Neuro-Genetic System for DAX Index Prediction

The task of stock index prediction is presented in this paper. The data is gathered at the target stock market (DAX) and two other markets (KOSPI and DJIA). The data contains not only raw numerical values from the markets but also indicators pre-processed in terms of technical analysis, i.e. oscillators and patterns. Statistical analysis and the genetic algorithm are used to create the proper sequence of inputs from all available variables. Selected data is input to a neural network trained with backpropagation with momentum. The prediction goal is the next day’s closing value of German stock market index DAX with consideration of Korean and USA stock markets’ indexes. The prediction is performed within a tight time-window in order to protect the model against changing relationships between variables. For each time-window the best neural network is evolved and applied. The evaluation is repeated for every time-window in order to discover a new set of proper