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題名 使用 LSTM 結合匯率模型和技術指標預測外匯走勢
Forecasting Foreign Exchange Trends using LSTM with Exchange Rate Model and Technical Indicators作者 廖浲諭
Liao, Feng-Yu貢獻者 蕭明福<br>廖四郎
Shaw, MingFu<br>Liao, Szu-Lang
廖浲諭
Liao, Feng-Yu關鍵詞 LSTM
外匯預測
匯率模型
總經指標
技術指標
LSTM
Foreign Exchange Forecasting
Exchange Rate Models
Macroeconomic Indicators
Technical Indicators日期 2023 上傳時間 10-Jul-2023 11:52:59 (UTC+8) 摘要 本研究嘗試結合總體經濟和技術指標的特性來預測外匯走勢，利用長短期記憶模型(LSTM)以總體指標和技術指標作為輸入變數，進行外匯漲跌方向的預測，總體指標參考4個匯率模型UIRP、PPP、MF和Taylor Rule作為挑選變數，將總體指標與技術指標分開預測再進行結合，比較此作法是否優於個別分開預測，且能觀察模型改善的效果的好壞，最後會對模型進行績效評估。實證結果顯示，總經指標使用LSTM以 PPP和Taylor Rule作為變數預測結果優於以UIRP和MF作為變數，以PPP和Taylor Rule為基底加入台灣外匯存底月變動和大盤月報酬變動 2個變數後，預測表現皆下降但報酬率皆上升，技術指標再經過篩選後以開盤價、最高價、最低價、CCI、MOM作為變數預測表現較佳，結合模型後相較於個別預測有明顯改善，以PPP結合技術模型準確率最高、Taylor Rule結合技術模型準確率次高，在績效評估上以Taylor Rule結合技術模型表現最佳，模型改善效果以Taylor Rule改善最大。
This study attempts to combine the characteristics of macroeconomic indicators and technical indicators to predict foreign exchange trends. It utilizes the LSTM with macroeconomic and technical indicators as input variables to forecast the direction of foreign exchange movements. The macroeconomic indicators are based on four exchange rate models: UIRP, PPP, MF, and Taylor Rule. The study compares the performance of combined forecasting with separately predicting macroeconomic and technical indicators to assess the effectiveness of the proposed approach.The empirical results demonstrate that using LSTM with PPP and Taylor Rule as variables yields superior predictions compared to using UIRP and MF. When incorporating two additional variables, the monthly changes in Taiwan`s foreign exchange reserves and stock market returns, based on PPP and Taylor Rule, the prediction performance decreases while the return rates increase. After filtering, the selected technical indicators including opening price, high price, low price, CCI, and MOM exhibit better predictive performance. The combined model shows significant improvements compared to individual predictions, with the highest accuracy achieved by combining PPP with the technical model and the second-highest accuracy achieved by combining Taylor Rule with the technical model. In terms of performance evaluation, the combined model of Taylor Rule and the technical indicators performs the best, and Taylor Rule contributes the most to the improvement in model performance.參考文獻 [1] Allen, H. and M. P. Taylor (1992), “The Use of Technical Analysis in the Foreign Exchange Market,” Journal of International Money and Finance, Vol. 113, 301-314.[2] Amat, C., Michalski, T., and Stoltz, G. (2018). Fundamentals and exchange rate forecastability with simple machine learning methods. Journal of International Money and Finance, 88, 1-24.[3] Chen, Yu-chin, and Kenneth Rogoff, 2003, "Commodity Currencies," Journal of Internationa Economics, Vol. 60, pp. 133-60[4] Engel, C. and K. D. West, (2005). Exchange Rates and Fundamentals, Journal of Political Economy. 113: 485-517.[5] Engel, C., Mark, N. C. and West, K. D. (2015). “Factor model forecasts of exchange rates.” Econometric Reviews, 34, 32-55[6] Frenkel, J. A. (1976). “A monetary approach to the exchange rate.” Scandinavian J. Econ. 78, no.2, 200-224.[7] Fischer, T., and Krauss, C. (2018). Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research, 270(2), 654-669.[8] Gourinchas, P.-O., and Rey, H. (2007). International financial adjustment. Journal of Political Economy, 115(4), 665-703.[9] Hau, H., and Rey, H. (2006). Exchange rates, equity prices, and capital flows. Review of Financial Studies 19, 273-317.[10] Hodrick, R. J., and Prescott, E. C. (1997). Postwar US business cycles: an empirical investigation. Journal of Money, credit, and Banking, 1-16.[11] Mark, Nelson C. (1995), “Exchange Rate and Fundamentals: Evidence on Long-Horizon Predictability,” American Economimc Review, 85, 201-218.[12] Mark, N. C. and D. Sul, (2001). Nominal Exchange Rates and Monetary Fundamentals: Evidence from a Small Post-bretton Woods Panel. Journal of International Economics, 53: 29-52.[13] Molodtsova, T. and D. H. Papell, (2009). Out-of-Sample Exchange Rate Predictability with Taylor Rule Fundamentals? Journal of International Economics, 77: 167-180.[14] Meese, Richard A. and Kenneth Rogoff (1983), “Empirical Exchange Rate Models of the Seventies: Do They Fit Out of Sample?” Journal of International Economics, 14, 3-24.[15] Nelson,M.Q., Pereira, A.C.M. and deOliveira,R.A. (2017). Stockmarketsprice movement prediction with LSTM neural networks. International Joint Conference on Neural Networks (IJCNN). Piscataway, NJ: IEEE, pp. 1419-1426.[16] Qu, Y., and Zhao, X. (2019). Application of LSTM neural network in forecasting foreign exchange price. In Journal of Physics: Conference Series (Vol. 1237, No. 4, p. 042036). IOP Publishing.[17] Rossi, B. (2013). Exchange rate predictability. Journal of economic literature, 51(4), 1063-1119.[18] Yıldırım, D. C., Toroslu, I. H., and Fiore, U. (2021). Forecasting directional movement of Forex data using LSTM with technical and macroeconomic indicators. Financial Innovation, 7, 1-36. 描述 碩士
國立政治大學
經濟學系
110258034資料來源 http://thesis.lib.nccu.edu.tw/record/#G0110258034 資料類型 thesis dc.contributor.advisor 蕭明福<br>廖四郎 zh_TW dc.contributor.advisor Shaw, MingFu<br>Liao, Szu-Lang en_US dc.contributor.author (Authors) 廖浲諭 zh_TW dc.contributor.author (Authors) Liao, Feng-Yu en_US dc.creator (作者) 廖浲諭 zh_TW dc.creator (作者) Liao, Feng-Yu en_US dc.date (日期) 2023 en_US dc.date.accessioned 10-Jul-2023 11:52:59 (UTC+8) - dc.date.available 10-Jul-2023 11:52:59 (UTC+8) - dc.date.issued (上傳時間) 10-Jul-2023 11:52:59 (UTC+8) - dc.identifier (Other Identifiers) G0110258034 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/145960 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 經濟學系 zh_TW dc.description (描述) 110258034 zh_TW dc.description.abstract (摘要) 本研究嘗試結合總體經濟和技術指標的特性來預測外匯走勢，利用長短期記憶模型(LSTM)以總體指標和技術指標作為輸入變數，進行外匯漲跌方向的預測，總體指標參考4個匯率模型UIRP、PPP、MF和Taylor Rule作為挑選變數，將總體指標與技術指標分開預測再進行結合，比較此作法是否優於個別分開預測，且能觀察模型改善的效果的好壞，最後會對模型進行績效評估。實證結果顯示，總經指標使用LSTM以 PPP和Taylor Rule作為變數預測結果優於以UIRP和MF作為變數，以PPP和Taylor Rule為基底加入台灣外匯存底月變動和大盤月報酬變動 2個變數後，預測表現皆下降但報酬率皆上升，技術指標再經過篩選後以開盤價、最高價、最低價、CCI、MOM作為變數預測表現較佳，結合模型後相較於個別預測有明顯改善，以PPP結合技術模型準確率最高、Taylor Rule結合技術模型準確率次高，在績效評估上以Taylor Rule結合技術模型表現最佳，模型改善效果以Taylor Rule改善最大。 zh_TW dc.description.abstract (摘要) This study attempts to combine the characteristics of macroeconomic indicators and technical indicators to predict foreign exchange trends. It utilizes the LSTM with macroeconomic and technical indicators as input variables to forecast the direction of foreign exchange movements. The macroeconomic indicators are based on four exchange rate models: UIRP, PPP, MF, and Taylor Rule. The study compares the performance of combined forecasting with separately predicting macroeconomic and technical indicators to assess the effectiveness of the proposed approach.The empirical results demonstrate that using LSTM with PPP and Taylor Rule as variables yields superior predictions compared to using UIRP and MF. When incorporating two additional variables, the monthly changes in Taiwan`s foreign exchange reserves and stock market returns, based on PPP and Taylor Rule, the prediction performance decreases while the return rates increase. After filtering, the selected technical indicators including opening price, high price, low price, CCI, and MOM exhibit better predictive performance. The combined model shows significant improvements compared to individual predictions, with the highest accuracy achieved by combining PPP with the technical model and the second-highest accuracy achieved by combining Taylor Rule with the technical model. In terms of performance evaluation, the combined model of Taylor Rule and the technical indicators performs the best, and Taylor Rule contributes the most to the improvement in model performance. en_US dc.description.tableofcontents 摘要 IAbstract II目次 III表次 IV圖次 V第一章 緒論 1第一節 研究背景與動機 1第二節 研究目的 1第二章 文獻回顧 2第一節 匯率模型預測相關文獻 2第二節 LSTM 模型相關文獻 3第三章 研究方法 4第一節 研究流程 4第二節 匯率模型 4第三節 長短期記憶模型 7第四節 模型衡量指標 11第四章 實證分析 12第一節 資料介紹 12第二節 模型預測能力 14第三節 模型績效評估 20第四節 優化交易策略 27第五章 結論與建議 30第一節 結論 30第二節 未來展望 30參考文獻 32附錄 34 zh_TW dc.format.extent 2532432 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0110258034 en_US dc.subject (關鍵詞) LSTM zh_TW dc.subject (關鍵詞) 外匯預測 zh_TW dc.subject (關鍵詞) 匯率模型 zh_TW dc.subject (關鍵詞) 總經指標 zh_TW dc.subject (關鍵詞) 技術指標 zh_TW dc.subject (關鍵詞) LSTM en_US dc.subject (關鍵詞) Foreign Exchange Forecasting en_US dc.subject (關鍵詞) Exchange Rate Models en_US dc.subject (關鍵詞) Macroeconomic Indicators en_US dc.subject (關鍵詞) Technical Indicators en_US dc.title (題名) 使用 LSTM 結合匯率模型和技術指標預測外匯走勢 zh_TW dc.title (題名) Forecasting Foreign Exchange Trends using LSTM with Exchange Rate Model and Technical Indicators en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) [1] Allen, H. and M. P. Taylor (1992), “The Use of Technical Analysis in the Foreign Exchange Market,” Journal of International Money and Finance, Vol. 113, 301-314.[2] Amat, C., Michalski, T., and Stoltz, G. (2018). Fundamentals and exchange rate forecastability with simple machine learning methods. Journal of International Money and Finance, 88, 1-24.[3] Chen, Yu-chin, and Kenneth Rogoff, 2003, "Commodity Currencies," Journal of Internationa Economics, Vol. 60, pp. 133-60[4] Engel, C. and K. D. West, (2005). Exchange Rates and Fundamentals, Journal of Political Economy. 113: 485-517.[5] Engel, C., Mark, N. C. and West, K. D. (2015). “Factor model forecasts of exchange rates.” Econometric Reviews, 34, 32-55[6] Frenkel, J. A. (1976). “A monetary approach to the exchange rate.” Scandinavian J. Econ. 78, no.2, 200-224.[7] Fischer, T., and Krauss, C. (2018). Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research, 270(2), 654-669.[8] Gourinchas, P.-O., and Rey, H. (2007). International financial adjustment. Journal of Political Economy, 115(4), 665-703.[9] Hau, H., and Rey, H. (2006). Exchange rates, equity prices, and capital flows. Review of Financial Studies 19, 273-317.[10] Hodrick, R. J., and Prescott, E. C. (1997). Postwar US business cycles: an empirical investigation. Journal of Money, credit, and Banking, 1-16.[11] Mark, Nelson C. (1995), “Exchange Rate and Fundamentals: Evidence on Long-Horizon Predictability,” American Economimc Review, 85, 201-218.[12] Mark, N. C. and D. Sul, (2001). Nominal Exchange Rates and Monetary Fundamentals: Evidence from a Small Post-bretton Woods Panel. Journal of International Economics, 53: 29-52.[13] Molodtsova, T. and D. H. Papell, (2009). Out-of-Sample Exchange Rate Predictability with Taylor Rule Fundamentals? Journal of International Economics, 77: 167-180.[14] Meese, Richard A. and Kenneth Rogoff (1983), “Empirical Exchange Rate Models of the Seventies: Do They Fit Out of Sample?” Journal of International Economics, 14, 3-24.[15] Nelson,M.Q., Pereira, A.C.M. and deOliveira,R.A. (2017). Stockmarketsprice movement prediction with LSTM neural networks. International Joint Conference on Neural Networks (IJCNN). Piscataway, NJ: IEEE, pp. 1419-1426.[16] Qu, Y., and Zhao, X. (2019). Application of LSTM neural network in forecasting foreign exchange price. In Journal of Physics: Conference Series (Vol. 1237, No. 4, p. 042036). IOP Publishing.[17] Rossi, B. (2013). Exchange rate predictability. Journal of economic literature, 51(4), 1063-1119.[18] Yıldırım, D. C., Toroslu, I. H., and Fiore, U. (2021). Forecasting directional movement of Forex data using LSTM with technical and macroeconomic indicators. Financial Innovation, 7, 1-36. zh_TW
