臺灣博碩士論文加值系統

臺灣是淺碟式經濟，出口額平均占GDP的50%以上。因此，預測外匯匯率對臺灣企業的永續發展至關重要。在財務金融文獻中，許多學者已經投入研究至外匯預測模型中。不同的預測模型，所得到結果也紛紛不一。其中，近年來，以基因演算法結合支援向量機(GA-SVM)為預測方法的首選。但數據上卻很少有相關文獻利用此模型來預測匯率。本研究基於資料探勘的GA-SVM模型用於預測匯率，以2015年1月5日至2017年11月30日為觀察期，使用其資料為學習資料，2017年12月1日到2017年12月28日之資料為預測資料。再運用平均絕對值誤差率(MAPE)和均方根誤差(RMSE)對匯率預測的準確性進行了驗證。結果表明，GA-SVM模型在MAPE、RMSE具有高的精準度。
關鍵字：基因演算法、支援向量機、匯率預測、均方根誤差

The amount of Taiwan’s export constitutes on average around more than 50% of GDP in recent years. Therefore, an accurate forecasting of foreign exchange rate (FOREX) is very critical to the sustainability of the firms in Taiwan. Although the FOREX forecasting is a very important issue in financial literature and numerous previous works has put endeavor into the search of best model for the FOREX forecasting. However, the results are still mixed. This might be attributed to the variations of modeling techniques. More recently, GA-SVM has evolved as a preferred approach in the prediction of financial arena. However, only a few researches have utilized the model to predict the FOREX to the knowledge of authors. The objective of this study is to examine the feasibility of using the GA-SVM to predict the USD/NTD exchange rate. The daily data of exchange rate of USD/NTD over the period from January 5, 2015 to November 30, 2017 served as training data. while the daily data from December 1, 2017 to December 28, 2017 is the holdout data set. The data-mining-based GA-SVM model is utilized to predict the said exchange rate. The accuracy of the exchange rate forecasting is validated by both MAPE and RMSE. Our empirical result suggests that the GA-SVM model presents a high accuracy in terms of MAPE, 0.0032, and RMSE, 0.0001.

Keyword: GA-SVM, FOREX Prediction, RMSE.

摘要 Ⅰ
ABSTRACT II
誌謝 Ⅲ
TABLE OF CONTENTS IV
LIST OF TABLES V
LIST OF FIGURES VI

CHAPTER I INTRODUCTION 1
1.1 Backgrounds 1
1.2 Motivation and Purpose of the Study 2
CHAPTER II LITERATURE REVIEW 4
2.1 Theory on the Detrainments of Foreign Exchange Rate 4
2.2 Related Literature on Foreign Exchange Rate Prediction 7
CHAPTER III METHODOLOGY AND DATA 12
3.1 Methodology 12
3.2 Data Sources 26
CHAPTER IV EMPERICAL RESULT 28
4.1 Analysis Sample Characteristic 28
4.2 Forecast Evaluation Index 30
4.3 Exchange Rate Prediction Test Results Based on GA-SVM 31
CHAPTER V CONCLUSION AND DISCUSSION 32
REFERENCES 33

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