Forecasting Performance Of Nonlinear And Nonstationary Stock Market Data Using Empirical Mode Decomposition
| dc.contributor.author | Al-Abd Awajan, Ahmad Mohammad | |
| dc.date.accessioned | 2019-04-03T02:46:43Z | |
| dc.date.available | 2019-04-03T02:46:43Z | |
| dc.date.issued | 2018-05 | |
| dc.description.abstract | The stock market indices are typically non-linear and non-stationary with high heteroscedasticity data, which affect the accuracy and validity of the results of traditional forecasting methods. Therefore, this study focuses on decomposition method to solve the problem of non-linearity and non-stationarity in data with high heteroscedasticity behavior to improve the accuracy of stock market forecasting. Recently, Empirical mode decomposition (EMD) method has been introduced as an effective technique for overcoming the non-linearity and non-stationarity in time series data. EMD presents several characteristics that other decomposition methods do not have. Thus, this thesis proposes five different techniques to forecast the stock markets by combining EMD with traditional forecasting techniques or bootstrapping EMD with traditional technique to solve the forecasting inaccuracy problem in financial time series data and therefore to obtain improving forecasting results. The five new techniques, namely, EMD with moving average, EMD with Holt-Winter (EMD-HW), EMD with random walk (EMD-RW), EMD with exponential smoothing method (EMD-EXP), and bootstrapping of EMD with HW (EMD-HW bagging) are proposed. These proposed five techniques are compared with eight traditional forecasting methods, ten different daily stock market indexes of over one thousand five hundred observations for each set are used. Based on five error measures (i.e. RMSE, MAE, MAPE, MASE, and TheilU), the results show that the five proposed techniques are more accurate than the existing forecasting techniques on the stock market forecasting. | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/7924 | |
| dc.language.iso | en | en_US |
| dc.publisher | Universiti Sains Malaysia | en_US |
| dc.subject | Decomposition method to solve the problem | en_US |
| dc.subject | improve the accuracy of stock market forecasting | en_US |
| dc.title | Forecasting Performance Of Nonlinear And Nonstationary Stock Market Data Using Empirical Mode Decomposition | en_US |
| dc.type | Thesis | en_US |
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