Buy-Side Trading Desks: The Need For Constant Evolution


By Gregg Dalley, Global Head of  Equities Trading, Schroders

Human traders and automation coexist within an effective trading desk to source liquidity and ensure best execution.

The buy-side trading desk is unrecognisable from two decades ago. Transaction volumes have doubled, more portfolio managers and investment teams are being serviced, and the number of financial instruments has increased at the same time as the size of the trading staff has almost halved.

The introduction of more extensive and sophisticated technology, extensive automation and improved trading tools has provided faster and enhanced data collection and analysis, removing many manual tasks.

These tasks require different skillsets to those acquired and earned by traders in the past, and Schroders has been developing them internally for several years to meet the challenges and opportunities provided by new technologies and regulatory changes. In particular, it is essential to identify the specific roles that machines and humans should best adopt, and also where they complement each other for greatest efficiency.

Broadly, the co-existence of machines and humans for a successful trading process is not only critical, but, in practice, inevitable.

Technology can be used to analyse data (including “Big Data”) rapidly and detect patterns, as well as calculate complex problems quickly. Automation facilitates transparent and auditable decision-making, and prevents emotion and bias affecting the process. Moreover, technology and automated systems are scalable and should be cost-effective.

However, human agency also has a fundamental role. Personal networks help extract information that can augment best execution, especially for block trades. Skilled, experienced staff can also best interpret and prioritise data that is not electronic in natures. It is also incredibly valuable around events, news flow or any occasions when a stock is not behaving as expected and therefore is harder to model.

Although algorithms (and artificial intelligence) are more efficient than humans for many activities within the trading cycle, people nevertheless need to devise and direct the algorithms. Here, most starkly, is the symbiosis of human agency and machine.

Indeed, interaction and collaboration between humans and machines is common throughout the trade cycle. Perhaps, it is most evident when sourcing liquidity to extract the best price for a transaction.

Sourcing liquidity
If liquidity were easily accessible, then it would be possible simply to plug an order into an algorithm and the trade would be filled at the best price. In that case, success would be determined by speed; it would be a latency race. Certainly, this does happen, but in practice, a broker with a large unwind position or holding a mid- or small-cap position does not place their order into the market for execution in a crossing network or a conditional order type to interact with. Instead, they will typically keep it on their pad, make calls to find the other side or wait from an inbound call from an indication of interest to maximise commission and generate more business.

Similarly, the buy-side dealing desk often acts as an internal sales trader, crossing up inbound liquidity opportunities with the portfolio managers, adding value to the investment process and reducing transaction costs.

In fact, both sell- and buy-side trading desks provide a valuable service for their firms by actively searching for the other side of a trade, rather than the passive, hands-off route of deploying an algorithm. Basically, a trader needs a variety of complementary tools and skillets, and there is a time and place for all of them.

The issue of liquidity and its alternative sources will be a major focus after the Markets in Financial Instruments Directive (MiFID) II is implemented in January 2018. In particular, the impact of systematic internalisers (SIs) will be examined.

Some industry participants fear that they might be a source of information slippage and shady practices; others hope they will turn out to be viable, alternative suppliers of liquidity. The truth is likely to depend on the integrity of the firms operating the SIs, but in any case, buy-side traders will have access to the data in order to make their judgements.

Impact of MiFID II
One purpose of MiFID II is to make trade execution more transparent, and lit exchanges are obvious venues to ensure that happens. There is a danger that trade volumes might migrate to SIs, which would likely motivate regulators to reduce their activities or even remove them altogether.

The attitude of the buy-side is ambivalent. On the one hand, the prominence of SIs will make it difficult for buy-side firms to capture the spread on a security because they cannot act as SIs themselves, yet third party orders might interact with external SIs before they route to the order book. On the other hand, the buy-side will have the information about the trades SIs have executed and it will be able to measure and monitor their performance.

For instance, will brokers provide external or internal Si’s with additional (even damaging) material about their clients, and would individual buy-side firms be told how they are classified. Also, will brokers tell their clients exactly which internal book they have dealt with?

In addition, the new regime might cause an unintended paradox: there might be a reduction in trading volumes without a corresponding decline in real liquidity.

First, the new reporting and additional tagging of orders will remove double counting and some of the inefficacies of the previous framework. Second, the new liquidity landscape will remove or, at least initially, reduce the amount of market making opportunities for high frequency traders while they figure out the new world and rebuild their models.

However, this will not cause a decline in real liquidly for the buy-side, although it may well reduce market noise and the volumes that are traded on a daily basis. After all, if you start flat and finish flat, then what is the net added value at the end of the day?

Simply: less volume, but undiminished liquidity? We will have to wait and see, but it will be interesting.

Nevertheless, if headline volumes fall, then there might be criticism that MiFID II is having a damaging impact on market activity. An accurate assessment will require a dispassionate analysis of transaction costs and a proper appreciation of real liquidity before and after MiFID II.

Schroder’s algorithm trading wheel allows the quantitative equity trading team to determine which sell-side firms are delivering the best and most consistent execution service. The effect is greater transparency, a reduction of human bias from broker or venue selection, and a more rigorous assessment of counter-party performance.

Schroders is also planning to incorporate portfolio managers’ alpha trading profiles, styles, strategies and algorithmic selection suggestions into the trading desk’s workflow where they have statistical significance.

Global collaboration and quantitative analysis and input is embedded in the buy-side trading process, and the objective is to utilise additional data in order to be better informed, which should improve decision-making and optimise final outcomes. Will Psomadelis took up the role of head of electronic strategy research working with our Schroders’ data insight unit to provide a quantitative service to the firm’s global trading team and create consistency worldwide.

One challenge we all face is that on 3 January 2018 a new era begins and all historical data will be far less relevant as the game changes and we have new rules and venues. Our job will be to put this puzzle together as quickly as we can, using a combination of quantitative data and human trader interpretation.

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