Chaotic analysis of predictability versus knowledge discovery techniques: case study of the Polish stock market
Increasing evidence over the past decade indicates that financial markets exhibit nonlinear dynamics in the form of chaotic behavior. Traditionally, Ate prediction of stock markets has relied on statistical methods including multivariate statistical methods, autoregressive integrated moving average models and autoregressive conditional heteraskedasticity models. In recent yearsy neural networks and other knowledge techniques have been applied extensively to the task of predicting financial variables. This paper examines the relationship between chaotic models and teaming techniques. In particular, chaotic analysis indicates the ripper limits of predictability for a time series. The teaming techniques involve neural networks and case-based reasoning. The chaotic models take the form of R/S analysis to measure persistence in a time series, the correlation dimension to encapsulate system complexity and Lyapunov exponents to indicate predictive horizons. The concepts are illustrated in the context of a major emerging market, namely the Polish stock market.
knowledge discovery chaotic models case-based reasoning backpropagation neural network