請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/15598完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 曹承礎(Seng-Cho Chou) | |
| dc.contributor.author | Man-Shiuan Shiu | en |
| dc.contributor.author | 許曼軒 | zh_TW |
| dc.date.accessioned | 2021-06-07T17:48:30Z | - |
| dc.date.copyright | 2020-08-21 | |
| dc.date.issued | 2020 | |
| dc.date.submitted | 2020-08-08 | |
| dc.identifier.citation | Aloui, R., Ben Aïssa, M. S., Nguyen, D. K. (2013). Conditional dependence structure between oil prices and exchange rates: a copula-GARCH approach. Journal of International Money and Finance, 32, 719-738. doi: 10.1016/j.jimonfin.2012.06.006 Bank for International Settlements. (2019). Triennial Central Bank Survey - Foreign exchange turnover in April 2019. Monetary and Economic Department. Retrieved from https://www.bis.org/statistics/rpfx19_fx.pdf Bollinger, J. (1992). Using bollinger bands. Stocks Commodities, 10(2), 47-51. Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32. doi: 10.1023/A:1010933404324 Capie, F., Mills, T. C., Wood, G. (2005). Gold as a hedge against the dollar. Journal of International Financial Markets, Institutions and Money, 15(4), 343-352. doi:10.1016/j.intfin.2004.07.002 Chen, T., Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In B. Krishnapuram, M. Shah (Chairs), Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785-794). New York, NY: Association for Computing Machinery. doi: 10.1145/2939672.2939785 Ciner, C., Gurdgiev, C., Lucey, B. M. (2013). Hedges and safe havens: An examination of stocks, bonds, gold, oil and exchange rates. International Review of Financial Analysis, 29, 202-211. doi: 10.1016/j.irfa.2012.12.001 Ding, L., Vo, M. (2012). Exchange rates and oil prices: A multivariate stochastic volatility analysis. The Quarterly Review of Economics and Finance, 52(1), 15-37. doi: 10.1016/j.qref.2012.01.003 Fleming, J., Kirby, C., Ostdiek, B. (1998). Information and volatility linkages in the stock, bond, and money markets. Journal of financial economics, 49(1), 111-137. doi: 10.1016/S0304-405X(98)00019-1 Gers, F. A., Schmidhuber, J., Cummins, F. (2000). Learning to forget: Continual prediction with LSTM. Neural Computation,12(10), 2451-2471. doi: 10.1162/089976600300015015 Hirabayashi, A., Aranha, C., Iba, H. (2009). Optimization of the trading rule in foreign exchange using genetic algorithm. In F. Rothlauf (Chair), Proceedings of the 11th Annual conference on Genetic and evolutionary computation (pp. 1529-1536). New York, NY: Association for Computing Machinery. doi: 10.1145/1569901.1570106 Hochreiter, S., Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780. doi: 10.1162/neco.1997.9.8.1735 Intercontinental Exchange. US Dollar Index. Retrieved from https://www.theice.com/publicdocs/ICE_USDX_Brochure.pdf Liu, C., Hou, W., Liu, D. (2017). Foreign exchange rates forecasting with convolutional neural network. Neural Processing Letters, 46(3), 1095-1119. doi: 10.1007/s11063-017-9629-z Murphy, J. (1999). Technical analysis of the financial markets: A comprehensive guide to trading methods and applications. New York, NY: New York Institute of Finance. Ranjit, S., Shrestha, S., Subedi, S., Shakya, S. (2018). Comparison of algorithms in foreign Exchange Rate Prediction. In 2018 IEEE 3rd International Conference on Computing, Communication and Security (ICCCS) (pp. 9-13). IEEE. doi: 10.1109/CCCS.2018.8586826 Reboredo, J. C. (2013). Is gold a safe haven or a hedge for the US dollar? Implications for risk management. Journal of Banking Finance, 37(8), 2665-2676. doi: 10.1016/j.jbankfin.2013.03.020 Theofilatos, K., Likothanassis, S., Karathanasopoulos, A. (2012). Modeling and trading the EUR/USD exchange rate using machine learning techniques. Engineering, Technology Applied Science Research, 2(5), 269-272. Wilder, J. W. (1978). New concepts in technical trading systems. Greensboro, NC: Trend Research. 徐維志（2015）。以隨機森林為模式之美金/歐元匯率交易預測研究（未出版之碩士論文）。輔仁大學統計資訊學系應用統計碩士在職專班，新北市。 戴月珍（2013）。美元兌新台幣匯率預測（未出版之碩士論文）。國立高雄應用科技大學金融資訊研究所，高雄市。 | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/15598 | - |
| dc.description.abstract | 外匯市場為最活躍的金融市場，每日的成交量非常龐大。外匯的波動深深地影響我們的生活，不論是對個人，或是對企業家，甚至影響了國家做經濟上的決策，因此，外匯的預測相當重要。希望能透過機器學習的技術，捕捉外匯市場的特性，做到更貼近的市場趨勢預估。 此篇研究選用的機器學習模型為隨機森林、xgboost演算法與長短期記憶模型，並藉此深入探討不同特徵對模型匯率趨勢預測的影響，如技術指標與總體經濟因子，與探究有無加入總體經濟因子對模型的影響程度。研究中，將探討如何利用機器學習的模型，來預測不同天數的外匯匯率市場趨勢，並採用相對應的交易策略，達到期望的投資績效。 | zh_TW |
| dc.description.abstract | The foreign exchange market is the most active financial market, and the daily trading volume is very large. The volatility of foreign exchange deeply affects our lives, whether it is for individuals, entrepreneurs, or even the government to make decisions. Therefore, forecasting foreign exchange is very important. We want to use machine learning technologies to capture the characteristics of the foreign exchange market and make predictions of the market trends. The machine learning models used in this study are random forests, xgboost algorithm, and long short-term memory model, and use the model to explore the impact of different features on the model, such as technical indicators and economic factors, and explore the influence of economic factors on the model's prediction. In the study, we will explore how to use machine learning models to predict the market trend of foreign exchange rates on different days, and use corresponding trading strategies to achieve desired investment performance. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-07T17:48:30Z (GMT). No. of bitstreams: 1 U0001-0308202021135800.pdf: 10562362 bytes, checksum: 2173d6610be9dec55da4ddd21845d69d (MD5) Previous issue date: 2020 | en |
| dc.description.tableofcontents | 目 錄 口試委員審定書 i 致謝 ii 中文摘要 iii 英文摘要 iv 目錄 v 圖目錄 vii 表目錄 ix 第一章 諸論 1 第一節 研究動機 1 第二節 研究目的 2 第三節 研究流程與論文架構 2 第二章 文獻探討 4 第一節 外匯預測 4 第二節 技術指標 5 第三節 總體經濟因子 7 第三章 研究方法 12 第一節 研究對象與資料來源 12 第二節 資料前處理與特徵工程 13 第三節 外匯匯率趨勢標籤設定 28 第四節 模型預測 29 第五節 投資策略與績效評估 32 第四章 研究結果 33 第一節 模型預測結果 33 第二節 交易策略與回溯測試結果 42 第三節 模型預測結果之特徵探討 46 第五章 結論與未來方向 53 第一節 結論 53 第二節 未來方向 54 參考文獻 55 | |
| dc.language.iso | zh-TW | |
| dc.subject | 技術指標 | zh_TW |
| dc.subject | 經濟變數 | zh_TW |
| dc.subject | 外匯預測 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | machine learning | en |
| dc.subject | economic variables | en |
| dc.subject | technical indicators | en |
| dc.subject | foreign exchange forecasting | en |
| dc.title | 機器學習於外匯趨勢預測與交易策略之應用 | zh_TW |
| dc.title | Foreign exchange rate trend forecasting and trading rules using machine learning | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 108-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.coadvisor | 盧信銘(Hsin-Min Lu) | |
| dc.contributor.oralexamcommittee | 周子元(Tzy-Yuan Chou) | |
| dc.subject.keyword | 外匯預測,技術指標,經濟變數,機器學習, | zh_TW |
| dc.subject.keyword | foreign exchange forecasting,technical indicators,economic variables,machine learning, | en |
| dc.relation.page | 60 | |
| dc.identifier.doi | 10.6342/NTU202002318 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2020-08-10 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
| 顯示於系所單位： | 資訊管理學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| U0001-0308202021135800.pdf 未授權公開取用 | 10.31 MB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。