Why control over Market Impact is an important differentiator for using FX algorithms

August 2019 in Provider Profiles

Broadly speaking what is Market Impact and why is it of concern to many buyside firms?

Market impact is the immediate change in supply and demand in the market whenever a trader, or strategy places orders in the market. In addition to the imbalance that might be the result of order arrival in the market, the executing party also incurs the risk of leaking information about the intent, urgency or size of any given order. This may negatively affect the outcome of the rate at which the trade is executed at, slippage, and/or leave information in the market about the positioning of the client. This may erode the alpha the client has.

Why does providing control over Market Impact help to differentiate FX algos over other types of risk transfer solution?

Essentially the use of execution algos is a way for the buy side to have a level of control over which market impact they can expect – on average. This is a systematic way of providing this instruction to the executing dealer. In the past this intent has been transmitted as an instruction to the voice dealer, describing the execution style the client wants. By using electronic execution algorithms, the client can clearly transmit this intent in a systematic way. This in turn enables the client to gather information and benchmark various execution styles in a quantitative and systematic way.

In what ways can buyside firms use FX algos to help them manage and fine tune the impact of their trading behaviour?

It is essential that the buyside firm, ideally together with the algo provider, establishes what the goal of each execution is, and that determines which metrics should be observed in order to benchmark the execution. With the use of this data foundation, as well as the empirical observations made by the algo provider on a large set of executions, this can be utilized by the buyside client to understand what the expected impact of a given set of strategies would be, on average.

How do leading FX providers like Nordea go about computing the level of expected Market Impact that their algos will be able to deliver?
We have a data driven approach to estimating the market impact. Essentially we look at the expected turnover in the market during the appropriate time frame. The average market impact of the implementation of a specific strategy is highly dependent on whether the information and the disturbances in market liquidity is appropriate for a given market condition.

Understanding how much we’re able to trade with an expected market impact per unit time, gives a baseline for the execution. It is clearly possible to execute faster than the optimal speed, and potentially incur some impact. This is really the key to understand – the urgency of the client can come with some cost, in the form of slippage, but this may be acceptable for the buyside client.

What analysis toolsets can be used to determine how effective an FX algo trading strategy has been in reducing Market Impact?

When looking at market impact, we can calculate this on a per sub-order, entire algo order or portfolio of algo orders. The statistical soundness of the analysis obviously improves with the number of observations. We are able to report the impact of the sub-orders and the entire algo as part of our TCA offering. Through our partnership with BestX, we are also able to provide these metrics on a portfolio of algo trades.

When choosing an algo type and its associated parameters clients face a choice of how to weigh market risk with the risk of causing Market Impact. What issues are important for them to consider here?

There is a fair amount of consideration to do here. If we look at the very basic premise of urgency, it is so that in order to reduce market risk, the execution should be done swiftly. A swift execution has a higher likelihood of causing market impact. Clients will have to consider their own urgency due to internal factors as well as external factors.

The external factors could be event risk, time of day and current market conditions, where the internal factors could be an internal cut-off time, or other workflow related factors. Another thing to consider is how the client is expected to benchmark the execution.

Some clients prefer strategies which attempts to track a benchmark, such as an average rate over a timeframe. If the client is bounded by such constraints in the execution methodology, the expected market impact follows as a result of these.

Editor’s note: Lars is a  speaker at next months webinar on Algorithmic FX trading in the Nordics. Please see more details here.