SSRN Author: Phil GoddardPhil Goddard SSRN Content
http://www.ssrn.com/author=2437900
http://www.ssrn.com/rss/en-usTue, 17 May 2016 01:01:55 GMTeditor@ssrn.com (Editor)Tue, 17 May 2016 01:01:55 GMTwebmaster@ssrn.com (WebMaster)SSRN RSS Generator 1.0REVISION: Solving the Optimal Trading Trajectory Problem Using a Quantum AnnealerWe solve a multi-period portfolio optimization problem using D-Wave Systems' quantum annealer. We derive a formulation of the problem, discuss several possible integer encoding schemes, and present numerical examples that show high success rates. The formulation incorporates transaction costs (including permanent and temporary market impact), and, significantly, the solution does not require the inversion of a covariance matrix. The discrete multi-period portfolio optimization problem we solve is significantly harder than the continuous variable problem. We present insight into how results may be improved using suitable software enhancements, and why current quantum annealing technology limits the size of problem that can be successfully solved today. The formulation presented is specifically designed to be scalable, with the expectation that as quantum annealing technology improves, larger problems will be solvable using the same techniques.
http://www.ssrn.com/abstract=2649376
http://www.ssrn.com/1496671.htmlMon, 16 May 2016 01:10:38 GMTREVISION: Solving the Optimal Trading Trajectory Problem Using a Quantum AnnealerWe solve a multi-period portfolio optimization problem using D-Wave Systems' quantum annealer. We derive a formulation of the problem, discuss several possible integer encoding schemes, and present numerical examples that show high success rates. The formulation incorporates transaction costs (including permanent and temporary market impact), and, significantly, the solution does not require the inversion of a covariance matrix. The discrete multi-period portfolio optimization problem we solve is significantly harder than the continuous variable problem. We present insight into how results may be improved using suitable software enhancements, and why current quantum annealing technology limits the size of problem that can be successfully solved today. The formulation presented is specifically designed to be scalable, with the expectation that as quantum annealing technology improves, larger problems will be solvable using the same techniques.
http://www.ssrn.com/abstract=2649376
http://www.ssrn.com/1433110.htmlFri, 02 Oct 2015 09:23:39 GMTREVISION: Solving the Optimal Trading Trajectory Problem Using a Quantum AnnealerWe solve a multi-period portfolio optimization problem using D-Wave Systems' quantum annealer. We derive a formulation of the problem, discuss several possible integer encoding schemes, and present numerical examples that show high success rates. The formulation incorporates transaction costs (including permanent and temporary market impact), and, significantly, the solution does not require the inversion of a covariance matrix. The discrete multi-period portfolio optimization problem we solve is significantly harder than the continuous variable problem. We present insight into how results may be improved using suitable software enhancements, and why current quantum annealing technology limits the size of problem that can be successfully solved today. The formulation presented is specifically designed to be scalable, with the expectation that as quantum annealing technology improves, larger problems will be solvable using the same techniques.
http://www.ssrn.com/abstract=2649376
http://www.ssrn.com/1424066.htmlSat, 29 Aug 2015 13:41:36 GMTREVISION: Solving the Optimal Trading Trajectory Problem Using a Quantum AnnealerWe solve a multi-period portfolio optimization problem using D-Wave Systems' quantum annealer. We derive a formulation of the problem, discuss several possible integer encoding schemes, and present numerical examples that show high success rates. The formulation incorporates transaction costs (including permanent and temporary market impact), and, significantly, the solution does not require the inversion of a covariance matrix. The discrete multi-period portfolio optimization problem we solve is significantly harder than the continuous variable problem. We present insight into how results may be improved using suitable software enhancements, and why current quantum annealing technology limits the size of problem that can be successfully solved today. The formulation presented is specifically designed to be scalable, with the expectation that as quantum annealing technology improves, larger problems will be solvable using the same techniques.
http://www.ssrn.com/abstract=2649376
http://www.ssrn.com/1422509.htmlMon, 24 Aug 2015 08:32:17 GMT