Automating The Trade Process

September 5, 2017

By Curt Engler, Head of Equities Trading, The Americas, JP Morgan Asset Management

Systemisation should be a gradual process with each stage of implementation tested for weakness until it becomes a solid foundation for further development.

New technologies have already had a profound impact on equity trading operations. Automation is well-embedded throughout the trade process and now systemisation is becoming more sophisticated, as trading desks leverage their quantitative skills and data analytics to further enhance order execution, strategy implementation and risk management.

However, the introduction of technology is neither indiscriminate nor hasty. Instead, a successful adoption of automated systems requires a deliberate and incremental approach. In practice, that means starting with the less glamorous stages in the trading cycle before moving to more complex applications.

At all levels, the technology is first used experimentally, tested and, if suitable, is then applied more extensively while constantly monitored and checked for any deficiencies.

Incremental approach
The trading desk at JP Morgan Asset Management receives up to 8,000 orders a day from its portfolio managers, so automating the workflow was an early priority. By synchronising our order management system with our execution management system, then placing and monitoring small orders before extending usage more widely, we created a more streamlined and efficient process that lay the foundation for building automated trading strategies.

Again, these were introduced gradually. The intention was to extract biases that human traders are susceptible to, while also weighing the benefits – lower costs, greater trade execution efficiency – against the risks of automation – reduced flexibility.

We first made trial order placements in both lit and dark venues, and then gradually increased the trade sizes once we were sure the process functioned smoothly and the results were successful.

Now, between 70% and 80% of the notional amount of our trades is electronic and crossed in the market using sophisticated algorithms. The system accommodates a wide range of individual order size for portfolio managers with a variety of investment styles, such as momentum and reversal.

Broker selection is also systematised based on performance metrics for stock category and market conditions, and on their ability to facilitate orders within a particular investment style and increasingly complex trading strategies.

Although there are regional nuances, the structure has been adopted throughout our global operations. It is also being replicated for different asset classes, such as derivatives.

Importance of quant skills
In order to achieve this high degree of automation, trading desks have forged a much tighter, day-to-day working relationship with technology specialists than ever before.

Indeed, it would not have been possible without a considerable investment in qualified staff specialised in quantitative research and trading analytics.

Their expertise is critical throughout the trading cycle. They collect, disentangle and interpret data from a multitude of internal and external sources. For instance, they can identify tiers of liquidity for individual stocks at specific times and at particular trading venues in order to optimise trading strategies and fulfil our responsibility to achieve best execution – measured by transaction cost analysis.

For example, data can indicate disruptive or even manipulated market behaviour in an alternative trading system, which might be a signal to avoid trying to fill an order at that venue.

We’ve also introduced predictive analysis, based on data mining and statistical modelling, into our trading structure, and developed visualisation tools to increase our understanding of possible scenarios that follow a trade decision.

New roles
Of course, there will always be an important role for human agency within the trading process, especially to manage trades during unusual circumstances or for unorthodox transactions.

However, humans are clicking the trade execution button less and less, and the trend will continue. Machines are becoming more adept at completing even the most complex orders, ranging from crossing large blocks to finding a buyer for an illiquid odd-lot.

On the other hand, dealing desks will still hire staff – but they are more likely to have quantitative skills than streetwise agility.

At JP Morgan Asset Management, we will continue to leverage on the automation and systemisation that is already in place and has been rigorously tested and proven. More orders will be incorporated into the systemised process, which will be continually assessed for its performance and risk controls.

Automation needs to be disciplined, but not rigid. Markets can be volatile and so the systems also need to be nimble and flexible.

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Professional Standards Within The Electronic Trading Industry

GlobalTrading

September 5, 2017

By Jim Northey, Senior Vice President, Strategy and Research, Itiviti

As the speed and complexity of automation rises, market participants have a duty to ensure that they pursue and maintain best practices.

Most professions have a set of ethics that guide behaviour – think of the Hippocratic Oath for doctors. In the absence of similar guidelines for our occupations in the hectic world of electronic trading, the Code of Ethics of the Institute of Electrical and Electronic Engineers (IEEE) has served me well since the early 1980s when I first joined the IEEE and the IEEE Computer Society. There is a degree of diligence associated with every profession.

When we examine some of the major recent crimes in our industry, it is clear that there was an enabling technology component and an enabling financial technologist. Would Nick Leeson have been able to take down Barings Bank without the assistance of an IT specialist who hid trades within the error account? Would Bernie Madoff have been able to continue his Ponzi scheme for so many years without the aid of technologists?

These are important questions we should ask ourselves. Every day we are faced with choices that separate someone merely putting in the time from someone that is committed to a profession. So, we should ask whether we actively promote best practices and standards for automation, operations, and testing, or merely do what is obligatory to meet regulatory requirements.

This is especially pertinent now. Electronic trading has grown in speed and complexity since the early days when the FIX timestamp only had a resolution down to the second.

The most advanced among us measure time in picoseconds or the equivalent the length of fibre optic cable in millimetres.

Yet, there are still firms that are struggling to implement basic time synchronization and will face significant challenges to comply with the Markets in Financial Instruments Directive (MiFID) II in Europe and the Consolidated Audited Trail in the United States.

There are several other serious failures of resolution within the industry:

• Information Security
Professional firms always encrypt their FIX network connections, use two factor authentication to access resources, and maintain access control lists and auditing on sensitive data. However, there is a disturbing laxity regarding protecting data in motion and at rest, and in fact, the CPMI-IOSCO white paper on cyber resiliency singled out the FIX Protocol as having information security issues.

The FIX Community is stepping up by creating a cybersecurity handbook and in defining the FIX session layer, which is a formalisation of the long-standing recommendation to operate FIX over a Transport Level Security (TLS).

• Resiliency
The best firms implement a business continuity plan that includes failover and hardening of key points of failure. The financial markets were early adopters of fault tolerant architectures, using systems made by Tandem and Stratus, for example.

The industry then migrated to high availability architectures, some of which have implemented quite sophisticated and optimized versions of the Byzantine quorum algorithm. Yet, just a few years ago, some alternative trading systems and brokers were running on a single platform with no or inconsistent backups and no physical access security.

• Operations
Best practices in the financial services industry finds one of the major global exchanges using continuous testing so extensively that it can push an update of its platform into production weekly. This is near the top in terms of achieving DevOps nirvana.

Some firms have fully integrated monitoring and alerting and a mature issue resolution practice. On the other hand, there are firms that lack full control over their operational environment and have to be informed by their customers when there is an outage or other failure.

• Testing
There are now market participants starting to adopt model-based testing that can exhaustively monitor all edge cases of complex systems. The best firms use a combination of virtualisation of both venue and customer interfaces to continuously test their systems. Yet, again, many firms perform minimal testing and only discover the edge cases in the production environment.

• Supporting Standards
While working on the MiFID II response within the FIX Community we found that many firms were unwilling to adopt the party component block to carry the additional party information, even though it is permitted under the FIX standard and would not require them to upgrade their FIX version.

Twenty-five years on since the inception of FIX, these firms push to obtain user defined fields so that they can add in relevant party information, instead of adopting standard fields or upgrading to more modern and feature rich versions of FIX. There are still remnants of FIX.4.1 in use in the industry.

We could continue elaborating these contrasts – but, you get the point. The question each of us must ask ourselves is whether we are professionals who are responsible for upholding high levels of standards and quality – or are we just time serving?

Now I will put in some time and do some introspection, and identify those areas where I am falling short of my ideals and values when it comes to my involvement in the electronic trading profession.

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Block Trade Routing

GlobalTrading

September 5, 2017

By Sanjay Awasthi, Director, Central Dealing Desk, Eastspring Investments

Different levels of stock liquidity determine whether the best way to transact a block trade is through an algorithm or by human intermediation.

The benefit of transacting in blocks and importance of such trades in achieving optimal price execution is broadly accepted by institutional investors. However, it is necessary to penetrate the surface and explore the intricacies of block trades. The respective roles of technology and human expertise in various situations need scrutiny in order to fully understand how to transact such blocks effectively.

Block transactions can be examined from a perspective that distinguishes between different levels of scale and liquidity, with particular relevance in emerging markets. At the outset, it may be appropriate to distinguish between these two distinct categories of tradeable securities, that is, stocks that are widely held by institutional investors and those that are narrowly held by institutional investors.

Stock categorisation
Widely-held stocks are generally actively traded. They comprise large- to mid-cap names and often form the core holdings of a diversified portfolio.

The narrowly held category tend to be illiquid and comprise small- to mid- cap and are typically the alpha picks of country (both domestic and foreign) funds and some regional funds. They are usually the main wealth creators and many grow to become large caps and dominant weightings in benchmark indices.

The size of a portfolio is another categorisation that is pertinent when considering how to treat blocks.

The individual order amount of a large fund, typically a global or a regional fund could be the equivalent of multiple days of the average daily trading volume (ADTV) of an active, widely-held stock. Often, it might make up a significant portion of the available free-float available in the market or even the total tradeable equity of a particular company.

Degrees of liquidity
Therefore, access to liquidity will determine whether an order can be filled, and the nature of it will ascertain how best this can be achieved. In practice, a trader needs to differentiate between liquidity that is either undiscovered or non-existent which could be in either widely held stocks or in narrowly held stocks. Assessing the nature of the liquidity available for a stock at a particular time guides the trader in the attempt to complete an order.

Undiscovered liquidity is available in the system as either an order on another dealer’s pad or expressed as an interest to trade by a market participant. These types of order can be matched and transacted as a block trade: some are more suitable for automation, others require the human touch.

Technology-driven block crossing networks such a Liquidnet, POSIT Marketplace and other “indication of interest” (IOI) platforms provide extremely efficient ways for dealers to access available liquidity.

Human agency
However, human intermediation is also essential to convert an “interest to trade” to the completion of the transaction. The buy-side still requires a network of trusted market relationships established and coordinated by skilled sales traders who can search for a matching interest to trade and close really large blocks.

Buy-side traders need to trust sell-side traders to ensure that information leakage is minimised and that their market isn’t spoiled. The sell-side trader should have critical mass, with access to a wide variety of counterparties, as well as discretion.

Testing the waters can be counter-productive if it disturbs the stock price. Timing can be important and that is where the expertise of a trader is most evident.

Tapping resources
Non-existent liquidity is a salient feature of narrowly-held stocks and of orders by large funds whose size is much more than the ADTV of a stock and a significant proportion of its free-float or total number of shares.

Completing these types of trades is tough, and in practice, they could be characterised as quasi investment banking mandates. Again, human networks, professional skills and personal sensitivity are essential ingredients for achieving a successful transaction.

For narrowly-held stocks, there is an additional complication. Many leading brokers do not regularly cover them, so often buy-side firms are forced to approach brokers beyond their usual list to source such liquidity.

Success in such transactions is highly dependent on market relationships and demonstrates the true value of a buy-side dealing desk.

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Disrupting The Disruptors

GlobalTrading

August 29, 2017

By Mark England, Senior Managing Director, Head of Asset Manager Sector Sales, Asia Pacific, State Street

The application of digital technology is shaking up the investment management industry, and incumbents need to rethink their business models.

Digitalisation is re-defining the product offering and processes of financial institutions. The challengers, who tend not to be tied to legacy technology and unimpeded by excessive regulation, are introducing highly accessible services that remove many of the barriers and costs traditionally associated with investing.

Established institutions, however, do have some notable advantages over these newcomers, namely their brand strength and reputations. This could give these organisations a competitive edge over the Fintech start-ups that are entering the market.

So what do the dominant financial institutions need to do if they are to co-exist alongside the digital disruptors?

Effectively managing data will be a huge determinant behind any financial institution’s success over the next few years. In a recent State Street Survey, industry leaders stated that they are putting data integration, intelligence and integrity at the core of what they do.

Data integration simplifies the aggregation process
Streamlining data management internally at any organisation can be highly effective. At present, numerous companies simply transfer their information into a data warehouse pending analysis, which can be cumbersome as the data needs to be cleansed and converted into a standardised format.

The same State Street research found that 52% of digital leaders were building an integrated, omni-channel approach, something which will simplify their operating model. The larger financial institutions tend to host a significant amount of data, and simplifying the aggregation process will be an important factor in them gaining a competitive edge over the Fintech disruptors.

Furthermore, new data warehousing technology is making it possible to analyse data in near real-time, irrespective of whether it is structured or unstructured. This can allow firms to verify the accuracy, integrity and timeliness of any data as it is being produced. If there is an issue with data, it can be addressed immediately in this environment.

Digital repositories enable investment firms to integrate third-party data, such as external benchmark data, with their own. Other unstructured data feeds such as social media posts, video/audio files and email text will eventually be incorporated into the process. Firms will then be able to leverage artificial intelligence to produce quality analysis of their data pools, which will allow them to identify trends or behavioural traits, enabling a superior product or service to be delivered.

Data intelligence is more than just a risk management tool
Having sophisticated data intelligence can help firms improve their performance and reduce risk. Many firms have bought into this concept, with 63% of digital leaders surveyed for State Street’s report stating that they were fully harnessing data and analytics to improve their decision-making processes. Using industry-leading technology to mitigate risk is supported by investors, with 39% of respondents expecting their investment firms to use the latest technology to provide sophisticated data analytics. Firms have recognised this, and many are deploying data analytics to identify risk and gain a real-time view of how shifting market conditions are impacting portfolios.

Data intelligence – or advanced data analytics as it is often known – is not just a risk management tool. It can also be used in predictive analysis for scoping out future investor trends, understanding client needs and finding new ways to benchmark performance. In addition, predictive analysis can be used to better align investment firms’ agendas with client needs.

Data intelligence can also be applied to expanding market share in new market segments, or targeting individuals through a highly-customised approach. Advanced data analytics can enable firms to segment their client base more clearly, and this is an approach already being adopted by 63% of digital leaders surveyed by State Street.

For example, many organisations only use net worth to categorise clients whereas predictive analysis can dig deeper into client behaviour, allowing firms to better segment their stakeholders.

Established market participants naturally have a brand and reputation advantage over the relative newcomers to the market. This is apparent in the State Street study, which found that 53% of investors acknowledged they trusted established wealth service brands more than new entrants. This is a huge advantage, and organisations should leverage that to their advantage. Sixty percent of respondents said reputation, brand and experience would be among the most important qualities investors look for in firms during the next five years.

Complacency is not an option
There is no denying that digital disruption is coming to the finance industry, and no firm can afford to be complacent. As the research in the State Street report illustrates, some investment firms are already at risk of being left behind.

However, for those institutions that are bold enough to rethink their business models around digital from the bottom-up, and are always looking to improve their client service, the future looks bright.

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Multi-Asset Trading: Art Or Science?

GlobalTrading

August 29, 2017

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

A reliance on electronic tools creates a danger that the human qualities and expertise of a multi-asset trading desk are not fully harnessed.

Multi-asset class trading is certainly not a new concept, yet it is one that is gathering steam as trading enterprises look not only to expand capabilities and access, but in some cases to narrow headcount and broaden responsibilities.

So, the question is: what exactly is the right trading desk model?

There are some that contend multi-asset traders should be able to trade any product in any asset class – that is, equity and fixed income – the definition of a generalist. Others endorse a desk consisting of a blend of specialists and generalists with traders that have an overarching knowledge of certain tradable instruments coupled with those that “sweat the small stuff” – flow versus focus. Both versions are certainly attainable, but are either the right option when put in the context of trading and overhead costs, execution quality and robust relationships with the sell-side?

There is a third option, one that is achievable and addresses the demands and needs of a firm that wants the knowledge and flexibility a full service multi-asset class trading desk can provide with the focus realised in a single asset class format. The solution is an asset class generalist who acts as an investment product specialist, or “Genspec”.

This is a trader who knows in detail the execution and operational landscape of the specific asset class they travel (equity or fixed income, not both), yet can dig for the gems to add value to the investment process and, ultimately, the client. Here it is critical that a sharpened quality of execution as well as market and product understanding be the standard, not the broad quantity of knowledge. It is this type of trader that is essential, for he or she understands both the art (the relationship) and the science (execution) of trading.

The science
For the better part of ten years the advent and advancement of electronic trading has transformed the way trading is approached, monitored, benchmarked and executed. It has given us speed that could not have been foreseen at conception, access to liquidity from multiple sources never envisioned and, in most cases, a narrowing of spreads spurred by increased competition for flow. Electronic venues have worked to help all traders not only execute trades in a more efficient and cost effective manner when compared to some traditional methods, but with partners who share the same aim of increased trading effectiveness on behalf of the end client. Or so we thought.

Electronic trading and enhanced capabilities in equities, futures and foreign exchange, have given the buyside trading community access and control like it has never had before. The baby steps into the fixed income marketplace should yield better results as lessons from the equity rollout are documented. This “science” is completely necessary to any successful multi-asset enterprise (or any trading discipline for that matter), but the focus need not be solely on this one path. The acceleration of the mind-set of bringing as much trading in-house as possible is a natural progression of the electronic age. “Just give us the tools we need and we can do it better and cheaper,” the buy-side says. Who can argue with that?

More control and greater access at a potentially lower impact point is a worthwhile pursuit regardless of the investment discipline or marketplace. But, does this thinking run the risk of pushing the pendulum too far from what truly makes a multi-asset class desk unique and viable?

The art
The answer is yes. Most market participants are aware of the issues that are still inherent in trading electronically, so that alone should lead to caution before putting all of one’s eggs in that basket. For the multi-asset trader, balance is the key. Here is where the traditional relationships with the sell-side, those that are often incorrectly viewed as archaic and costly, strike that necessary balance.

Trading multi-asset requires an understanding that is born from knowing that one solution, whether that be product type, trading venue or broker relationship, does not solve all. Having options – in trading tools (order and execution management systems, alternative trading systems), in products (listed/over-the-counter (OTC)/structured) and relationships (traditional/derivatives/electronic) – is the best arrow in the quiver. Here’s the “art.”

Access to capital, natural flow, inventory axes, as examples, lend options not only for better execution quality, but to better relationships with the broker community as core competencies are discovered and can be fully utilized in the future. These are factors that promote the now fading human touch.

Since all assets types do not trade electronically, such as OTC and structured products, building these necessary partnerships with the sell-side helps the multi-asset trader in two important ways.

First, it provides greater access to trade ideas that can work to reduce overall trading costs while gaining the desired exposures. Second, it increases the knowledge footprint of the enterprise not only by gaining access in potentially better, more esoteric ways, but allows for an increased awareness of the regulatory and operational environments that is critical to any trader these days, especially for those that trade in grey areas, such as OTC.

The second point is an exciting by-product of this balanced solution, because it is the trading desk where intuitive, problem-solving, exposure enhancing ideas originate. Being the effective repository of execution quality and end-to-end trade processing is invaluable and permits greater communication with, as well as greater confidence from, supported investment managers. It will also spur conversations with investment professionals who are interested in learning more about gaining traditional exposures in non-traditional ways.

Striking the balance
Concentrating too heavily on any one element of trading restricts both innovation and opportunity. Successful multi-asset trading efforts should be a combination of the right environment, the right tools and the right relationships.

The focus is to be a front-to-back expert in the asset class one trades; to have access to liquidity and products that lends options to the trade process and yields cost effective results; and an understanding with the sell-side that they are partners, not adversaries.

Crucially, they are partners that provide ideas and solutions, yet partners that must accept and agree that the client is the most important part of any trade, not their own revenue stream. It is here that art indeed blends with science and where the “Genspec” trader lives and thrives.

Why be a slave to the pendulum when you can be its master?

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Developments In The World Of FIX

GlobalTrading

August 29, 2017

By Courtney Doyle McGuinn, Operations Director, FIX Trading Community

Now that Quarter 1 is ticked off the calendar, of course the countdown continues in earnest to January 2018 and the go-live date for MiFID II compliance.

The FIX MiFID working groups continue to meet regularly and most recently FIX Extension Pack 228 was published to extend the FIX Protocol to meet the requirements of MiFID II and MiFIR and covers a second set of critical data requirements identified by the sub-working groups on transparency, and order data and recordkeeping.

That brings FIX to four extension packs that are available for firms to download and implement as part of their work on MiFID II: EP206 (clock synchronization), EP216 (post-trade flagging obligations) and EP222 (critical data requirements identified by the sub-working groups on transparency, and order data and recordkeeping).

A few other things to highlight: work on the cybersecurity front continues with the further development of the FIX Cybersecurity Best Practices and the development of FIX over TLS Standard which involves extending FIX to meet cybersecurity requirements.

Additionally, there is a strong push underway for adoption of FIX for post-trade and there will be a new page on the site to showcase those firms that have implemented FIX in this space to date, so if you wish to be added to the list please contact the FIX Program Office.

Finally, please be on the lookout for the launch of the new FIX website, which will be revealed by the summer!

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ETFs For Fixed Income Liquidity

GlobalTrading

August 22, 2017

By Sean Cunningham, Head of Capital Markets for iShares and Index Investing APAC, BlackRock

Exchange Traded Funds can be an increasingly important vehicle for investors to access depleted fixed income liquidity, reduce costs and improve trade efficiency. There are regional variations, but their popularity in Asia is set to grow.

Exchange Traded Funds (ETFs) are a well-established and still rapidly growing alternative to mutual funds. They combine the benefits of diversification in a passive portfolio with the liquidity and security of a stock exchange listing. Most attention focuses on the wide array of equities vehicles available, but increasingly, bond ETFs are gaining traction.

It’s not difficult to understand why. During the past few years, regulatory proscriptions and stricter capital adequacy ratios have imposed balance sheets constraints on banks that had provided liquidity to the fixed income market through their ability and willingness to hold inventory or take short positions. The Volker Rule and Basel III have curtailed banks’ capacity to hold large bond inventories.

At the same time, new government issuance volumes soared as central banks implemented quantitative easing policies, while companies across the credit spectrum raised long-term debt at historically low borrowing costs from investors hungry for incremental yield.

Hence, a vibrant primary market sometimes contrasts with a tepid secondary market. New investment grade bond issues are typically over three times oversubscribed. However, less than a third of large issues trade daily, and bid-ask spreads have widened more than 70% since 2007.

Nevertheless, the bifurcation has also provided market participants and industry service providers with an opportunity.

ETFs can provide liquidity and low costs
As one of the world’s largest bond investors, BlackRock is a beneficiary of this environment. Yet for several years, we have been enthusiastic about the merits of fixed income ETFs as a complement to active investment strategies. They comprise only around 0.6% of the underlying bonds markets, compared with equity ETFs which make up about 4% of the underlying, but they are set to grow in popularity.

In several ways, ETF trading is a potential precursor of the future operation of the bond market, exhibiting low cost, transparent, on-exchange trading in a standardised, diversified product. ETFs can enhance price discovery, provide investors with low execution costs to establish a diversified portfolio, and increase bond market liquidity and transparency.

ETFs combine characteristics of both stocks and traditional open-end mutual funds. Like a stock, an ETF can be bought and sold on the exchange intraday; like an open-end fund, ETF shares can be created or redeemed during the trading day – although, with the difference that these primary trades are facilitated by a group of institutional firms, known as approved participants (APs) who have entered into an agreement with the ETF’s distributor.

Primary trades do not require securities purchases or sales by the ETF. Instead APs present a basket of securities to the ETF provider in exchange for ETF shares. APs also act as agents for creations and redemptions on behalf of their clients, whether market makers or end-investors.

ETF liquidity can be additional to the underlying bond market liquidity because buyers and sellers can offset each other’s transactions without having to trade in the underlying market. Being able to trade fixed income ETFs on a stock exchange, away from the bond market itself, can provide a layer of additional liquidity that is not present in many other financial instruments.

The bid-ask spread for one of BlackRock’s High Yield ETFs (which was launched in early 2007 on the eve of the global financial crisis) averages one basis point (bp), compared with 50bp for a basket of US high yield corporate bonds, and 14bp for one of our Euro HY ETFs compared with 85bp for the equivalent basket.

Stress tests
Even during periods of market stress, ETF shares are at least as liquid as the underlying portfolio securities. For instance, according to BlackRock and Bloomberg research, more than $1 billion shares (12% of total cash bond trading) of the ETF mentioned above were traded in a single day in June 2013 in the wake of former Fed chairman Ben Bernanke’s taper speech the previous month, yet there was no underlying impact.

Furthermore, the shares of the High Yield ETF often traded at premium to the portfolio’s net asset value during the weeks of uncertainty following the Fed’s signal that it intended to reduce its asset purchases.

Again, in December 2015, when there was a pronounced risk-off market in high yield, the corresponding BlackRock ETF traded more than $32 billion for the entire month. At the same time, the amount of net redemption for the fund was around $334 million, so the ratio of volume that cleared on the exchange away from the underlying market was roughly 20-to-one. In normal times, the ratio is a still impressive nine-to-one.

In fact, most trading happens on the stock exchange, and the underlying isn’t actually traded in the bond market. Daily trading volumes of this particular ETF regularly amount to $1 billion, whether the bond markets are risk-on or risk-off. Since 2008, liquidity in the fund has grown 371 times versus a 52 times growth in assets.

Bond ETFs have endured multiple stressed markets including the 2008 financial crisis, European sovereign debt crisis, US Treasury downgrade, taper tantrum, oil sell-off of 2014 and high yield corporate bond sell-off and fund “gating” seen in late 2015. During times of stress, fewer corporate bonds tend to trade over-the counter, while bond ETFs often see increased trading volumes.

Artificial Intelligence: The Core Of A New Generation Of Agency Algorithms

eugene_kanevsky

August 22, 2017

By Eugene Kanevsky, Global Head of Electronic Trading, CLSA

AI technology is an indispensable tool for traders to gain an edge in the algorithm arms race, predicting future trends, volume, volatility and price and allowing agency algorithms to optimise execution.

The concept of artificial intelligence (AI) conjures up visions of a future world yet AI already influences us in our daily lives and has done for some time. From simple things such as suggesting friends on Facebook or proposing our next Amazon purchase to delivering news of interest direct to our phones and even through the apps that guide us by analysing real-time traffic conditions to suggest faster routes.

The financial markets are also changing and algorithmic trading, previously seen as the leading edge technology, is now seen as static or even outdated unless it is augmented with AI technology.

At CLSA we have launched the next generation of algorithms based on our proprietary AI machine learning framework, called “ADAPTIVE”, reflecting the ability to continually adapt in real time to the market.

How is AI used in the next generation of algorithms?
Recently we have heard a lot about the use of AI, predominantly with regard to stock classification initiatives. In effect, historic stock trading patterns have been analysed and classified into similar groups using machine learning techniques. These groupings then assist traders in selecting more appropriate trading strategies.

AI models essentially redefine and expand upon the traditional groupings traders understood and used daily, such as large- or small-cap, liquid or illiquid and wide or narrow spreads. These models based upon stock classification are developed and continually refined to reflect trading pattern changes.

The objective of AI stock classification is to optimise algorithm selection for stocks, and while the development offers some benefits, it does not address the fundamentals of the algorithms used to trade in the market.

At CLSA, we wanted to know: “how will the stock trade next?” We knew that by answering this question correctly and regularly with a high degree of certainty would provide far more value to our clients’ execution.

We base the CLSA’s ADAPTIVE platform upon a proprietary neural network.

Neural networks are loosely inspired by neuroscience and the most effective machine learning method known today. This trained neural network generates real-time signals projecting what is going to happen next in the market enabling the ADAPTIVE algorithm strategies to adjust execution plans as they trade, rather than rely on static predefined scenarios.

Our neural network is currently trained to predict short-term and long-term price movements, volatility and trading volume, but this is just the beginning in a process of continual development.

Why is AI more essential than ever?
In recent years, we’ve seen a growing number of non-traditional market participants exerting their influence on the Asian trading landscape, in some markets reportedly accounting for half if not more of the total traded volume. Much of this liquidity is automatically created by various types of quant algorithms.

Algorithm strategies need to be smart enough for this new world and that’s where the implementation of effective AI proves invaluable.

Algorithm trading has always been an arms race. For many years that arms race was largely limited to brokers with their algorithm quant and development teams releasing the next iteration of their latest, greatest and smartest enhancement to their suites of strategies, be that quarterly or annually.

However, equipped with nimble, shorter release cycles and increasing utilisation of AI technology for prediction, it’s not hard to understand why short-term horizon non-traditional trading houses have found short-term profitable opportunities in many Asian markets at the expense of long-term investors.

In response, brokers have grown the number of quants and technologists in their algorithmic development teams in an attempt to shorten release cycles and keep up with the trading landscape changing at an ever increasing rate.

The man-power versus automation scenario has played out in many industries, but it’s always more striking when it’s in your own area of expertise. Now, with the benefit of hindsight, observing ADAPTIVE do its work, taking just minutes to detect new patterns, multiple times during the trading day and at different price levels, the futility of more people working harder against smarter technology becomes apparent.

CLSA’s ADAPTIVE short term price prediction signals achieve an accuracy of over 95% in volatile securities. We have implemented this AI technology in our agency algorithms to provide clients direct access to this impressive technology.

Earlier I mentioned the challenges of competing with non-traditional market participants such as HFT and other short horizon strategies. While identifying short-term price displacements is an important element when improving execution, the ADAPTIVE framework also predicts future trends, volume, volatility and price, allowing the agency algorithms to optimise execution for the more traditional agency trading benchmarks, such as VWAP, IS, Inline or Close. Results have been impressive, and we see a win-ratio when compared with traditional algorithms in excess of 80%.

Is best execution really all about the smarter machine?
One of the common misconceptions is that this new generation of agency algorithms with embedded AI are black boxes, and the sales traders’ role will be merely to enter the client order and then let the technology do its work. We do not agree. AI algorithms are there as an empowering tool for the sales trader and dealer to use within the overall trading plan.

Buy-side traders, with their knowledge of the investment objective and fundamentals, are essential in mapping out the appropriate trading strategy. The sell-side trader brings detailed knowledge of the market and current sentiment: AI algorithms provide an edge in the marketplace.

CLSA’s interpretation of best execution is the sum of all the parts. We continue to believe in local market knowledge, the value of sourcing block liquidity and the importance of delivering timely and relevant information to our clients. Leading the way in market execution technology in Asia is a critical component.

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Buy-Side Trade Surveillance: Drivers, Challenges And Implications

GlobalTrading

August 22, 2017

By Michael O’Brien, Vice President, Head of Product Management, Global Risk & Surveillance, Nasdaq

Regulatory developments are continuing to play a major role in the buy-side’s adoption of trade surveillance technology, according to a recent survey.

There is no doubt that there has been a shift within buy-side firms globally over recent years – buy-side institutions are increasingly relying on direct execution for trades and less on their sell-side counterparts. In fact, in Nasdaq’s 2016 Global Compliance Survey, 52% of buy-side firms cited that they are relying less on sell-side institutions for trade execution.

However, with increased direct execution, and thus increased trading volumes, from buy-side firms comes additional focus from global regulators and new requirements from regulations, such as MiFID II and MAR. While traditionally, buy-side firms have relied on their sell-side counterparts to ensure regulatory compliance, they are now faced with the need to demonstrate that their own systematic surveillance processes and controls are in place.

In a recent buy-side analysis, Nasdaq and Aite Group set out to determine the current state of trade surveillance adoption on the buy-side – further investigating its specific drivers, challenges and implications.

Based on study participants’ responses, it was evident that regulatory drivers have and will continue to play a significant role in the adoption of trade surveillance technology on the buy-side. For global buy-side firms, there is a need to focus on both global regulations as well as local regulations in regions where they invest.

Further affirming this, in Nasdaq’s 2016 Global Compliance Survey 64% of buy-side firms noted that they were concerned with global regulation and how it would impact their firms. MiFID II and MAR were the two most notable regulations of concern.

Managing reputational risk
Increased regulatory focus on the buy-side is forcing these firms to take a closer look at how they are currently managing their monitoring processes and whether these processes need to be adapted to effectively manage reputational risk and avoid costly fines. Almost all participants in the study stressed that they believed that upholding and protecting the reputation of their respective firms is the most important compliance function. This was also reiterated in Nasdaq’s Global Compliance Survey, with 64% of respondents noting that this was the most important role of the compliance team.

As a result of regulatory pressure and the importance of effectively managing reputational risk, buy-side firms are showing more concern over specific types of manipulation – most significantly, insider trading. With several public cases and fines in recent years, buy-side firms are well-aware of the potential reputational risk that insider trading poses, even when inadvertently. Study participants overwhelmingly noted insider trading as their foremost compliance concern.

While insider trading is the main concern for buy-side compliance teams, buy-side firms face a number of other potential market abuse scenarios and sophisticated manipulation techniques that can pose significant reputational and financial risk to their firms, such as front-running from sell side counterparts, speed manipulation, layering and spoofing.

Obstacles to implementation
While there are clearly compelling drivers for trade surveillance implementation on the buy-side, numerous firms remain in the beginning phases of adoption. While there are several reasons for this, some of the key findings uncovered in the study implied that a major challenge in implementing trade surveillance measures on the buy-side is the inability to effectively consolidate disparate data sources, including trading activity, and electronic and audio communications, among others.

Furthermore, some buy-side firms do not currently have the appropriate technology framework to support trade surveillance requirements.

Despite the challenges, global and regional regulations continue to place additional pressure on buy-side firms to implement systematic trade surveillance and monitoring processes – further driving the shift from a traditionally reactive compliance culture, to a more proactive and even soon-to-be predictive compliance culture. Buy-side firms must adapt in order to remain compliant and protect their organisations from potential reputational risk.

For additional information, contact MarketTech@nasdaq.com for the full report.

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