Andrew Papanicolaou is an assistant Professor in the Department of Finance and Risk Engineering. He holds a B.S. from University of California at Santa Barbara (2003), an M.S. from University of Southern California (2007), and a Ph.D. in Applied Mathematics from Brown University (2010). His research focuses on filtering theory, parameter estimation, stochastic control, and financial mathematics. Specific problems he’s studied include model selection and calibration for pricing of volatility derivatives, statistical inference for hidden economic indicators, and optimal strategies for investment in markets with unobserved factors. His past appointments were as a postdoctoral fellow and lecturer at Princeton in the department of Operations Research and Financial Engineering from 2010 to 2013, and as a lecturer at the University of Sydney in the School of Mathematics & Statistics from 2013 to 2015.
An optimal control strategy for execution of large stock orders using long short-term memory networks
Using a general power law in the Almgren and Chriss model and real data, the authors simulate the execution of a large stock order with an appropriately trained LSTM network.
In this paper, the authors study the dynamics of Chicago Board Options Exchange volatility index (VIX) futures and exchange-traded notes (ETNs)/exchange-traded funds (ETFs).