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
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;
Conference_Titel :
Decision and Control (CDC), 2010 49th IEEE Conference on
Conference_Location :
Atlanta, GA
Print_ISBN :
978-1-4244-7745-6
DOI :
10.1109/CDC.2010.5717310