Professional execution desk traders pride themselves on
leveraging their specialist expertise to deliver alpha when
handling stock acquisitions/disposals. In a perfect world,
conditions would enable them to maximise this. Their workload
would be perfectly allocated and balanced so that they would
always be handling a manageable number of orders in stocks where
they had a strong understanding of the relevant technical and
Reality, however, paints a different picture. In practice, traders often find themselves having to cope with unexpected surges in the number of orders; often orders are in stocks that are unfamiliar. Under these conditions, a conventional execution algorithm is limited, as it typically cannot deliver the predictive intelligence of an experienced human trader. As such, it is similar to a highly featured and sophisticated car that lacks a driver to anticipate road conditions and hazards.
Assistant, not replacement
In view of that, the logical next step is to add a measure of
predictive intelligence capable of steering execution algorithms
in a manner that assists, rather than competes, with human
traders. When execution algorithms first appeared, many traders
saw them as a threat to their job security. Over time this
perception has changed; algos are seen as useful productivity
tools; specifically, algos are viewed as an "extra set of tools"
in the execution tool-kit that can be actively managed to deliver
superior execution performance. Therefore, any new tool that
seeks to add predictive modelling to algorithmic execution needs
to perform in a similar manner, by acting as the trader's
assistant - not replacement.
In order to achieve that, the predictive capabilities need to be well-integrated with existing execution algos and intelligent order routing. If they aren't, then the potential workflow benefits will be lost and execution quality may suffer as the trader will be obliged to manually adjust the algo and routing settings based on the guidance of the predictive model.
But how good is your algo?
While accurate prediction is valuable in its own right, execution
performance ultimately depends on the quality of the algo it
interacts with. Having a good idea where the stock will be in
five minutes time is of limited use if the underlying algo is
dumb. To seek the best results, especially in the U.S. equity
markets where liquidity is spread across lit and dark market
venues, the underlying algo has to offer smart posting and
intelligent order routing.
If the algo is closely monitoring real time trading and is capable of quickly shifting orders to venues showing the highest liquidity, then it can take full advantage of the intelligent prediction. For example, in the previously mentioned situation, the predictor anticipated that the market was poised to break down; it therefore made sense to grab as much liquidity as possible while the opportunity was there. The algo needs to maximise representation while seeking to minimise information leakage through techniques such as hidden orders and randomised anonymous acronym display orders, in order to maximize the predictive advantage.
Think like a trader
Any effective price predictor needs to function in much the same way as an expert human trader. It will therefore take into account a variety of factors, such as market depth, consolidated tape activity, current activity on the trader's own order, bid/offer spread/size, technical indicators and any imminent reports/earnings releases. A further factor, crucial when attempting to predict short term price behaviour, is relative performance - i.e. how is the stock performing relative to its sector and the market as a whole?
Source: CQG, inc. © 2009. All rights reserved worldwide. www.cqg.com
Such performance has a tendency to mean revert. For example, Figure 1 shows a 5 minute GE bar chart with below it the moving linear regression of the difference between GE's and the Dow's percentage returns, together with the difference's long term average. As can be seen, while the stock frequently over/underperforms the index, this does not persist for extended periods, nor diverge completely.
Bloomberg Tradebook's B-SmartSM AUTO
An example of the type of integrated intelligent assistant outlined here is Bloomberg Tradebook's B-SmartSM AUTO. This solution provides advanced trader support with a tightly-integrated statistical price prediction model to Tradebook's proven B-Smart execution algorithm. B-Smart already offers four different aggression levels (REACT, PASSIVE, NORMAL and AGGRESSIVE) that the trader can specify according to his/her view on current conditions in a stock. B-Smart AUTO moves things to the next level by enabling the aggression settings to be directly controlled by the price prediction model. For example, if a trader is working a large buy order and the predictor anticipates the stock is about to break down sharply, it will automatically become more passive, move from normal to react mode, and wait for the market to fall before re-engaging execution.
This characteristic is particularly valuable for fine tuning switches between execution modes on the core algorithm. If the stock is strongly outperforming the market and the algorithm is in aggressive mode in order to maintain the necessary acquisition rate, sooner or later the stock will exceed a significant threshold. (For example, two standard deviations above the long term mean of relative stock versus sector/index performance.) When it does, this might influence the predictor model to switch the algorithm's execution mode back to passive, in anticipation of the stock pulling back and range trading.
One size doesn't fit all
A robust price predictor can obviously add huge value to
execution performance and desk workflow. However, it cannot
forecast individual circumstances, such as the urgency of each
trade. These circumstances would obviously affect the way in
which a human trader would handle a trade, particularly the
points at which he/she would switch algo execution modes.
Therefore, to deliver maximum value, any practical predictor model needs to be able to accept a parameter input from the human trader indicating trade urgency. The predictor can then take this into account along with stock performance to determine the optimum execution mode switching points for the underlying algo.
Putting it all together
Combining statistical price prediction with a smart algorithm is
highly significant in its own right. However, it is possible to
further maximise the benefits by making the new technology part
of a multilayered execution strategy. If the trader's EMS is
capable of managing cascading "What if?" scenarios, then assorted
price/volume/trade completion/news triggers can be set that will
flip between strategies when triggered.
For example, if a stock price is above a maximum threshold, the order could be handled by a combination of predictive model and smart algo. Below that level and down to a minimum threshold it could be dealt with by a dynamic VWAP strategy. Finally, below the minimum threshold, the order could be handled by a straightforward arrival price strategy.
The addition of intelligent price prediction to the trader's toolbox is a major step forward. Apart from generally boosting productivity, it offers the sort of multipurpose wrench that can save the situation on those days when reality intrudes that bit too much. Predictors at the cutting edge are also agnostic; they can offer automated price guidance irrespective of a stock's liquidity or trading profile. As a result, they add value across a wide range of trading scenarios - from mid-sized orders in unfamiliar but liquid names, to relatively large (and typically time consuming) orders in thinly traded small caps.
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