政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/146599

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/146599

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政大機構典藏 > 商學院 > 金融學系 > 學位論文 > Item 140.119/146599

請使用永久網址來引用或連結此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/146599

題名:	以深度學習預測外匯超額報酬之研究 Deep Neural Network for Foreign Exchange Return Prediction
作者:	蔡玄中 Cai, Syuan-Jhong
貢獻者:	林建秀 Lin, Chien-Hsiu 蔡玄中 Cai, Syuan-Jhong
關鍵詞:	外匯預測利差交易策略動能交易策略價值交易策略深度學習 Exchange rate prediction Carry trade Momentum Value Deep learning
日期:	2023
上傳時間:	2023-08-02 14:11:08 (UTC+8)
摘要:	外匯走勢除市場供需影響走勢外，也深受該國政府政策，以及總體經濟的變化影響。因此本研究利用各國政策數據，例如利率決策、政府債務餘額佔GDP比例、經常帳餘額佔GDP比例、政府治理、外匯存底等五個因子作為各別因子，總體因子則以美國的數據為主，其中有VIX、TED利差、美國貨幣基數、AAA等級公司債殖利率、美國消費者物價指數、外國向美國購買證券、美國向外國購買證券等七個總體因子，另外也會納入過去文獻常探討的四個交易策略，市場投資組合、利差交易、動能交易、價值交易等四個傳統四因子。本研究將會把每個國家的各別因子與總體因子、傳統四因子進行交乘，將得到的60因子(5+(7+4)5)分別放入DNN模型以及OLS模型進行匯率變化預測。接著再將兩個模型的預測結果加上各國利差得到的外匯超額報酬建構HML(High Minus Low)投資組合，並與市場投資組合、利差交易、動能交易、價值交易等四個交易策略績效進行比較。本研究最後會進行Permutation Importance，以判斷哪個因子對本研究的DNN模型重要性最高。實證結果發現，DNN模型的樣本外R^2顯著優於OLS模型，在各個交易策略中，DNN模型所建構的投資組合績效也是最好的。在因子重要性中，以政府債務餘額佔GDP比例、外匯存底、政府效能、外國向美國購買證券、美國向外國購買證券、美國消費者物價指數、美國貨幣基數、價值交易等幾個因子的重要度較高。上述的結果也顯示DNN模型在當下的經濟環境中，能夠有效應用因子所蘊含的資訊，這也是傳統線性模型所沒有的特性。 Foreign exchange trends are influenced not only by market supply and demand but also by government policies and macroeconomic changes in each country. In this study, DNN model uses various factors related to government policies as individual, and use US data as macroeconomic factors. Besides, this study also uses four strategies that has been discussed for long time, market portfolio, carry trade, momentum, and value. The individual factor will be multiplied and combined with macroeconomic factors and four strategy factor. Therefore, this study will use sixty (5+(7+4)5) factors and separately input these factors into DNN and OLS models to predict the change of foreign exchange rate. Then, the predicted result will plus the interest rate differentials of each country, and get the predicted excess return. Next, use the predicted excess return to construct the High Minus Low portfolio. The performance of the HML portfolio will then be compared with the four trading strategies: market portfolio, carry trade, momentum, and value. Finally, the study will perform Permutation Importance to determine the most influential factor in the DNN model for this research. Empirical results reveal that the out-of-sample R^2 of the DNN model is higher than that of the OLS model. Furthermore, among the various strategies, the portfolio constructed by the DNN model exhibits the best performance. The factor importance analysis indicates that factors such as public debt, foreign exchange reserves, political stability, monetary base, CPI, gross purchases of domestic U.S security, gross purchases of foreign security from U.S, and value hold greater significance. These findings highlight the effectiveness of the DNN model in utilizing the information embedded in the factors within the current economic environment, a characteristic that traditional linear models lack.
參考文獻:	[1] Amat, C., Michalski, T., & Stoltz, G. (2018). Fundamentals and exchange rate forecastability with simple machine learning methods. Journal of International Money and Finance, 88, 1-24. [2] Ang, A., & Chen, J. (2010). Yield curve predictors of foreign exchange returns. AFA 2011 Denver Meetings Paper, [3] Asness, C. S., Moskowitz, T. J., & Pedersen, L. H. (2013). Value and momentum everywhere. The Journal of Finance, 68(3), 929-985. [4] Avramov, D., Cheng, S., & Metzker, L. (2023). Machine learning vs. economic restrictions: Evidence from stock return predictability. Management Science, 69(5), 2587-2619. [5] Batista, G. E., & Monard, M. C. (2002). A study of K-nearest neighbour as an imputation method. His, 87(251-260), 48. [6] Brunnermeier, M. K., Nagel, S., & Pedersen, L. H. (2008). Carry trades and currency crashes. NBER macroeconomics annual, 23(1), 313-348. [7] Burnside, C., Eichenbaum, M., & Rebelo, S. (2011). Carry trade and momentum in currency markets. Annu. Rev. Financ. Econ., 3(1), 511-535. [8] Chaboud, A. P., & Wright, J. H. (2005). Uncovered interest parity: it works, but not for long. Journal of International Economics, 66(2), 349-362. [9] Cover, T., & Hart, P. (1967). Nearest neighbor pattern classification. IEEE TRANSACTIONS ON INFORMATION THEORY, 13(1), 21-27. [10] Della Corte, P., Sarno, L., & Tsiakas, I. (2009). An economic evaluation of empirical exchange rate models. The Review of Financial Studies, 22(9), 3491-3530. [11] Eichenbaum, M. S., Johannsen, B. K., & Rebelo, S. T. (2021). Monetary policy and the predictability of nominal exchange rates. The Review of Economic Studies, 88(1), 192-228. [12] Engel, C., & West, K. D. (2005). Exchange rates and fundamentals. Journal of Political Economy, 113(3), 485-517. [13] Feng, G., He, J., & Polson, N. G. (2018). Deep learning for predicting asset returns. arXiv preprint arXiv:1804.09314. [14] Filippou, I., Rapach, D., Taylor, M. P., & Zhou, G. (2022). Exchange rate prediction with machine learning and a smart carry portfolio. Available at SSRN 3455713. [15] Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine learning. The Review of Financial Studies, 33(5), 2223-2273. [16] Hoerl, A. E., & Kennard, R. W. (1970). Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12(1), 55-67. [17] Hu, Z., Zhao, Y., & Khushi, M. (2021). A survey of forex and stock price prediction using deep learning. Applied System Innovation, 4(1), 9. [18] Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. The Journal of Finance, 48(1), 65-91. [19] Khalaf, G. (2012). A proposed ridge parameter to improve the least square estimator. Journal of Modern Applied Statistical Methods, 11(2), 15. [20] Kroencke, T. A., Schindler, F., & Schrimpf, A. (2014). International diversification benefits with foreign exchange investment styles. Review of Finance, 18(5), 1847-1883. [21] Lakonishok, J., Shleifer, A., & Vishny, R. W. (1994). Contrarian investment, extrapolation, and risk. The Journal of Finance, 49(5), 1541-1578. [22] Meese, R. A., & Rogoff, K. (1983). Empirical exchange rate models of the seventies: Do they fit out of sample? Journal of International Economics, 14(1-2), 3-24. [23] Menkhoff, L., Sarno, L., Schmeling, M., & Schrimpf, A. (2012a). Carry trades and global foreign exchange volatility. The Journal of Finance, 67(2), 681-718. [24] Menkhoff, L., Sarno, L., Schmeling, M., & Schrimpf, A. (2012b). Currency momentum strategies. Journal of Financial Economics, 106(3), 660-684. [25] Menkhoff, L., Sarno, L., Schmeling, M., & Schrimpf, A. (2017). Currency value. The Review of Financial Studies, 30(2), 416-441. [26] Molodtsova, T., & Papell, D. H. (2013). Taylor rule exchange rate forecasting during the financial crisis. NBER International Seminar on Macroeconomics, [27] Muthukrishnan, R., & Rohini, R. (2016). LASSO: A feature selection technique in predictive modeling for machine learning. 2016 IEEE international conference on advances in computer applications (ICACA), [28] 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. [29] Raza, A. (2015). Are Value Strategies Profitable in the Foreign Exchange Market? 28th Australasian Finance and Banking Conference, [30] Rossi, B. (2013). Exchange rate predictability. Journal of Economic Literature, 51(4), 1063-1119. [31] Taylor, J. B. (1993). Discretion versus policy rules in practice. Carnegie-Rochester Conference Series on Public Policy, [32] Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1), 267-288.
描述:	碩士國立政治大學金融學系 110352020
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0110352020
資料類型:	thesis
顯示於類別:	[金融學系] 學位論文

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