbundling of research provision and trade execution in itself is unambiguous, so it is important that a fund manager avoids stepping over that line, or is even suspected of doing so. The onus is on the fund management firm and its traders to be fully cognizant of brokerages that have been selected as authorised providers of (qualitative and quantitative) research material and ensure that there is no suspicion that it is being used as an inducement to trade. Moreover, the regulator is also wary of a buy-side firm that relies on an in-house developed AI system for its trading and investment decisions, rather than a process featuring directly accountable individuals. Ultimately, humans, not machines, must be held accountable. Meanwhile, clients too might legitimately complain that a trading decision, which is integral to a buy-side firm’s investment process, should not be the product of a machine-generated computation. After all, they are paying the firm for the skills and experience of its fund managers and traders. Nevertheless, AI is a useful tool for helping trade some, but not all, asset classes. The main determinant is the comprehensiveness of accessible data. Information about exchange-traded equities is usually sufficiently extensive and reliable for AI systems to operate, but far less so for fixed income and foreign exchange markets where most trading occurs over-the-counter (OTC), directly with sell-


FOCAL POINT | 9

“If a platform’s software is provided by a sell-side firm, and it promotes a decisionmaking process based on AI, then it could be viewed as an inducement to trade.” side counterparties or anonymously via broker intermediaries. Executed transactions and posttrade information can be opaque. Price makers are conscious of market impact, especially during bouts of illiquidity, hence there is an emphasis on protecting market flows. Fixed income markets are characterised by brokerages’ inventory supplies, risk capacity and niche expertise; although AI might identify trading patterns, ultimately a human trader’s networks and experience are a more reliable way of achieving best execution. Foreign exchange markets are generally opaque, especially spot trades where price activity is determined by myriad influences. Speculative behaviour remains a powerful force, but less so now that hedge funds are under tighter regulatory scrutiny. Instead, “genuine” transactions that support or hedge investment and corporate treasury decisions dominate the spot market and AI will struggle to determine useful patterns among those trades. MiFID II seeks to address some if not all the above issues as it aims to create a level playing field for all market participants and all markets with a push for greater disclosure and transparency. Now, transactions that involve foreign exchange products – options, non-deliverable forwards and currency and interest rate swaps – are reportable to the market within 15 minutes of trading. Trading data on various venues including Multi Trading Facilities (MTF), aka

Ambrose Tan, Head of Dealing - Asia Pacific, Aberdeen Standard Investments multi-dealer platforms, will also be captured. New legislation seeks to monitor not just post-trade but pre-trade analysis as well. While these developments may spell a positive for AI trading, we go back to the fundamental issue of human accountability. Will AI create an environment in which volatility spikes up in the face of speedier data crunching? Disparate jurisdictions and rules A more fundamental problem is the confusingly disparate regulatory regimes throughout the world that force buy-side firms, in particular, to be

Q2 • 2018 | GLOBALTRADING


10 | FOCAL POINT

“A fundamental problem is the confusingly disparate regulatory regimes throughout the world that force buy-side firms, in particular, to be circumspect about the adoption of some technologies.” Europe, in Japan, brokers are not allowed to charge fees directly for research, but instead bundled in transaction charges. If the various worldwide regulatory bodies could agree on common policies and standardised rules, then they would help money managers and their clients achieve their objectives more easily. More importantly, consistency would allow a successful adoption and implementation of the new technologies that are increasingly available.

circumspect about the adoption of some technologies – such as AI – that should in theory benefit the investment and trading processes. Some financial regulators in Asia do manage to combine a firm oversight (across all asset classes), while also being receptive to global regulatory trends and open to the development of new trading technologies. Elsewhere in Asia, there are several emerging markets with diverse regulatory regimes which are a challenge to institutional investors with a global or regional mandate. Yet, perhaps a standout example of regulatory anomaly is actually between the rules in two developed jurisdictions. In a direct contradiction to the unbundling requirements of MiFID II throughout

GLOBALTRADING | Q2 • 2018

Aberdeen Standard Investments is a brand of the investment businesses of Aberdeen Asset Management and Standard Life Investments. The views expressed in this article are the author’s and not the company’s.


INSIGHT | 11

Buy Side + FinTech + Big Data = ? By Nicholas Greenland, Managing Director, Global Head of Broker/Dealer Relations at BNY Mellon Investment Management

The rapid evolution of technology and the surging availability of datasets, means that assessing and choosing which products and firms to work with can be overwhelming, so industry cooperation is essential. Asking people on the buy-side about Fintech – more specifically big data and machine learning – and its potential application to their business elicits a broad range of responses ranging from excitement and existing involvement, to frustration and the lack of belief that opportunities can be grasped. I believe that the buy-side can be split into three pockets: those who believe there are great opportunities within big data and machine learning, those for whom it’s “just not in my business” and those who struggle to understand how this new technology can be relevant to them. Summing up some of these challenges, Mahmood Noorani, founder of Quant-Insight, recently said: “We

need to understand the question that we are trying to answer, not try to reverse engineer the question from the answer”. For many, such experiences have been quite the opposite, so the ability to both bridge business challenges and understand and deliver technology has never been more valuable. Stepping back, the core belief (and at times, hope) articulated in conversations with buy-side peers is that alternative datasets and the evolving FinTech space can offer value by addressing not only our trading and investment management requirements, but also our clients’ needs. With this in mind, the broader issue is actually about identifying the right questions that need to be answered, searching for the right answers and then understanding how to execute on what is now possible. This is against the backdrop of industry evolution, where some are now looking at whether the sell-side model of high-touch and low-touch trading makes sense for us. For the buy-side, the former is the focus

Q2 • 2018 | GLOBALTRADING


12 | INSIGHT

“Alternative datasets and the evolving Fintech space can offer value by addressing not only our trading and investment management requirements, but also our clients’ needs.” cross-asset trading workstation to be delivered using components from separate providers and seems to be gaining mindshare as it promises greater levels of customisation above and beyond what is normally attainable.

Nicholas Greenland, Managing Director, Global Head of Broker/Dealer Relations at BNY Mellon Investment Management for trade advisory/value creation (for example, research-led cross-asset liquidity consultancy and derivative implementation ideas) and the latter is more focussed on streamlining and scaling execution coupled with research (for example, how best to aggregate fragmented liquidity). Regardless, both approaches need to lever technology to meet their full potential. Technology options With these business drivers and with MiFID II stimulating changes in technology and trading processes, the question for many is: what else can we do to optimise and expand upon what we have? For example, do we need an execution management system (EMS)? If so, what is the best implementation strategy and technology solution? The main options currently being discussed include adopting cross-asset platforms from an existing firm, an amalgamation of best of breed platforms, or leveraging container-like technology to create one’s own “trading app store”. The latter allows a bespoke

GLOBALTRADING | Q2 • 2018

It also allows for a flexible “swap in and out” of components as technology evolves and potential for faster delivery of solutions as these become available, for instance, as some sell-side firms look to expose some of their toolsets. This is against the backdrop that few asset managers, with notable exceptions, consider building their own technology as being a true differentiator. Regardless of the approach: do implementing new trading tools mean that all the questions will have been answered? No. The broad themes of data and tools for pre-trade and in-flight decision support (which may or may not be within an “out of the box” EMS), and other functionality to assist price formation for those wishing to be price-setters on all-to-all venues, will keep many on the buy-side focused internally for some time. Looking externally, the question is actually what should and can we be doing together as an industry. I think there are two main areas. Buy-side initiative Firstly, given the rapid evolution of technology and the surging availability of datasets, assessing and choosing which products and firms to work with can be overwhelming. This is where the industry can work together through trade bodies, in partnership with specialist investment firms or with larger sell side firms, all of whom can curate such conversations for their own members, clients and partners.


INSIGHT | 13

By leveraging such organisations, the buy-side has the ability to hone in more rapidly on the FinTech firms that are ready to engage with sophisticated financial institutions, have correctly articulated opportunities facing our industry and have identified meaningful problems that they can help us solve.

solutions. One recent suggestion is supporting initiatives such as OpenFin-led FDC3 to enable standardised connectivity for our industry’s desktop applications. It would then be up to the individual firms to follow their own path and add their edge through tailored implementation and use of such tools, including the input of carefully sourced and manipulated data sets.

“The broad themes of data and Collaboration to foster evolution part of a global multi-boutique investment tools for pre-trade and in-flight Being management organisation, I am aware of the power of co-operation and scale that companies acting together decision support, and other can bring, as well as the importance of independence. The two are not mutually exclusive. I also believe functionality to assist price members of trade bodies should utilise them as a forum for collaboration and to foster evolution in the formation for those wishing market structure. Members are increasingly more in their thoughts and feedback to the market to be price-setters on all-to-all proactive and this is evident in the Investment Association’s venues, will keep many on the position paper on “Last Look” in foreign exchange. buy-side focused internally for some time.” “The sell-side, for many years, has been at the vanguard of The sell-side, for many years, has been at the vanguard driving and paying for evolution of driving and paying for evolution in market structure and technology. It has had existing teams and in market structure and demonstrable pedigree in collaborating with competitors to deliver mutual goals. For the cynic, this technology.” has been to support “just enough” innovation without too much disruption. The buy-side however, has less pedigree with arguably fewer resources to do so. With some recent notable exceptions (for example, Luminex and Turquoise Plato), this has meant that we have historically relied upon the sell-side to facilitate collaboration to encourage innovation. Looking ahead, there is a real opportunity for buy-side firms to strategically collaborate and partner (including with the sell-side) to the benefit of its clients and industry.

Long may the buy-side seek to collaborate ever more closely and proactively push for market evolution. This will help us all more clearly demonstrate that trading is a core part of the investment management process and that the industry is cooperating to act in the best interests of our clients.

So, secondly, I would encourage buy-side firms to articulate several problem sets, for instance around how best to enhance either high- or low touch trading, so we can actively collaborate to find the potential

Q2 • 2018 | GLOBALTRADING


14 | INSIGHT

VWAP Trap: Volatility And The Perils Of Strategy Selection

By Erin Stanton, Managing Director, Trading Analytics, ITG Historical performance indicates that traders should reduce their use of VWAP strategies during spikes in market volatility. When it comes to improving trading performance, selecting the right strategy is crucial. An ITG survey of buy-side traders last year found that 85% believe strategy selection has the most potential to affect trading performance, far outstripping the importance of broker choice or venue selection. But before traders choose a strategy, they would be well served to consider prevailing market conditions. The relationship between algorithmic trading strategies, trading costs and volatility has been well documented, including in a 2011 paper by Ian Domowitz. Domowitz found that usage of a VWAP strategy in a high-volatility environment added an eyebrow-raising 18 basis points (bp) of impact costs versus a VWAP trade executed in a low-volatility environment.

GLOBALTRADING | Q2 • 2018

We employed a similar analytical framework leveraging ITG’s broker-neutral algo Global Peer database, focusing on two recent periods in the US with differing market conditions, as measured by ITG’s Smart Market Indicators (SMIs). Our SMIs compare current volume, volatility and spread to historical averages, allowing users to quickly identify abnormally favourable or unfavourable market conditions, and react to those conditions accordingly. In October 2017, volatility measured by ITG’s SMIs was relatively normal at the 58th percentile, whereas in February 2018, SMI-measured volatility jumped to the 71st percentile. Despite this observed shift in volatility, the general use of a VWAP strategy in the US remained quite consistent between the two time periods. During the more volatile period, liquidity-seeking algos were used less while implementation shortfall (IS) usage nearly doubled.


INSIGHT | 15

Value Traded by Algo Strategy

Source: ITG

VWAP as an algo strategy is generally used to achieve two objectives: 1) to implement a trading strategy that minimizes costs against a VWAP benchmark, and 2) to take an in-line approach to implementation against an arrival benchmark. Irrespective of the driver behind the selection of a VWAP algo, during the more volatile period costs increased by two times against the VWAP benchmark and nearly three times against the IS benchmark. Recognizing that VWAP as a strategy can be affected by single outlier trades, we also reviewed the median IS costs, which increased from -1.3bps to -4bps.

“We observed in Asia that VWAP use spiked from a typical level of 10% to around 30% during the high-volatility periods caused by specific market events. This increase came at a cost.” Parallels in Asia Pacific trading We took a similar look last year at the use of VWAP in Asia Pacific trading around the extreme volatility events of Brexit and the 2016 US election. We observed that VWAP use spiked from a typical level of 10% to around 30% during the high-volatility periods

Erin Stanton, Managing Director, Trading Analytics, ITG caused by specific market events. This increase came at a cost. Compared against a VWAP benchmark, the difference between low-volatility and high-volatility periods was a significant 2.7bps. And while the difference in cost against an IS benchmark between an implementation shortfall algo and a VWAP algo became less pronounced, the standard deviation of cost became much wider for VWAP trades in Asia Pacific trading. VWAP Trading Statistics

Source: ITG

Algo performance evaluation should not consider just the cost of what was executed, but should also consider the opportunity cost for what does not get completed. Without considering the number of shares left on the table, we would say that in the lowervolatility environment of October, VWAP was still the most expensive strategy in US trading. But when considering the total cost of the order, including the unfilled shares, we can see that IS was the most

Q2 • 2018 | GLOBALTRADING


16 | INSIGHT

expensive strategy, averaging 18bps of slippage (unfilled shares were priced using the trade date close and benchmarked against IS). This picture changes in the higher-volatility regime of February, though. Total costs for all strategies, except IS, increased between October and February, while VWAP spikes to -28bps of total implementation cost. October Filled Cost vs. Total Cost Including Opportunity Cost

Source: ITG

February Filled Cost vs. Total Cost Including Opportunity Cost

“It is difficult to offer a definitive reason for the continued use of underperforming VWAP strategies during periods of higher volatility.” No herd immunity It is difficult to offer a definitive reason for the continued use of underperforming VWAP strategies during periods of higher volatility. It is possible that some traders are focused on working more difficult parts of their order books and they seek to put some trades on a “participate without too much risk” setting through VWAP. It is also possible that traders looking to remain in line with their index benchmarks are hopeful that VWAP is the best way to preserve that correlation when trading conditions are unfavourable. Even if this year doesn’t see the same return of volatility that some are predicting, traders would be well served to consider lightening up on use of VWAP strategies during any sharp spikes in volatility, because an “in-line” print can result in sub-par trading performance.

Source: ITG

“Algo performance evaluation should not consider just the cost of what was executed, but should also consider the opportunity cost for what does not get completed.” GLOBALTRADING | Q2 • 2018


INSIGHT | 17

Widening The Net To Devise Sophisticated Trading Algorithms

By Stuart Baden Powell, Head of Asia Electronic Product, Macquarie, and Professor Dan Li of Hong Kong University The only constant in algorithmic trading is change and continual improvements are necessary to evolve with innovation in both technology and market dynamics. There is a major change underway within the trading industry as the focus shifts towards a more sophisticated and advanced quantitative and scientific execution logic. At Macquarie we have embraced this move, and increasingly, a similar approach is evident among several of our buy-side counterparties. This shift has parallels in other industries. For instance, the airline industry offers a historical similarity where, over the years, human pilots have obtained new skills and adapted to shifting cultures in response to technological change. Most importantly, the value of human agency has diminished as automated processes have superseded an individual’s experience skill and intuition for functions such as landing an aircraft. Yet, humans retain a role that is

skilled in a different way, namely an ability to understand and interpret complex technology. This developmental “human in/out of the loop” process is what we are seeing on the buy- and sell-side trading desks. Sourcing algorithm complexity With skillsets from diverse academic and industry backgrounds and wider computational improvements built-in, Macquarie has pre-positioned for this change and sourced algorithmic logic not only from within finance, but also by looking to more sophisticated industries. Opportunities to map across aero/ astronautics, or Silicon Valley firms provides tried and tested logic in similar non-linear environments. A crucial advantage from a multi-disciplinary approach is the ability to develop more complex algorithms and strategies to enhance trade execution performance, and differentiate a firm’s capability from the overcrowded and commonplace.

Q2 • 2018 | GLOBALTRADING


18 | INSIGHT

“A crucial advantage from a multi-disciplinary approach is the ability to develop more complex algorithms and strategies to enhance trade execution performance, and differentiate a firm’s capability from the overcrowded and commonplace.” Indeed, in a recent paper co-written by Dan Li, entitled “The Competitive Landscape of High-Frequency Trading Firms” *, the authors found that the majority of computerized trading in the Canadian market even in recent years is still concentrated in fairly simplistic algorithmic trading strategies. Just three major, basic strategies generate a large amount of trades, and even more orders. These algorithms respond to market conditions and trading signals in a similar fashion, and pursue near identical profit/cost reduction opportunities. More importantly, this similarity leads to heightened competition within each strategy space. The market is crowded and it is becoming increasingly difficult for any generic algorithm to stand out. One particular strategy investigated concerns posting or supplying liquidity. It was found that strategies primarily providing liquidity generate lower trading revenues, regardless of whether the market is going up, down or staying flat. Limitations of simple strategies A natural question to ask is how the increased competition affects the market in general. For the most part, the research focuses on volatility over various intraday horizons, because volatility

GLOBALTRADING | Q2 • 2018

Professor Dan Li, Hong Kong University, Stuart Baden Powell, Head of Asia Electronic Product, Macquarie

management is central to trading performance. In a marketplace where algorithmic traders tend to employ similar strategies, short-term volatilities are dampened. A further investigation suggests that the fall in market volatility is driven by the portions of short-horizon volatility related to both the permanent and temporary price impacts of trades.

“We are only at base camp for algorithmic practices in Asia, so that many current segments of underlying logic could quickly be deemed legacy.” On one hand, competition among traders could lead to faster revelation of hard-information signals, and a reduction of adverse selection costs. On the other hand, competition in provision orders might also lower the compensation posting algorithms earn, which in turn explains the reduction in volatility that stems from the temporary price impact.


INSIGHT | 19

At Macquarie, we have uncovered similar usage patterns in Asia. Although it varies by market and by client the majority use a small handful of algorithmic strategies. Away from the people side and onto the product side, our research suggests that we are only at base camp for algorithmic practices, so that many current segments of underlying logic could quickly be deemed legacy. For example, we have done extensive work around prediction. If we are aiming to predict short-term direction and magnitude yet the majority of the sample is VWAP algorithms, results are rarely profound; this is amplified if we work off a fixed time constraint. In fact, predictive logic using a fixed “finish time” is actually largely superfluous. A far superior logic is time-flexible start and finish intervals leaning on specific market conditions.

“The ability to automatically adapt to what can be bid/ ask bounce, flutter, volatility, momentum or reversion reinforces the needs for more sophisticated algorithmic modelling.”

The next stage in algorithmic modelling Our work tells us that many problems can be formulated using supervised or reinforcement learning but several considerations exist to reach optimal solutions. If you start with a tabula rasa, you need a substantial amount of repeatable examples to guide the algorithm and this is where reinforcement learning can struggle. Alternatively, the widely-known “greedy algorithm” can lock into a suboptimal action loop, often referred to as the “optimization versus exploration” dilemma, and can lead to best execution challenges. At Macquarie, we think algorithmic logic has moved beyond these processes. These difficulties can be acute where some market participants use simple algorithms and some use more advanced models often manually switching in high or low volatility conditions. The ability to automatically adapt to what can be bid/ask bounce, flutter, volatility, momentum or reversion reinforces the needs for more sophisticated algorithmic modelling. Overall, these findings call for more sophisticated buy-side algorithms that build on recent, wider developments in machine learning and strategic design. * Boehmer, Ekkehart, Dan Li and Gideon Saar, “The Competitive Landscape of High-Frequency Trading Firms” in The Review of Financial Studies, 2017.

We also dig into machine learning; a crucial element given that our dynamic market is prone to high impact, fat-tail exogenous events. How does “a decision process” loosen itself and adapt when the statistical and sensory feeds are far from static? Similar to computational map-making using a LIDAR and Feature Space, it could learn by creating a feature vector or some form of training set. Alternatively, an algorithm can also use “deep reinforcement learning” or a materially simplified one or two layer approach, more correctly termed “shallow learning”. In fact, we see this shallow learning in place today.

Q2 • 2018 | GLOBALTRADING


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OPINION | 21

Fixed Income Trading: Big Data Boost By Carl James, Global Head of Fixed Income Trading, Pictet Asset Management Access to more data is improving the efficiency and raising the sophistication of the buyside fixed income trade execution process. Fixed income buy-side firms increasingly have the capability to analyse data to achieve a better outcome for clients and to satisfy regulatory requirements. To some degree the reason is quite simple: until recently data was sparse, now it is becoming more and more abundant. Banks make money when there is opacity, but regulators are forcing them to be more transparent about their activities, justify their fees and validate transaction pricing across asset classes. Moreover, sophisticated technology is getting cheaper which encourages disintermediation and disruption to traditional business models by new

entrants. Banks and brokerages are compelled to respond, or else suffer shrinking market share. The buy-side is in a similar position. The ineluctable rise of passive investing and the intrusion of robowealth advisers are piling pressure on established asset managers. Truly, the tectonic plates are shifting. The technology needed to gather and make sense of the information and then derive recommendations is rapidly improving. Indeed, we have just launched a five-person trading technology team to take advantage of these new opportunities. The information is generated internally and accumulated from external sources. The internal data accrues from messaging from counterparties, orders, trades and their hit ratios and a myriad

Q2 • 2018 | GLOBALTRADING


22 | OPINION

“Sophisticated technology is getting cheaper which encourages disintermediation and disruption to traditional business models by new entrants: the tectonic plates are shifting.” other derivations and interpretations. External data, for instance, declared orders and reported trades, was previously proprietorial and therefore scarce or expensive, but is now widely available. Although there isn’t yet a consolidated tape, measures such as the Trade Reporting and Compliance Engine (TRACE) for over-the-counter bond transactions in the US is a step in that direction. In the UK, the London Stock Exchange’s approved publication arrangement (APA) has raised the level of trade reporting in the fixed income markets, with some third party vendors now aggregating APAs. The product captures a significant part of the total market, and usually large enough to draw valid conclusions especially when used in conjunction with internal data. Post-trade analysis is critical. As more data is available, a trader becomes better able to gauge whether they handled an order correctly (for example, a request for quote), whether they approached the best counterparty for a particular bond (through examination of hit-ratios) and whether they transacted at the optimum time in the day. It is also worth emphasising that an important effect of the Markets in Financial Instruments Directive (MiFID) II is to ensure that a fixed income trader’s experience and intuition is underpinned by evidence. A decision to execute a trade and the process towards that decision must be seen to be rational.

GLOBALTRADING | Q2 • 2018

Carl James, Global Head of Fixed Income Trading, Pictet Asset Management

“As more data is available, a trader becomes better able to gauge whether they handled an order correctly, whether they approached the best counterparty for a particular bond and whether they transacted at the optimum time in the day.”


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Electronic and automated trading of liquid, benchmark bonds is evolving, with increasingly reliable and systematic recalibrations of constituent bond issues taking place. One obvious benefit for traders is that more bandwidth is created for them to deploy their skills and exploit their networks to concentrate on illiquid or esoteric bond issues. Limits to machine learning effectiveness Of course, the potential of artificial intelligence (AI) and machine learning (ML) for the trading process has attracted a lot of attention. However, despite the hype, their deployment in fund management in general is at a very early stage: there simply isn’t yet the commercial imperative for their application.

“Greater electronification of fixed income trading is likely to lead to behavioural changes, as orders get focussed on more tightly defined data points and as the underlying process becomes more methodical.”

Furthermore, many bond issues trade infrequently. Although most European equities trade around 500 times a day, many bond issues rarely trade for days or even weeks. This, perhaps, is the most significant reason why ML has restricted relevance to the fixed income markets – at least, beyond the regularly traded, liquid benchmark issues which are more amenable to systematic trading. The extensive use of algorithms caused a behavioural shift in the equities markets, by reducing the size of individual trades. Greater electronification of fixed income trading is also likely to lead to behavioural changes, as orders get focussed on more tightly defined data points and as the underlying process becomes more methodical. The difficulty for buy-side firms is how to embrace new technologies successfully. There isn’t a standard model with an unambiguous record of success – partly because it’s too early to make an accurate assessment, and partly because many buy-side firms have historically relied (and in many cases still do) on the sell-side for technologies and systems, for instance their trading algorithms. In any case, the buy-side firms need to decide quickly – and must expect to fail as they experiment. Ultimately, it’s only possible to solve problems and adapt to external innovations to the extent of your capabilities. As in the solution to the riddle, how do you eat an elephant? Answer: one bite at a time.

Portfolio managers might use ML to track a benchmark (either explicitly or covertly); traders might eventually find a use for ML if orders are delivered in a less sequential fashion than now. However, the fragmentary nature of the fixed income markets and, despite the proliferation of data, the still incomplete knowledge of all liquidity sources, different trading protocols, diverse instruments and partial price information inherent in an over-the-counter market limits the efficacy of ML in the trading process.

Q2 • 2018 | GLOBALTRADING


24 | OPINION

The Itiviti Perspective On Current Trends In Quality Assurance

By Vaibhav Shukla, Senior Vice President, Global Services, Itiviti Automating quality assurance across a range of procedures provides cost benefit and productivity improvements. The financial industry is constantly evolving, driven by a mix of new business and regulatory requirements. The implementation of the Markets in Financial Instruments Directive (MiFID) II in Europe, for instance, is spurring a tectonic technology transition. With these rapid changes in the industry and the rising cost of errors in production systems, the quality assurance function is gaining importance, and is increasingly seen as a businesscritical investment and not just a cost centre. Automated testing and common use cases Automation is a natural evolution evident in any industry, be it agriculture, vehicles or financial software. The core driver is the need is to automate “well defined, repeatable, and costly” operations. Instead of devoting efforts

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to manual testing, a similar effort spent on designing the test harness and automating the test executions may produce a huge cost benefit and productivity increase. A computer can run 4,000 test scenarios on a complex infrastructure system in a matter of minutes — where it would have taken days or even weeks as manual task. As a rule of thumb: if you must do the same task twice you should automate. One area where we see clients benefit from automating quality assurance is where unit testing, integration testing, and regression testing is integrated with the automated software build systems within a continuous integration process. Other areas with strong cases for automation are change management and service virtualization. To illustrate, imagine that you’re investing in new infrastructure software for managing client flow, but the software is still under development. Traditionally, you would wait for the software


OPINION | 25

“As a rule of thumb: if you must do the same task twice you should automate.” to be completed. Automated testing can save time by virtualizing the new environment for your clients to test against and validate their interfaces before going live. Additionally, you can use all the known client flows to continuously test against your software in development to ensure it will be fit for the purpose and scope. Quality assurance in the light of MiFID II The introduction of MiFID II was a momentous event impacting European trading operations even before it was implemented. Every aspect of trading was affected by this regulation and therefore practically all aspects of trading infrastructure software have required testing in light of the new requirements. At Itiviti, we have supervised re-development and re-certification of nearly every client connection as well as platform changes from all venues in the FIX and native protocols. We offer venue emulators and venue simulators enabling clients to test their workflows in both FIX and native protocols using our MiFID II Certification Service and our industry leading on-boarding solution, Itiviti Conductor. We are using the same tools internally to manage and assist the quality assurance process for new Itiviti products and upgrades. Our clients’ view on VeriFIX by Itiviti In March 2017, Itiviti asked TechValidate, an independent third-party research firm, to survey clients who use our Quality Assurance product VeriFIX by Itiviti, to discern product usage and evolving trends in testing automation. This survey provided essential guidance, helping us design the new enterprise offering in the area of automated testing. Clients identified the following as the most common challenges in quality assurance: • Continuous integration and regression testing. • Unit and isolation testing with multiple protocols.

Vaibhav Shukla, Senior Vice President, Global Services, Itiviti

“Instead of devoting efforts to manual testing, a similar effort spent on designing the test harness and automating the test executions may produce a huge cost benefit and productivity increase.” • Session level testing with the end points including using service virtualization. • Test harness design to catch black swans/ unexpected or known race conditions.

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26 | OPINION

Testing automation efforts are mainly focused on first three aspects: Automated testing on the rise. The proliferation of automated testing is evident from the way clients are using VeriFIX by Itiviti. Among those who use later versions of VeriFIX (6.0 or higher), nearly three quarters reported using it for regression testing while just 38% use it for manual click testing as seen in the chart below.

Quality assurance teams the main users. VeriFIX by Itiviti is today predominantly used by quality assurance teams with nearly 72% of the user base identifying themselves as such – a change from the early days of this product when it was primarily used by support desks.

50% of the users of older versions (4.x, 5.x) identify themselves as support staff, whereas 82% of the users of newer versions (6.1, 6.3) are quality assurance. Another trend influencing product design is that quality assurance has become an integral part of the development and deployment process. Conclusion We expect continued strong momentum towards automated testing, driven by increasingly complex and comprehensive testing needs as well as transformative industry trends and events such as MiFID II. We envision a highly integrated continuous testing environment and are currently investing in next-level automation that will further reduce the cost of test design and management building an enterprise-level platform.

500–2000 or more automated test cases. Users who perform regression testing will quickly build a critical mass of automated test cases – 61% have created 500–2000 or more. As the volume increases, test case management including re-usable framework, collaboration, and version control becomes an important element of an enterprise solution.

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OPINION | 27

Adapting To Constant Change By Neil Bond, Partner, Equity Dealer, Ardevora Asset Management

In an evolving trading landscape, the buyside needs to embrace technologies to extract liquidity wherever they find it. Exchanges have been around for a great many years and will likely be around for many more. They play a crucial role in the listing of securities, but are also secure trading places, and data vendors. They have not remained unchallenged even if the politicians seem to be aligned with their interests, and they have had to evolve. Recently we have seen IEX enter the trading, listing and data arena. Spotify has also come to market avoiding the traditional channels. So the primary exchanges

need to keep on their toes which they have been doing by way of conditional order type venues for large-in-scale (LIS) orders and periodic auctions for smaller orders. The MiFID changes this year have been the most dramatic to the UK market since “Big Bang” in the 1980s when trading left the floor and went screen based. So, what do traders need to keep up in this constantly changing trading landscape? I believe that new technologies and trading styles and venues need to be embraced. One thing that does seem to remain constant is a trader’s need to source liquidity. The trend toward passive investing has

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28 | OPINION

“Many of the tools that traders dreamt of are now a reality and help us not only make informed decisions but also demonstrate how we arrived at those decisions.” had a somewhat detrimental effect on liquidity, with stocks being bought and sent to the index trackers’ vaults, unlikely to see the order book again for years. In the past, you could ask a market maker and be quoted a reasonable size (maybe “a penny out in a hundred”), but that was when balance sheets were healthy and accessible, spreads were wide and commissions were high. Market makers cannot operate like that anymore and instead of making thousands of pounds in a large trade, have to make the same amount but over thousands of trades. We need to understand what the costs are when accessing the liquidity and the price of urgency. The resurgence of quant fund managers could also create a vein of liquidity that has been absent since the quant crisis of 2007, when some of the smartest fund managers suffered unprecedented losses. The use of artificial intelligence in stock selection is increasingly popular, but caution is needed in the form of human supervision and intervention to avoid a crash. The data available now has increased immensely and processing power has also accelerated greatly. Many of the tools that traders dreamt of are now a reality and help us not only make informed decisions but also demonstrate how we arrived at those decisions.

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Neil Bond, Partner, Equity Dealer, Ardevora Asset Management

Where trading commissions are fully segregated from research payments, traders can freely trade where they find best execution and use sophisticated tools to aid them in their broker/venue selection analysis. At a glance now I can see a host of visuals together on a dashboard that explain the factors that determine whether or not putting a block together at current levels is the right price. Two-tier market We seem to be transitioning to a two-tier market place where one set of trading strategies is needed for larger orders and a completely different set for sub-LIS orders. The options for block trading are well catered for by high touch sales traders, conditional venues and the traditional block crossing venues POSIT & Liquidnet. For sub-LIS trades things get a little more complicated. It is a world frequented by high frequency traders and electronic liquidity providers who tend to have extremely short holding periods, but


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“The trend toward passive investing has had a somewhat detrimental effect on liquidity, with stocks being bought and sent to the index trackers’ vaults, unlikely to see the order book again for years.”

Increased transparency was one of the aims of MiFID II, and one area where this has succeeded is giving buy-side traders more granularity into the counterparties they are trading against. Some larger money managers have trading volumes big enough to gather the data in order to make educated decisions. However, most of us do not have a large enough data set to do this and still rely on data collection and analysis from our brokers. The selection of trading venues, particularly non-bank SIs needs to be approached quantitatively – some may have strengths in small- and mid-cap stocks, others may have strengths in particular regions or sectors. Now that all trading data has to be reported, it would be nice to think we could end up with a consolidated tape that would be beneficial to all market participants, but for that to happen data costs would need to be addressed and these revenue streams are proving to be an insurmountable hurdle.

they are trying to convince the world that they want to trade larger sizes and hold positions for longer. Understanding the toxicity (normally measured by market impact and reversion) has become a core requirement for traders who now have to decide between the lit market, dark pools (assuming double volume caps are not in effect), periodic auctions and systematic internalisers for their smaller orders.

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30 | OPINION

ESG: Can Hedge Funds Save Our Oceans? By Philippe Burke, Portfolio Manager, Apache Capital

There are several ways that large asset managers could demand a change in behaviour from publically traded polluters. We’ve all read alarming reports of collapsing fish populations, giant rotating ocean gyres filled with consumer plastic, ocean acidification, blanching coral reefs, and melting glaciers. Our disappearing marine life is a classic illustration of the “tragedy of the commons”, and our inability to self-correct is sometimes explained as the natural outcome of an intractable prisoner’s dilemma. An illustration follows. Dilemma Let’s suppose there are two rational neighbours live by a pond where a thriving population of 100 fish frolic, growing at a net rate of 10% per month. The two neighbours meet and agree to protect & maintain the pond’s stock of 100 fish, and share in the growth bounty equally, with each harvesting only 5 fish from the pond per month. The problem with the agreement is that it would in fact be rational for each neighbour to cheat. To see that, consider the possible actions of each neighbour: 1) if neighbour A decides to cheat (catches 6 fish instead of 5) and believes that B will not cheat (will

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catch only 5 fish), A gets 100% of the benefit of cheating, and the communal loss (i.e. diminished fish stock, that could be countered by harvesting a bit less next month) is born equally by A and B; similarly 2) if A thinks B will in fact cheat (catch 6 fish), then it is again in A’s interest to cheat (catch 6 fish), because not cheating would mean that A would bear half the cost of B cheating, with none of the upfront benefits in extra fish. That cost/benefit assessment is, of course, the same from B’s perspective. So rather than both neighbours not cheating, which would in fact be in both A’s and B’s best long term interest (e.g. being able to harvest 5 fish per month for ever), it is short-term rational for both neighbours to cheat, and that of course is how we end up with fish populations in free-fall in oceans across the globe. But the environmental problem is a bit more complex than this simple example suggests: in addition to rapidly falling fish populations, what fish stock remains is becoming increasingly toxic from rising ocean pollution. Most ocean pollution comes from the land (e.g. fertilizers, pesticides, mine tailings, plastics). The table below summarizes statistics for the Pacific coastal regions, for illustration. In short, the Pacific accounts for


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“At the very least, counting on the current system to selfcorrect without exogenous intervention would seem reckless, considering the extinction risks of both populations.” roughly 50% of global ocean waters, and 99% of commercial fishing is done within 200 miles of coast lines, and within 500 meters of the surface. We have calculated the area of the “donut” of Pacific Ocean coastal waters where most of the fishing is done. This area also corresponds to where most of the ocean trash is dumped annually. Assuming that toxins account for 2% of trash, and that fish have a concentration of trash-emitted toxins 50 times higher than ambient waters, we find that on average, large fish accumulate toxic concentration levels in their flesh in the range of the EPA’s maximum recommended threshold within two years of life in Pacific coastal waters. How can this problem be addressed? Consider the parties that must reach a lasting agreement for this pollution and depletion problem to be reversed: it is no longer an agreement among fishermen to harvest responsibly, but also among nations to hold pollution in check. Our lake is now the Pacific Ocean, and our two neighbours are now China and the US. If one neighbour nation grows more rapidly, that nation will likely also increase its share of ocean pollution, impairing the resources and health of both neighbours, and both parties will have an incentive to harvest fish more quickly before the rapidly diminishing fish stock becomes even more toxic. So any agreement between neighbours must include both harvesting and pollution limitations, but as was the case in our simple lake example above, it may be short-term rational for both/ either neighbour to cheat on any agreed quotas. A closer look To address this problem, let’s begin by modelling the dynamics between the human and fish populations. At

its simplest, we have rising human population leading to an increase in GDP, an associated rise in industrial pollution, as well as greater harvesting of fish for food. Rising pollution and diminishing fish stock in turn causes higher concentration of pollution in fish populations, resulting in greater food-toxicity and deaths for humans. What outcome can we expect for human and fish populations over time? We have a number of modelling alternatives, including the Schaefer harvesting model and the Lotka-Voltera predator-prey model. In what follows, we will employ a modified set of Lotka-Voltera logistic equations and examine these dynamics over time. -> Summary: Humans grow in numbers, pollute the ocean, and consume a diminishing supply of increasingly polluted fish, ultimately poisoning themselves. -> Variables: H(t) and F(t) are the populations of Humans and Fish at time (t), α1 & α2 are the unconstrained growth rates of the Human & Fish populations, β1 & β2 are the natural constraints on Human and Fish population growths, α1/β1 & α2/β2 are the Human and Fish population saturation, is the Fish population’s impact on Man (from pollution poisoning), is the Man population’s impact on Fish (from harvesting) that is: Human population growth = dH/dt = H * [ α1 – β1*H - 1*F ] Fish population growth = dF/dt = F * [ α2 – β2*F - 2*H ] Let’s examine the critical points (pairs of H and F) from equations (i) and (ii): -> In Equation (i), dH/dt = 0 if either: (a) H = 0 (that is, Humans become extinct); substituting into (ii), we obtain: α2*F – β2*F^2 + 0 = 0 => F = [-α2 +/– (α2^2)^.5] / (-2*β2) or (b) the bracketed term [ α1 – β1*H - 1*F ] is zero. -> In Equation (ii), dF/dt = 0 if either: (c) F = 0 (that is, Fish become extinct); substituting into (i), we obtain: α1*H – β1*H^2 + 0 = 0 => H = [-α1 +/– (α1^2)^.5] / (-2*β1) or

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32 | OPINION

“Large asset managers could demand a change in behaviour from publically traded polluters by using their voting rights, divesting their holdings, and shorting the stock of polluters.” (d) the bracketed term [ α2 – β2*F - 2*H ] is zero. In short, we now have these 3 critical pairs of Human and Fish population: (H, F) = (0 , [-α2 +/– (α2^2)^.5] / (-2*β2)) Humans become extinct (H,F) = [-α1 +/– (α1^2)^.5] / (-2*β1) , 0) Fish become extinct (H,F) = (0 , 0) Humans and Fish become extinct The last critical population pairs has both Humans and Fish in existence, and we obtain these by setting the 2 bracketed terms above to zero, and solving for H and F: [ α1 – β1*H [ α2 – β2*F -

1*F ] = 0 2*H ] = 0

(i): dH/dt = H * [ α1 – β1*H - 1*F ] = zero (ii): dF/dt = F * [ α2 – β2*F - 2*H ] = zero and analyse the local dynamics (local linearization) of the H & F populations around the non-zero critical values Hc and Fc computed in (v) above: for the Human population, we take its partial derivative with respect to Humans and Fish populations around their critical values Hc & Fc; and we then repeat for the Fish population. This gives is the Jacobian matrix: dH/dh (Hc, Fc) dF/dh (Hc, Fc)

dH/df (Hc, Fc) dF/df (Hc, Fc)

= α1 – 2*β1*Hc - 1*Fc - 1*Hc - 2*Fc α2 – 2*β2*Fc - 2*Hc

To get a sense of the actual dynamics between the two evolving and co-dependent populations, we need to select parameter values for our model. An illustration follows, assuming unconstrained growth rates of 1% and 2% for Human and Fish populations, environmental limitations of 0.8% and 1.9% respectively, and crossspecies encroachment (that is, negative impact from fishing and pollution) of 1% and 1.5% respectively. Input Values α1: 1.0% β1: 0.8% 1: 1.0%

α2: 2.0% β2: 1.9% 2: 1.5%

(iii) (iv) <--- unconstrained growth rate for humans and fish

(iii) => H = [α1 – 1*F] / β1, <--- growth limitation from environment for H & F and substituting into (iv) gives us the critical values Fc & Hc in which neither the Fish nor the Human populations become extinct:

Fc = ( 2*α1)/ β1 – α2 ( 1* 2/ β1) – β2

With these parameter values, we are now able to specify our model, and calculate the eigenvalues of our system:

Hc = α1 - 1 * [( 2*α1)/ β1 – α2]/[( 1* 2/ β1) – β2] β1 (v)

Determinant: 79.240 -λ (118.875)

Dynamics What are the dynamics of the system around these non-zero critical pairs of Human and Fish populations? We begin by setting equations (i) and (ii) equal to zero:

Which gives us eigenvalues of:

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<--- growth limitation from other species: H to F, & F to H

99.050 (152.555)

Lambda 1: 4.057 Lambda 2: -77.372


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Result Eigenvalues of opposite sign are characteristics of a saddle path, indicating unstable dynamics between humans and fish populations in our model, as specified. Different input parameters result in different (nonsaddle path) dynamics, so confidence in the dynamics of our system would require solid empirical support to estimate the input parameters. That said, to the extent the parameters used above are realistic, our model would suggest that the environmental dynamics at play today { growing human population -> increasing pollution spilling into primary fishing zones -> combined with rising fishing hauls to feed more humans -> resulting in more toxicity entering the human food chain} are likely to be unstable for both human and fish populations over time, and endogenous corrective forces in the system may not be sufficient to result in a stable long term equilibrium in which both populations survive. At the very least, counting on the current system to self-correct without exogenous intervention would seem reckless, considering the extinction risks of both populations. Potential solutions To have staying power, solutions must address the perverse incentive of pollution and over-fishing highlighted in our prisonerâ&#x20AC;&#x2122;s dilemma example: pollution and over-fishing currently benefit the perpetrators up front, while costs are shared inter-temporally by the entire human and fish populations. Correcting that externality is likely to be an essential element of any stable, long term solution.

Pacific to find ways to grow sales (and contribute to GDP) in more sustainable, environmentally sustainable ways. Private Solution: Large asset managers could demand a change in behaviour from publically traded polluters by using their voting rights, divesting their holdings, and shorting the stock of polluters. Jana Partners, a large hedge fund, recently announced plans to launch an ESG activist fund this year. To get a sense of effectiveness, consider that a concerted plan by the largest asset managers to divest from the equity of the 20 largest publically traded polluters would likely result in a drop in the stock prices of these polluters, all else equal. As illustrated in Table 2, gains from a market-neutral short position (properly sized and disclosed) could be used by an impact fund to finance pro-environmental activities. Table 2: Environmental Activism Step 1: start with 30,000 public equities Step 2: use negative environmental filters (e.g. energy use per $US of sales, CO2 emissions, environmental files) to identify the worst polluters Step 3: use negative financial filters (e.g. low revenue growth, high leverage, high price-to-book, high P/E) to select, among the worst polluters, those that have a poor financial profile Step 4: short their stock (against a market-long) Step 5: use gains (extracted from the shareholders of weak polluters) to finance environmental clean-up work.

Public Solution: One relatively direct solution would be for the World Trade Organization to appoint trusted independent third party with the task of measuring ocean pollution off of the coasts of both Pacific neighbours, reporting the results daily with full transparency and monitoring, and whenever pollution exceeded a certain pre-set sustainable threshold levels for a period of time, the offending party would find the price of its export goods taxed in foreign markets, with tax proceeds used by a competent NGO to clean up ocean pollution and help revitalize marine life. The economic incentive to pollute would be lowered by the tax on the offending nationâ&#x20AC;&#x2122;s export profits, and if pollution did not fall below the sustainable threshold, funding would exist to counter its negative environmental impact. As importantly, there would now be market incentives for businesses on both sides of the

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34 | EUROPE

MiFID IIâ&#x20AC;&#x2122;s Unintended Consequences By Gianluca Minieri, Deputy Global Head of Trading, Amundi

Connectivity between communities and venues is the key to exploiting the scale, breadth and the depth of the European trading market. When in 2007 the European Union (EU) regulators decided to implement Markets in Financial Instruments Directive (MiFID) I, they did it with the intention of laying the foundation for a comprehensive, single regulatory framework for European financial markets, which would help develop and enhance market-based funding of European economies. The main principle underlying the legislation was that the European economy was getting insufficient funding from the financial markets due to the high cost of transactions, such as commissions charged

GLOBALTRADING | Q2 â&#x20AC;˘ 2018

by trading venues. According to this theory, these transaction costs were impeding the development of secondary markets, which, in turn, could be detrimental to market liquidity. The primary purpose of MiFID I was therefore to bring down the transaction costs for investors and secondly to facilitate the creation of a large secondary market which could eventually enhance liquidity by promoting and incentivising competition among trading venues. With hindsight, our view is that MiFID I missed the objective of enhancing the level of liquidity in lit markets and reducing the cost of trading for investors. In fact, its effect on liquidity was the fragmentation across a plethora of trading venues, which made it


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a consultation process on the proposal to target dark pool trading and clamp down on access to dark liquidity, we (the buy-side) were very vocal on these topics. A group of buy-side asset manager representatives travelled to Brussels a number of times to provide evidence (through empirical statistics) to the regulators to show that restricting dark liquidity would not lead to any enhancement in the price formation process nor would it lead to a shift of liquidity from dark to lit venues. Buy-side consensus During those meetings, I remember how little conflicting views there were among the asset managers consulted. At the end of the day, our shared concern focused on ensuring that we did not lose the ability to invest money efficiently in the best interest of our clients.

Gianluca Minieri, Deputy Global Head of Trading, Amundi

“It is clear that MiFID’s latest incarnation is no closer to forcing liquidity to lit markets and that this comes mainly from an oversight of the needs of real money investors.” more difficult for the buy-side to understand where to find liquidity. Therefore, when during the long road that led to the birth of MiFID II the EU authorities launched

Our opinion was very clear on this topic: capping dark pools might have led to suppressing rather than encouraging liquidity formation, with the risk of impacting negatively on long-term institutional investors, the very firms that are meant to be protected. Our experience as large size traders was that trading on the primary exchanges could be significantly more expensive than in dark pools, especially in large size blocks, given the decreased market impact that dark pools offered compared with trading in lit markets. Dark liquidity was criticised for obscuring price discovery but yet we had not seen any evidence supported by robust statistical analysis showing a negative impact on price formation from dark trading. In fact, a higher level of trading in dark pools had been associated with improved lit market price quality. As widely predicted by many professional investors, MiFID II remains challenged in its aim to bolster liquidity on traditional stock exchanges. At the end of the first quarter following introduction, MiFID II has led to: 1. An almost instantaneous move from broker crossing networks (BCNs) to Systematic Internalisers (SI). Although both buy- and sellside firms are still familiarising themselves with the SI regime, SIs are certainly among the biggest beneficiaries of the new MiFID II requirements. Liquidity previously exchanged in BCNs has de

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36 | EUROPE

facto moved to SIs, where investors feel they have the advantage of tailoring their liquidity needs more appropriately than BCNs, given the greater transparency requirements of SIs vis-à-vis BCNs (”Trading Under MiFID II: Initial Impressions from ITG”, The Trade, January 2018); 2. An increase in block trading. Fidessa’s Top of the Blocks report shows that the proportion of dark traded as Large In Scale blocks reached a record 28.7% on 12 January compared to 12% in January last year (“Moving towards the light”, The Trade Magazine, 22 March 2018). This is a trend that existed well before MiFID II and that was strengthened by the implementation of the regulation. Venues that offered Large-In-Scale (LIS) trading experienced significant growth and confirmed the scepticism of real money investors towards the capacity of lit exchanges to absorb large size trades while protecting them from predatory strategies. Platforms like Turquoise Plato, CBOE LIS and Liquidnet have all experienced a rise in volumes given the interest of buy-side players to keep trading in size and get their blocks done; 3. Exchange operators such as CBOE Global Markets set new records on their periodic auctions book, recording double-digit growth in their average daily notional value traded. The new record was set on 12 March and was 20% higher than the previous record of €488 million seen on 6 february (“Cboe Periodic Auction sets periodic auction record as dark caps arrive”, The Trade, 13 March 2018). The ban of BCNs acted as a boost for periodic auctions, where investors find a cheap, transparent way of matching their orders.

confidentiality in order to protect their investors and generate performance. The choice of trading venue is first and foremost driven by the opportunity to execute their clients’ orders with the best possible conditions. A skewed playing field That is why rules aimed at forcing to trade on a particular category of venues can be counterproductive to liquidity. Today the reality is that, even after MiFID II, if a large trade is spotted entering the market, that order is open to abuse by speculators. It is like a game of cards where the other players can see your hand.

“Capping dark pools might have led to suppressing rather than encouraging liquidity formation, with the risk of impacting negatively on longterm institutional investors, the very firms that are meant to be protected.”

It is clear that MiFID’s latest incarnation is no closer to forcing liquidity to lit markets and that this comes mainly from an oversight of the needs of real money investors. And these needs can all be linked to a very simple concept: institutional long-term investors keep the interests of their clients at the core of their investment decisions.

In our view, block trading venues will keep growing in popularity until institutional professional investors will be satisfied that they can trade on a level playing field where their trading data are protected and transparency rules are consistent with the liquidity level of the asset being traded.

They represent the interests of a wide variety of clients, from institutional to high net worth individuals to pensioners. They trade large blocks of assets with the primary objective of doing it in the best market conditions and with the appropriate level of

Until that time, block trading will not only grow in volume but will be key to unlocking greater levels of liquidity directly from the buy-side to execution venues, although at this stage it is unlikely a wholesale shift from lit to dark trading venues.

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â&#x20AC;&#x153;Block trading venues will keep growing in popularity until institutional professional investors are satisfied that they can trade on a level playing field, where their trading data are protected and transparency rules are consistent with the liquidity level of the asset being traded.â&#x20AC;?

Connectivity between communities holds the key to exploiting the scale, breadth and depth of the European trading market and is one of the most important drivers in establishing a sustainable future for block trading. Buy-side professional investors are and remain in favour of regulation and indeed suggest a greater role for market supervisors to create and maintain a trading environment in which best practises are encouraged through greater transparency, comparability and choice between service providers. However, a lot of work still has to be done in creating a level-playing field for safe standard trading protocols, which will determine the way trading data are transmitted and published and, consequently, the willingness of buy-side investors to confidently show their orders on lit markets.

Q2 â&#x20AC;˘ 2018 | GLOBALTRADING


38 | ASIA

Transforming Trading With Machine Learning By Lee Bray, Asia Pacific Head of Equity Trading, J.P. Morgan Asset Management The firm’s Asia Pacific equity trading team aims to have around 50% of trading automated with machine learning by the end of this year. J.P. Morgan Asset Management has invested significant resources in building machine learning tools to enhance global equity trading, as the rise of

GLOBALTRADING | Q2 • 2018

artificial intelligence and automation transforms how we conduct business. The firm’s Asia Pacific buy-side equity trading team developed a model utilizing machine learning to help its portfolio managers make the execution of trading orders more effective and cost efficient. The


ASIA | 39

“By creating a systematic, adaptive model able to alter actions based on mathematical patterns, we’re transitioning equity trading to be more scientific and quantifiable.”

Lee Bray, Asia Pacific Head of Equity Trading, J.P. Morgan Asset Management

proprietary model, which was developed by the firm’s quantitative analysts and traders, uses data patterns to find the optimal execution strategy for trading orders. The Asia Pacific trading desk is on track to have 50% of equity trading flow driven by machine learning by end of 2018

companies like Facebook and Google. By creating a systematic, adaptive model able to alter actions based on mathematical patterns rather than relying on human input, we’re transitioning equity trading to be more scientific and quantifiable. Currently the model gives trading recommendations to human traders, but increasingly it is taking over an automated role in executing trades. As its application becomes more scalable, it has the potential to generate cost savings for JP Morgan Asset Management’s clients through greater trading efficiency.

With myriad options available for executing any given order, particularly smaller or more routine orders, an intelligent model can identify the best execution more efficiently than a human. The artificial intelligence model “learns” constantly about the best outcomes for trading orders and adapts by recalibrating as market conditions change and new information is delivered. Using these algorithms to pinpoint the probability of best performance, the machine learning environment auto-routes and executes accordingly. To develop the model with machine learning, we tapped into techniques more commonly found at

Q2 • 2018 | GLOBALTRADING


40 | AMERICAS

Electronic Trading In The Brazilian Markets By Dr. Christian J. Zimmer, Quantitative Research, Itaú Asset Management and Chair LatAm, FIX Community, and Leandro Pereira, IT Manager, Itaú Asset Management

Fixed income trading in Brazil is now much more efficient, and attention is moving to equity order matching and stock-lending. Brazil witnessed significant advances in electronic fixed income trading in 2017. The country’s notoriously high inflation rates means fixed income is an especially important asset class in Brazil, so improvements in trading infrastructure and systems have a substantial and supportive impact on the country’s savings and investment industry.

Progress was made following a FIX Community event in 2016, where representatives from the buy-side and sell-side in Brazil recognised that innovation was needed in the local fixed income markets. In particular, sell-side firms agreed to move towards electronic trading as long as the buy-side was supportive. A local FIX Protocol (FPL) committee decided in 2017 that it should focus on three major issues for enhancement. These were: fixed income electronic trading, intraday trade matching for equities, and stock lending.

However, electronification in the fixed income market historically always lagged a considerable way behind equities. There are several reasons for this, including fragmented liquidity, an entrenched human trading culture, buy-and-hold investment preferences and a requirement for pre-trade submission of cash and securities to meet T+0 settlement.

At this time, fixed income securities were tradeable on the BM&FBovespa and Cetip (later merged into B3) and on a Bloomberg platform, fragmenting an already illiquid market in private and public debt. As Cetip was the most liquid market at this time, a pilot project between Itaú Asset Management (IAM) and Cetip was initiated to route fixed income orders.

GLOBALTRADING | Q2 • 2018


AMERICAS | 41

Leandro Pereira, IT Manager, Itaú Asset Management, Dr. Christian J. Zimmer, Quantitative Research, Itaú Asset Management

Pilot project So, an arrangement was made with Cetip, where IAM can route the orders to the market, such as send orders to the brokers in the market. Basically, there are three types of fixed income order: • Market Order The market order is the most traditional kind of trade, used by the entire market, in this kind of order the buy side sends the order to a broker indicating the limited price or limited yield (usually limited yield). This kind of order is defined by the value 0 (zero) at the tag #828. • On behalf of Orders on “behalf of” are similar to the above market orders, the only difference is the tag #448, where the buy side identifies the broker that will appear at the Cetip platform. This kind of order is used only when the buy side decided to create an order in a order book. • Cross opportunity

This type of order is chosen when the seller and buyer are management desks of the same institution, trading the same security, but on opposite sides of the transaction. In the Brazilian market the central bank doesn’t allow trades between two accounts without a broker intermediating the trade even for an internal crossing opportunity the buy-and the sell-order must be sent via a broker. From the sell-side perspective, an order originated from an internal crossing opportunity is very different to a market order. The FIX message for this kind of order has special tags filled by the buy-side, in order to indicate to the sell-side the details of the trade as follow: #828: Trade Type: 3 (Cross opportunity). #548: Cross ID: Identifier for the cross order. (The ID is used to link the buy and sell orders, and only two orders can have the same ID per session).

Q2 • 2018 | GLOBALTRADING


42 | AMERICAS

One of the problematic workflows for the fixed income industry is in the post-trade process, because all trades must be registered with the central bank for T+1, and the cut off time is 7:00pm. 1. Allocation Every trade scenario has a specific FIX message, but all orders are pre-allocated within FIX 4.4 using repeating groups with the tags #79 and #80. That was the practice for the market at the beginning of the project, but was abandoned without any major issues for the participants. 2. Settlement command Due to a convention of the Brazilian market, the seller is responsible for the origination of a number (unique in the day) to be used in the settlement process at the central bank. This number is called “command”. In this project we used the custom tag #8000 in the repeating group, to accommodate the settlement command. 3. Settlement account The settlement account is a number defined by the broker, and represents the agency or principal account to be used in the settlement process at the central bank. During this project we included this number in the custom tag #8001 and the broker provides that information for each order fill sent. 4.Validation Since the post-trade validation is included during the trading process, the broker has to implement the following validation before acknowledging the message: a. Verify if all accounts indicated at the repeating group of the tag #79 are allowed by the brokerage. b. Verify if the security identifier of the tag #48 match with the other securities information in the message, that is maturity date and ticker. c. Verify if the yield of the order is fair, according to the spreads currently executed in the market. Once the order is executed, the broker has to send the execution price for the order, even when a target yield was provided as a reference for the execution. This allows the buy-side to verify if the price is correct, corresponding to the yield provided. The participating firms may then send the order to the

GLOBALTRADING | Q2 • 2018

central bank for registration, without any order post-trade process. And what are the plans for the next few years? Although there is always place for improvement, we understand that the process for fixed income trading is now relatively well designed and effective. Now, it is time for the rest of the market to follow the trend and board the electronic trading train. We believe that there is also a pressing need to improve the real-time matching of Brazilian equity trades, which is still a remarkably manual process.

“Sell-side firms agreed in 2016 to move towards electronic trading as long as the buy-side was supportive.” Matching of equity trades in Brazil The process for matching equity trades in Brazil is the following: 1. When trades are completed – that is, after the market closes - the buy-side sends a flat file (a “tordist”) to the sell side. This file specifies how all trades made on the omnibus accounts should be distributed to the final accounts that trade under this umbrella. 2. The Brazilian exchange, as the central counterparty clearing (CCP) for all equity trades, demands an allocation of all trades that are executed on a trade-by-trade level. This means that if an omnibus account sends one trade to the exchange and receives 10 fills, then 10 distributions must be indicated for each fund participating in the trade. So, if five funds participate in the trade, the buy-side sends 5*10=50 designations. It is not permissible to work on an average price basis.


AMERICAS | 43

3. The sell-side receives the distribution and inserts it into a system of the exchange, called Sinacor. After processing all trades and distributions, the exchange “clears” all trades and thus allows the sell-side to close the day. 4.When all trades and allocations are approved, the sell-side sends a confirmation file to the buy-side (“pesq”). 5. If nothing is wrong, the buy-side matches the tordist-file with the pesq-file and then inserts the accepted allocation into the middle- and back-office systems. This process is only for physical allocation: a separated file is sent from the sell-side to the buy-side with all the transaction data, and depending on the buy-side systems, these values are confirmed with the internally calculated result based on the physical allocation file.

available from the CLBC (the exchange’s entity responsible for equity custody). Simple commands can be used to inform new borrowing and lending operations, renewals or cancelations. For the buy-side, the problem is less in the operations aspect of executing a deal, but more on the dealarrangement aspect and finding liquidity. A first step in the direction of creating a deal-arrangement platform is with the intraday price (rate) broadcasting of deals sent to the CBLC. We hope that within the next year and a half, we will have devised a more efficient solution for equity trade matching, and also have found ways to improve and automate the stock lending process.

“There is also a pressing need to improve the real-time matching of Brazilian equity trades, which is still a remarkably manual process.” Sometimes trade allocations do not match - perhaps because of an execution error - and maybe the error is only identified after the market has closed. This causes an end-of-day panic, putting pressure on trading desks and middle offices. But, creating a point-to-point FIX-allocation process is cumbersome and some players do not have the systems to post their information into a FIX engine. Stock lending and borrowing The liquidation part of the stock lending and borrowing process is easily automated, with APIs

Q2 • 2018 | GLOBALTRADING


Join us and drive change To find out more, please contact us at fix@fixtrading.org

fixtrading.org

FIX Trading Community is the non-profit, industry-driven standards body at the heart of global trading. The organisation is independent and neutral, dedicated to addressing real business and regulatory issues impacting transparency, and reduced costs and risks for all market participants. Demonstrate your firmâ&#x20AC;&#x2122;s commitment to FIX Trading Community by becoming a member of this unique organisation.


FIX | 45

In Memory Of Tim Healy It is with a heavy heart that we announce the passing of our dear colleague and friend, Tim Healy, who was FIX’s Global Marketing and Communications Director and worked tirelessly for the past several years to raise awareness of FIX’s efforts to engage and help the industry. As many of you know, Tim had been battling a rare form of kidney cancer that had unfortunately spread to his brain. He always employed a strong will and positive outlook; he never gave up the good fight and pushed through, working right until the end, even managing to attend the EMEA Trading Conference held in March of this year. Tim had 30 years of experience within the financial services sector. He started his career in the City working for US Equity broker, Dean Witter Reynolds, and worked his way through the ranks to become a Sales Trader. He later went on to work for PaineWebber and Citigroup in a similar role. Prior to his position at the FIX Trading Community, Tim worked for a number of different vendors and brokers involved in the electronic trading sector where he was focused on sales and account management. The community will miss his kind, gentle smile, his sharp wit and calming presence which made him hugely popular with his colleagues and peers alike. We will be forever grateful for his contributions to the FIX Trading Community. As we mourn the loss and cherish the memories we have with Tim, we ask you to please continue to keep his family in your thoughts. He is greatly missed. FIX Trading Community

Q2 • 2018 | GLOBALTRADING


46 | 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 Charles River Development Chi-X Global Inc Cinnober Financial Technology AB 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 Gamma Three Trading, LLC

Premier Global Members

GLOBALTRADING | Q2 • 2018

GATElab GETCO Asia GMO Goldman Sachs GreySpark H2O Asset Management Haitong International Securities HCL Technologies Higher Frequency Trading 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. MarketAxess Marshall Wace Asset Management


FIX TRADING COMMUNITY MEMBERS | 47

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 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 Trading Technologies 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.fixtradingcommunity.org

www.bloomberg.com

BSO Network

www.bsonetwork.com

Commonwealth Bank of Australia www.cba.com.au

FactSet

www.factset.com

Fix8 Market Tech www.fix8mt.com

Kx Systems kx.com

Mongol Securities Exchange (MSX) www.msx.mn

MUREX

www.murex.com

Perpetual Motion Research www.ipmcons.com

Quantitative Brokers quantitativebrokers.com

Sensiple

www.sensiple.com

Vontobel

www.vontobel.com/en-int

XTRD

www.xtrd.io

Premier Global Members

Q2 • 2018 | GLOBALTRADING


48 | LAST WORD

My City

Singapore By Ambrose Tan, Head of Dealing - Asia Pacific, Aberdeen Standard Investments

Best thing about your city? It’s super-efficient and super-safe. Worst thing about your city? The heat and humidity, at least during the day. I wouldn’t complain if our office was next to a beach.

building and in the middle of it, so literally, in front of me is my fixed income trader; on my left, my FX trader and on my right, my equity trader.

Getting to work? Driving, as I prefer to be boss of my own destiny. It takes me about 20 minutes (12 km) if I’m early. Public transport is great provided you’re close to it.

Where to take guests to dinner? I don’t entertain much, but if I do, it would be Ubin Seafood in Hillview or Wine Connection behind my office, which has a good wine selection and reasonable tapas.

View from your desk? I’m on the ground floor of a heritage

Relaxed spot with family or friends? My home or a friend’s home.

GLOBALTRADING | Q2 • 2018

Best place to stay when visiting? Depends on the type of visit, but I’d say Raffles Hotel and Shangri-la. Older hotels have their charm and rooms should be bigger - not that I’ve stayed in them! Best tourist site? Head to Singapore Botanic Gardens, a UNESCO heritage site, and if you’re lucky they’ll also have free concerts during weekends. For the super touristy, go to Sentosa Island.


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