Computational finance, statistical machine learning, scientific computing, fintech
Faculty Innovation Award, Fall 2018
M.F. Dixon, N. Polson and V. Sokolov, Deep Learning for Spatial-Temporal Modeling: Dynamic Traffic Flows and High Frequency Trading, to appear in Applied Stochastic Models in Business and Industry, 2018
M.F. Dixon, A High Frequency Trade Execution Model for Supervised Learning, High Frequency, 1(1), pp. 32-52, 2018.
C. Akcora, M.F. Dixon, Y. Gel and M. Kantarcioglu, Bitcoin Risk Modeling With Blockchain Graphs, to appear in Economic Letters, 2018.
M.F. Dixon, Sequence Classification of the Limit Order Book using Recurrent Neural Networks, J. Computational Science 24, pp. 277-286, 2017.
M.F. Dixon, D. Klabjan, and J. H. Bang, Classification-based Financial Markets Prediction using Deep Neural Networks, Algorithmic Finance 6(3-4), pp. 66-99, 2017.
M.F. Dixon, J. Chong and K. Keutzer, Accelerating Value-at-Risk Estimation on Highly Parallel Architectures, Concurrency Computat.: Pract. Exper 24(8), Wiley, pp. 895-907, 2012.
Intel funded research in computational finance