JP Morgan pulls plug on deep learning model for FX algos
US bank turns to less complex models that are easier to explain to clients
JP Morgan has phased out a model that leverages machine learning technology for foreign exchange algorithmic execution, citing issues with data interpretation and the complexity involved.
The US bank had implemented what it calls a deep neural network for algo execution (DNA), which uses a machine learning framework to optimise order placement and execution styles to minimise market impact.
Launched in 2019, JP Morgan said at the time that the move would replicate reinforcement learning
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