The problem with using neural networks in market microstructure is the latency at inference time. Market makers and HFTs need to compute decisions on the order of microseconds. That’s not feasible with large, deep networks.
With specialized hardware you can get close. But you’re still talking about a mid-single digit number of microseconds on inference alone. The competitor using linear models can get down to hundreds of nanoseconds. If you’re in FPGA world, that kind of latency advantage is worth way more than a 30% accuracy improvement from using a complex ML model.
With specialized hardware you can get close. But you’re still talking about a mid-single digit number of microseconds on inference alone. The competitor using linear models can get down to hundreds of nanoseconds. If you’re in FPGA world, that kind of latency advantage is worth way more than a 30% accuracy improvement from using a complex ML model.