The potential benefits for corporates in algorithmic trading
Curtis Pfeiffer, Chief Business Officer at Pragma Securities explains how corporates could stand to benefit from using algorithms for FX execution…
Why should corporates consider using algorithms for FX execution?
Corporations want to maximise profit, and a penny saved is a penny earned. Algorithmic trading can contribute to the bottom line by significantly reducing FX trading costs. Corporations trade on the order of $70 trillion a year – roughly the same as the total global GDP. On such large amounts, basis points matter.
That’s why, to fulfil their mission, corporate treasurers are increasingly focused on ensuring that they get best execution on their FX transactions, which includes using the best available trading tools and practices.
What advantages do algorithms have over other trading techniques?
With the speed at which trading is conducted today, the proliferation of trading venues, and sheer levels of information that is processed, it is simply impossible for a human trader to stay on top of all the data that the market is generating.
There are four core benefits to algo execution:
- Breaking up a large order into multiple smaller pieces means, on average, paying less than trading in a block
- Building algorithms on top of an aggregated liquidity pool effectively narrows the spreads being traded on
- Algorithms have the ability to provide liquidity as well as to take prices, allowing patient traders to capture part of the bid-offer spread
- Automation frees treasurers and traders to focus more of their time on those issues where human intelligence and judgement add the most value.
What factors should investors consider when choosing an FX algorithm?
First, corporations should understand the bank’s liquidity model for their algorithmic offering – principal, agency or hybrid.
Bank algos access liquidity differently depending on the model. A pure principal algo accesses just the host bank’s liquidity, which also provides indirect access to other liquidity pools in the marketplace. Agency models do not interact with the host bank’s liquidity, but are able to provide liquidity on ECNs as well as taking prices, potentially capturing part of the bid-offer spread for the customer.
Hybrid models can offer the best of both worlds, though customers should understand how the bank manages its dual role as principal and agent. Corporations should assess the liquidity pool underlying each bank’s algorithms to determine which model will be most effective.
Second, corporations should be satisfied that their bank provider has first class algorithmic trading tools – either through a major investment it has made in algorithmic trading research and development internally, or by partnering with an algorithmic technology specialist. Smart algos have sophisticated order placement logic, change their behaviour based on pair and time-specific liquidity patterns, and make intelligent and dynamic use of the real-time liquidity available across venues – for example based on order fulfilment rates.
Provided liquidity and investment checks out, corporations can consider algorithmic trading as another service their banks provide, and direct flow as part of the overall banking relationship.
Finally, best practice is to use TCA after the fact to track performance across bank providers and make sure all is as expected.
How can investors ensure that they maintain control over their trading strategies, for example, in fast moving markets?
One of the reasons it’s important to use high quality algos is that the technology itself can build in protections, such as optionally slowing or stopping trading when spreads spike. In addition, stretching trades out over time makes them more robust to bad executions resulting from momentary market anomalies. In addition, depending on the liquidity demand being made, simple measures like limit prices can ensure that executions don’t fall outside traders’ expectations.
How can algorithms be used as a transaction cost analysis tool and satisfy the concerns of stakeholders over FX execution?
Basic TCA best practice is to track execution shortfalls for each order – for example, the difference between the market price at the time an order is picked up by the trading desk and the average price of the execution. Then, the average shortfall can be compared across providers, and compared across different methods of trading, such as RFQ.
One of the challenges corporates face is that the number of trades they make may not be sufficient to provide meaningful comparisons – there’s just too much noise. With algorithmic trading, banks can often give timestamped prices on individual child orders executed by the algorithm. This allows a customer to compare the child order executions to other prices available in the marketplace, and to estimate the effective spread that they are trading on.
The mechanics of this kind of TCA are challenging, but third-party TCA providers are offering increasingly sophisticated tools. This kind of granularity can significantly improve a corporation’s ability to recognise statistically significant patterns between different providers or methods of trading.