Quantitative Trading:Sourcing Liquidity And Managing Momentum

Michele Patron, Senior Quantitative Trader, AllianceBernstein talks to Stuart Baden Powell, Head of European Electronic Trading Strategy, RBC Capital Markets about sell-side algorithms, efficient sourcing of liquidity, the need for pre- and post-trade transparency and high frequency trading.

Stuart Baden Powell, RBC:
Recently, there has been much discussion about improvements in sell-side agency algorithms: some would argue that the core ‘building blocks’ of scheduled and opportunistic algorithms remain virtually identical, built around the same underlying models; others would point to a more radical shift away from mere incremental enhancements. Regardless of view, what is clear is that the buy-side is taking control of its execution destiny. Concerns about a reduction in trust, together with insufficient transparency of internal operations from many brokers have all contributed towards the shift. Whilst some buy-side firms will purchase off-the-shelf, canned algorithms from the sell-side, marginally tweak them and call them their own, other institutional firms are taking matters more into their own hands. Quantitative trading has been of huge importance to hedge funds over recent years. However, there are now a few select long only houses moving to incorporate quantitative trading in-house and link this to their own fundamental trading strategies. AllianceBernstein would fall into that latter bracket – Michele, you have worked at both CQS and BGI and now run European Quantitative Trading at AllianceBernstein. Could you talk us through what you are up to?

Michele Patron, AllianceBernstein:
I think that the discussion about how much buy-side firms should rely on the sell-side for trading research should have a definite answer by now: it is well-recognised that saving transaction costs represents an important source of alpha – even for medium turnover strategies. In addition, the wealth of information that buy-side firms have about their own flow cannot be achieved by counterparties, especially in multi-broker interaction scenarios: an accurate estimate of an alpha decay profile, which could be based on simple internal factors (i.e. order reason or PM strategy), will give the buy-side an important trading advantage.

At AllianceBernstein, we see our counterparties as partners, both in the high and low touch space. Within Quant Trading – which is globally headed by Dmitry Rakhlin – we continually try to analyse and customize execution algorithms, after we have had open discussions with our key counterparties. The ‘building blocks’ that you referred to earlier, are in principle easy to understand, and all the algo offerings out there can be bucketed into a few categories: the sell-side can offer us a hedge with smarter technology and expertise around execution tactics. There are some very smart options available in the market to minimize the latency arbitrage effect on client flows. A good solution can be achieved without tweaking the most relevant variable for this problem – system latency.

Two key tasks for a buy-side trading desk are: sourcing liquidity efficiently – especially for high AD V orders – and managing momentum – for low AD V. Having the ability to provide electronic solutions to address the latter, gives traders the opportunity to concentrate on the first task.

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