Genetically Optimized Artificial Neural Network for Financial Time Series Data Mining

Abstract

This paper examines stock prices forecasting and trading strategies’ development with means of computational intelligence (CI), addressing the issue of an artificial neural network (ANN) topology dependency.

Simulations reveal optimal network settings. Optimality of discovered ANN topologies’ is explained through their links with the ARMA processes, thus presenting identified structures as nonlinear generalizations of such processes. Optimal settings examination demonstrates the weak relationships between statistical and economic criteria.

The research demonstrates that fine-tuning ANN settings is an important stage in the computational model set-up for results’ improvement and mechanism understanding. Genetic algorithm (GA) is proposed to be used for model discovery, making technical decisions less arbitrary and adding additional explanatory power to the analysis of economic systems with CI.

The paper is a step towards the econometric foundation of CI in finance. The choice of evaluation criteria combining statistical and economic qualities is viewed as essential for an adequate analysis of economic systems.