This report examines a specific subset of the cryptocurrency marketplace in 2018 from the perspective of a Tier I investment banking FX business and trading model. Cryptocurrencies have existed in theory for decades, and Bitcoin – which is 2018’s most commonly-traded cryptocurrency – is now 10 years old. To date, discussion of cryptocurrencies within the capital markets industry generally has largely been highly divisive, with crypto evangelists praising them on the one hand, and sceptics dismissing them and their underlying distributed ledger technology (DLT) as a fad.
SARON® Futures: Transitioning the CHF-denominated market smoothly to the new risk free rate
SARON®Futures: Transitioning the CHF-denominated market smoothly to the new risk free rate.
Following the introduction of the Three-Month SARON®Futures to help the Swiss market transition to a new risk-free rate, Eurex spoke with Martin Bardenhewer, Head of Financial Institutions & Multinationals, Zürcher Kantonalbank and Co-chair of the Swiss National Working Group on Reference Rates (NWG), Pascal Anderegg, Interest Rate Derivatives Trader, Zürcher Kantonalbank, and Michel Erni, Head of Market Rates & Director at Basler Kantonalbank.
How can the SARON Futures help the market transition to the new risk-free rate?
Martin Bardenhewer, Head of Financial Institutions & Multinationals, Zürcher Kantonalbank and Co-chair of the Swiss National Working Group on Reference Rates (NWG): Futures have been the most important instruments for the short end of the LIBOR-Swap curve. We expect the dominance of futures for short tenors to continue, and therefore, SARON Futures are set to be a key instrument for liquidly trading in each segment of the SARON curve.
Michel Erni, Head of Market Rates & Director at Basler Kantonalbank:The input from the market side for a smooth transition becomes much more profound with the launch of SARON Futures, bearing in mind that the SARON IRS market was established not very long ago, in April 2017. The introduction of new SARON Futures completes the base for a new benchmark curve for the risk-free rate.
What are the key challenges for SARON and the SARON Futures?
Martin Bardenhewer: CHF is a small currency compared to most other IBOR currencies. Allocating liquidity to a small number of derivative contracts on SARON is key to a smooth transition away from LIBOR. Only if hedge instruments are liquid, or at least are recognized as due to become liquid in the near future, can interest rate risk in cash products like loans be managed without additional costs. Incentives to keep a LIBOR book will weaken quickly as soon as liquidity shifts from LIBOR derivatives to SARON derivatives: that is why the new SARON Futures fill a gap.
Michel Erni: The market and all associated products have to adapt to a completely new methodology, which means changing from a forward-looking LIBOR to a backward-looking SARON. Some areas affected are, for example, the mortgage business, treasury, and possibly resulting in an increase in derivatives, to reduce this uncertainty to plan interest rate costs in advance.
How important is transparency post-LIBOR and how does SARON respond to the changing regulatory environment?
Martin Bardenhewer: SARON is calculated from transactions and binding quotes on CHF GC repos, by far the most important CHF money market segment. There are hardly any unsecured trades in the CHF money market, since rising capital requirements have made this segment inefficient.
Michel Erni: Because of the financial crisis and LIBOR scandals, regulators focus strongly on transparency, and this is the reason why SARON is based on Repo transactions, in contrast to LIBOR. This transparency – and hence, security – does come with a price: if we look for example at well-known LIBOR-based mortgages, clients do know their interest rate costs for the upcoming period after fixing of LIBOR in advance of this period. With SARON, however, overnight rates have to be compounded and thus clients come to know their final interest rate costs only after the period.
How smooth was the transition readiness to the new risk-free rate?
Pascal Anderegg, Interest Rate Derivatives Trader, Zürcher Kantonalbank: For the time being, SARON swaps, introduced in 2017, have fully replaced CHF Tomorrow-Overnight-Index Swaps. So far, they have not gained significant market share from LIBOR swaps. Without the imminent necessity to switch to SARON, most of the liquidity in the OTC IRS market remains in LIBOR. We assume that many institutions are still in the process of adjusting IT-systems and risk models to be able to handle SARON-based derivatives. By continuing to promote more SARON-based derivatives, the NWG is supporting the CHF market in the enhancing of transition readiness away from LIBOR. In this respect, SARON Futures play an important role, as they allow market participants to move away from LIBOR-based futures for the hedging of short-term interest rate risks. Moreover, the futures, if sufficiently liquid, will be used to strip the short end of the swap curve, making the SARON swap market more liquid and robust.
How does SARON Futures help to efficiently trade CH-denominated ETD & OTC products?
Pascal Anderegg: Having SARON Futures visibly on Central Limit Order Books (CLOBs) will facilitate the pricing of short-term forward starting swaps, which can be used for the hedging of changes in monetary policy rates.
Michel Erni: As there are still big interest rate hedging needs in the market for short- and long-term interest rates, a liquid alternative for CHF-3m-Futures is key for all participants. We now see more and more businesses based on SARON, and both the SARON Futures and SARON IRS help to eliminate basis risk. We expect more transfers from LIBOR-based hedging to SARON in the near future. Being prepared early is very important in our view.
How important are the new SARON Futures to having robust fixed income markets?
Pascal Anderegg: Currently, the fixed income market is marked against LIBOR swaps. As the CHF LIBOR swap market will most likely cease to exist by the end of 2021, it is important to have a robust SARON swap market in place. Having SARON Futures visibly on screens will help build confidence in the SARON swap market. However, it cannot be stressed enough how important it is for market participants to make the necessary adjustments to their systems and models and start relying on the SARON swap market. The launch of the SARON Futures should be the starting signal to switch from LIBOR to SARON.
Michel Erni: In a benchmark curve for robust money and fixed income markets, futures contracts have to be considered as part of the pricing, and improve credibility overall.
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Derivatives More Transparent And Accessible
By Sanjay Awasthi, Director, Eastspring Investments**
Increased adoption of OTC derivative technology is giving buy-side traders better pricing and reduced operational risk. More technological options will only help improve systems and trading methods and styles.
For exchange-traded derivatives, including futures and options contracts, electronic trading is relatively straightforward and the markets can be accessed electronically via electronic messaging protocols and broker algorithms.
However, with respect to Over the Counter (OTC) derivatives across foreign exchange (FX) and fixed income derivatives the increased acceptance and adoption of technology are more complex and innovative. These are much larger markets and traditionally, there have been issues around transparency and pricing.
Historically, financial derivative contracts were OTC and over time, as they were standardised and gained wider acceptability, we began to see them trading on exchanges in the listed space. However, FX and fixed income derivatives continue to trade on OTC markets.
These OTC markets still work on the basis of Request for Quotes (RFQ), which require traders to ask a few counterparties, typically banks for quotes and trades are executed based on the best available price The traditional approach involves calling a broker or sending messages in chat rooms.
There are a few inherent limitations to this method.
First, it limits the trader’s ability to reach out to multiple counterparties and consequently, a buy-side trader may not get the best possible price.
Second, it is time-consuming, which is increasingly relevant as many trading desks and traders are handling multiple asset classes.
Third, this process is prone to errors as manual entry of trading particulars invites the risk of fat finger order mis-entry.
The OTC FX and fixed income derivative markets have been around for a long time and operate in this particular manner for various reasons. However, explanation of these reasons may not be germane to what we are now discussing.
The electronic RFQ platforms, be they Bloomberg, Tradeweb, FX connect, etc., facilitate ease of trading without significantly changing the way these OTC
markets operate. As a result, we are increasingly seeing new technology being implemented into Request for Quotes in the OTC derivative space.
The obvious advantages to a buy-side trading desk are as follows:
• One is able to immediately request more counter-parties for pricing. This increases the chances of getting better pricing for client mandates.
• Linking the buy-side order management systems to RFQ systems and markets electronically makes trading less time-consuming, seamless and free of obvious manual errors.
• The quotes are stored electronically and are available for assessment of trading quality on a tangible basis. Electronic records assist TCA on pricing and counter-party reviews which adds quantitative and objective dimensions to the whole process.
• Across most jurisdictions, there are increasing regulatory and compliance requirements around trading OTC derivatives. RFQ systems bring transparency in terms of electronic audit trail, controls and reporting, which in turn significantly enhances compliance and helps avoid regulatory oversight.
• The RFQ systems bring in an STP element to the OTC market which significantly reduces operational risk.
Most institutional investors have concerns around transparency in OTC derivative markets and this, in turn, limits their use of these products. RFQ systems which bring in increased transparency makes one more inclined to use OTC derivatives to achieve portfolio objectives.
RFQ systems even facilitate ease of accessing quotes in listed derivative like options, especially longer-dated relatively illiquid options.
Another area where technology will have a significant impact is in post-trade operations around these OTC contracts. These contracts being bilateral are governed under cumbersome ISDA contracts which increases operational complexity and risk. Evolution of technology which can facilitate ‘smart contracts’ as a protocol to digitally facilitate, so as to electronically record all contract conditions and performance can completely transform the OTC derivative space.
** The print version of this article incorrectly listed Sanjay’s title. It has been corrected here.
My City: Paris
By Laurent Albert, Global Head of Execution, Natixis Asset Management Finance
Best thing about your city?
Definitely the architecture and its many restaurants. Paris is a multicultural city with a wonderful diversity of neighbourhoods ranging from colourful Montmartre to the historical “Quartier Latin” without forgetting the authentic Chinatown of Paris, there is always something new to discover.
Worst thing about your city?
People use to say the taxi but I guarantee you this is not true. With the emergence of ride-hailing services like Uber and private drivers, the competition has had a beneficial effect!
Getting to work?
I use public transport because it is for me the fastest and most efficient way. Paris is also a city very well served by transport.
View from your desk?
We look out onto the Austerlitz train station that provides us with the ability to escape the Paris hustle and bustle if need be! Our setting also provides us with great views of the city, especially on a clear day.
Where to take guests to dinner?
The train bleu with its majestic décor and is located close to our offices! Or the Georges restaurant on the roof of the George Pompidou Museum for its magnificent night view of Paris.
Relaxed spot with family or friend?
Hôtel Costes In a neo-baroque setting by Jacques Garcia, here you are in a unique patio, surrounded by small lounges and more intimate spaces that transition to a lively atmosphere at night!
Best place to stay when visiting?
The 6th arrondissement (Boulevard St Germain) is a neighbourhood very popular with Parisians and tourists alike. You are next to the Seine river, and can combine shopping and sightseeing, visit Luxembourg gardens on foot and walk along the river to the Louvre Museum. Bistros and restaurants are everywhere, with lots of great addresses and culinary references.
Best tourist site?
Eiffel Tower, Louvre Museum, simply UNIQUE. I would also recommend the streets and alleys of the “Quartier Latin” for a truly authentic experience.
Tradeweb Targets Options Illiquidity
With limited liquidity dispersed across 15 options exchanges, how can institutions most efficiently find the other side of large trades?
Tradeweb is aiming to deepen electronic options markets via solicitation of competitive bid and ask prices from multiple liquidity providers. In May the trading platform operator introduced request-for-quote (RFQ) trading for options, building on the protocol it pioneered and subsequently established in fixed income and derivatives and more recently, exchange-traded funds.
There are few markets globally with more angst over paper-thin liquidity than in the U.S. listed options market. Market participants say the options space can be a mile wide but an inch deep. There are a ton of bid-ask prices on the screen, but the majority of displayed bids and offers aren’t anywhere near the quantities that institutional investors are looking for.
“The market is extremely fragmented and there’s very little firm size posted on-screen, anywhere,” said Adam Gould, Head of U.S. Equity Derivatives at Tradeweb. “RFQ is a way to help solve for that.”
Under Tradeweb’s RFQ protocol, institutional investment managers request full-size price quotes from multiple market makers simultaneously, who in turn revert with their best bid or offer. In just six months Tradeweb already has 11 liquidity providers live on the RFQ platform, and trades have been executed on more than 125 single options plus various multi-leg strategies, according to Gould.
The most liquid options are the broad index ETFs of SPY, SPX, QQQ, VIX, and IWM; heavily traded single-stock options include Apple, Bank of America, and General Electric. Through the first 10 months of 2018, 35% of options volume market-wide traded in just five tickers, according to OCC data, while 68% of volume traded in the top 50 tickers.
Vast Universe
There were 4,336 listed companies in the U.S. at the end of last year, according to the World Federation of Exchanges. Most publicly traded equities have an options chain with dozens of individual put and call options, each with their own prices and expiration dates. With 15 — soon to be 16 — options exchanges, it’s easy to see how most names in the options market get spread too thin.
As evidence of the breadth of the options market, a broker recently told Belmont Capital Group Managing Partner Stephen Solaka that there are more than 1 million strike prices overall. “A lot of the names are less liquid, with screen markets that are very wide,” Solaka told Markets Media. “RFQ is something that can be helpful as a better way to source liquidity in less-liquid securities.”
Solaka, whose firm manages more than $1 billion, noted the liquidity bifurcation that has vexed options market participants for several years now. “As the number of market makers has decreased, there’s less liquidity in the non-top-tier names,” he said. “RFQ can make things easier in that you don’t have to call 10 people for quotes. And because it’s a competitive situation, there will potentially be better price discovery.”
And Tradeweb trading data supports that – customers using Tradeweb RFQ have executed trades at the full size requested at 4 to 22 cents better than the National Best Bid and Offer (NBBO), according to Gould. On average, he said the best liquidity available on Tradeweb has been 90% at or better than on-screen prices and for 85% larger size than available on-screen.
Gould also noted that pricing that comes back from different options market makers can vary substantially, at least for most of the market. AAPL, GE, Bank of America and other heavily traded names don’t have this issue, but when one moves down the liquidity scale to Children’s Place, Dish Network, and Stanley Black & Decker, quotes can be scattershot.
2018 Tailwind
Broad volatility across financial markets has driven a robust options market in 2018. Year to date through October, equity options volume increased 21.3% to 20.7 million cleared contracts per day on average, according to OCC.
After mostly flat options volumes through much of the 2010s, this year’s expansion has been welcomed by market operators, trade handlers and end-user investors. But as it is mostly attributable to exogenous macro factors, the growth is not necessarily sustainable. As exchange leaders have stated at industry events for years, the Holy Grail is organic market growth led by innovation that addresses a market-structure issue, and by extension makes options easier and cheaper to trade. Although it’s early days, Tradeweb seems like it could be one of those much needed innovations.
“RFQ essentially provides an aggregate best bid and offer from multiple market makers in competition,” Gould said. “It’s a huge improvement from where a trader would be able to execute on-screen, both in price and in size.”
“It also streamlines traders’ workflow, and provides the electronic audit trails that everyone cares a lot about these days,” Gould continued. “When someone’s looking for a quote over the phone or in a chatroom, there’s a lot of room for error. Stripping out as much manual entry as possible is something that clients are embracing.”
To be sure, innovating is easier said than done, and the roadsides of options and other markets are littered with innovations that never caught on. But Tradeweb is optimistic that RFQ has staying power. “It’s a very big market, and we’re addressing a real issue that’s out there,” Gould said. “There’s a lot of room to run.”
Big Data: Navigating the Hype of AI and Machine Learning
By David Firmin, MD and Head of Global Trading Research, Instinet
With all the big talk about big data these days, the financial services industry must make better sense of machine learning and artificial intelligence (AI) applications.
Definitions vary widely and discussions are often vague, so it can be hard to determine what is real and what is spin. Without consistency in what these terms mean, it can be a challenge understanding how these advances in processing power have changed the way algo engines work or enhanced the tools that deliver analytics and insights.
What are the criteria that define big data? Is there a standard definition?
The 3Vs: Volume, Velocity, and Variety, are often used to differentiate simple data from big data. Any big data project would factor in these criteria, but we would include a 4th V: Value. Value refers to the quality of the data, as well as the quality of its return on investment (ROI) relative to how it is being used. This is an element we believe all constituencies should actively evaluate, define, and build into their programs.
THE “4Vs”
Volume
Volume is a function of the depth and breadth of data and their sources. The term “alternative data” means non-traditional sources that can now be applied to quantitative analysis. We see many more sources, as well as larger quantities of data, and possibly also greater frequency and/or lower-latency real-time increments of data— all of which combine to dramatically increase the overall volume of data that has to be captured, stored, processed, and analysed.
Velocity
With new sources of data such as social media, machine data, and mobile applications streaming into the ecosystem in real time, support for high velocity extends to not only how swiftly data is captured and collected, but all the way through the process until the application of that data drives a resulting business action. The time horizons between data capture and results have been massively compressed, especially for industries with business models that critically depend upon low-latency capabilities and capacity, such as financial services.
Variety
Data variety refers to the many sources and types of data being consumed, both structured and unstructured.
Structured data: This includes the typical market or tick data and transaction reference data that traders or quants have contended with for years. These datasets have predetermined formats that are designed to fit into systems analysed easily.
Unstructured data: Examples of unstructured data include social, sentimental, and voice data. You can find drastic variations between these data points, and they will need to be constructed into a machine-readable format for analysis (becoming structured data). Trade emails, voice, and IM data are good examples of what is captured for compliance and risk analysis.
Value
The final V—Value—is by far the most important. It characterises the potential ROI and strategic impact of big data on your day-to-day business activities or organisation. As we think about the Value factor, the first order of business is to assess the quality of your data. If the content you are collecting is not trustworthy or clean, the entire process is corrupted. More isn’t necessarily more if you cannot be assured that the data being collected is going to add value. By the same token, if the way in which you are applying the data is not well considered, i.e., if you are not “asking the right questions of that data,” then you will not extract benefit from the process. The age-old story of “garbage in, garbage out” certainly applies to the process of big data management, but we can add a new maxim, as well: bad question, bad result.
It’s important to be mindful that big data isn’t a virtue unto itself. Its value lies in its effective application to a specific problem or model.
Why doesn’t everyone use big data analysis?
A tall order.
Big data digitises the sheer volume of information that is being produced globally and synthesises that information to deliver benefits, improve efficiencies, or advance an organisation’s goals. That’s a rather tall order.
To do this, an enterprise must first be able to develop strategies, operations, and the right resources to plan and manage the logistics of all this information.
Requires enterprise-wide change.
There are several imperatives:
A cultural change needs to happen. Traditional financial firms are not set up to take advantage of the data that’s available today. They must adopt new mindsets and skill sets in order to realise the benefits that new technology can bring.
You must have a modern data platform in place to support your big data strategy across the enterprise. Some financial institutions lack the systems and technologies to integrate siloed data and model data to produce insights that they can incorporate into their operations.
Traders and analysts need to be comfortable and effective in applying new techniques. Workflow needs to change, along with their tools and strategies. Committing to these imperatives is not easy.
It requires a change in your business model that aligns the organisational structure, your processes, and technology to create a robust, secure, and scalable data management infrastructure. It also requires having uniquely talented people—not only data scientists, but IT and business people—who know how to pursue the 4Vs and ask the right questions. This mix of talent can be difficult to find, especially when so much of this technology is still new.
Not quick. Not cheap. No guarantees.
The upfront investment of time and resources can be a challenge to firms of all types. Depending upon the nature, size, and mission of a firm, committing to a major, long-term investment such as a big data project can be hard to sell to executive management and boards, since quantifying the tangible benefits and understanding the timeline for reaping the ROI can require a leap of faith.
Analytics has always been important to trading. How has big data analysis changed the way data is used in financial services?
Since Instinet launched electronic trading in 1969, technology has been playing an ever-expanding role in the financial sector. Big data is a significant factor in the most recent rapid evolution of electronification. It is pushing the industry to new heights and across functions such as idea generation, analytics, execution, risk management, regulatory compliance, marketing, and client relationship management.
Big data technology has enabled the storage and analysis of data sets not possible before. Many firms are putting greater emphasis on new data management platforms that enable them to integrate and deliver data and analytics in real time, rather than using numerous separated analytics engines. Using the latest technologies, data infrastructure, and processing methods to harvest greater intelligence from increasingly higher volumes of data is becoming a competitive necessity.
The existence of big data makes the following more possible:
– Real-time responsiveness. Incorporating low-latency stimuli from alternative sources into existing strategies and the behaviour of live orders.
– Heuristic capabilities. Going beyond static models.
– Machine learning. Combining advanced computational analysis and simple automation.
– Artificial intelligence. Handing over decision-making discretion to the platform.
What is the relationship between big data, machine learning, and artificial intelligence?
Big data is the core fuel that drives technologies like machine learning and AI. These technologies are dependent upon the advanced computational capabilities and characteristics (the 4Vs) of the underlying data.
Machine learning is a way of distilling patterns and/or achieving automation that is genuinely heuristic. Instead of writing millions of lines of code with complex rules to perform a task, you can develop technology that can look at a lot of data, recognise patterns, and learn from the data. “Learning” requires feeding huge amounts of data to the algorithms and allowing them to adjust and improve. While machine learning may be considered an evolution or extension of known statistical methods, it requires new data logistics and analytical skill in order to derive signals that are relevant to the investment process, and drive conclusions or actions in a way that delivers against the goals with precision and consistency.
Artificial intelligence (AI) is an attempt to build machines that can perform tasks that are characteristic of human intelligence. This includes understanding language, recognising objects and sounds, learning, and problem-solving. AI in financial services puts greater discretion over decision making into the technology versus the human operators. This means that AI offerings are replacing certain aspects of human labour or effort, empowering technology to perform these explicit tasks with a degree of pre-determined control.
Don’t try to replace all human involvement.
Businesses need to be mindful of where to apply machine learning and other new data processing technologies. Machine learning is the first step in the integration of big data analytics with automation. This is where technology utilises big data to learn and respond or adapt. Machine learning still allows for human insights and judgment to drive it forward—it allows you to use automation to recognise patterns, or remove bias, in a way that is faster than what a human could do.
Using artificial intelligence means you are asking the technology to make decisions on your behalf. This level of discretion is something that must be designed and weighed carefully. It’s analogous to the difference between using a GPS guidance tool versus a self-driving car.
At the end of the day, machine learning and other data processing technologies should not seek to replace the experience, judgment, and insight of the human trader, but rather they should amplify his/her capabilities and complement his/her intuition.
The Weakest Link: balancing algorithmic expertise and fundamental understanding in trading
By Max Rybinski, Head of Proprietary Trading, JM Asset Management Ltd.
As electronic trading extends further from institutional into retail trading, how well do today’s traders know the building blocks of the tools they employ?
Electronic trading rules Hong Kong’s trading, just as it does around most of the developed world. The popularization of online discount brokers, targeted social media advertisements, sophisticated platforms, along with faster data connectivity, have also created a swell of retail trader confidence in electronic trading tools.
Unfortunately, many of these retail traders will lose most, if not all, of their money in a very short period. Easy access to trading technology is to blame. It has become very affordable, accessible and complex trading strategies have very simple execution options that only require a click of a mouse.
Algos without fundamentals
Our new reality is that most retail traders here in Hong Kong specialize in using electronic trading tools, but can barely scratch the surface when trying to understand the underlying strategy. Therefore, when the market conditions are in flux, traders should know how the strategy will behave and have the skills to adjust accordingly.
In the institutional trading space, the current generation of traders has all come up after the mass adoption of electronic trading, which raises the question whether they may also be overly reliant on pre-built electronic execution strategies.
Having started my career on manual trading desks, working with traders with a wide range of successful trading strategies, and now developing algorithmic strategies to execute in the market, I have seen the benefits of each approach. More importantly, I have seen the skills that each approach requires and fosters among the traders that employ it.
I do not intend to demean the quality of the trading talent in Hong Kong, but if the majority of traders merely supervise a fully automated electronic system, are they familiar enough with the fundamentals of developing a trading strategy? By no fault of their own, the industry has shifted to a fully electronic system and developing the understanding of the fundamental underpinnings of algorithmic strategies has not been sufficiently addressed.
There are core fundamental skills that appear to be receding from the industry. If these are reduced, will it inhibit a trader’s ability to understand and service their clients’ needs? Are we confident enough to ensure best execution to an algorithm while not fully understanding the client or portfolio manager’s fundamental intentions, even including HFT firms and market makers?
Strategy Construction
Most traders will understand dynamics around spotting and sourcing liquidity in the market, but they must also be familiar with the tenets of financial strength (Altman Z, Beneish M and Piotroski F Score), value metrics and sector weight. For example, traders need to track the sector performance to correctly weigh order sizes for each trade they need to execute.
Besides these fundamental investment concepts, traders should also have a deeper understanding of the technical analysis that their algorithmic strategies are built on, to help them determine realistic targets, safety stops and also validate momentum.
A good example of a skill manual traders know well, but not all electronic traders know is harmonic ranges. Each instrument and its timeframe display habitual movements, which are especially visible using a price bar instead of a traditional time bar. Finding the harmonic range helps a trader determine realistic targets and stops. Typically, a trader would attempt to enter in a new range on the early pivot and target two-thirds of the remaining move. The stop may be placed slightly outside of the range at the point of range violation.
Knowing the position of your trend is also another basic, yet overlooked aspect. A trend is simply a general direction, which the price is developing or changing. Traders can confirm the trend on the second point and attempt to enter on the second higher low (long entries) or lower high (short entries) and on each consecutive point thereafter. Trend analysis is important because certain algorithms can overreact in choppy markets, making inopportune entries, and traders must understand the trend in the signals the algorithm is processing.
This naturally brings us to momentum. Momentum oscillators are another favourite tool of manual traders, which are calculated from the price movement. Some complex momentum calculations will only attempt to make the oscillator more sensitive or lagging depending on the desired behaviour of the strategy.
These indicators are only useful to help traders gain confidence to execute the trade. A momentum indicator is a small but useful validation to help a trader time their trade entry.
Fully Automated and Semi-automated
At our firm, we believe in mixing the best of electronic execution and human insight. Below is my trading chart, including our proprietary banding and momentum oscillator tools. We use these to identify the changing trends and price speed when entering trades. Using simplified tools allows our traders to focus primarily on trade management, position sizing and market speed.
Fully automated systems are useful for arbitrage and marketing making strategies, but require a large budget for infrastructure and development. Semi-automated systems have a much smaller budget, but require trader discretion to execute a trade efficiently in the chosen direction. Both systems provide distinct advantages, however, experience has shown that a semi-automated system in the hands of a talented trader will outperform purely automated strategies. A semi-automated trading tool paired with a talented trader brings the best of technology and human talent that will always outperform each element trading on their own.
The image above is the semi-automated tool that we use, which includes the sophisticated institutional metrics and options that professional traders desire, but it has been further simplified so the trader only needs to focus on the direction they want to enter. Once the trader confirms the trend direction, the tool will attempt to enter at the next most advantageous price range and handle all the position management.
Each member of our trading team specializes in different areas of analysis, but all agree that the basic trading foundations maximize our performance. Only by blending the skills and technical understanding of manual traders with the programming and quantitative abilities of modern algorithmic strategies can today’s traders provide the best returns for their clients and their firms.
ING Brings AI to Bond Trading
By Shanny Basar, Markets Media
ING has created an artificial intelligence tool with Dutch asset manager PGGM to help investors make quicker decisions on which bonds to buy and sell.
The Dutch bank originally developed Katana to help its traders provide liquidity and respond more quickly to requests for quotes from clients. Katana provides traders with a visualization of relevant historic and real-time data and the tools’ algorithms provide forward-looking predictions of the price that will win an RFQ within a certain confidence range.
ING has now developed Katana Lens with PGGM, who managed €215bn ($244bn) in pension assets on June 30 2018.
The banks’ emerging markets credit desk in London tested Katana last year and found that faster pricing decisions were made for 90% of trades, there was a 25% reduction in trading cost and the bank made the best price four times more frequently.
Santiago Braje, global head of credit trading at ING Bank, told Markets Media: “Katana Lens has been co-created with PGGM and is completely different as it solves the selection problem for the buy-side and helps them decide what to buy and sell. For example, for 2,000 bonds you have two million possible trade combinations if you buy one bond and sell another.”
Braje explained that Katana Lens systematically scans a universe of investable bonds chosen by the user. The tool identifies possible opportunities based on the current yield or spread against historical behaviour for similar underlying risks over the past six to 12 months.
Androniki Menelaou, lead data scientist, Katana Lens at ING Bank, told Markets Media: “The algorithm scans the market in an agnostic way following a data-driven approach. We have built an easy interface so users can filter the trading ideas to match their portfolio.”
Braje continued that clients have said they have seen opportunities they might have otherwise missed.
“They have been able to make decisions very quickly and trade smaller tickets more frequently,” Braje added. “Opportunities identified by Lens have also been profitable which is relevant in this environment where the market is on a downward trend.”
There has been an increase in electronic trading in fixed income which is expected to continue under MiFID II, the regulations that come into force in the European Union this year. MiFID II extends best execution requirements into fixed income for the first time, and introduces pre-trade and post-trade transparency. In addition, the buy side is facing margin pressure and looking to make its workflows more efficient. As a result investors are sending electronic RFQs more often and for smaller sizes, as well as using more algorithmic trading.
“Katana Lens presents trading ideas but is not linked to execution yet,” said Braje. “Going from idea generation to execution is in our plans, but we are quite a few steps away from auto-executing on its recommendations.”
Menelaou added: “Katana Lens is complementing investment managers who make the final decision on whether to trade. It is an augmented intelligence which helps them navigate the market.”
Katana Lens currently covers emerging markets corporates and sovereigns and European sovereigns but coverage will be expanded as trials of the system continue. Development of Lens is funded by the ING Innovation Fund who monitor progress and the system is expected to be fully launched in the first half of next year.
Technology as a differentiator
Fixed income trading used to be a cash cow but over the past decade low volume, low volatility, low rates and a high cost of capital have been eating into profits according to consultancy Greenwich Associates.
Kevin McPartland, head of research for market structure and technology at Greenwich said in the study, Trust and Data Drive Fixed Income Dealer Growth, that relationships and balance sheet still matter but the ability to manage both in a more quantitative way can mean the difference between profit growth and a year-on-year decline.
“This idea has driven both large and middle-market dealers to focus their technology efforts on data, analytics and workflow automation, while looking to increase the data management literacy of everyone on the desk,” added McPartland.
The consultancy’s survey of US fixed income dealers found that investing in technology is the top approach for bulge-bracket banks, and second only to organic growth among middle-market dealers.
“For the first time since Greenwich Associates began asking dealers how they stand out, nearly 20% of the global banks point to the provision of technology tools to clients as a differentiator,” said the report. “This would include pre and post-trade analytics, execution algorithms (where appropriate) and other trading tools.”
Some bulge bracket dealers in the survey reported a current fiscal year technology budget of $10m to continue building out their corporate bond trading desk, while middle-market dealers have increased their spending from roughly $900,000 annually to over $1m.
Data Science and the Trading Desk
By Terry Flanagan, MarketsMedia
Francis Bacon, René Descartes and Isaac Newton were among pioneers who advanced the idea of making conclusions based on observation and evidence, rather than just reasoning.
Centuries later, institutional brokers are incorporating tenets of the scientific method into their own pursuits of buying and selling blocks of equity.
The nutshell premise is that data and proof walk, conjecture talks. This is especially the case in a rapidly evolving market with a multitude of promising — but untested — trading options.
“At UBS in the Americas our view is that the equity ecosystem continues to evolve and become increasingly complex in terms of new order types, new venues and new sources of liquidity,” said Todd Lopez, Head of Americas Cash Equities at investment bank UBS. “There continues to be more competition and diversity in liquidity sources. To effectively navigate this environment we need to understand in forensic detail when and how to access these sources and leverage new order types.”
Sell-side trading desks utilizing data isn’t new. What is new is the level of sophistication of buy-side investment managers, who need to see evidence that a methodology works. Brokers need to show, not just tell.
“Our clients are becoming increasingly sophisticated in how they measure results and are pushing us harder to optimize our capabilities to solve their specific use cases,” Lopez said. “They require empirical evidence that taking a particular approach will result in lower implementation costs of trading.”
‘Significant Differentiator’
“A broker’s client base is diverse and each buy-side customer may have varying order flow and therefore different liquidity needs,” said Curt Engler, Head of Equity Trading, Americas, at J.P. Morgan Asset Management. “The ability to test varying theories and quantify the results, especially client- specific needs, should be a significant differentiator for algorithmic providers.”
In the early 17th century, Galileo Galilei used the scientific method to contradict the long-accepted Aristotelian notion that the rate at which objects fall is proportional to their weight. He did this by dropping two balls of different weights onto ramps, which slowed speeds and enabled more precise time measurement. When the balls reached the ground at the same time, the theory that objects fall with the same acceleration regardless of mass was proven.
In 2018, trading desks are out to prove or disprove their own theories, in complex, high-speed electronic markets rather than backyards. UBS is doing so via a framework which is designed to improve algorithmic performance by allowing for controlled experimentation with different trading hypotheses.
There are plenty of new developments for trading desks to work with. For instance, recently exchange operator Nasdaq launched Midpoint Extended Life Order, which is meant to unite counterparties with longer-term investment horizons. Conceptually, the order type is attractive, as large institutions with buy-and-hold clientele prefer to trade with each other rather than with market participants who make their money moving in and out of markets quickly.
UBS ran an internal pilot program to determine if M-ELO lives up to its promise. “We collect a statistically significant number of observations, which helps us understand where this new order type may or may not make sense,” Lopez said. “We can then work with clients and use this data to further optimize their execution process.”
Another market dynamic whose impact needs to be tested is the rise of electronic liquidity providers. Years ago many institutional buy-side participants were wary of trading with ELPs given their roots in high-frequency trading strategies, but that has subsided as the proprietary traders moved into the mainstream and are now major liquidity providers in many markets.
ELPs represent one counterparty option for UBS and its clients, in addition to other liquidity sources such as UBS PIN, within the UBS ATS in the Americas, to help maximise crossing with UBS retail and institutional flow. The firm is taking a fresh look at ELPs on a top-down basis, i.e. assessing the trade-execution efficiency of the entire ‘parent’ order, in addition to the individual ‘child’ orders that emanate from the parent.
“The ability to source liquidity bilaterally, which in the US has primarily been through ELPs, has been expanding,” Lopez said. “That presents a potential opportunity in the effort to reduce implementation costs for clients and our traders.”
“This is something we have been focused on; trying to quantify what if any benefit is realized at both the child and parent order level by incorporating these liquidity sources into the execution process,” Lopez added. “Ultimately we want to prove that this does actually reduce the slippage from clients’ primary objective function.”
Rigour Needed
Institutional investment managers, who have a direct economic interest in the rigour of the sell side’s testing, are watching closely.
“As the buy-side focuses more on higher level optimization of trading performance at the strategy and urgency level, we are leaning more on the sell-side to provide optimization around implementation of strategy in the marketplace, especially around access to liquidity,” said Enrico Cacciatore, Senior Quantitative Trader and Head of Market Structure & Analytics at Voya Investment Management.
“To really understand and evaluate quality of liquidity, we need to understand intention, cost expectation, time decay, and potential for adverse selection,” Cacciatore explained. “If we are only getting large block prints in conditional venues during adverse situations, we need to be able to quantitatively evaluate what would potentially have been the outcome if we decided to not interact with the block liquidity and instead trade more passively over a longer duration.”
Cacciatore is formalizing a committee, comprised of both buy-side and sell-side market participants, that will work to normalize child-order reason codes. The aim is to add transparency around intention of broker routing, and also better evaluate liquidity sourcing for best execution.
“Understanding the reasons for a route and a fill are important. We intend to release new functionality to let clients know when they interacted with conditional liquidity in UBS ATS,” said Lopez.
“Having controlled, structured efforts to gather data and assess the effectiveness of new order types or other configurations is the right approach,” J.P. Morgan’s Engler said. “Only by collecting data over a significant number of observations can we better configure our routing decisions, whether on child orders or assessing broker algorithms, on an apples-to-apples basis.”
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Outsourcing Can Help ATSs Jump Regulatory Reporting Hurdles
By Paul Roland, Global Head of Markets & Services, Banks & Brokers, Nasdaq
Alternative Trading Systems (ATSs) in the US already spend a significant amount of resources on regulatory reporting – both internally and on outside legal counsel.
On July 18, 2018, the Securities and Exchange Commission (SEC) amended Regulation ATS to enhance transparency and oversight of ATSs. It did so by introducing a new filing, the Form ATS-N, which will increase the regulatory reporting burden and absorb even more resources. However, ATSs can jump this reporting hurdle more easily by outsourcing the operation of their platform to an expert in running marketplaces. The equities markets have evolved substantially since Regulation ATS became effective in 2000. For starters, there are now many more ATSs, and these platforms are a significant source of liquidity in National Market System (NMS) stocks. According to the SEC, ATSs now account for about 11.4% of the total dollar value traded in NMS stocks1.FINRA estimates that more than 30% of the total NMS volume of shares traded occurs over the counter2, and 54.7 billion shares3 were traded on ATSs in the second quarter of 2018 alone. Moreover, NMS Stock ATSs have been a source of innovation within the US equities markets. They have become more complex and sophisticated, and some platforms now offer features similar to registered national securities exchanges, such as The Nasdaq Stock Market, which are required to be more transparent in their activities.With the SEC’s recent public focus on competition4, ATS operators will likely play a key role in shaping innovation and market structure. To this end, the SEC has introduced a new Form ATS-N to enhance transparency and oversight of the platforms that trade stocks listed on national securities exchanges. Existing NMS Stock ATSs will be required to file a Form ATS-N no earlier than January 7, 2019 and no later than February 8, 2019. As of January 7, 2019, an entity seeking to operate as an NMS Stock ATS will be required to file a Form ATS-N.The disclosures on Form ATS5 are relatively minimal compared to what will need to be disclosed on Form ATS-N in the future. In many ways, the standards of disclosure on the new form are ‘exchange like’. That is, the amount and level of description will look more similar to an exchange’s rulebook than in years past, and similar to a MiFID II disclosure. The effort and expertise to complete the initial operation report on Form ATS-N, along with the hours preparing a cessation of operations report, are not trivial. The requirements on the 20-page Form ATS-N are extensive.6
The SEC estimates that an NMS Stock ATS will spend about 127.4 hours completing the form, about nine hours preparing each amendment to Form ATS-N, and about two hours preparing a notice of cessation. The disclosures on Form ATS-N have been designed to inform market participants about how the ATS operates. They include the order types and market data used on the ATS, fees, the ATS’s execution and priority procedures, and any procedures to segment orders on the ATS.
Market participants will also be able to assess potential conflicts of interest and risks of information leakage arising from the ATS-related activities of the ATS’s broker-dealer operator and its affiliates. Whenever an ATS makes any change to the operation of the platform, including types of securities traded, or the types of subscribers, they must update their Form ATS-N, as they do now on Form ATS. If the number of rule filings that stock exchanges make annually is any comparison, the reporting burden is enormous: exchanges propose dozens of rule changes per year.
Where Experience Meets Outsourcing
To compete in today’s environment, ATSs need to acquire and maintain advanced technology and retain internal and external compliance experts. These costs have put enormous pressure on broker-dealers’ balance sheets, and have left them searching for ways to reduce costs in order to achieve capital efficiency. Some conclude that they cannot meet these demands entirely alone, and it makes sense to investigate outsourcing the operation of their trading platform, including regulatory support, as an alternative to doing it in-house. Ultimately, outsourcing enables ATSs to concentrate their efforts on enhancing core competencies aimed at adding value for clients and generating revenue. Broker-dealers should look for an outsourcer that can support their platform holistically. A partner should be able to provide support services related to operations, compliance, surveillance, supervisory, recordkeeping and reporting obligations.
Nasdaq actively invests in technology and services for bank and broker venues from design and build, to hosting, and throughout all operations. Currently powering 100+ of the world’s market infrastructure organisations, including exchanges, clearinghouses, central securities depositories and regulators, in over 50 countries with end-to-end, mission-critical technology solutions, Nasdaq effectively manages outsourced venues with exchange-grade expectations, both from a technology and regulatory perspective.
