Separating Trade Execution And Research

December 5, 2017

By Stephane Loiseau, Managing Director, Head of Cash Equities & Global Execution Services – Asia Pacific, Société Générale

MiFID II’s commission unbundling provisions will have a widespread impact on global financial services and lead to the emergence of more independent research platforms.

The implementation of MiFID (Markets in Financial Instruments Directive) I a decade ago established a harmonised investment services framework in Europe. It also led to a wide-spread adoption of low-key unbundling of stock trading commissions, separating a broker’s advisory service from order execution.

However, MiFID II, which will come into force on 3 January 2018, will have much more explicit prescriptions and a wider ranging impact, affecting not just how European fund managers conduct business, but also on buy-side firms in Asia and the US.

The legislation considers payment for research (or any other service) through trade execution commissions an inducement and is therefore not allowed. The regime also sets tougher rules on the transparency of research costs, which must be disclosed by asset managers.

It is likely that the regulation will equally impact US and Asia firms whether they trade in the EU or not. It also means that US and Asia subsidiaries in the EU will have to conform.

Currently, there is limited unbundling of trade commissions and research in Asia, mainly due to the complexity and diversity of the region’s markets. Yet, there has been a prevailing awareness that advisory services and order execution should be separated, albeit informally. The advent of MiFID II has hardened the perspective, solidifying a previously nebulous undertaking to distinguish between the two services.

In the US, where the Securities and Exchange Commission prohibits cash payment for research from the profit and loss account, a majority of fund managers said according to various press reports that MiFID II is likely to have an impact on their business models as they intend to unbundle commissions, although it’s not clear how they will do so or even value research.

Global adoption of unbundling
There are two main reasons why Asia and US fund managers are becoming more MiFID II aware. First, there is a commercial motive: they operate in a highly competitive market, and asset owners are likely to select fund managers who demonstrate best practice. This is especially important when pitching for sovereign fund mandates in Asia and the Middle East whose activities are subject to close public scrutiny, and where maximizing performance, therefore also minimizing costs, are amongst the key objectives.

Second, there is a business imperative: the scope of MiFID II outside Europe is still unclear, but it is likely to be wide and probably extra-territorial, so fund managers need to be prepared to avoid being blindsided, while adoption on a worldwide basis will facilitate internal operational efficiency for the largest firms.

The MiFID II provisions cover all types of research, including macroeconomic analysis and strategic advice, as well as stock, rates and credit recommendations. Although the separation of trade execution and research costs for equities has already been applied by some buy-side firms, fixed income is entering new territory. There are no explicit commissions paid for dealing in bonds, and there is no connection between research costs and trading spreads.

For all asset classes, the sell-side needs to determine how the buy-side should pay for different components and methods of delivery, distinguishing between basic web content, discrete research portals and premium services such access to a star analyst, strategist or economist.

Meanwhile, a buy-side firm basically has three options for allocating costs. First, the transactional method funds a Research Payment Account (RPA) that incorporates a Commission Sharing Agreement (CSA) with sell-side firms, separating trade execution costs from research costs.

Second, the accounting method funds segregated Research Payment Accounts (RPA) internally, matching invoices from research providers with their application to clients’ proportional interest in a portfolio. The third way is simply to pay for research from the firm’s profits. This has the attraction of being subject to less regulatory oversight, but some firms might take one of the two RPA routes if they have already implemented a degree of payment unbundling and allocation system for their clients.

Certainly, the adoption of the RPA method will make buy-side firms more aware of the value of specific research and this closer scrutiny will put pressure on sell-side firms to produce consistently high-quality content.

An unforeseen consequence of MiFID II’s rule might be concentration risk, with fewer banks and brokerages providing research and a reduction in trade execution counterparties as some sell-side firms, starved of commission income, pull out of markets. Perversely, this could lead to a re-bundling, creating an oligopoly between the largest investment banks and asset managers.

Independent research platforms
On the other hand, independent research providers should be given a boost. Regulation is pushing buy-side firms towards them in order to ensure there is no suspicion of tacit links between trade execution and research, and independent firms with low costs should be able to compete on price with banks and brokerages struggling with high fixed costs and vulnerable revenue streams.

However, research specialists need to establish credibility with fund managers, especially if they are a new entrant to the market up against well-established bank incumbents. There are companies that aggregate research, package and sell it to fund managers, but an alternative model, that takes advantage of new technologies, offers a more efficient and customised service.

Last year, Société Générale signed an agreement with “Smartkarma”, a curated online platform for investment insight focused on the Asian markets, to provide its institutional clients access to equity research based on real-time demand and matched to individual investment mandates using predictive technology.

Customers receive research provided by around 400 highly ranked analysts, academics, data scientists and strategists covering more than 1600 companies across 15 Asian markets. Many of the analysts previously worked at major financial institutions and prefer the independence of working in smaller, autonomous firms.

A platform such as Smartkama offers fund managers an ecosystem that is fully compliant with MiFID II’s unbundling prescriptions, avoiding suspicions of brokerage inducement while ensuring access to the best research, judged on its own merits.

Independent research platforms are likely to develop further as many sell-side firms are forced to choose between maintaining costly own research capabilities or focussing on trade execution. A bifurcation of function seems to be an inevitable consequence of MiFID II’s unbundling provisions.

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Unplugging Alpha In Indian Markets

GlobalTrading

December 5, 2017

By Jyoti Rai, Associate Director, Business Development & Advocacy, Edelweiss Prime Brokerage Services

The investment case for India is compelling, overseas access is becoming easier, capital markets are developing rapidly and domestic financial firms are adopting the latest technologies.

Today, India is witnessing a paradigm shift owing to a convergence of steadfast vision, inclusive economic growth and deep policy reforms that are intended to benefit all Indians. In spite of global headwinds, India’s GDP rate has been sustainable with the government achieving a fine balance between fiscal prudence and growth.

Phases of reform
During a period of market liberalization in the early nineties, India introduced a raft of reforms that effectively ended the license raj, the expression for the serpentine assortment of bureaucratic regulations that often stifled business and enterprise. Private companies were allowed to enter key sectors, including manufacturing, oil and steel, foreign direct investment was encouraged and important regulators such as the Securities and Exchange Board of India (Sebi) and the Insurance Regulatory and Development Authority (Irda) were established.

Economic growth was volatile during this transition phase, but it was a prelude to spectacular annual growth rates from 2004, reaching over 9% a year towards the end of the decade. Foreign portfolio investment flowed into the country’s expanding capital markets and the rupee strengthened.

The 2008 global financial crisis revealed that this hyper-expansion was unsustainable and fragile, and a period of steady consolidation was nurtured from 2008 to 2013. Prudent risk management insulated India from the worst of the fallout from the worldwide recession, but not from some domestic financial scandals.

High profile exits from India by Fidelity and Walmart could have shaken the confidence of policy makers committed to a careful and circumspect reform, but they remained firm and introduced important regulatory frameworks such as the General Anti Avoidance Rule and the BEPS Report.

Meanwhile, there has been significant reform and development across all sectors of the economy, including telecommunications, banking, asset management, aviation, fast-moving consumer goods and hospitality, as well as a vibrant technology sector.

Investor access to India growth
In a major restructuring three years ago, Sebi introduced the Foreign Portfolio Investment (FPI) regime that categorised non-resident investors into three groups determined by their risk profiles and know-your-client requirements. Registration procedures were also made simpler.

We at Edelweiss, have been seeing an influx of FPI registrations coming through during the past three years. Interestingly, the number of FPI registrations has gone up exponentially in the last calendar year, climbing to a high of over 8,500. This saw a further spike after the recent prohibition on offshore derivative instruments on Indian derivatives contracts. Building on the “ease of access” syntax, we have ensured that a new FPI does not have to approach multiple service providers – Edelweiss India is an authorized custodian and conducts the required KYC to issue new FPI certificates, in addition to helping apply for India tax ID, trade and execution set-up and futures clearing.

Edelweiss has set-up dedicated KYC and on-boarding teams at our offshore locations in Asia, London and New York as well, which has reduced the FPI set-up and Go-Live timelines. COOs across fund houses have appreciated this “one-stop-shop” approach along with dedicated hand-holding for the nuanced requirements of getting an FPI ID for India.

Investment rules are now clearer, and overseas funds are able to tap into India’s increasingly sophisticated capital markets more easily than before. The average daily trading volume of the National Stock Exchange and Bombay Stock Exchange combined is $3.5 billion and daily volume of the top five single futures contracts is between $75 million and $80 million.

Emerging trends in India’s financial markets include:

Domestic savings are moving to equities
Investments in mutual funds are rising steadily,
growing fourfold in the past five years.
Corporate bond markets are opening up and
becoming exchange traded
Currency markets are more accessible, with
offshore participation allowed
New products such as REITs and InVITs offer great
opportunities
Distressed assets and the private debt market are
growing rapidly
Markets are supported by robust technology and
infrastructure

Direct market access
Indian financial services companies such the Edelweiss Group provide a range of high technology products and services to a large and diversified client base that includes corporations, institutions and individuals. Edelweiss’s offerings span multiple asset classes and consumer segments across domestic and global geographies.

As one of the country’s leading institutional equities businesses, Edelweiss delivers seamless execution and innovative research products to more than 300 active institutional investors. It is a pioneer in algorithmic trading in India and offers a complete suite of proprietary, exchange approved algorithms, tailored to suit the Indian markets.

Edelweiss offers direct market access (DMA) and FIX connectivity, catering to over 200 clients and also provides Direct Strategy Access (DSA) services with both one-touch and no-touch DSA options. Moreover, Edelweiss-DMA has a working relationship with major FIX connectivity providers that enables easy and quick on-boarding of clients with the platform. In addition, Edelweiss offers ultra-low latency DMA to high frequency trading clients.

The system is tested robotically for around one million cases and conditions and is continually upgraded with the latest certified hardware and software designs and coding techniques. Production support and monitoring is also automated.

The investment case for India is clearly compelling and policy makers are keen to ease foreign access to the country’s high potential capital markets in a steady and sober fashion. Meanwhile, domestic financial firms with ambition are prepared to facilitate overseas investment through the adoption of the latest technologies.

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News : Xavier Rolet leaves the LSE after bitter boardroom row

Lynn Strongin Dodds

November 29, 2017

According to published reports, Xavier Rolet has stepped down as CEO of the London Stock Exchange with immediate effect after a protracted and contentious boardroom row over the leadership of the exchange.

Chairman Donald Brydon will also not stand for re-election in 2019 “as he and the Board believe that at that point it would be in shareholders’ interests to have a new team at the helm to steer the future progress of the company.”

Rolet, who was due to leave next year after an eight year reign as CEO, said in a statement, “Since the announcement of my future departure on 19 October, ‎there has been a great deal of unwelcome publicity, which has not been helpful to the company. At the request of the Board, I have agreed to step down as CEO with immediate effect. I will not be returning to the office of CEO or director under any circumstances. I am proud of what we have achieved during the past eight and a half years. CFO David Warren will step in as interim CEO.”

The storm has been brewing over the past couple of weeks and there been reports that some of his staff disliked his management style. However, Rolet is widely seen as being integral to reviving the exchange’s fortunes and the exchange group has come under fire over his planned departure from activist investor, The Children’s Investment Fund, which holds a 5% per cent stake

Christopher Hohn, who heads TCI, has accused the LSE of forcing Rolet out against his will, seeking to extend his contract to 2021. He accused the board of gagging him with confidentiality clauses.

Bank of England governor Mark Carney also weighed in. Speaking at a press conference covering the BoE’s latest Financial Stability Report, Carney said, “Xavier Rolet has made an extraordinary contribution as head of the LSE over the last nine years. But everything comes to an end. We were appraised of the succession plan before it was announced — the agreed succession plan.”

He added: “In some respects, we are a bit mystified about the debate because we knew about the succession plan and stayed close to the situation. We can’t envisage a situation where a CEO stays beyond the agreed period. But it’s in the interest of all parties involved that clarity is provided as soon as possible.”

The departing Rolet will be paid his salary of £800,000 for 12 months of gardening leave, and a potential bonus worth £1.6m. He also has a number of long-term incentives under which he could theoretically earn £10.2m.

©BestExecution 2017

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Machine Learning And The Future of Finance

GlobalTrading

November 28, 2017

By Elliot Noma PhD, Managing Director, Garrett Asset Management

Artificial intelligence has conquered games and image recognition, but will it master investing? The short answer is yes, but how soon and how complete?

Machine learning methods have had impressive recent successes. These include defeating humans at chess, Jeopardy, poker and Go, as well as providing superior image and speech recognition. Developers strive to create tools that automate decision making and that can mimic or exceed human performance for specific tasks.

The range of tasks and the variety of methods influence current successes and the way forward. This means that there can be large differences in the short-term outlook of machine learning methods in finance, with some areas quickly embracing artificial techniques (AI) while other areas require the development of new methods.

AI and machine learning are often used interchangeably since they convey a general idea that software can make intelligent decisions, and intelligence implies the ability to learn over time. However, the ability to learn new concepts is different from the appearance of intelligent behaviour. Some methods, such as unsupervised learning, need little guidance, while others such as support vector machines require extensive training. Linear regression models have a great deal of statistical theory backing their application, but for neural nets the theory is still being developed. Moreover, models of learning vary based on the criteria used to evaluate success.

AI is being applied to finance in several ways. These include the automation of many tasks, handling basic functions and the ability to change over time as the software adapts to new market conditions.

Strengths
1. Automation: When incorporated into data feeds, models extract information from inputs, classify them into useful categories and initiate actions autonomously or through human intervention. These actions are consistent and can be improved over time. Automation allows quicker evaluation of inputs for an investor trying to determine the utility of a new dataset. It enables a detailed look at large amounts of data produced by sensors in the Internet of Things. Trading models can be developed quickly as older models become obsolete.

2. Handle the mundane: Software excels at monitoring every-day occurrences. It can easily monitor the performance of a physical device to determine that the device is functioning correctly.
Tabulating the performance of even the largest pool of loans is done easily and accurately. Over time, machine learning tools can become increasingly sensitive to deviations from what is expected and are therefore highly useful tools in fraud detection. Different detectors can also be linked together to identify deviant behaviour.

3. Adapt over time: Machine learning techniques require training data so they can predict the best actions in normal situations. As data sets expand, the software can be adapted to increase the number of features that it can monitor. This makes decision making more nuanced. In addition, as new categories are added in supervised learning, the granularity of the decision process improves with more data.

However, machine learning methods need customisation for different domains. For instance, neural nets are usually trained on images of a standard size to better fit the internal network. A neural net analysing text may be better structured using a different internal configuration. In finance, these challenges may extend beyond just the geometry of connections making up the network.

Challenges
1. Time to learn: Machine learning techniques require training data so they can excel at predicting the best actions in normal situations. They are weakest when classifying unusual situations where there are few training cases and exceptional drivers dominate. Also, some data may arrive at specified intervals such as the announcements of central banks or quarterly corporate financial reports. In contrast, other fields can accelerate their data collection by increasing their web traffic of recruiting evaluators. Using a variety of models to analyse data from multiple sources may help here, as techniques such as boosting maximize the contribution of each model and data set.

Furthermore, learning can take place at several levels and for different purposes. For instance, you may talk to a chatbot which appears to exhibit intelligence. It answers your questions and may have some learning as you continue to converse with it – but only so far. Another example is an algorithm which ostensibly learns to detect images of cats, but might be actually creating a rule on the brightness of the background.

These structures point to differences in our expectations of how AI learns and what it retains. This makes the individual methods impressive in many contexts, but lacking in aspects of intelligence that we take for granted in our interactions with other people. These include a memory of context and our ability to notice deviations from our expected context. Humans also assume that certain information will be learned even if we are not aware we are learning it. We also expect that when asked, we can give some justification for our decisions and perceptions.

2. Continuous stream of input data: Another challenge is modelling a continuous flow of information whose time boundaries may be unknown or indeterminate. This is unlike image processing which analyses individual images in isolation or game-playing programs which use well-defined conclusions that can be used unambiguously to define victory or defeat. In contrast, the time horizon of financial investments is usually not fixed as personal and business circumstances change over time as does the evaluation of investment success.

3. Lack of stationarity: Images of cats do not change over time as our image processing capabilities improve. However, trading strategies and markets as a whole adapt depending on external events and actions by traders and money managers. In some markets, new trading algorithms may have a very short useful life. Often these changes are due to events outside the world of finance, so they are beyond the horizon of most models.

4. Crowded trades: The use of common data sets and common machine learning methodologies can lead to crowded trades which limit profitability for some strategies. It also moves overall market risk from individual portfolios to the market as a whole, similar to how the convergence of risk management methods increased systemic risk. However, there is a wide range of methodologies for machine learning and the number of datasets is expanding as new tools and measures are created, which mitigates concentration risk.

5. Lack of transparency: Of greater concern is the acceleration of the investment process and the decreased insight into why decisions are made. Combined with the lack of understanding of how non-normal markets are handled by software this should raise concerns about how the system copes with periods of high volatility or events such as the “flash crash” of May 2010. Software and hardware are prone to bugs, and humans need to keep a tight control of the behaviour of machines as they take over more and more tasks.

Applications
Among the various applications across finance – credit, operations, trading cycle – each has different characteristics that play to the current strengths and weaknesses of machine learning. One cannot generalise within such broad categories, but certain specific applications are most compelling.

For instance, many operational and trade execution functions can take advantage of automation to recognise the normal. Routine execution and trading functions can be best conducted using algorithms in normal markets. Some brokerage and banking functions can be streamlined as investment advisers are cued to sales opportunities by software that considers customers’ past trades and investment preferences. Credit verification already uses a high degree of automation and could be made even more accurate by considering more complex patterns of the borrower and adjusting the weights to these factors based on changes seen in customer behaviour.

All these software tools are used within the world of human activities that have consequences for individuals and whose rules are set by humans. For the immediate future, humans will need to give oversight to computer activities and continue to handle the exceptional situations. This is especially true since the reasons a program makes a decision are very different from the heuristics used by humans.

Also, methods for computers to explain the logic behind their decisions to humans are still in their infancy. This lack of communication is especially important when determining the blind spots of an algorithm. Software cannot dictate an investor’s preference for risk or intuitions about specific opportunities. Much remains outside the view of models such as the activities of governments and central banks to affect the markets, especially during exceptional situations.

Just as other machines in other areas of activity have not fully replaced people, machine learning algorithms are unlikely to replace people altogether. Software will, however, change the number of people doing specific tasks and the skillsets of those remaining. The one certainty is that machines will further disrupt the financial industry.

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How Much Security Is Enough?

GlobalTrading

November 28, 2017

By Mark Vos, Chief Information Security Officer, Iress

Cybersecurity has become a significant and increasing cost of doing business, but by striving for a “best fit” solution, it can be a business enabler.

As a security and risk professional, I am often asked: “how much security is enough?” It seems a simple enough question, but it manages to trip up so many people. So what is the right answer? Is there a nice sound bite that one can give? Well, not really.

In a dynamic environment of increasing security threats, firms have a big challenge on their hands to ensure they continue to:

Get their security governance structure right and clearly articulate roles and responsibilities
Obtain executive level buy-in and sponsorship
Base security investments on risk
Use security as a business enabler, not just a cost
Establish a security awareness programme
Continue to assess and adjust their security capabilities to changes in the environment

It is certainly complicated. Barely a day passes without a press report relating to a security issue, and all financial services organisations now face greater security threats to their people, assets and operations from such diverse sources as:

Terrorism
Fraud and financial crime (both internal and external)
Organised crime, including money laundering
Information security threats from hackers and computer viruses

The level of complexity involved in managing such a diversity of threats means that cybersecurity has become a significant and increasing cost of doing business. The challenge is to develop a holistic approach to security management that responds to each of these demands in a coordinated, cost effective, and efficient way.

Where are firms focusing their InfoSec investment?
Our larger clients are spending millions in transforming their security functions and improving their security management practices across a range of areas, including:

Risk management
Information security
Fraud and investigations
Forensics
Anti-money laundering
Physical security
Business continuity
Crisis management.

For many, this investment represents a significant shift away from the manner in which they have traditionally managed security. It is also placing huge demands on their security teams to develop new management skills, and places demand on their partners and service providers.

The best fit model
It is becoming more common for organisations to strive for a “best fit” solution as opposed to obtaining “best practice” in every security matter. It’s about being commercial and pragmatic in the way security is managed. Conforming to best practice is an extremely expensive exercise that does not necessarily deliver business benefits equal to or greater than the expenditure required to get there. A best fit model is about understanding what the risks are, and applying the most appropriate risk mitigation strategy to reduce them, as opposed to applying the best practice processes regardless of the associated risk.

So how much security is enough? A good place to start is to identify the top risks your business is likely to face and find commercially pragmatic solutions that remediate those risks. And that’s exactly what firms must be focused on doing right now Global-scale cyberattacks such as the Wannacry ransomware attack and, more recently, the huge malware attack that brought chaos to the Ukraine before spreading internationally, can inflict real damage on an organisation, both in its ability to function and its reputation.

They are also a big reminder of the risks we all face – but let’s keep things in perspective. The reality is, you’re far more likely to suffer an internal security breach than from an external threat. According to a recent PWC report, half of the worst cybersecurity incidents were due to inadvertent human error.

When it comes to information security, people and process are critical. You can have the best patch management practices in the world, but if your employees aren’t being vigilant, you’re wide open to many different types of attack. The bottom line is that your company culture is what will ultimately define your security posture and its effectiveness.

What are you up against?
However good your defences, you need to work on the assumption that malware will get through from time-to-time. At that point it will be your diligence and awareness that makes the difference. So what sort of nasties are you up against?

Bots and Zombies
Ransomware
Rootkits
Spyware
Trojan horse
Virus

What these do is exploit vulnerabilities – either those of a system or an individual. Every 40 seconds, a company is hit with ransomware (in the first quarter, 2016 it was every two minutes).

By far the most common delivery vehicles for ransomware are attachments sent directly to your users in increasingly believable emails from seemingly trustworthy sources. A review by IBM Security found that the number of ransomware-infected emails sent this year has already increased 6,000% compared with 2016.

Cyber criminals are looking for an easy target and it’s your employees they are more likely to target, rather than your software. Humans have now moved ahead of machines as the top target for cyber criminals.

Awareness and breaking bad habits remain the biggest challenges when it comes to fighting phishing. A 2016 study on IT security infrastructure by the Friedrich-Alexander University, reported that 78% of respondents knew about the risk of unknown links in emails, yet they click anyway! So what can you do?

Don’t leave InfoSec to the IT department
Ten years ago, the job title “Information Security Analyst” didn’t exist. Today, there is a genuine worldwide shortage of qualified and experienced InfoSec specialists. They are in high demand, and with good reason. As the cyber threat grows and evolves, so must your cyber defence resources.

Three years ago, we set up a dedicated global information security team tasked with protecting our environment and those of our clients’. We recruited specialist subject matter experts who could educate others and keep up with ever-evolving cyber threats and techniques. The team was integrated into the business, not set apart as a traffic cop.

It’s their responsibility to perform and communicate information security within the business and make it everyone else’s responsibility too. It quickly became obvious that if we were going to do this successfully, we needed to take a client centric approach to everything we did. That meant:

Defining metrics of the effectiveness of information security and providing that to the board to get their buy-in on commensurate information security investment
Having a team that could influence colleagues and internal stakeholders
Communicating information security in a clear and effective manner
Focusing on the company culture, driving the importance of protecting client data, and other sensitive data

Transforming Legacy Systems

GlobalTrading

November 28, 2017

By Jay Boyd, Application Services Technology Officer, Invesco

Implementing large-scale systems programmes requires commitment and discipline at all stages of the transformation process as asset managers aim to take advantage of new technologies.

The asset management industry is at a crossroads. It is facing several external factors that are causing many firms to reassess their operating models. New financial technology is providing many exciting opportunities for firms to expand their capabilities, but it is going to be through the transformation of their legacy systems and processes that they will achieve the leverage they are looking for in preparation for the future.

The transformation of legacy systems and processes are typically large, expensive programmes that touch many parts of the organisation. They usually involve changing a substantial, end-to-end process that has been in place for many years, whose scope is not fully known and whose architects are often no longer available.

There are two primary objectives: First, introduce new systems and processes and achieve all of the stated upfront goals. Second, replace the hidden cottage industry processes that have been established over the years on top of the legacy systems. These are usually undocumented, opaque, and their scale and scope are unclear.

There are many components to establishing and completing a large-scale transformation programme, but six are critical:
• Clear vision
• Executive sponsorship
• Well-defined road map
• Programme leadership
• Planning
• Strong execution

Clear vision
It all begins with the vision. If a vision cannot be executed, it is nothing more than fantasy, therefore it should be aspirational, but also achievable. It should be put together in such a way that everyone in the firm understands what it is and why it is important.

Large-scale programmes take time, resources and money. They often build a foundation for future growth and scale, but can also be unpopular since they are not necessarily the “latest and greatest” hot items. It can feel like a home repair to some – necessary but what does it get me?

This makes it easy for people to question the programme. It is often perceived as being in the way of what they want or believe is important. Staff need to see that it is more than repairing your home, that it is vital for growth.

Executive sponsorship
Executive buy-in is absolutely key to the success of a programme. Executive support ensures that it will have the resources, continued prioritisation, air-cover, and the necessary cheerleading. An executive sponsor should be spreading the benefits of the programme and counter nay-sayers sceptical of the programme’s value, fearful of the changes the it will bring or even jealous that it could derail one of their own priorities. Sponsorship can also help ensure that the true cost estimates are transparent, and not being glossed over.

Road map
Once you have your vision, you begin to build your road map. The vision is “where you want to go” and the road map is “how you are going to get there.”

It should define the approximate timeframe to deliver the entire body of work, each discrete project and its timeframe, the primary deliverables for each project and the value or benefit derived from each project. You are looking to deliver value in incremental pieces that make sense, without waiting until the big bang at the end of the programme. Solution architects are required to design the work appropriately, and provide insight and direction on how to integrate the different pieces as they emerge.

Programme leadership
When a firm decides to take on a large-scale initiative, it must put into place a management and resource structure that will get the job done as fast and as efficiently as possible. Create the leadership team of the programme by carving out the best people within the firm for the job, and then making this their full-time job. While the firm may find it necessary to bring in external expertise to help with various aspects of the programme, they should be putting as many of their own resources in leadership positions as possible.

Where external leadership is necessary, it is best to put “two-in-a-box” so that someone from the organisation can learn new skills from external experts. This is a both an investment in the firm and an investment in one of its key leaders.

The programme director must be someone who is capable of making decisions and empowered to make them. They must be someone who can sell the vision of the programme, inspire team members about its value, and initiate and drive through change.

The programme manager is another key position. They must be willing to help moderate disputes, resolve design issues, and ensure consistent communications across the programme. Often this role is the ‘glue’ the pulls the entire program together.

Planning
The planning phase itself is typically a time-boxed period when requirements, timeframes and resources are defined. Here, you should identify many of the unknowns of the vison and road map phases. You will write any necessary RFPs, evaluate the responses and make vendor selections, plan the execution phase and build materials for the project governance process.

Execution
There are three crucial issues to highlight: First, an understanding that plans can go wrong. The programme needs to be flexible enough to adapt and adjust. Implementation will take longer than expected and be more complex than anticipated. A balance must be found between scope expansion and scope control to accommodate the likelihood that the plan will change. Providing the programme with a limited contingency bucket can be an effective way to control reasonable scope expansion for the unknowns, while proper oversight from a programme steering committee can provide a cap for scope control.

Second, is decision making. Mangers must have the ability to make decisions with less than perfect information, make decisions that are best for the programme, and be able to pivot if the decision is not working as expected.

Third, is having a robust change-management plan in place. Change management is how the programme will engage the business, where new processes are established, training is conducted, manuals written and test plans and cases devised.

Conclusion
These elements may seem to be basic requirements to ensure a successful large-scale transformative programme, but they are often overlooked or not sustained. There is also one more critical component. There should be assessment points that allow for decisions to be made to either continue to the next phase of the programme, pivot it or if necessary discontinue the programme. This prevents the firm from having to make a single big commitment, and reassures stakeholders that there are controls in place that deal with the unexpected.

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From Risk Analysis To Returns Forecasts, Machine Learning Is Guiding Investments

GlobalTrading

November 20, 2017

With Gary Kazantsev, Head of Machine Learning, Gideon Mann, Head of Data Science, and Bruno Dupire, Head of Quantitative Research, Bloomberg

Machine learning cannot do everything people can do, but the technology is finding more widespread use in finance.

What is the biggest misconception about machine learning in finance?

Gary Kazantsev: That it is some sort of a magic wand that will solve hard problems in contravention of truths known from basic statistics. No amount of machine learning will help if the problem you are trying to solve is ill posed, or you don’t have a sufficient amount of data, or if you aren’t careful about issues like non-stationarity and bias.

Gideon Mann: One major misconception is that machine learning can do things that people cannot do — that it can magically accomplish things that tax human ability. Typically, the biggest impacts of machine learning come by automating simple and straight-forward human decisions, but doing it on a cost basis that makes various processing economical. This, in turn, leads to the appearance of magic.

How advanced is machine learning in finance today?

GK: It depends. The range of problems being attacked and the methods used is now vast and rapidly expanding. We are familiar with organizations which do end-to-end strategy development (from portfolio selection to execution) as an ensemble machine learning problem. There are also plenty of firms who are now only starting to investigate this field.

The level of acceptance of new technology in financial institutions varies depending on their acceptable risk profile, specific requirements for interpretability and transparency of models, and even geographical region (which influences the available pool of talent familiar withcutting edge research). This applies to machine learning even more so than many other technologies.

Bruno Dupire: Machine learning is still in its early stage, but catching up very quickly. Quantitative finance is a natural field for it as learning to establish links between input data and returns is very valuable. Data, both structured (security price time series, fundamentals) as well as unstructured (text from news/tweets/call transcripts, net searches, satellite images) are systemically exploited and the array of methods is ceaselessly expanding.

Random forests, support vector machines, knowledge graphs, recurrent nets, LSTM (long short-term memory), convolution nets, GAN (generative adversarial networks). It has changed a lot since I initially used neural nets to forecast financial time series in 1987.

How are sophisticated clients using machine learning in their workflow,and how is it impacting investment strategies?

GK: We have seen everything from counterparty risk analysis to optimal execution, and from predicting bankruptcy risk to forecasting returns, earnings or unemployment statistics. It’s also being used in portfolio construction, sentiment analysis of financial news and so on. Machine learning is becoming an integral part of the toolbox used in creation of systematic strategies.

What is driving investment and attention in machine learning in the financial industry?

GM: Machine learning has had an enormous effect on other industries and has driven significant growth. Think Google, Amazon, Facebook. There are also an increasing number of financial firms that have been able to harness machine learning to drive value. Finally, the pressure to trim costs has focused firms inward to see if they can do more with less, and enhancing employee productivity through augmentative technology has become more appealing.

What new Bloomberg machine learning application or tool are you most proud of and why?

BD: We are building a machine learning prototyping suite that enables the user to access scikit-learn, TensorFlow and our own functions, in a very user-friendly interactive environment. It offers multiple ways to visualize the data, the progress of the learning and how the algorithm operates.

GM: We have made significant investments in our neural network infrastructure, and because of our efforts have seen numerous examples of deployed neural network models. From these, the effort in understanding tables has particularly made me proud as it demonstrates the power of these new technologies on a thorny old issue.

GK: I am particularly proud of the work we have done on question answering. We have been able to make an impact on the way clients use the Bloomberg terminal despite this being a very challenging open problem.

Can you give one prediction for the future?

GM: I think the future is likely to be increasingly characterized by fairly stable periods interrupted by very rapid changes as the speed at which information and technology gets disseminated increases.

GK: Sea levels will rise, markets will fluctuate, and deep learning will not give us true human-level artificial intelligence.

GD: I think the community will soon realize that neural nets, however deep they are, cannot solve every problem. We are likely to observe a merging of neural nets and logic-based systems, especially when data is scarce. For advanced tasks, it is not enough to let data drive the learning process, one also needs to inject expert knowledge, leading to hybrid systems.

Implementation Of MiFID II Testing Requirements By Trading Venues And Investment Firms

GlobalTrading

November 20, 2017

By Matthias Burghardt, Head of Xitaro Exchange System Development, Boerse Stuttgart

If you consider MiFID II testing requirements are a challenge, you might have a problem with your established processes today.

Boerse Stuttgart is approaching the last quarter of a two-year Markets in Financial Instruments Directive (MiFID) II project which is dominated by development and testing activities. MiFID II requires trading venues and investment firms to implement substantial changes in existing processes and technology. As Boerse Stuttgart Group operates not only a trading venue but also an investment firm, EUWAX AG, requirements have to be fulfilled for both entities. Before work was carried out an examination was needed to identify the differences and commonalities in requirements between investment firms and trading venues.

MiFID II testing requirements on investment firms and trading venues are based on Articles 17 and 48 and are further specified in the regulatory technical standards 6 and 7. Article 17 requires investment firms engaged in algorithmic trading to ensure their systems are fully tested. Article 48 requires regulated markets to ensure their trading systems can perform orderly trading under conditions of severe market stress and meet strict testing criteria. In addition, regulated markets shall require members to carry out appropriate testing of algorithms and provide environments to facilitate such testing. According to Article 18(5) these requirements do not only apply to regulated markets but to multilateral trading facilities (MTFs) and organised trading facilities (OTFs) as well.

Regulatory Technical Standards (RTS) 6 and 7 provide the details on MiFID II’s testing requirements for investment firms and trading venues, respectively.

There are six areas to consider when implementing MiFID II: Staffing, general testing methodology, conformance and algorithm testing, testing environments, stress testing and the role of self-assessments. Let’s explore in more detail.

1) Staffing: You need to have a sufficient number of qualified and expert staff to manage your trading systems and algorithms. The requirements on investment firms and trading venues are remarkably similar, but investment firms need to have staff with technical knowledge of trading systems, algorithms and strategies. Trading venues need to have staff with knowledge of the trading systems, algorithms and the types of trading undertaken by the members.

2) General testing methodology: MiFID II requirements on the general testing methodology may not be new, but it’s wise to check your processes and documentation. The goal of testing is to ensure that systems do not behave in an unintended manner. But, similar to the staffing requirement, stakes are higher for investment firms than for trading venues.

Investment firms should establish clearly delineated methodologies to develop and test their systems, algorithms or strategies. They should also adapt their testing methodologies to the trading venues and markets where the trading algorithm will be deployed. On the other hand, trading venues are required to make use of clearly defined development and testing methodologies and be able to demonstrate at all times that they have taken all reasonable steps to avoid their trading systems contributing to disorderly trading.

3) Conformance and algorithm testing: Investment firms and trading venues must work together to ensure conformance of the investment firm’s trading algorithms with the trading system. Trading venues must require their members to test the conformance of the investment firm’s algorithmic trading systems with the system of the trading venue. In particular, conformance testing should prove that the systems interact as intended, verify basic functionalities, test connectivity and recovery. Trading venues should document the results by issuing a conformance test report.

In addition to conformance testing which covers only the basic functionality, trading venues must require members to certify that their algorithms have been tested to avoid contributing to or creating disorderly trading conditions. This is a task investment firms can do without any interaction with the trading venue. However, before an algorithm is deployed, investment firms must certify and explain their algorithm testing activities to the trading venues. Trading venues are expected to include all testing obligations in their rules and regulations. Critically, conformance testing is made a condition in the due diligence for members of trading venues.

4) Testing environments must be strictly separated from production environments in both investment firms and trading venues. Investment firms should use a testing environment separated from production. Some firms may opt to use testing environments provided by a trading venue, direct electronic access (DEA) provider or vendor, but they need to retain full responsibility. They also need to use their testing environment for stress tests.

Trading venues should provide a conformance testing environment and require members to use it. Despite there being no strict requirement for members to use it, they should also provide access to an algorithmic testing environment which is as realistic as possible.

5) Stress tests shall be used by investment firms and trading venues, respectively, to verify their systems’ performance. Investment firms must – as part of their annual self-assessment – test that algorithmic trading systems can withstand increased order flows or market stresses. This is done by running high message and trade volume tests using twice the number/volume of the last six months maximum. Trading venues should – in the context of their self-assessment – simulate adverse scenarios, including members’ activities in all trading phases, segments and instruments.

Adverse scenario tests should be based on an increased number of messages received (baseline is the highest number of messages per second during the last five years), unexpected behaviour and a random combination of normal and stressed market conditions. It is important to note that stress tests are executed separately by investment firms and trading venues and have a different focus. Investment firms concentrate on testing an increased system load whereas trading venues concentrate their testing activities on adverse scenarios and an unexpected behaviour of their operational functions. There is no requirement regarding common stress testing activities.

6) Self assessments should be regarded by investment firms and trading venues as an opportunity to determine their specific MiFID II implementation needs. Investment firms are required to perform an annual self-assessment considering nature, scale and complexity of their business. Similarly, trading venues should perform a self-assessment at least once a year, but before the deployment of a trading system.

According to RTS 6 recital 8, compliance with the specific organisational requirements for investment firms should be determined according to a self-assessment. Similarly, trading venues should – according to RTS 7 recital 5 – lay down their requirements with respect to their systems and apply them in conjunction with a self-assessment since not all trading models present the same risks. Therefore, some organisational requirements may not be appropriate for certain trading models. In particular, the specific requirements to be set should be considered according to the nature, scale and complexity of the algorithmic trading activity.

In other words, the European Securities and Markets Authority (ESMA) acknowledges that: (a) investment firms and markets are not necessarily equal in terms of nature, scale and complexity; (b) the specific application of requirements may take these differences into consideration; and (c) self-assessments could be considered a chance to explain the specific implementation measures.

If you consider MiFID II testing requirements a challenge, you might have a problem with your established processes today.

Most of the requirements are probably already fulfilled by markets and their participants. MiFID II imposes the same standards on each investment firm and on each trading venue making it a level playing field. However, requirements on investment firms are higher than on trading venues. You probably do not have to implement completely new processes, but you may need to verify this and update your documentation. And last but not least, self-assessments need to be performed at least annually and don’t forget they are also a chance to explain your MiFID II compliance to your regulator.

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Catalysts For Change: HKEX Hosting Services Ecosystem Forum 2017

GlobalTrading

November 20, 2017

By Rupert Walker, Managing Editor, GlobalTrading

New products, the extension of China Connect and service upgrades are strengthening HKEX’s role as a leading financial hub.

Hong Kong Exchanges and Clearing (HKEX) continues to develop its markets infrastructure and introduce new products across asset classes to meet the requirements of investors and issuers. In addition, it is consolidating its position as the global offshore renminbi (CNH) hub, and facilitating further trading linkages with Mainland China through an extension of the Connect programme.

HKEX’s Data Centre plays a key role for all market participants through the range and quality of its technology, with around a half of cash equities and derivatives trading now transacted through its co-location facilities, said Jonathan Leung, Senior Vice President and Head of Hosting Services at HKEX, to delegates at the HKEX Hosting Services Ecosystem Forum 2017 held on 25 May.

The hosting part of the Data Centre’s capabilities include tier 4 data centre specification, low latency direct market feeds for cash and derivatives trading, an interactive ecosystem environment supporting trading, broad telecommunications carrier access and flexible power and space packages offering both racked and caged environments.

Prudent technology investment
Indeed, HKEX has a long history of technological innovation, although it is always cautious about introducing unproven systems, preferring a prudent approach, according to Richard Leung, Deputy Group CIO and Chief Technology Officer at HKEX. It first created an electronic central clearing and settlement system in 1992 and in the following year set up a first-generation order matching and execution platform for cash transactions, extending it to derivatives in 1995. HKEX is upgrading its platform to deploy the latest open systems technology, but with a minimum of disruption for users.

In 2011, HKEX Orion, a major technology upgrade programme that enabled HKEX to offer hosting services, was launched, and now the next generation systems are being developed and deployed.

HKEX is naturally examining fintech innovations, but is aware of their risks so is wary about adopting them, said Leung. Distributed ledgers might improve post-trade operations and HKEX is reviewing its possibilities, but is concerned about the security risks of selecting a blockchain system when there are competing designs and encryptions available that could make its choice redundant.

There are several applications of the Cloud that exchanges could use and HKEX introduced virtualisation six years ago, but there are data sovereignty concerns. Big data is actually quite easy to collect, but acquiring and applying the right analytical tools is the challenge.

There is no first mover advantage from adopting these new technologies in the financial market infrastructure space; instead there are dangers from moving too quickly, argued Leung. Retaining customers’ trust and ensuring data security must be HKEX’s priority, he added.

Nevertheless, HKEX is continuing to expand the scope and quality of its hosting services as an ecosystem for participants, rather than as a commercial enterprise.

HKEX hosted as of August 2017 a historical high of 592 trading participants on its securities market. The momentum of southbound Stock Connect trading has increased significantly. Products such as Exchange Traded Funds (ETFs) have added to market turnover, and HKEX is keen to further promote Leveraged and Inverse Products (L&I Products).

Meanwhile the average daily turnover of securities market turnover for the first seven months of 2017 was HK$77.4 billion ($9.9 billion), an increase of 16% compared with HK$66.7 billion for the same period last year.

New products and services
The roadmap for 2017/2018 includes the potential launch of the “Next Generation” Orion Trading Platform for the securities market by at the end of this year, and continued development of the Stock Connect schemes to potentially include more products, said Kenneth Kok, Head of Cash Trading, Markets Division at HKEX.

The recently implemented phase 2 of the closing auction session (CAS) includes regulated short-selling. For those participants concerned about the risks, it is important to note that CAS phase 1, launched in July 2016, caused no increase in price volatility, despite warnings by critics, noted Kok. Instead, it led to a substantial take-up by institutional investors allowed to execute trades at closing prices.

Julien Martin, Head of FIC Product Development, Market Development at HKEX explained three main pillars of HKEX’s strategy. First, the growth of the cash market through Mainland and international investors and the implementation of Bond Connect; second, the development of exchange-traded rates and credit derivatives and of deliverable futures; and third, the expansion of over-the-counter (OTC) clearing capabilities, especially for renminbi, he said.

Bond Connect, which was launched in July, strengthens Hong Kong’s role in offshore renminbi, making the territory an even more relevant international financial centre.

The initial focus of Bond Connect is on northbound investment into Mainland China’s vast domestic bond market. The objective is to minimise inconvenience for international investors through cooperation with Mainland China’s foreign exchange authorities, the implementation of a nominee structure, no quotas and eligibility similar to the existing investor access to the interbank bond market.

The onshore Rmb10 trillion interbank bond market is set to overtake Japan as the world’s second largest bond market within 10 years, yet foreign investors own less than 2% of it. The People’s Bank of China has a target of 10-15% foreign ownership, and there are strong policy incentives, such as a stable renminbi (now benchmarked against a basket of 24 currencies) and liquid short-dated bonds with attractive yields.

HKEX is gaining market share in offshore renminbi from banks, because of restrictions and costs. The USD/CNH futures contract on the exchange is one of the most active in the world, traded by numerous clients and supported by eight market makers. Plans are being prepared for it to trade overnight, and three new indices have been introduced in partnership with Thomson Reuters. In addition, HKEX was the first exchange to launch physical USD/CNH options .

Benefits of Hong Kong ETFs
The global ETF market eclipsed $4 trillion in assets in the second quarter of this year and is expected to grow to over $6 trillion in the next five years. ETFs have grown enormously in popularity among long- and short-term retail and institutional investors attracted by their liquidity, quick access and cost efficiency.

While Asia Pacific’s ETF market is in the earlier stages of development, Hong Kong has been a leader since 1999. Asia-based investors are becoming increasingly aware of the diversity and the tax advantages of ETFs listed in Hong Kong, but more education is needed, said Brian Roberts, Head of ETPs, Market Development at HKEX.

ETFs make up between 6 and 7% of HKEX’s average daily cash market turnover, which is impressive considering ETFs make up less than 2% of Hong Kong’s equity market capitalisation. As more institutional and retail investors become aware of the advantages of ETFs, HKEX could experience further growth, according to Roberts. Retail investor participation in ETFs is around 10% compared with 15%-to-20% of the cash market, so their involvement in ETFs should grow as they learn more about their advantages.

Interest in ETFs and the size of the market are growing in Asia, not least because of the tax benefits of Hong Kong-listed products, agreed Sean Cunningham, head of capital markets for iShares and index investing APAC at Blackrock. Clients have gone to the US and Europe for liquidity, but there has been an increase in the number of Hong Kong products that overseas investors can trade in their own time zone, he said.

News : Threat of tick-size wars prompts ESMA rethink

Ian Rycott

November 20, 2017

THREAT OF TICK-SIZE WARS PROMPTS ESMA RETHINK.

By Flora McFarlane.

Regulators are consulting on changes to share-trading rules to prevent an exodus of equities trades from multilateral venues to systematic internalisers (SIs), which could benefit from the capacity to price trades at smaller increments, under MiFID II.

The proposed change to Regulatory Technical Standard (RTS) 1 proposes a clarification that SI quotes should “reflect the price increments applicable to EU trading venues.”

Questions had arisen over the potential disparity between prices quoted on an SI and other markets. The SI quotes do not have to reflect the tick sizes (the minimum price increment in the limit order book) applicable on-venue to the quoted financial instrument.

Prices and quotes on multilateral trading facilities (MTFs) and regulated markets (RMs)for shares, depositary receipts and certain exchange traded funds (ETFs) always have to comply with the minimum tick size regime of MiFID II, but SIs do not. ESMA seeks to address the concerns that there would be an uneven playing field as a result of different rule.

Tick size wars have been a constant concern in the US and Europe since competition began to move trading away from regulated markets in the early- to mid-2000s. In 2010, when the European Union reviewed MiFID I it also raised concerns regarding tick size rules.

A report by academics at the University of Bergen and the University of Stavanger last year looked at the 2009 activity of trading venues Chi-X, Turquoise and BATS Europe, on reducing their tick sizes for stocks listed at the Oslo Stock Exchange (OSE). That led to the OSE reducing its own tick sizes. The paper found that ﬁnd that markets with small tick sizes capture market shares and that tick reductions negatively affect the stock liquidity in markets that keep larger tick sizes.

The current consultation sets out that in order to ensure that quotes reflect market conditions, they may need to be linked to the minimum tick sizes that trading venues are held to. Although the concern is that the tick size regime may be excessively favour SIs, some market commentators believe they are still highly competitive.

Duncan Higgins, head of electronic sales at agency broker ITG says, “The changes proposed in the consultation aren’t a surprise to us or others in the market. SIs will still be able to improve on size or price and we will be connecting to a range of SIs of different flavours.”

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THREAT OF TICK-SIZE WARS PROMPTS ESMA RETHINK.

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