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函數型資料分析在高頻匯率共整合配對交易之應用

為了解決eurusd預測的問題，作者王婕瑜 這樣論述：

摘要 iAbstract ii目次 iii表目次 iv圖目次 v第一章 緒論 1第一節 研究背景 1第二節 研究動機與目的 3第三節 研究架構 3第二章 文獻探討 5第一節 價格均數回歸現象(Mean Reversion Phenomenon) 5第二節 配對交易策略 6第三節 配對交易於高頻資料研究 11第四節 函數型資料分析 14第三章 研究方法 16第一節 函數型資料分析(Functional Data Analysis, FDA) 18第二節 函數型單根檢定(Functional Unit Root Test) 26第三節 函數型共整合檢定(Func

tional Cointegration Test)29第四節 函數型向量誤差修正模型 38第五節 動態配對交易避險模型 40第四章 實證結果與討論 44第一節 資料選取與樣本期間 44第二節 函數型單根檢定 47第三節 函數型共整合檢定 49第四節 函數型匯率預測 53第五節 配對交易實證結果 54第五章 結論與建議 57第一節 研究結論 57第二節 研究建議 57參考文獻 59

評估機器學習模型以預測資產價格

為了解決eurusd預測的問題，作者瑞華 這樣論述：

The aim of this thesis is to evaluate machine learning models for the purpose of predicting asset prices. The models are limited to neural networks, specifically the multilayer perceptron, the recurrent neural network, and the pi-sigma network. The asset used as forecasting target is the EURUSD. Th

e thesis discusses and compares the differences between the neural network architectures, and uses a custom fitness function during the models’ training. Results show that the three models can be used as forecasting tools to build profitable trading strategies. To enhance the validity of the results

, the performance of the three models are compared to the discoveries of other researchers, and among the three neural networks, the pi-sigma network displays the best forecasting performance, in line with what other studies show as well. Besides comparing the performance of the three models, the th

esis also discusses how the forecasts can be improved by combining several machine learning models. The thesis also discusses potential reasons for why some scholars achieve dissatisfying forecasting results when testing similar types of neural networks, and provides some suggestions of how results

might be improved.