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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/50161完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳釗而(Jau-er Chen) | |
| dc.contributor.author | Eugene-Yuan Kow | en |
| dc.contributor.author | 寇先元 | zh_TW |
| dc.date.accessioned | 2021-06-15T12:31:15Z | - |
| dc.date.available | 2016-08-24 | |
| dc.date.copyright | 2016-08-24 | |
| dc.date.issued | 2016 | |
| dc.date.submitted | 2016-08-04 | |
| dc.identifier.citation | [1] Vladimir N. Vapnik. Statistical Learning Theory. Wiley-Interscience, 1998.
[2] Andrew Ng. Cs229 lecture notes. CS229 Lecture notes, 1(1):1–3, 2000. [3] Leo Breiman. Random forests. Machine Learning, 45(1):5–32, 2001. [4] Yoav Freund and Robert E Schapire. A desicion-theoretic generalization of on-line learning and an application to boosting. In European conference on computational learning theory, pages 23–37. Springer, 1995. [5] Tristan Fletcher, Fabian Redpath, and Joe D’Alessandro. Machine learning in fx carry basket prediction. In Proceedings of the World Congress on Engineering, volume 2. Citeseer, 2009. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/50161 | - |
| dc.description.abstract | 本篇論文主要是探討機器學習演算法，利用股票市場以及原物料市場等，所萃取出來的特徵值，對於匯率市場投資組合的短期漲跌走向的預測能力。有別於傳統計量經濟的方法，本篇論文採用了隨機森林 支撐向量機 決策樹自適應增強算法，納入模混和模型中的三種模型。由這三種模型以及傳統經濟變數，機器學習演算法可以有效的預測市場違反鞅系統的時機點，進以返回交易訊號。由機器學習演算法所找尋到交易策略不僅給我們較好的預測正確率也同時擁有相較對照組有高的獲利能力。 | zh_TW |
| dc.description.abstract | This paper studies the forecasting capability of machine learning models with economic features. The machine learning model constructed is based on Random Forest, Support Vector Machine, Decision Tree with Adaptive Boosting, and Hybrid Model. With the information of past risk metrics, our models signify the predictability of the currency market instability. The predictability comes from the fact that our machine learning model observes the violation of martingale restriction in the currency market portfolio. Furthermore, we apply the resulting outputs from the model to the forex carry trading strategy. The profitability of the corresponding trading strategy is significantly higher than those from a long-term holding strategy and the benchmark strategy constructed by the VIX. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-15T12:31:15Z (GMT). No. of bitstreams: 1 ntu-105-R02323025-1.pdf: 2383526 bytes, checksum: 522d50a7e34bc22a5d0d28aae8b470dd (MD5) Previous issue date: 2016 | en |
| dc.description.tableofcontents | 口試委員會審定書……………………………………………………………… i
誌謝……………………………………………………………………………… ii 中文摘要…………………………………………………………………………. iii 英文摘要…………………………………………………………………………. iv 第一章 前言………………………………………………………………….. 1 第二章 模型………………………………………………………………….. 6 分類問題……………………………………………………………… 6 2.1支撐向量機…..…………………………………………………………… 7 2.2 決策樹與隨機森林…………………........................................ 8 2.3自適應增強………………………………………………………………… 9 2.4 混合模型…………………………………………………………………. 9 2.5 模型設定…………………………………………………………………. 9 第三章 機器學習方式………….……………………………………………... 10 3.1 平衡問題……………………………………………………………….. 10 3.2 樣本篩選……………………………………………………………….. 11 3.3 資料說明……………………………………………………………….. 11 3.4 交易訊號…………………………………………………………………..12 3.5 市場波動性與市場不穩定性……………………………………………..12 3.5.1 人造波動交易訊號………………………………………………….12 3.5.2 機器波動交易訊號………………………………………………….12 第四章 實證分析…………………………………………………………………13 4.1模型訓練………………………………………………………………13 4.2實證結果……………………………………………………………………14 4.3測試資料集與模型評估……………………………………………………14 4.4.決定K-means分群演算法對應交易訊號………………………16 4.5獲利評估……………………………………………………………..16 4.5.1匯率投資組合與策略比較…………………………………………..16 第五章 總結與討論……………………………………………………………..18 參考文獻…………………………………………………………………….…… 20 附錄………………………………………………………………………………. 21 | |
| dc.language.iso | en | |
| dc.title | 以機器學習技法探討貨幣利差市場投資組合策略及市場風險 | zh_TW |
| dc.title | Machine learning on Forex Carry Forecasting with Economics Feature | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 104-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 林軒田(Hsuan-Tien Lin),徐世勛(Shih-Hsun Hsu) | |
| dc.subject.keyword | 機器學習,波動度,預測,貨幣投資組合,經濟特徵值,隨機森林,支撐向量機,決策樹自適應增強算法, | zh_TW |
| dc.subject.keyword | Machine Learning,Economics Features,Random Forest,Support Vector Machine,Decision Tree with Adaptive Boosting,Hybrid Model,Forex Carry,Volatility Forecasting, | en |
| dc.relation.page | 25 | |
| dc.identifier.doi | 10.6342/NTU201601126 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2016-08-04 | |
| dc.contributor.author-college | 社會科學院 | zh_TW |
| dc.contributor.author-dept | 經濟學研究所 | zh_TW |
| 顯示於系所單位： | 經濟學系 | |
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|---|---|---|---|
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