Stock Index Prediction Using Back Propagation Neural Networks
| Autor: | Chang-Chieh Chen, 陳昌捷 |
|---|---|
| Rok vydání: | 2015 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 103 The main purpose of this study is to construct a Back Propagation Neural Network ( BPNN) model on MATLAB for predicting the Taiwan stock exchange capitalization weighted stock (TAIEX). The data ranging from 2014.01.02 to 2014.07.31 is selected. The duration is 7 months and there are total 140 recorders. The weighted indexes and technical analysis indicators are screened as the input parameters by using Pearson correlation coefficient. Specifically, the indicators, that the r values are more than 0.7, are selected as the input parameters, and there are total 17 input parameters. The input parameters are divided into three groups where the r values are 0.7, 0.8, and 0.9, respectively. Finally, Mean Absolute Percentage Error (MAPE) is used to evaluate the accuracy of the models. The results show that the MAPE of the prediction for index closed point is 0.6315%. In the short term (8 days) prediction, the accuracy is up to 87.5%. The accuracy of the short term prediction trends for the weighted index is 71.42%。 |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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