Execution Analytics: Real-Time Challenges and Directions in Institutional Trading

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Execution Analytics: Real-Time

Challenges and Directions in

Institutional Trading

Dushyant Shahrawat, Senior Research Director

Capital Markets, TowerGroup June 2011

Executive Summary

The importance of complex event processing (CEP) in securities trading is rapidly expanding. New use cases for real-time analysis of multiple, complicated data and information streams – the essence of CEP - will penetrate more parts of the transaction lifecycle. One area in which TowerGroup expects to see the greatest increase of CEP is execution analytics. Execution analytics, a cousin to transaction cost analysis (TCA), is the process by which a trade is analyzed while it is being worked. This real-time analysis has as its goal the creation of actionable information that may result in changes to an order such as adjustment of parameters on an algorithm, a change of strategy selection, or even cancellation of the order.

This form of live feedback requires a technology platform that enables the rapid absorption, filtering, and analysis of multiple types of data. Complex event processing is the technology that enables such analysis. The live feedback determines where liquidity is resident and where a trade is experiencing slippage against its benchmark, so that an appropriate response can be triggered. One of the key benefits of CEP is improvement in execution quality. In addition, the broker dealer that offers CEP-based execution analysis is providing a higher level of client service than its peers. Over the next 3 years, TowerGroup expects that execution analytics will become standard practice. For now, however, such an approach is a strategic differentiator that can be used to attract incremental order flow from the buy-side.

This paper looks at real-time analytics and CEP in securities trading in the context of the critical roles they play in the management of diverse data sets and other inputs in the institutional trading process. It describes some of the data analysis challenges traders face in the evolving high frequency, multi-asset, global trading world, and then offers a view of the expected future state of the business. Lastly, it offers prescriptions and lessons from leading firms regarding the means they are employing to successfully master an increasingly fast-paced and complex trading environment.

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 2011 The Tower Group, Inc.

Analyzing Analytics

The market for real-time data and data analysis is rapidly expanding in securities trading – not just in the number of users but, more importantly, in the number of uses. The evolution is perhaps not all that surprising: the first applications of CEP in capital markets were in the trade execution process (for example, dynamically updating smart order routers), and it was only a matter of time before new ones were discovered, such as to analyze the impact of news on stock price movements.

The execution story has of course not been completed yet. Broker dealers and the buy side will constantly tinker with their algorithms to implement new trading strategies and move into new asset classes, including bonds, FX, and derivatives. Each of these instruments will require new versions of pre- and during-the-trade data intake and assessment, such as yield curve or prepayments impacts. But there are other approaches that are being introduced today, such as compliance and risk management, or are mere ideas that won’t become mainstream for a few years, e.g., harvesting and integration of sentiment from social media. Progressive firms, those looking to gain an edge, will embrace these new uses.

Analytics covers a wide range of activities, from pre-trade credit checks to post-trade exposure management. The means exist to produce these types of analytics today, but they are oftentimes inadequate to the demands increasingly being placed on securities firms from regulation, heightened risk management concerns, and overall trading business decision-making. Particularly counterparty credit risk management, of which analytics is a key component, will be a critical concern. In this regard, firms will have to grapple with the challenges of siloed data and organizations, disjointed analytics applications, and the lack of an integrated view of enterprise-wide exposures. Market forces also demand quick decision making and execution. The challenge is to perform the necessary risk management checks without getting bogged down in lengthy and cumbersome data analysis.

In point of fact, real-time analytics is a relative value; “real-time” is a function of the needs of the user. For compliance personnel, end-of-day will be sufficiently “real-time” for their routine purposes. For others such as high frequency traders, real-time will be measured in microseconds. But there are very few users (if any) that are looking for less data or data with less timely availability. As a result, data and analytics access has to become faster than it ever was in the past. This is true across all forms of analytics, as improved data management is a necessary pre-condition to better supervision of execution, counterparty, operational, and other forms of risk.

Securities firms are in the business of assuming and successfully managing risk. Regulators must try to limit the systemic impacts of that risk assumption, and other stakeholders must try to ensure it does not threaten the firm’s survival. Pension plans are underfunded; yields on high-quality fixed income and other instruments are anemic. To generate competitive absolute returns and be able to match assets with liabilities, portfolio managers have no choice but to engage in more complex portfolio strategies and products. They will look everywhere for performance – including their trading desk.

The complexity and interconnectedness of risks will force institutions to reevaluate their processes and technology to improve the way they measure and manage risk. Institutions are compelled by good business practice, stakeholder pressure, and regulatory fiat to do so. Further, various forms of risk now interact with each other as never before. For example, instrument risk on a firm’s own holdings in credit default swaps transforms into counterparty exposure when (often indecipherable) positions of trading partners are taken into account. Rather than remain a singular process, risk management must develop into a compilation of tools and techniques designed to provide transparency into such interconnected risks.

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Data Management Requirements On the Trading Floor

The securities business is not being spared the “Big Data” challenge many industries are experiencing: explosive growth in the volumes, sources, and complexities (including unstructured data) of trading information. Data must be accessed and analyzed as quickly as possible in order for it to be of value to traders. In the US alone, message traffic (as measured by trade and quote (TAQ) data), will have increased 455% from 1994 through 2011. In order to handle the data management challenges such volumes entail, institutions are working hard to bring their securities trading data infrastructure under control.

Exhibit 1 highlights this trend in data volumes, and projects continued growth, despite the recent slowdown in overall equities trading volumes, over the next 2 years.

Exhibit 1

**US Equities* Trade and Quote Volumes**

(1994 – 2013P)

Source: NYSE TAQ Database, TowerGroup estimates

0 100,000 200,000 300,000 400,000

Trade Quote Total

Trades and quotes (In Millions)

New York Stock Exchange Trade and Quote Volumes (1994–2013P)

Note: Numbers to the right of the vertical line are projections TowerGroup derived by extrapolating historic trends.

Estimated 136% increase in trade and quotes from 2010 to 2013

N ot e: * Inc lude s t rade s and quot es for al l s ec ur iti es li st ed on the N ew Y or k St oc k E xc hange (N YS E) and Am er ic an St oc k Ex change (A M EX ), as w el l as N as daq N at ional M ar ke t S ys te m (N M S) and Sm al lC ap issu es.

In the face of this data torrent, securities firms are employing a variety of methods to keep up with demands from the trading desk to reduce latency. Some of those methods include co-locating servers next to those of the exchanges, dramatically increasing bandwidth and application processing speeds, and otherwise optimizing network infrastructure.

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In the case of co-location, the sponsoring broker will often incorporate its own risk check tool into the buy-side customer’s server. The broker will then take responsibility for ensuring that appropriate limits are maintained over the course of the day with respect to key order parameters such as share size, notional limits, price, etc. The broker will also measure and monitor the overall latency performance of the trading infrastructure (in the information “hops” it controls) in order to give its client a window on its ability to compete on latency with other high-speed trading firms.

Increasingly, traders need to execute not only quickly but in multiple asset classes. An efficient work flow and careful risk management necessitate they be able to do so on the same front-end trading system no matter what product the desk is trading. As electronic trading has spread to asset classes other than US equities, the need to collect, process, and store exponentially greater amounts of data has also risen.

This trend presents a firm’s data management infrastructure with serious challenges. For example different vendors, market centers, and brokers often use different security masters for both securities and venues. ISIN, CUSIP, SEDOL, RIC Codes, and Bloomberg symbologies are all being employed by various market participants at any given time. Trade and quote data presents an additional challenge, in that it must often be cleansed, enriched and integrated into analytics or other data streams before it can be acted upon. And instruments such as options have more associated data such as strike price, expiration date, etc., making them even more data-intensive than traditional products.

The global hunt for alpha has made it necessary to collect data from far flung locales as well. A trader is much more likely today to trade an international basket of stocks when looking for exposure to a certain industry. For example, a strategy may consist of gaining exposure to semi-conductor companies that trade in the US, Tokyo, London, and Hong Kong. To effectively execute such baskets, the trader needs to ensure that trading systems, including front-end, smart order routers, algorithms, and back-end all are supplied with and able to store correct data from these diverse sources.

However uniform across the region’s algorithms are intended be, the reality is that there will be significant differences and changes to child orders to account for local order types, as well as nuances in local market micro-structure. For example, a liquidity removal algorithm chosen for a multi-region trade may have many of the same parameters attached to it (levels of aggression, time-slicing, etc) across the regions. The historical and live data it is based upon, however, as well as the order types and destinations of the child orders will vary widely according to local market conditions. All of those divergences will require dramatically greater real-time data management – in effect creating a complex event processing environment that must be harnessed in order for the strategy to be carried out effectively.

Although the goal of a multi-legged, international, cross-asset trade has been around for some time, going forward traders will increasingly expect to carry out such trades seamlessly and with simplified work flow in terms of order entry and management. Because each leg of the trade is dependent upon at least one other leg for the strategy to work, each algorithm must ensure that it can adjust its own actions in concert with what is occurring in the orders related to it. Only in a complex event processing environment can an algorithm be structured, and then directed in real-time to interactively adjust its trajectory. Such real-time adjustments can include the need to speed up or pause in response to price or volume action, or because one of the child orders is simply stuck in an unclear execution status.

There is an increased regulatory need to process and store all of this data as well - scrutiny over best execution practices, OATS reporting, and other regulatory requirements make it essential

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that firms store this data in a manner in which it can be easily accessed and parsed. Questions from investors or regulators about a particular execution that are answered quickly and completely are less likely to resurface, eliminating the drain on traders’ and managers’ time and energy better devoted to high-value activities.

Human Trader Risk and Real-Time Processing

No matter how extensively electronic means supplant traditional phone-based trading, humans will still be integral to the trading process. As a result, trading will still be subject to the errors of commission or omission that may befall a trader. Both buy-side and sell-side firms must stay ahead of such errors, and so traders are being subjected to increasingly sophisticated risk checks, both upon order entry and as the order is being worked. As with other trading risk management tools, these checks are entirely dependent on the timely consumption and analysis of live and historical market data.

Managing human risk on order entry is more complex than simply limiting “fat finger” errors. A robust best-practice in this regard is to allow traders only to enter orders below a certain threshold, typically a percentage of the security’s average daily volume, share quantity or notional value, or some combination of all these and more. Inputs into setting this threshold include the order type or strategy used, the trader’s seniority, the volatility of the stock, and other factors. Best practice dictates that the limit-setting process accounts for the fact that certain order types and algorithms are more aggressive than others, meaning they are likely to be completed very quickly, and are therefore harder to correct if an error is made. On the other hand, some orders are executed on a “passive” basis, meaning there is likely time to adjust and recover if an error is made, therefore less stringent limits may be imposed on such orders. Given the speed of many algorithms, it is imperative that key information elements are processed as quickly as possible in order to ensure an adjustment can be made in a timely manner.

There is of course a tradeoff between speed and risk control. Institutions want to optimize that trade-off by using the most effective complex event processing tools possible to accelerate ongoing analytics associated with the order. Once an order has passed entry checks, risk and execution checks must continue throughout its life, potentially affecting its completion or performance unless a CEP engine is incorporated into the trading workflow. Orders need to be continuously monitored by the algorithm provider, the buy-side trader, or both to ensure they are behaving as expected.

For example, a CEP engine can analyze instantaneously what kind of impact on price can be expected from price movements or events in other markets, indices or securities, allowing the algorithm to more rapidly adjust to an anticipated movement in the instrument’s price. If, as a result of those events, the price then moves beyond a predefined threshold, the order can be automatically paused, and may need to be re-released by the trader or his sell side coverage person. Thanks to the CEP layer, both the algorithm engine and the trader are provided with a degree of timely and well-founded discretion based on comprehensive information and analysis. Though this functionality is increasingly available to traders themselves, there is a need in the industry to move this analysis up a level within a securities firm. Head traders need flexibility in

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determining appropriate thresholds, for example for lower priced stocks which will have looser trading bands than higher priced stocks. Trading heads also need visibility across all markets, asset classes, and traders, in order to see firm-wide the impacts individual CEP and other analyses are revealing on an individual-trader basis.

As a result of the assimilation of CEP and real-time analytics into trading, the sell-side coverage model and methodology is changing. Above all, it is becoming more electronic and data dependent. Traders are now being presented with market color and feedback in digital form in near real time. Best practices are still emerging, but the most successful broker dealers and the most demanding buy-side traders are operating with detailed, actionable execution analysis as the order is working (and while there is time for adjustment). What was once verbal color given by a sales trader about liquidity sourcing or benchmark slippage is now presented electronically as it happens.

An example of “electronic color” is an analysis of dark pool fills in terms of number of executions, size of the executions and price relative to the bid/ask at the time and overall action in the stock. Though some broker dealers (or even the buy side) may want to keep the market color process phone-based to provide greater context, broker dealers will still need to perform the analysis electronically for internal and regulatory purposes. As a consequence, they are also more likely to begin sharing that analysis with clients, if for no other reason than because they can. Reports that were once given daily, weekly, or monthly are now generated as the trades happen so that adjustments can be made to strategies to improve their performance immediately. The ability of a broker to provide this service will attract order flow as buy-side traders and portfolio managers reap the benefits of near-instantaneous market intelligence.

Where is The Industry Going?

Over the next three years, CEP will be used to improve supervisory risk management. Head traders need, and will acquire, a “dashboard view” of all activity by all traders in all asset classes at any given time, as well as the ability to drill down into the data from each of those perspectives.

CEP will also have an important role to play in managing the trade execution process itself. Historically, execution management systems have been largely broker-owned as well as single broker, and therefore only able to track orders worked with that particular broker, and only those that are worked electronically. While order management systems have been broker-neutral for the most part, integration with EMS has been clumsy and slow in terms of real-time drop copies. The EMS has also essentially been unaware of the current status of non-electronic flow.

Particularly with the emergence of smart order routing and liquidity removal algorithms, this lack of smooth integration has left traders unable to take full advantage of the algo provider’s execution possibilities. In addition, OMS did not have the ability to handle market data efficiently or comprehensively (full depth of book, etc), again leaving the trader frustrated at the lack of transparency into true market conditions.

This situation is changing in several important ways. To begin with, EMS have evolved as a result of market pressures to be authentically broker-neutral, with little bias in terms of preferencing particular brokers, execution methods or other factors. Additionally, OMS are improving their ability to handle large amounts of traffic to and from execution and data sources. Reports for manual orders as well, that were formerly given by phone, are now sent back automatically via electronic means.

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While dashboarding is increasingly prevalent, and represents a high-value step forward in the trading process, several complexities are entailed in its implementation. For example, head traders will need to easily, quickly and accurately assign different permissions to different trading staff. They will also need to be able to adjust permissioning on-the-fly, and according to a staff member’s level and area of responsibility: for example for a global head trader, a head US equities trader, a head industrials or healthcare trader, etc. CEP tools’ capabilities enable institutions to manage these assignments seamlessly, and in real-time, which will be critical to the parallel goals of speed, trader efficiency and risk control.

The head trader(s) will be assessing aggregate risk throughout the day as traders send and interact with individual orders or baskets. Each trader is likely aware of what his or her counterparts are doing as they carry out other elements of the strategy, and often will work in concert with one or more of them. Rarely, however, do they have the full picture – necessitating the creation of the in-depth, cross-trader view the head trader can interpret and, where needed, take action on.

A single event, or a multitude of them, can affect volume and volatility profiles of stocks or sectors temporarily, or for days, weeks, months or even years at a time. As a result, reference data relating to stock behavior will in many cases be both real-time streaming, and rolling, incorporating historical trends. A “new normal” is constantly being established, sometimes driven by news affecting a stock, and sometimes simply due to demand-driven or other unknown factors forcing traders to suddenly trade abnormally aggressively in order to achieve a certain trading goal.

Risk and trading managers will as a result need to be able to adjust and, occasionally, remove controls quickly, accurately, and easily. A trader repeatedly getting rejected when attempting to liquidate a position as the market plummets is a recipe for disaster, so flexibility will be key in any execution analytics and control system. CEP and other high-volume data management tools are well-suited to address the trading desk’s need for rapid, precise control of both trading and risk management.

The following graphic depicts the overall structure of an effective execution analytics approach for an institutional trading firm.

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Process Requirements for Robust

Execution Analytics

Actionable Intelligence

Parameters on an Algorithm Change of Strategy Selection Outright Cancellation of an Order

Execution Analytics Platform

Outright Rejects Actionable Rejects Large Orders Unexecuted Orders Orders Away from Market

Security Specific Inputs

Equity (Symbol, Side, Limit, Shares, etc.) Options (Price, Strike, Month, Contracts, _etc.) Global / Region (Symbol, Side, Month, etc.)

Raw Data Inputs

Last Print _{Volume, Depth of Market}Current Bid / Offer, Benchmarks Price Action Since Order _Started Historical ADV

Conclusion

Managing securities trading data based on a robust process and technology infrastructure will be vitally important for the securities firm of the future. The unavoidable impact of the high-speed, low latency market environment is that more demands than ever before are being placed on the buy- and sell-side to operate at a rapid pace. In the context of stricter regulatory requirements, risk management concerns, and the profitable operation of the firm, it is becoming all but impossible to remain in business without integrated, high-performing software and hardware platforms to address “the need for speed.”

This need extends beyond data taxonomies into real-time access and interpretation of diverse and complicated data sources, including those across asset classes, geographies, markets, and traders’ books. The need to respond to these forces in ever-faster cycles will drive nearly all steps in the trading lifecycle to become more timely, as real-time becomes the mantra of trading desks. Complex event processing is increasingly being implemented as a key component of an institution’s real-time strategy. There is too much at stake in terms of revenues, order flow, execution quality, and reputation to ignore the contributions CEP is able to provide on the trading floor. Successful trading, and the sophisticated analytics that underlie it, depend on it.

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Sybase commissioned TowerGroup to conduct independent research and analysis into practices and trends relating to the use of real-time analytics and complex event processing in institutional trading. The content of this report is the product of TowerGroup and is based on independent, unbiased research not tied to any vendor product or solution. Although every effort has been taken to verify the accuracy of this information, neither TowerGroup nor the sponsor of this report can accept any responsibility or liability for reliance by any person on this research or any of the information, opinions, or conclusions set out in the report.