Financial engineering modelling using computational intelligent techniques: Financial time series prediction

Abstract

Prediction of financial time series is described as one of the most challenging tasks of time series prediction, due to its characteristics and dynamic nature. In any investment activity, having an accurate prediction system will significantly benefit investors by guiding decision making, especially in trading, asset management and risk management. Thus, the attempts to build such systems have attracted the attention of practitioners in the market and also researchers for many decades. Furthermore, the purpose of this thesis is to investigate and develop a new approach to predicting financial time series with consideration given to their dynamic nature. In this thesis, the prediction procedures will be carried out in three phases. The first phase proposes a new hybrid dynamic model based on Ensemble Empirical Mode Decomposition (EEMD), Back Propagation Neural Network (BPNN), Recurrent Neural Network (RNN), Support Vector Regression (SVR) and EEMD-Genetic Algorithm (GA)-Weighted Average (WA) to predict stock index closing price. EEMD in this phase is introduced as a preprocessing step to historical observation for the first time in the literature. The experimental results show that the EEMDD-GA-WA model performance is a notch above the other methods utilised in this phase. The second phase proposes a new hybrid static model based on Wavelet Transform (WT), RNN, Support Vector Machine (SVM), Nave Bayes and WT-GA-WA to predict the exact change of the stock index closing price. In this phase, the experimental results showed that the proposed WT-GA-WA model outperformed the rest of the models utilised in this phase. Moreover, the input data that are fed into the hybrid model in this phase are technical indicators. The third phase in this research introduces a new Hybrid Heuristic-Rules-based System (HHRS) for stock price prediction. This phase intends to combine the output of the hybrid models in phase one and two in order to enhance the final prediction results. Thus,to the best of our knowledge, this study is the only one to have carried out and tested this approach with a real data set. The results show that the HHRS outperformed all suggested models over all the data sets. Thus, this indicates that combining di↵erent techniques with diverse types of information could enhance prediction accuracy.