DocumentCode :
2570589
Title :
Stock market trading via stochastic network optimization
Author :
Neely, Michael J.
Author_Institution :
Electr. Eng. Dept., Univ. of Southern California, Los Angeles, CA, USA
fYear :
2010
fDate :
15-17 Dec. 2010
Firstpage :
2777
Lastpage :
2784
Abstract :
We consider the problem of dynamic buying and selling of shares from a collection of N stocks with random price fluctuations. To limit investment risk, we place an upper bound on the total number of shares kept at any time. Assuming that prices evolve according to an ergodic process with a mild decaying memory property, and assuming constraints on the total number of shares that can be bought and sold at any time, we develop a trading policy that comes arbitrarily close to achieving the profit of an ideal policy that has perfect knowledge of future events. Proximity to the optimal profit comes with a corresponding tradeoff in the maximum required stock level and in the timescales associated with convergence. We then consider arbitrary (possibly non-ergodic) price processes, and show that the same algorithm comes close to the profit of a frame based policy that can look a fixed number of slots into the future. Our approach uses a Lyapunov optimization technique previously developed for optimizing stochastic queueing networks.
Keywords :
Lyapunov methods; investment; optimisation; profitability; stochastic processes; stock markets; Lyapunov optimization; ergodic process; investment risk; optimal profit; stochastic queueing networks; stock market trading; Algorithm design and analysis; Heuristic algorithms; Manganese; Optimized production technology; Resource management; Stochastic processes;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Decision and Control (CDC), 2010 49th IEEE Conference on
Conference_Location :
Atlanta, GA
ISSN :
0743-1546
Print_ISBN :
978-1-4244-7745-6
Type :
conf
DOI :
10.1109/CDC.2010.5717310
Filename :
5717310
Link To Document :
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