The Predictive Power for the Quarterly Earnings Per Share based on Time Series and Artificial Intelligence Model
碩士 === 國立成功大學 === 財務金融研究所 === 92 === The purpose of this study is to compare the forecasting ability among the ARIMA model, the Transfer Function model, the Artificial Neural Network model and the Genetic Algorithm model. To evaluate the forecasting accuracy, there are two dimensions taken into co...
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ndltd-TW-092NCKU53040102016-06-17T04:16:56Z http://ndltd.ncl.edu.tw/handle/29179758900041335723 The Predictive Power for the Quarterly Earnings Per Share based on Time Series and Artificial Intelligence Model 季每股盈餘之預測能力--根據時間序列及人工智慧模型 Ya-Jung Shih 施雅蓉 碩士 國立成功大學 財務金融研究所 92 The purpose of this study is to compare the forecasting ability among the ARIMA model, the Transfer Function model, the Artificial Neural Network model and the Genetic Algorithm model. To evaluate the forecasting accuracy, there are two dimensions taken into consideration: 1) the deviation between the actual quarterly EPS value and the forecasted quarterly EPS value, and 2) the changing direction from quarter to quarter between the actual quarterly EPS value and the forecasted quarterly EPS value. In the aspect of the deviation between the actual quarterly EPS value and the forecasted quarterly EPS value, the empirical results show that the Transfer Function model outperforms the ARIMA model. Therefore, the settings of time lags of the Transfer Function model are adopted to the other two models. The empirical results reveals that the Genetic Algorithm model shows the best forecasting accuracy in both dimensions while the Artificial Neural Network model shows the worst forecasting accuracy in both dimensions. In addition, both of the quarterly basic EPS data and the quarterly diluted EPS data were applied in forecasting future quarterly basic EPS. There is not enough evidence to support that using the diluted EPS data would yield higher accuracy than using the basic EPS data in the aspect of deviation. However, the empirical result shows that using the basic EPS data outperforms using the diluted EPS to forecast future basic EPS in the aspect of predicting the directions. Syou-Ching Lai Hung-Chih Li 賴秀卿 李宏志 2004 學位論文 ; thesis 57 en_US |
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碩士 === 國立成功大學 === 財務金融研究所 === 92 === The purpose of this study is to compare the forecasting ability among the ARIMA model, the Transfer Function model, the Artificial Neural Network model and the Genetic Algorithm model. To evaluate the forecasting accuracy, there are two dimensions taken into consideration: 1) the deviation between the actual quarterly EPS value and the forecasted quarterly EPS value, and 2) the changing direction from quarter to quarter between the actual quarterly EPS value and the forecasted quarterly EPS value.
In the aspect of the deviation between the actual quarterly EPS value and the forecasted quarterly EPS value, the empirical results show that the Transfer Function model outperforms the ARIMA model. Therefore, the settings of time lags of the Transfer Function model are adopted to the other two models. The empirical results reveals that the Genetic Algorithm model shows the best forecasting accuracy in both dimensions while the Artificial Neural Network model shows the worst forecasting accuracy in both dimensions.
In addition, both of the quarterly basic EPS data and the quarterly diluted EPS data were applied in forecasting future quarterly basic EPS. There is not enough evidence to support that using the diluted EPS data would yield higher accuracy than using the basic EPS data in the aspect of deviation. However, the empirical result shows that using the basic EPS data outperforms using the diluted EPS to forecast future basic EPS in the aspect of predicting the directions.
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Syou-Ching Lai |
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Syou-Ching Lai Ya-Jung Shih 施雅蓉 |
author |
Ya-Jung Shih 施雅蓉 |
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Ya-Jung Shih 施雅蓉 The Predictive Power for the Quarterly Earnings Per Share based on Time Series and Artificial Intelligence Model |
author_sort |
Ya-Jung Shih |
title |
The Predictive Power for the Quarterly Earnings Per Share based on Time Series and Artificial Intelligence Model |
title_short |
The Predictive Power for the Quarterly Earnings Per Share based on Time Series and Artificial Intelligence Model |
title_full |
The Predictive Power for the Quarterly Earnings Per Share based on Time Series and Artificial Intelligence Model |
title_fullStr |
The Predictive Power for the Quarterly Earnings Per Share based on Time Series and Artificial Intelligence Model |
title_full_unstemmed |
The Predictive Power for the Quarterly Earnings Per Share based on Time Series and Artificial Intelligence Model |
title_sort |
predictive power for the quarterly earnings per share based on time series and artificial intelligence model |
publishDate |
2004 |
url |
http://ndltd.ncl.edu.tw/handle/29179758900041335723 |
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