Before revision:  We describe the training of a recurrent neural network (RNN) and show (i) there is little gain from re-training the model on a frequent basis; (ii) that there are distinct intra-day classifier performance trends; and (iii) classifier accuracy quickly erodes with the length of prediction horizon. Our findings suggest that our computationally tractable approach can be used to directly evaluate the performance sensitivity of a market making strategy to classifier error and can augment traditional market simulation based testing.
After revision:  Our approach directly evaluates the performance sensitivity of a market making strategy to classifier error and can augment traditional market simulation based testing.