TY - JOUR
T1 - Solving the Optimal Trading Trajectory Problem Using a Quantum Annealer
AU - Rosenberg, Gili
AU - Haghnegahdar, Poya
AU - Goddard, Phil
AU - Carr, Peter
AU - Wu, Kesheng
AU - De Prado, Marcos López
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/9
Y1 - 2016/9
N2 - We 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.
AB - We 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.
KW - Optimal trading trajectory
KW - portfolio optimization
KW - quantum annealing
UR - http://www.scopus.com/inward/record.url?scp=84982278624&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84982278624&partnerID=8YFLogxK
U2 - 10.1109/JSTSP.2016.2574703
DO - 10.1109/JSTSP.2016.2574703
M3 - Article
AN - SCOPUS:84982278624
SN - 1932-4553
VL - 10
SP - 1053
EP - 1060
JO - IEEE Journal on Selected Topics in Signal Processing
JF - IEEE Journal on Selected Topics in Signal Processing
IS - 6
M1 - 7482755
ER -