Publications

Farnsworth, Grant V.; Kelly, John A.; Pryor, Richard J.

Genetic programming (GP) has proved to be a highly versatile and useful tool for identifying relationships in data for which a more precise theoretical construct is unavailable. In this project, we use a GP search to develop trading strategies for agent based economic models. These strategies use stock prices and technical indicators, such as the moving average convergence/divergence and various exponentially weighted moving averages, to generate buy and sell signals. We analyze the effect of complexity constraints on the strategies as well as the relative performance of various indicators. We also present innovations in the classical genetic programming algorithm that appear to improve convergence for this problem. Technical strategies developed by our GP algorithm can be used to control the behavior of agents in economic simulation packages, such as ASPEN-D, adding variety to the current market fundamentals approach. The exploitation of arbitrage opportunities by technical analysts may help increase the efficiency of the simulated stock market, as it does in the real world. By improving the behavior of simulated stock markets, we can better estimate the effects of shocks to the economy due to terrorism or natural disasters.

Robinson, Allen C.; Farnsworth, Grant V.; Robinson, Allen C.

Algorithms for higher order accuracy modeling of kinematic behavior within the ALEGRA framework are presented. These techniques improve the behavior of the code when kinematic errors are found, ensure orthonormality of the rotation tensor at each time step, and increase the accuracy of the Lagrangian stretch and rotation tensor update algorithm. The implementation of these improvements in ALEGRA is described. A short discussion of issues related to improving the accuracy of the stress update procedures is also included.