2.3k citations · 2.3k across the 9 of their papers we have counts for
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math.PR2016
Stochastic Gradient Descent in Continuous Time
Justin Sirignano, Konstantinos Spiliopoulos
Stochastic gradient descent in continuous time (SGDCT) provides a computationally efficient method for the statistical learning of continuous-time models, which are widely used in…
q-fin.ST2016
Deep Learning for Mortgage Risk
Justin Sirignano, Apaar Sadhwani, Kay Giesecke
We develop a deep learning model of multi-period mortgage risk and use it to analyze an unprecedented dataset of origination and monthly performance records for over 120 million mo…
q-fin.TR2016
Deep Learning for Limit Order Books
Justin Sirignano
This paper develops a new neural network architecture for modeling spatial distributions (i.e., distributions on R^d) which is computationally efficient and specifically designed t…