Research On Times Series Stock Forecasting With Neural Network And Wavelet Transform

Predicting stock data with traditional time series analysis has proven to be difficult. An artificial neural network may be more suitable for the task. This is because assumptions about a suitable mathematical model need not to be made before the forecasting. Furthermore, a neural network has the ability to extract useful information from large sets of data, which often is required for a satisfying description of a financial time series.In this thesis, we propose a stock forecasting model based on neural network and wavelet transform. Technical as well as fundamental data are used as input to the network. Wavelet technique is implemented to the time series data, decomposing the data into number of wavelet coefficient signals. The decomposed signals are then fed into neural network for training. Then trained network is used to forecast using the same wavelet technique to reconstruct.Matlab is used as a simulation tool and results showed that the model was capable of producing a reasonable forecasting accuracy in short term load forecast.