Evaluation of normalization methods on neuro-genetic models for stock index forecasting

With the rise of artificial intelligence technology and the growing interrelated markets of the last two decades offering unprecedented trading opportunities, technical analysis simply based on forecasting models is no longer enough. To meet the trading challenge in today´s global market, technical analysis must be redefined. Before using the neural network models some issues such as data preprocessing, network architecture and learning parameters are to be considered. Data normalization is a fundamental data preprocessing step for learning from data before feeding to the Artificial Neural Network (ANN). Finding an appropriate method to normalize time series data is not a simple task. This work evaluates various normalization methods used in ANN model trained with gradient descent (ANN-GD), genetic algorithm (ANN-GA), and functional link artificial neural network model trained with GD (FLANN-GD) and genetic algorithm (FLANN-GA). The study is applied on daily closing price of Bombay stock exchange (BSE) and experimental result.