博碩士論文 107225010 完整後設資料紀錄

DC 欄位	值	語言
DC.contributor	統計研究所	zh_TW
DC.creator	黃子菱	zh_TW
DC.creator	Tzu-Ling Huang	en_US
dc.date.accessioned	2020-7-28T07:39:07Z
dc.date.available	2020-7-28T07:39:07Z
dc.date.issued	2020
dc.identifier.uri	http://ir.lib.ncu.edu.tw:444/thesis/view_etd.asp?URN=107225010
dc.contributor.department	統計研究所	zh_TW
DC.description	國立中央大學	zh_TW
DC.description	National Central University	en_US
dc.description.abstract	本文的主要目標是建構可提供超額報酬的彈性投資組合。我們基於機器學習技術和卡爾曼濾波演算法進行共整合配對交易策略，並且使用五種不同的門檻來生成交易訊號。根據獲得的實證結果，我們認為以公差極限作為門檻的策略是更保守的投資組合，而使用風險價值界限作為門檻的策略是更積極的投資組合。此外，我們在冠狀病毒COVID-19大流行期間獲得了更高的報酬率。	zh_TW
dc.description.abstract	The main objective of this thesis is to build a resilience portfolio that provides excess returns. Based on machine learning technology and the Kalman filter algorithm, we conduct a cointegration pairs trading strategy that uses five different thresholds to generate trading signals. According to the empirical results obtained, we believe that the strategy using the tolerance limits as the threshold is a more conservative portfolio, while the strategy using the Value at Risk bounds as the threshold is a more aggressive portfolio. In addition, we obtaine a higher rate of return during the coronavirus COVID-19 pandemic.	en_US
DC.subject	配對交易	zh_TW
DC.subject	統計套利	zh_TW
DC.subject	共整合	zh_TW
DC.subject	機器學習	zh_TW
DC.subject	卡爾曼濾波	zh_TW
DC.subject	風險價值界限	zh_TW
DC.subject	公差極限	zh_TW
DC.subject	Pairs trading	en_US
DC.subject	Statistical arbitrage	en_US
DC.subject	Cointegration	en_US
DC.subject	Machine learning	en_US
DC.subject	Kalman filter	en_US
DC.subject	Value at Risk bounds	en_US
DC.subject	Tolerance limits	en_US
DC.title	Pairs trading based on statistical learning	en_US
dc.language.iso	en_US	en_US
DC.type	博碩士論文	zh_TW
DC.type	thesis	en_US
DC.publisher	National Central University	en_US