政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/131509

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/131509

English | 正體中文 | 简体中文 | Post-Print筆數 : 27 | Items with full text/Total items : 113656/144643 (79%)
Visitors : 51695930 Online Users : 620

RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

Scope

please add "double quotation mark" for query phrases to get precise results

please goto advance search for comprehansive author search

Adv. Search

Home ‧ Login ‧ Upload ‧ Help ‧ About ‧ Administer

Goto mobile version

政大典藏 > College of Commerce > Department of Money and Banking > Theses > Item 140.119/131509

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/131509

Title:	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
Authors:	黃開雋 Huang, Kai-Chun
Contributors:	林建秀 Lin, Chien-Hsiu 黃開雋 Huang, Kai-Chun
Keywords:	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
Date:	2020
Issue Date:	2020-09-02 11:49:57 (UTC+8)
Abstract:	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.
Reference:	[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 .
Description:	碩士國立政治大學金融學系 107352023
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0107352023
Data Type:	thesis
DOI:	10.6814/NCCU202001275
Appears in Collections:	[Department of Money and Banking] Theses

Files in This Item:

File	Description	Size	Format
202301.pdf		1452Kb	Adobe PDF2	2	View/Open

All items in 政大典藏 are protected by copyright, with all rights reserved.

社群 sharing

著作權政策宣告 Copyright Announcement

1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.

DSpace Software Copyright © 2002-2004 MIT & Hewlett-Packard / Enhanced by NTU Library IR team Copyright © - Feedback