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題名 機器學習對外匯報酬之預測
Forecast Foreign Exchange Returns with Machine Learning作者 蔡伯甯
Tsai, Bo-Ning貢獻者 林建秀<br>賴廷緯
蔡伯甯
Tsai, Bo-Ning關鍵詞 機器學習
自編碼器
主成份分析
隨機森林
極限梯度提升樹
外匯超額報酬
集成方法
Machine Learning
Autoencoder
PCA
Random Forest
XGBoost
Foreign Exchange Excess Return
Ensemble Method日期 2022 上傳時間 1-Apr-2022 15:06:21 (UTC+8) 摘要 本研究首先使用機器學習的模型，比較主成份分析（PCA）與自動編碼器（Autoencoder）兩種方式做降維後的資料擬合之結果，並且以測試集的R^2衡量表現，結果顯示，經過自動編碼器預訓練後的資料能更大幅度的提升模型性能。下一步，使用前面訓練好的模型作為弱學習器，以簡單平均的方式做集成，比較三個弱學習器與集成後的預測表現，再以模型預測結果作為買賣訊號來建構外匯投資組合，同時，加入利差策略與動量策略作為比較基準，觀察投資組合的績效表現，根據實驗結果，集成後的投組明顯優於個別機器學習模型，而機器學習模型又優於傳統策略。
In this study, we compare two different techniques which are principal component analysis (PCA) and autoencoder(AE) for reducing the dimensionality of data prior to modeling, and deploy machine learning models for data fitting to observe their results. Then, we measure performance by R^2 on the test set. The results showed that the data pre-trained by AE can greatly improve the model performance. The next step is to use previously trained models as weak learners to combine them by simple average method and compare its result to weak learners. After that, we adopt the result of model prediction as a trading signal to construct a foreign exchange portfolio. Moreover, we also add traditional strategies which are carry strategy and momentum strategy as the benchmarks to observe the portfolio performance. According to the experimental results, the composite is better than all weak learners, and all weak learners are better than the traditional strategies.參考文獻 [1] 石川、劉洋溢、連祥斌(2020)。因子投資與實踐。北京：電子工業出版社。[2] Alkhatib, K., Najdat, H., Hmeidi, L., Shatnawi, M. (2013). Stock Price Prediction Using K-Nearest Neighbor(kNN) Algorithm. International Journal of Business, Humanities and Technology, 3(3).[3] Andersen, T.G., Bollerslev, T., Diebold, F.X., & Vega, C. (2002). Micro Effects of Macro Announcements: Real-Time Price Discovery in Foreign Exchange. The American Economic Association, 93, 38-62.[4] Asness, C.S., Moskowitz, T.J., & Pedersen, L. (2013). Value and Momentum Everywhere. Journal of Finance, 68, 929-885.[5] Baldi, P., & Hornik, K. (1989). Neural networks and principal component analysis: Learning from examples without local minima. Neural networks, 2, 53-58.[6] Basak, S., Kar, S., Saha, S., Khaidem, L., & Dey, S.R. (2019). Prediction the direction of stock market prices using tree-based classifiers. The North American Journal of Economics and Finance, 47, 552-567.[7] Breiman, L. (1996a). Bagging Predictors. Machine Learning, 24(2), 123–140.[8] Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.[9] Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and Regression Trees. Wadsworth.[10] Carhart, M.M. (1997). On Persistence in Mutual Fund Performance. Journal of Finance, 52, 57-82.[11] Chen, T., & Carlos G. (2016). Xgboost: A scalable tree boosting system. A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785-794.[12] Clemen, R.T. (1989). Combining Forecasts: A Review and Annotated Bibliography. International Journal of Forecasting, 5, 559–583.[13] Ebrahimpour, D.H., & Kouzani, D.A. (2007). FACE RECOGNITION USING BAGGING KNN.[14] Fama, E.F.(1984). Forward and spot exchange rates. The Journal of Monetary Economics, 14, 319-338.[15] Fama, E.F., & French, K.R.(1992). The Cross-Section of Expected Stock Returns. Journal of Finance, 47, 427-465.[16] Fama, E.F., & French, K.R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33, 3-56.[17] Fama, E.F., & MacBeth, J. (1973). Risk, return and equilibrium: Empirical tests. The Journal of Political Economy, 81, 607-636.[18] Friedman, J.H. (2001). GREEDY FUNCTION APPROXIMATION: A GRADIENT BOOSTING MACHINE. The Annals of Statistics, 29, 1189-1232.[19] George, A. (2012). Anomaly Detection based on Machine Learning: Dimensionality Reduction using PCA and Classification using SVM. International Journal of Computer Applications, 47, 5-8.[20] Gu, S., Kelly, B. & Xiu, D. (2020). Empirical Asset Pricing via Machine Learning. The Review of Financial Studies, 33, 2223-2273.[21] Gu, S., Kelly, B. & Xiu, D. (2021). Autoencoder asset pricing models. Journal of Econometrics, 222, 429-450.[22] Hasen, L.P., & Hodrick, R.J. (1980). Forward Exchange Rates as Optimal Predictors of Future Spot Rates: An Econometric Analysis. The Journal of Political Economy, 88, 829-853.[23] Hasen, L.K., & Salamom, P. (1990). Neural network ensembles. IEEF Transactions on Pattern Analysis and Machine Intelligence, 12, 993-1001.[24] Hiton, G.E. and Salakhutdinov, R.R. (2006). Reducing the Dimensionality of Data with Neural Networks. Science, 33, 504-507.[25] Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. Journal of Finance, 48, 65-91.[26] Jiang, Z., Ji, R., & Chang, K.C. (2020). A Machine Learning Integrated Portfolio Rebalance Framework with Risk-Aversion Adjustment. Journal of Risk and Financial Management, 13, 155.[27] Jurczenko, E., & Teiletche, J., (2019). Macro Factor Mimicking Portfolios. Available at SSRN 3363598.[28] Kelly, B.T., Pruitt, S., & Su., Y. (2018). Characteristics Are Covariances: A Unified Model of Risk and Return. Available at SSRN 3032013.[29] Kroencke, T. A., Schindler, F., & Schrimpf, A. (2014). International diversification benefits with foreign exchange investment styles. Review of Finance, 18, 1847-1883.[30] Levy, R.A. (1967). Relative Strength as a Criterion for Investment Selection. Journal of Finance, 22, 595-610.[31] Lustig, H., Roussanov, N., & Verdelhan, A. (2011). Common risk factors in currency markets. The Review of Financial Studies, 24, 3731-3777.[32] Lustig, H., & Verdelhan, A. (2007). The cross-section of foreign currency risk premia and US consumption growth risk. The American Economic Review, 97, 89–117.[33] Menkhoff, L., Sarno, L., Shmeling, M., & Schrimpf, A. (2012a). Carry Trades and Global Foreign Exchange Volatility. Journal of Finance, 67, 681-718.[34] Menkhoff, L., Sarno, L., Schmeling, M., & Schrimpf, A. (2012b). Currency momentum strategies. Journal of Financial Economics, 106, 660-684.[35] Okunev, J., & White, D. (2003). Do Momentum-Based Strategies Still Work in Foreign Currency Markets?, Journal of Financial and Quantitative Analysis, 38(2), 425-447.[36] Qian, B., & Rasheed, K. (2010). Foreign Exchange Market Prediction with Multiple Classifiers. Journal of Forecasting, 29, 271-284.[37] Qian, X.Y. (2017). Financial Series Prediction: Between Precision of Time Series Models and Machine Learning Methods. arXiv: 1706.00948v3.[38] Simpson, M.W., Ramchander, S., & Chaudhry, M. (2005). The impact of macroeconomic surprises on spot and forward foreign exchange markets. Journal of International Money and Finance, 24, 693-718.[39] Steele, B.M. (2009). Exact bootstrap k-nearest neighbor learners. Mach Learn, 74, 235-255.[40] Struck, C., & Cheng, E. (2019). The Cross-Section of Returns: A Non-Parametric Approach. Available at SSRN 3494141.[41] Waskle, S., Parashar, L., & Singh, U. (2020). Intrusion Detection System Using PCA with Random Forest Approach. International Conference on Electronics and Sustainable Communication System, 2020, 803-808.[42] Yu, L., Lai, K.K. & Wang, S. (2008). Multistage RBF neural network ensemble learning for exchange rates forecasting. Neurocomputing, 71, 3295-3303.[43] Zhang, D., Wang, X., Gao, L., & Gong, Y. (2021). Predict and Analyze Exchange Rate Fluctuations Accordingly Based on Quantile Regression Model and K-Nearest Neighbor. Journal of Physics: Conference Series, 12-16.[44] Zhou, Z., & Yu, Y. (2005). Adapt Bagging to Nearest Neighbor Classifiers. Journal of Computer Science and Technology, 20, 48-54. 描述 碩士
國立政治大學
經濟學系
108258042資料來源 http://thesis.lib.nccu.edu.tw/record/#G0108258042 資料類型 thesis dc.contributor.advisor 林建秀<br>賴廷緯 zh_TW dc.contributor.author (Authors) 蔡伯甯 zh_TW dc.contributor.author (Authors) Tsai, Bo-Ning en_US dc.creator (作者) 蔡伯甯 zh_TW dc.creator (作者) Tsai, Bo-Ning en_US dc.date (日期) 2022 en_US dc.date.accessioned 1-Apr-2022 15:06:21 (UTC+8) - dc.date.available 1-Apr-2022 15:06:21 (UTC+8) - dc.date.issued (上傳時間) 1-Apr-2022 15:06:21 (UTC+8) - dc.identifier (Other Identifiers) G0108258042 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/139564 - 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) 108258042 zh_TW dc.description.abstract (摘要) 本研究首先使用機器學習的模型，比較主成份分析（PCA）與自動編碼器（Autoencoder）兩種方式做降維後的資料擬合之結果，並且以測試集的R^2衡量表現，結果顯示，經過自動編碼器預訓練後的資料能更大幅度的提升模型性能。下一步，使用前面訓練好的模型作為弱學習器，以簡單平均的方式做集成，比較三個弱學習器與集成後的預測表現，再以模型預測結果作為買賣訊號來建構外匯投資組合，同時，加入利差策略與動量策略作為比較基準，觀察投資組合的績效表現，根據實驗結果，集成後的投組明顯優於個別機器學習模型，而機器學習模型又優於傳統策略。 zh_TW dc.description.abstract (摘要) In this study, we compare two different techniques which are principal component analysis (PCA) and autoencoder(AE) for reducing the dimensionality of data prior to modeling, and deploy machine learning models for data fitting to observe their results. Then, we measure performance by R^2 on the test set. The results showed that the data pre-trained by AE can greatly improve the model performance. The next step is to use previously trained models as weak learners to combine them by simple average method and compare its result to weak learners. After that, we adopt the result of model prediction as a trading signal to construct a foreign exchange portfolio. Moreover, we also add traditional strategies which are carry strategy and momentum strategy as the benchmarks to observe the portfolio performance. According to the experimental results, the composite is better than all weak learners, and all weak learners are better than the traditional strategies. en_US dc.description.tableofcontents 第一章、 緒論 1第一節、 研究背景及動機 1第二節、 研究目的 4第三節、 論文架構及章節介紹 4第二章、 文獻回顧 5第一節、 因子策略文獻探討 5第二節、 機器學習文獻探討 7第三章、 樣本資料與因子建構 16第一節、 樣本資料 16第二節、 策略因子建構 21第四章、 研究方法 23第一節、 利差策略與動能策略 24第二節、 機器學習流程 25第三節、 資料預處理 26第四節、 維度縮減 27第五節、 機器學習算法 32第六節、 模型評估指標 44第五章、 實證結果 46第一節、 模型評估 46第二節、 投資組合累積報酬 47第三節、 投資組合績效評估指標 49第六章、 結論與建議 54參考文獻 56 zh_TW dc.format.extent 2230978 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0108258042 en_US dc.subject (關鍵詞) 機器學習 zh_TW dc.subject (關鍵詞) 自編碼器 zh_TW dc.subject (關鍵詞) 主成份分析 zh_TW dc.subject (關鍵詞) 隨機森林 zh_TW dc.subject (關鍵詞) 極限梯度提升樹 zh_TW dc.subject (關鍵詞) 外匯超額報酬 zh_TW dc.subject (關鍵詞) 集成方法 zh_TW dc.subject (關鍵詞) Machine Learning en_US dc.subject (關鍵詞) Autoencoder en_US dc.subject (關鍵詞) PCA en_US dc.subject (關鍵詞) Random Forest en_US dc.subject (關鍵詞) XGBoost en_US dc.subject (關鍵詞) Foreign Exchange Excess Return en_US dc.subject (關鍵詞) Ensemble Method en_US dc.title (題名) 機器學習對外匯報酬之預測 zh_TW dc.title (題名) Forecast Foreign Exchange Returns with Machine Learning en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) [1] 石川、劉洋溢、連祥斌(2020)。因子投資與實踐。北京：電子工業出版社。[2] Alkhatib, K., Najdat, H., Hmeidi, L., Shatnawi, M. (2013). Stock Price Prediction Using K-Nearest Neighbor(kNN) Algorithm. International Journal of Business, Humanities and Technology, 3(3).[3] Andersen, T.G., Bollerslev, T., Diebold, F.X., & Vega, C. (2002). Micro Effects of Macro Announcements: Real-Time Price Discovery in Foreign Exchange. The American Economic Association, 93, 38-62.[4] Asness, C.S., Moskowitz, T.J., & Pedersen, L. (2013). Value and Momentum Everywhere. Journal of Finance, 68, 929-885.[5] Baldi, P., & Hornik, K. (1989). Neural networks and principal component analysis: Learning from examples without local minima. Neural networks, 2, 53-58.[6] Basak, S., Kar, S., Saha, S., Khaidem, L., & Dey, S.R. (2019). Prediction the direction of stock market prices using tree-based classifiers. The North American Journal of Economics and Finance, 47, 552-567.[7] Breiman, L. (1996a). Bagging Predictors. Machine Learning, 24(2), 123–140.[8] Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.[9] Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and Regression Trees. Wadsworth.[10] Carhart, M.M. (1997). On Persistence in Mutual Fund Performance. Journal of Finance, 52, 57-82.[11] Chen, T., & Carlos G. (2016). Xgboost: A scalable tree boosting system. A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785-794.[12] Clemen, R.T. (1989). Combining Forecasts: A Review and Annotated Bibliography. International Journal of Forecasting, 5, 559–583.[13] Ebrahimpour, D.H., & Kouzani, D.A. (2007). FACE RECOGNITION USING BAGGING KNN.[14] Fama, E.F.(1984). Forward and spot exchange rates. The Journal of Monetary Economics, 14, 319-338.[15] Fama, E.F., & French, K.R.(1992). The Cross-Section of Expected Stock Returns. Journal of Finance, 47, 427-465.[16] Fama, E.F., & French, K.R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33, 3-56.[17] Fama, E.F., & MacBeth, J. (1973). Risk, return and equilibrium: Empirical tests. The Journal of Political Economy, 81, 607-636.[18] Friedman, J.H. (2001). GREEDY FUNCTION APPROXIMATION: A GRADIENT BOOSTING MACHINE. The Annals of Statistics, 29, 1189-1232.[19] George, A. (2012). Anomaly Detection based on Machine Learning: Dimensionality Reduction using PCA and Classification using SVM. International Journal of Computer Applications, 47, 5-8.[20] Gu, S., Kelly, B. & Xiu, D. (2020). Empirical Asset Pricing via Machine Learning. The Review of Financial Studies, 33, 2223-2273.[21] Gu, S., Kelly, B. & Xiu, D. (2021). Autoencoder asset pricing models. Journal of Econometrics, 222, 429-450.[22] Hasen, L.P., & Hodrick, R.J. (1980). Forward Exchange Rates as Optimal Predictors of Future Spot Rates: An Econometric Analysis. The Journal of Political Economy, 88, 829-853.[23] Hasen, L.K., & Salamom, P. (1990). Neural network ensembles. IEEF Transactions on Pattern Analysis and Machine Intelligence, 12, 993-1001.[24] Hiton, G.E. and Salakhutdinov, R.R. (2006). Reducing the Dimensionality of Data with Neural Networks. Science, 33, 504-507.[25] Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. Journal of Finance, 48, 65-91.[26] Jiang, Z., Ji, R., & Chang, K.C. (2020). A Machine Learning Integrated Portfolio Rebalance Framework with Risk-Aversion Adjustment. Journal of Risk and Financial Management, 13, 155.[27] Jurczenko, E., & Teiletche, J., (2019). Macro Factor Mimicking Portfolios. Available at SSRN 3363598.[28] Kelly, B.T., Pruitt, S., & Su., Y. (2018). Characteristics Are Covariances: A Unified Model of Risk and Return. Available at SSRN 3032013.[29] Kroencke, T. A., Schindler, F., & Schrimpf, A. (2014). International diversification benefits with foreign exchange investment styles. Review of Finance, 18, 1847-1883.[30] Levy, R.A. (1967). Relative Strength as a Criterion for Investment Selection. Journal of Finance, 22, 595-610.[31] Lustig, H., Roussanov, N., & Verdelhan, A. (2011). Common risk factors in currency markets. The Review of Financial Studies, 24, 3731-3777.[32] Lustig, H., & Verdelhan, A. (2007). The cross-section of foreign currency risk premia and US consumption growth risk. The American Economic Review, 97, 89–117.[33] Menkhoff, L., Sarno, L., Shmeling, M., & Schrimpf, A. (2012a). Carry Trades and Global Foreign Exchange Volatility. Journal of Finance, 67, 681-718.[34] Menkhoff, L., Sarno, L., Schmeling, M., & Schrimpf, A. (2012b). Currency momentum strategies. Journal of Financial Economics, 106, 660-684.[35] Okunev, J., & White, D. (2003). Do Momentum-Based Strategies Still Work in Foreign Currency Markets?, Journal of Financial and Quantitative Analysis, 38(2), 425-447.[36] Qian, B., & Rasheed, K. (2010). Foreign Exchange Market Prediction with Multiple Classifiers. Journal of Forecasting, 29, 271-284.[37] Qian, X.Y. (2017). Financial Series Prediction: Between Precision of Time Series Models and Machine Learning Methods. arXiv: 1706.00948v3.[38] Simpson, M.W., Ramchander, S., & Chaudhry, M. (2005). The impact of macroeconomic surprises on spot and forward foreign exchange markets. Journal of International Money and Finance, 24, 693-718.[39] Steele, B.M. (2009). Exact bootstrap k-nearest neighbor learners. Mach Learn, 74, 235-255.[40] Struck, C., & Cheng, E. (2019). The Cross-Section of Returns: A Non-Parametric Approach. Available at SSRN 3494141.[41] Waskle, S., Parashar, L., & Singh, U. (2020). Intrusion Detection System Using PCA with Random Forest Approach. International Conference on Electronics and Sustainable Communication System, 2020, 803-808.[42] Yu, L., Lai, K.K. & Wang, S. (2008). Multistage RBF neural network ensemble learning for exchange rates forecasting. Neurocomputing, 71, 3295-3303.[43] Zhang, D., Wang, X., Gao, L., & Gong, Y. (2021). Predict and Analyze Exchange Rate Fluctuations Accordingly Based on Quantile Regression Model and K-Nearest Neighbor. Journal of Physics: Conference Series, 12-16.[44] Zhou, Z., & Yu, Y. (2005). Adapt Bagging to Nearest Neighbor Classifiers. Journal of Computer Science and Technology, 20, 48-54. zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202200371 en_US
