JP Morgan noted a rise in customer adoption of adaptive forex algorithms at the peak of market volatility in March, according to a customer report by The TRADE's FX eCommerce team.
The report found that due to the recent volatile market conditions, customers are increasingly using adaptive algo orders compared to limit-based strategies. JP Morgan said this was a "remarkable change in customer behavior" compared to other major liquidity events such as Brexit and the 2016 US election.
A breakdown of order types by liquidity events in the report found that during the Brexit and US election liquidity events in 2016, around 30% of Algo's order types were adaptive and 60% limit-based. However, during the March Corona Virus liquidity event, JP Morgan said adaptive order types have increased to around 50% of Algo order types, while limit-based orders have decreased to around 20%.
Adaptive orders show a modified behavior based on market structure variables such as volume, market impact, bid / offer spreads. Traders can use adaptive Algo order types to buy different amounts at different time intervals based on the estimated activity, which essentially means that they adapt to market conditions. On the other hand, non-adaptive algae such as limit-based orders fulfill orders based on a fixed time interval or a fixed limit price.
Foreign exchange markets experienced tremendous volatility in March due to the corona virus pandemic, resulting in widened bid-offer spreads that have not been seen since the global financial crisis and a decline in market depth even for currency pairs that were considered liquid .
The increasing challenge of targeting a specific price, increased market impact risk, increased customer convenience in adaptive algo logic, and reduced concerns about time risk have been cited by JP Morgan as factors that have contributed to the increasing use of adaptive orders are considered limit-based orders.
JP Morgan added in its customer report that customers generally opted for the algo execution rather than risk transfer for tickets with a fictional size of more than $ 10 million in the period under review, accounting for around 60% of this bank's FX order tickets this size were traded via an algorithm.
Last year, JP Morgan used machine learning technology for its forex algorithms when the institution introduced a Deep Neural Network for Algo Execution (DNA) tool to bundle existing algae into a single execution strategy.