This article deals with emerging issues in trading floor support, connectivity and the challenges in finding alpha, as well as examining the regulations, practices and systems that support the trading processes of investment banks.
Capital not only flows across borders, but it also flows faster than ever before. Over the past decade, capital markets across trading venues have undergone a sea change. Markets have witnessed innovation both in tradable securities as well as their underlying supporting technologies. Product innovation and regulatory responses have shaped the trading practices of capital markets. The past decade has also witnessed interlocked trading pits and interlocked asset classes. Multi asset classes, cross border trading and a multi regulatory environment have created both difficulties and opportunities in finding beta and alpha in trading. A study by the Securities Industry Association estimates that between 3.5 and 4.0 million securities instruments are traded globally. If one multiplies this number by the attributes and statistical facts, one can comprehend the complexities involved in this process.
The ability to manage content, data and available information on a real-time basis propels differentiation among financial institutions. Often, this decides who will be the winner in the market; firms unable to gather and respond to information from multiple sources will forgo business opportunities. In a world that increasingly relies on the availability of real-time data and information, latency is a crucial factor.
What we are witnessing in the markets today is a trend towards adopting more machine-based content generation to advise traders about the steps they need to take. Trading practices have evolved a great deal from the good old days of floor trading and outcry systems thanks to automated direct market access and dark pools. This has resulted in increasingly sophisticated broker front-end systems. Today, the ability of a trader depends on the efficiencies and flexibility of a multi asset trading support environment. It is important to have technology that helps to create a platform for building order management systems and execution systems catering to multiple asset classes. Trading is no more a response to the available liquidity and price discovery process through self-regulated execution venues such as exchanges; it is now an algorithm driven market-making process. As the Chairman of BAI puts it, ‘as long as trading practice is complying with regulations, no strategies can be termed as bad or good practice, just because they offer superior advantage to a group of traders or institutions who are in the ring’.
It is a fact that firms are reinventing systems that can support their trading strategies to enable competitive advantage. As a trader from one of the leading brokerage houses captures it, ‘A book value of positive or negative based on mark to market is decided on the basis of milli and micro seconds’ data. In responding to these challenges firms are investing millions to split the micro seconds further and ensure real-time data. From the trading perspective it is always a combination of price and time priority.’
Firms have achieved a greater degree of success in addressing a trader’s requirements of high frequency, algorithm-supporting systems. Technology innovations both from the hardware and software perspective have pushed the needle significantly in that direction. However, it is our view that a synchronised trading platform is still miles away. Integrating portfolio management applications in conjunction with both order management and execution systems has not been attempted by many. Integrating order management systems and execution systems with the portfolio decision support applications is still a challenge for many trading organisations. Considering the disparate application and IT landscape, such old issues as integrating data, analysing information on the markets originating from multiple source systems (both internal and external) and handling significant surge in order volume, continue to be challenging. Even in the more sophisticated algorithmic trading systems, issues such as real-time insights, views of the trade life cycle and source of order type, and the level of larger trading basket strategies reflected by these orders, are not yet fully addressed. These systems’ lack of comprehensive ability is still posing challenges to traders. In addition, a few questions such as inconsistency in messaging standards (on the positive side, there are so many standards!) while communicating between various trade support applications and difficulties in complex event processing, are hurdles impeding the achievement of the required latency for these systems.
Even in dark pools, which do not reflect the market but provide either a single priced auction at set intervals or continuous blind crossing, latency is still an issue. Seamless communication remains a challenge while a question hangs over the effectiveness of selective information sharing.
What does all this mean for the future of automation? Which trends will gain momentum in the years to come? The consensus view of industry participants indicates the following:
• Accessing dark pools: Most of the dark pools fall into the categories of continuous blind crossing or internal crossing. However, there is difficulty in understanding whether the trader is dealing with resident or transient orders. Sometimes, though the order appears as retail, it may in reality come from a proprietary trader, who surrogates an order to gain knowledge from institutional activity. Although dark pools help with greater liquidity, there is no potential for proprietary traders to take advantage of buy-side order origination. The trader who wants to exploit a dark pool needs to be aware of this. The ability to understand these transient orders can help significantly. From a technology perspective, it is important to build target pattern analytical ability. Systems can capture the matching details and price movements to arrive at a pattern, which can provide insight to the buy-side on crisscrossing of resident and transient orders.
• Understanding best execution possibility: The Chartered Financial Analysts Institute defines the best execution process as the trading process firms seeking to maximise the value of a client portfolio within the client’s stated investment objectives and constraints. What this definition subtly highlights is that there are two different processes which need to be adhered to by a trading organisation. One is related to the stated portfolio objectives, which cover types of asset classes, markets and the possible return on investment sought by customers. These are related to the trading objectives. The second important dimension in this definition relates to the definition of constraints. Customers may impose certain restrictions on frequency of portfolio churning, or specify asset classes to avoid given personal and religious views etc. The trader mechanisms for executing these orders should take these constraints into account before initiating execution. This often poses difficulties in the pre-compliance process. It could, in volatile market price conditions, potentially result in a loss for the trading organisations. It is also important to note that emerging regulations are increasingly holding traders or trader organisations accountable for these violations. In addition, increasing sophistication at the customer end is forcing trading organisations to be transparent in the deals they close as part of an agreed mandate. Trading platforms need to address how they capture not only the benchmark price but also why it was the ‘best execution’ price possible during market trading hours. The system needs to increasingly address both liquidity and the cross-markets reference price for the traded instruments for the trader to be in a position to validate the action.
• Agency and proprietary trading: Agency trading continues to suffer from asymmetric information and incomplete contracts. Asymmetric information prevents the institutional investors from monitoring trade execution perfectly. Considering that volatility risk finally blows over as a debit entry in the general ledger of institutional investors and fund houses, execution traders’ interest in protecting institutions often comes under question. However, thanks to increasing sophistication of trading technology, and real-time monitoring and communication, the relationship between execution houses, that is investment banks, and fund houses, that is buy-sides, is continuously getting redefined. An increase in the ability of firms’ buy-side portfolio managers to gather the same market insight as their counterparts, namely investment banking traders, is propelling communication channel innovation towards providing real-time insight. In a fiercely competitive marketplace, it is customer loyalty and exit barriers that define the long-term success of an investment bank. From the buy-side firms’ perspective, the industry is witnessing a greater reliance on pure-play agency brokerage houses than proprietary trading organisations. A survey conducted by the TABB Group revealed that a majority of buy-side firms preferred exclusive agency brokerage against other offerings such as local presence, execution cost, capital commitment, pre-trade analytics, direct market access and algorithms. It is interesting to note that conflict of interest concerns, an old school thought, outweigh the benefit offered through other offerings. A trade and post-trade platform needs to take these developments into account when defining information sharing strategies between execution traders and buy-side firms.
• Risk measurement and management: Traditionally, risk measurement processes have been developed on the concept of value at risk, economic capital and risk adjusted return on capital. With respect to market risk, two important tools are deployed on the risk grids of investment banks. They base their risk understanding on sensitivity analysis (including durations and Greeks), stress testing and VaR. These have become standard ways in which market risks are measured and mitigated. However, reflecting the key learning from the financial turmoil, which shook the foundations of risk measurement practice, a new dimension is gaining momentum: measuring and monitoring liquidity. Reflecting the shifted priority, traders are demanding better integration of their order management and execution systems to the middle office function of risk measurement, liquidity understanding and stress testing if the market ceases to exist for a certain asset class. Though at a concept level, it appears simple, from the perspective of tool development and deployment it is a great challenge. Tighter integration between multiple functions will further propel the process and streamline it within investment banks.
In summary, a trading organisation has to re-look at the way it organises trade floor support IT. Integration, flexibility with centralised control over processes, and standardisation of communication protocols to build not the system but the platform to support multi asset classes as well as multi type customers, will gain further momentum in the industry in the years to come.
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