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題名 機器學習與傳統模型對外匯報酬之因子分析
The Factor Analysis on Foreign Exchange Return between Machine Learning and Traditional Factor Models作者 黃開雋
Huang, Kai-Chun貢獻者 林建秀
Lin, Chien-Hsiu
黃開雋
Huang, Kai-Chun關鍵詞 機器學習
外匯超額報酬
隨機森林
梯度提升
神經網路
Machine Learning
Excess Foreign Exchange Return
Random Forest
Gradient Boosting
Neural Networks日期 2020 上傳時間 2-Sep-2020 11:49:57 (UTC+8) 摘要 本研究主要是以機器學習模型為基礎，透過機器學習方法找出是否有潛在的因子能夠讓傳統上使用的因子外對外匯的超額報酬提供更高的解釋力，本研究使用樹模型、梯度提升、具隱藏層的神經網路以及隨機森林模型。本研究先在樣本期間(1997/01 至 2019/05)以HML投組法將19國匯率資料建構出利差、動能以及價值交易策略因子來取得傳統模型使用的因子。除了傳統上常使用的因子外，我們同時加入了其他的總經因子以及個別國家因子進入我們的模型之中，透過因子重要性分析我們發現不同的因子對於不同的模型會有不一樣的影響程度，但是除了市場因子外，並未有一個新加入的因子能夠顯著影響到所有的機器學習模型，故我們在結論處提出未來能夠改進的方向。
This paper tries to find some latent factors that can help us explain excess foreign return efficiently except using traditional factors which are market factor, value factor, momentum factor, and carry trade factor. We use three kinds of machine learning models in this paper which are random forest model, gradient boosting tree model, and neural network with hidden layer from one to five models. First, we use 19 countries’ foreign exchange data from Jan. 1997 to May. 2019 to build traditional factors by HML method. Then, we also put some macro factors and country-specific factors into machine learning models. Last, we specify which factor can affect the explanation ability separately by ranking variable importance in each model.參考文獻 [1] 郭秀樺(2018)。外匯報酬之利差、動能及價值交易策略成因分析。國立政治大學金融研究所碩士論文，台北市。[2] Barroso, P., & Santa-Clara, P. (2015). Beyond the carry trade: Optimal currency portfolios. The Journal of Financial and Quantitative Analysis, 50, 1037–1056.[3] Brunnermeier, M. K., Nagel, S., & Pedersen, L. H. (2009). Carry trades and currency crashes. NBER Macroeconomics Annual, 23, 313–348.[4] Butaru, F., Qingqing C., Brian C., Sanmay D., Andrew L., & Akhtar S., (2016), Risk and risk management in the credit card industry, Journal of Banking & Finance 72, 218~239.[5] Chordia, T., & Shivakumar, L. (2002). Momentum, business cycle, and time-varying expected returns. The Journal of Finance, 62, 985–1019.[6] Fama, E., & French, K. (1988). Business cycles and the behavior of metals prices. The Journal of Finance, 43(5), 1075–1093.[7] Fama, E. F., & MacBeth, J. (1973). Risk, return and equilibrium: Empirical tests. The Journal of Political Economy, 81, 607–636.[8] Freyberger, Joachim, Andreas N., and Michael W., (2017), Dissecting characteristics nonparametrically,Technical report, University of Wisconsin-Madison.[9] Harvey, R., & Wayne F., (1999), Conditioning variables and the cross-section of stock returns, Journal of Finance 54, 1325~1360.[10] Heaton, J., NG P., & JH W., (2016), Deep learning in finance[11] Hutchinson, M., Andrew L., and Tomaso P., (1994), A nonparametric approach to pricing and hedging derivative securities via learning networks, The Journal of Finance 49, 851~889.[12] Jingtao,Y. Yili L., & Chew T., (2000), Option price forecasting using neural networks, Omega 28, 455~466.[13] Khandani, E., Adlar K., & Andrew L., (2010), Consumer credit-risk models via machine learning algorithms, Journal of Banking & Finance 34, 2767~2787.[14] Lewellen, J. (2015), The cross-section of expected stock returns, Critical Finance Review 4, 1-44[15] Lustig, H., Roussanov, N., & Verdelhan, A. (2011). Common risk factors in currency markets. The Review of Financial Studies, 24, 3731–3777.[16] Menkhoff, L., Sarno, L., Schmeling, M., & Schrimpf, A. (2012b). Currency momentum strategies. Journal of Financial Economics, 106,660–684.[17] Moritz, Benjamin, & Tom Z., (2016), Tree-based conditional portfolio sorts: The relation between past and future stock returns, Available at SSRN 2740751 .[18] Rapach, E., Jack S., & Guofu Z., (2013), International stock return predictability: what is the role of the united states? The Journal of Finance 68, 1633~1662.[19] Raza, A., Marshall, B. R., & Visaltanachoti, N. (2014). Is there momentum or reversal in weekly currency returns? Journal of International Money and Finance, 45,38–60.[20] Shihao, G.,Bryan,K., & Dacheng, X.(2019). Empirical Asset Pricing via Machine Learning. NBER[21] Sirignano, J., Apaar S., & Kay G., (2016), Deep learning for mortgage risk, Available at SSRN 2799443 . 描述 碩士
國立政治大學
金融學系
107352023資料來源 http://thesis.lib.nccu.edu.tw/record/#G0107352023 資料類型 thesis dc.contributor.advisor 林建秀 zh_TW dc.contributor.advisor Lin, Chien-Hsiu en_US dc.contributor.author (Authors) 黃開雋 zh_TW dc.contributor.author (Authors) Huang, Kai-Chun en_US dc.creator (作者) 黃開雋 zh_TW dc.creator (作者) Huang, Kai-Chun en_US dc.date (日期) 2020 en_US dc.date.accessioned 2-Sep-2020 11:49:57 (UTC+8) - dc.date.available 2-Sep-2020 11:49:57 (UTC+8) - dc.date.issued (上傳時間) 2-Sep-2020 11:49:57 (UTC+8) - dc.identifier (Other Identifiers) G0107352023 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/131509 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 金融學系 zh_TW dc.description (描述) 107352023 zh_TW dc.description.abstract (摘要) 本研究主要是以機器學習模型為基礎，透過機器學習方法找出是否有潛在的因子能夠讓傳統上使用的因子外對外匯的超額報酬提供更高的解釋力，本研究使用樹模型、梯度提升、具隱藏層的神經網路以及隨機森林模型。本研究先在樣本期間(1997/01 至 2019/05)以HML投組法將19國匯率資料建構出利差、動能以及價值交易策略因子來取得傳統模型使用的因子。除了傳統上常使用的因子外，我們同時加入了其他的總經因子以及個別國家因子進入我們的模型之中，透過因子重要性分析我們發現不同的因子對於不同的模型會有不一樣的影響程度，但是除了市場因子外，並未有一個新加入的因子能夠顯著影響到所有的機器學習模型，故我們在結論處提出未來能夠改進的方向。 zh_TW dc.description.abstract (摘要) This paper tries to find some latent factors that can help us explain excess foreign return efficiently except using traditional factors which are market factor, value factor, momentum factor, and carry trade factor. We use three kinds of machine learning models in this paper which are random forest model, gradient boosting tree model, and neural network with hidden layer from one to five models. First, we use 19 countries’ foreign exchange data from Jan. 1997 to May. 2019 to build traditional factors by HML method. Then, we also put some macro factors and country-specific factors into machine learning models. Last, we specify which factor can affect the explanation ability separately by ranking variable importance in each model. 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 2第三節 論文架構及章節介紹 2第二章 文獻回顧 2第一節 傳統模型文獻探討 2第二節 機器學習文獻探討 4第三章 樣本選擇與研究方法 5第一節 樣本選擇 5第二節 模型建構 8第三節 研究方法 12第四章 實證結果分析 24第一節 機器學習模型樣本外R2結果 24第二節 機器學習模型重要性分析 24第五章 結論 30參考文獻 31 zh_TW dc.format.extent 1487732 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0107352023 en_US dc.subject (關鍵詞) 機器學習 zh_TW dc.subject (關鍵詞) 外匯超額報酬 zh_TW dc.subject (關鍵詞) 隨機森林 zh_TW dc.subject (關鍵詞) 梯度提升 zh_TW dc.subject (關鍵詞) 神經網路 zh_TW dc.subject (關鍵詞) Machine Learning en_US dc.subject (關鍵詞) Excess Foreign Exchange Return en_US dc.subject (關鍵詞) Random Forest en_US dc.subject (關鍵詞) Gradient Boosting en_US dc.subject (關鍵詞) Neural Networks en_US dc.title (題名) 機器學習與傳統模型對外匯報酬之因子分析 zh_TW dc.title (題名) The Factor Analysis on Foreign Exchange Return between Machine Learning and Traditional Factor Models en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) [1] 郭秀樺(2018)。外匯報酬之利差、動能及價值交易策略成因分析。國立政治大學金融研究所碩士論文，台北市。[2] Barroso, P., & Santa-Clara, P. (2015). Beyond the carry trade: Optimal currency portfolios. The Journal of Financial and Quantitative Analysis, 50, 1037–1056.[3] Brunnermeier, M. K., Nagel, S., & Pedersen, L. H. (2009). Carry trades and currency crashes. NBER Macroeconomics Annual, 23, 313–348.[4] Butaru, F., Qingqing C., Brian C., Sanmay D., Andrew L., & Akhtar S., (2016), Risk and risk management in the credit card industry, Journal of Banking & Finance 72, 218~239.[5] Chordia, T., & Shivakumar, L. (2002). Momentum, business cycle, and time-varying expected returns. The Journal of Finance, 62, 985–1019.[6] Fama, E., & French, K. (1988). Business cycles and the behavior of metals prices. The Journal of Finance, 43(5), 1075–1093.[7] Fama, E. F., & MacBeth, J. (1973). Risk, return and equilibrium: Empirical tests. The Journal of Political Economy, 81, 607–636.[8] Freyberger, Joachim, Andreas N., and Michael W., (2017), Dissecting characteristics nonparametrically,Technical report, University of Wisconsin-Madison.[9] Harvey, R., & Wayne F., (1999), Conditioning variables and the cross-section of stock returns, Journal of Finance 54, 1325~1360.[10] Heaton, J., NG P., & JH W., (2016), Deep learning in finance[11] Hutchinson, M., Andrew L., and Tomaso P., (1994), A nonparametric approach to pricing and hedging derivative securities via learning networks, The Journal of Finance 49, 851~889.[12] Jingtao,Y. Yili L., & Chew T., (2000), Option price forecasting using neural networks, Omega 28, 455~466.[13] Khandani, E., Adlar K., & Andrew L., (2010), Consumer credit-risk models via machine learning algorithms, Journal of Banking & Finance 34, 2767~2787.[14] Lewellen, J. (2015), The cross-section of expected stock returns, Critical Finance Review 4, 1-44[15] Lustig, H., Roussanov, N., & Verdelhan, A. (2011). Common risk factors in currency markets. The Review of Financial Studies, 24, 3731–3777.[16] Menkhoff, L., Sarno, L., Schmeling, M., & Schrimpf, A. (2012b). Currency momentum strategies. Journal of Financial Economics, 106,660–684.[17] Moritz, Benjamin, & Tom Z., (2016), Tree-based conditional portfolio sorts: The relation between past and future stock returns, Available at SSRN 2740751 .[18] Rapach, E., Jack S., & Guofu Z., (2013), International stock return predictability: what is the role of the united states? The Journal of Finance 68, 1633~1662.[19] Raza, A., Marshall, B. R., & Visaltanachoti, N. (2014). Is there momentum or reversal in weekly currency returns? Journal of International Money and Finance, 45,38–60.[20] Shihao, G.,Bryan,K., & Dacheng, X.(2019). Empirical Asset Pricing via Machine Learning. NBER[21] Sirignano, J., Apaar S., & Kay G., (2016), Deep learning for mortgage risk, Available at SSRN 2799443 . zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202001275 en_US
