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研究生:孫艾
研究生(外文):SUN AI
論文名稱:基於資訊系統演算法的匯率預測研究
論文名稱(外文):Research on Exchange Rate Forecasting Based on Information System Algorithm
指導教授:張瑞芳張瑞芳引用關係
指導教授(外文):Jui-Fang, Chang
口試委員:王天津陳榮方廖斌毅蔡正發張瑞芳
口試委員(外文):Tien-Chin,WangJung-Fang,ChenBin-Yih,LiaoCheng-Fa,TsaiJui-Fang,Chang
口試日期:2017-07-19
學位類別:博士
校院名稱:國立高雄應用科技大學
系所名稱:國際企業研究所
學門:商業及管理學門
學類:企業管理學類
論文種類:學術論文
論文出版年:2017
畢業學年度:105
語文別:英文
論文頁數:141
中文關鍵詞:匯率預測資訊模型
外文關鍵詞:Exchange rate predictioninformation models
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Abstract
Along with the rapid development of financial globalization, our country faces complicated financial risks and foreign exchange risks. The subprime mortgage crisis, the sovereign debt crisis in areas with the euro, etc., spurred a global financial crisis and an economic recession and hence caused exchange rate prediction to evolve into an important economic issue, drawing wide attention. However, the foreign exchange market is a non-linear system with multiple variables, in which correlations between all factors are perplexing, exacerbating the difficulty of exchange rate prediction.
As a complex non-linear system, exchange rate prediction methods have developed into a time series prediction from a parametric regression. However, in real applications, exchange rate fluctuations and varying trends are very complex, and the execution speed of the algorithm must surpass the variation speed of exchange rate at the same time as the exchange rate is precisely predicted. Although numerous studies pertaining to exchange rate prediction methods are currently available, the majority of the algorithms have been constrained by their complexity, and relevant research analysis has not been conducted on the applicability to data sets of the algorithms commonly used in exchange rate prediction. On account of this, three major method types are selected in this dissertation as the methodological basis of the research: the algorithm based on the empirical risk minimization principle, the algorithm based on the structural risk minimization principle, and the statistical filtering algorithm. Methods representative of algorithms theoretically applicable to exchange rate prediction are separated from the three major methods, namely, the Radial Basis Function Neural Network (RBFNN), the Least Squares-Support Vector Machine (LS-SVM), and the Kalman Filter (KF). The three methods mentioned above are selected in this dissertation to represent the three major methods, and explore their precision, efficiency, and applicability concerning exchange rate prediction. In addition, we contrast the three major types of algorithms according to test results, analyze the applicability of the different algorithms to data sets, and offer a novel train of thought and technological research on solving the problem of exchange rate prediction.
The main sections of the dissertation are as follows:
1. The widely-used type of neural network, RBFNN, is introduced into the field of exchange rate prediction based upon the empirical risk minimization principle. This method both inherits the empirical risk minimization principle and introduces the kernel functions of RBF, has a higher prediction accuracy, simple structure, fast training speed, and different from the ordinary feedforward neural networks, with the best approximation performance and overall optimization.
2. This dissertation takes LS-SVM to represent the methods based on the structural risk minimization principle used for exchange rate prediction, since the methods based on the empirical risk minimization principle have lower prediction accuracy in circumstances of insufficient data. Addressing the issue of slower computation and convergence speeds of the traditional SVM algorithm, this method solved the problem of quadratic programming with LS on the premise of ensured minimal structural risks. Therefore, adopting this method may ensure the accuracy of the algorithm in cases of small sample size, as well as completing the prediction faster.
3. Addressing the deviation existing in both the prediction results from each type of method and in the exchange rate data, this dissertation proposes an exchange rate method based on the Kalman Filter. This method is representative of statistical filtering algorithms and may internally reduce noise in the two models to acquire more accurate prediction values. Therefore, adopting this method may effectively utilize the accuracy of the two models and allow the acquisition of more precise prediction values by statistical means.

ABSTRACT I
CHAPTER 1 INTRODUCTION 1
1.1 RESEARCH BACKGROUND 1
1.2 PURPOSE AND IMPLICATIONS OF THE RESEARCH 6
1.3 MAIN RESEARCH INSTRUCTURE 13
CHAPTER 2 LITERATURE REVIEW 17
2.1 THEORETICAL BACKGROUND 17
2.2 EXISTING PROBLEMS 28
CHAPTER 3 RESEARCH METHODOLOGY 36
3.1 EXCHANGE RATE PREDICTION ALGORITHMS BASED ON THE PRINCIPLE OF THE MINIMIZATION OF EMPIRICAL RISK 36
3.1.1 Problem Description and Feasibility Analysis 36
3.1.2 Fundamental Principles of RBFNN 39
3.1.3 Exchange Rate Prediction Methods Based upon RBFNN 46
3.2 EXCHANGE RATE PREDICTION METHOD BASED ON THE STRUCTURAL RISK MINIMIZATION 50
3.2.1 Problem Description and Feasibility Analysis 50
3.2.2 Fundamental Principle of LS-SVM 53
3.2.3 Exchange Rate Prediction Method Based on LS-SVM 58
3.3 EXCHANGE RATE PREDICTION METHODS BASED ON STATISTICAL FILTERS 61
3.3.1 Problem Description and Feasibility Analysis 61
3.3.2 Fundamental Principle of KF 64
3.3.3 Exchange Rate Prediction Method Based on KF 67
3.4 CHAPTER SUMMARY 90
CHAPTER 4 EMPIRICAL STUDY 92
4.1 DATA DESCRIPTION 92
4.2 EXCHANGE RATE PREDICTION TEST RESULTS BASED ON RBFNN 97
4.2.1 Daily-step Exchange Rate Prediction 97
4.2.2 Monthly-step Exchange Rate Prediction 98
4.2.3 Quarterly-step Exchange Rate Prediction 100
4.2.4 Test Results Analysis and Conclusion 102
4.3 EXCHANGE RATE PREDICTION TEST RESULTS BASED ON LS-SVM 103
4.3.1 Daily-step Exchange Rate Prediction 103
4.3.2 Monthly-step Exchange Rate Prediction 105
4.3.3 Quarterly-step Exchange Rate Prediction 107
4.3.4 Test Results Analysis and Conclusion 109
4.4 EXCHANGE RATE PREDICTION TEST RESULTS BASED ON KF 112
4.4.1 Daily-step Exchange Rate Prediction 112
4.4.2 Monthly-step Exchange Rate Prediction 114
4.4.3 Quarterly-step Exchange Rate Prediction 116
4.4.4 Test Results Analysis and Conclusion 118
4.5 CHAPTER SUMMARY 119
CHAPTER 5 CONCLUSION 123
REFERENCES 126

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