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題名 應用機器學習預測利差交易的收益
Application of machine learning to predicting the returns of carry trade
作者 吳佳真
貢獻者 蔡瑞煌
吳佳真
關鍵詞 機器學習
利差交易
類神經網路
TensorFlow
圖形處理單元
Machine learning
Carry trade
Artificial neural networks (ANN)
TensorFlow
Graphic processing unit (GPU)
日期 2017
上傳時間 10-Aug-2017 09:46:03 (UTC+8)
摘要 本研究提出了一個類神經網路機制，可以及時有效的預測利差交易(carry trade)的收益。為了實現及時性，我們將通過Tensorflow和圖形處理單元(GPU)來實作這個機制。此外，類神經網路機制需要處理具有概念飄移和異常值的時間序列數據。而我們將透過設計的實驗來驗證這個機制的及時性與有效性。
在實驗過程中，我們發現在演算法設置不同的參數將影響類神經網路的性能。本研究將討論不同參數下所產生的不同結果。實驗結果表明，我們所提出的類神經網路機制可以預測出利差交易的收益的動向。希望這個研究將對機器學習和金融領域皆有所貢獻。
This research derives an artificial neural networks (ANN) mechanism for timely and effectively predicting the return of carry trade. To achieve the timeliness, the ANN mechanism is implemented via the infrastructure of TensorFlow and graphic processing unit (GPU). Furthermore, the ANN mechanism needs to cope with the time series data that may have concept-drifting phenomenon and outliers. An experiment is also designed to verify the timeliness and effectiveness of the proposed mechanism.
During the experiment, we find that different parameters we set in the algorithm will affect the performance of the neural network. And this research will discuss the different results in different parameters. Our experiment result represents that the proposed ANN mechanism can predict movement of the returns of carry trade well. Hope this research would contribute for both machine learning and finance field.
參考文獻 Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., ... & Ghemawat, S. (2016). TensorFlow: Large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv: 1603.04467.
Alexius, A. (2001). Uncovered interest parity revisited. Review of International Economics, 9(3), 505-517.
Babaeizadeh, M., Frosio, I., Tyree, S., Clemons, J., & Kautz, J. (2016). Reinforcement learning through asynchronous advantage actor-critic on a gpu. arXiv preprint arXiv:1611.06256.
Barzdins, G., Renals, S., & Gosko, D. (2016). Character-Level Neural Translation for Multilingual Media Monitoring in the SUMMA Project. arXiv preprint arXiv:1604.01221.
Basu, S., & Meckesheimer, M. (2007). Automatic outlier detection for time series: an application to sensor data. Knowledge and Information Systems, 11(2), 137-154.
Bekaert, G., & Hodrick, R. J. (1993). On biases in the measurement of foreign exchange risk premiums. Journal of International Money and Finance, 12(2), 115-138.
Bernardo, A., & Ledoit, O. (1999). Approximate arbitrage. Finance. Retrieved from http://www.anderson.ucla.edu/documents/areas/fac/finance/18-99.pdf
Bilson, J. F. O. (2013). Adventures in the Carry Trade. Retrieved from http://www.cmegroup.com/education/files/bilson-adventures-in-the-carry-trade.pdf
Brunnermeier, M. K., Nagel, S., & Pedersen, L. H. (2009). Carry trades and currency crashes. In Daron Acemoglu, Kenneth Rogoff, Michael Woodford (Eds.), NBER Macroeconomics Annual 2008 (Vol. 3), (pp. 313-347). Chicago: University of Chicago Press.
Burnside, C. (2011). The cross-section of foreign currency risk premia and consumption growth risk: comment. The American Economic Review, 101(7), 3456-3476.
Burnside, C., Eichenbaum, M., Kleshchelski, I., & Rebelo, S. (2006). The returns to currency speculation. NBER Working Papers, 12489.
Burnside, C., Eichenbaum, M., Kleshchelski, I., & Rebelo, S., (2011). Do peso problems explain the returns to the carry trade? The Review of Financial Studies, 24(3), 853-891.
Clinton, K. (1998). Transactions costs and covered interest arbitrage: theory and evidence. Journal of Political Economy, 96(2), 358-370.
Elwell, R., & Polikar, R. (2011). Incremental learning of concept drift in nonstationary environments. Neural networks, IEEE Transactions on, 22(10), 1517-1531.
Fama, E. F. (1984). Forward and spot exchange rates. Journal of Monetary Economics, 14(3), 319-338.
Frankel, J. A. (1980). Tests of rational expectations in the forward exchange market. Southern Economic Journal, 46(4), 1083-1101.
Frenkel, J. A., & Levich, R. M., (1975). Covered interest rate arbitrage: unexploited profits? Journal of Political Economy, 83(2), 325-338.
Froot, K. A., & Ramadorai, T. (2008). Institutional portfolio flows and international investments. Review of Financial Studies, 21(2), 937-971.
Froot, K. A., & Thaler, R. H. (1990). Foreign exchange. The Journal of Economic Perspectives, 4(3), 179-192.
Fujii, E., & Chinn, M. D. (2000). Fin de Siècle real interest parity. NBER Working Papers, 7880.
Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., & Bouchachia, A. (2014). A survey on concept drift adaptation. ACM Computing Surveys (CSUR), 46(4), 44.
Hodge, V. J., & Austin, J. (2004). A survey of outlier detection methodologies. Artificial Intelligence Review, 22(2), 85-126.
Hodrick, R. J. (1991) the Empirical Evidence on the Efficiency of Forward and Futures Foreign Exchange Markets, 2nd. edn. London, UK: Routledge.
Huang, S. Y., Lin, J. W., & Tsaih, R. H. (2016, July). Outlier detection in the concept drifting environment. In Neural Networks (IJCNN), 2016 International Joint Conference on (pp. 31-37). IEEE.
Huang, S. Y., Yu, F., Tsaih, R. H., & Huang, Y. (2014). Resistant learning on the envelope bulk for identifying anomalous patterns. In Neural networks (IJCNN), 2014 International Joint Conference on, 3303-3310.
James, J., Marsh, I. W., & Sarno, L. (2012). Handbook of Exchange Rates. New Jersey : Wiley.
Jordà, Ò., & Taylor, A. M. (2012). The carry trade and fundamentals: Nothing to fear but FEER itself. Journal of International Economics, 88, 74-90.
Kuan, C. M., & Liu, T. (1995). Forecasting exchange rates using feedforward and recurrent neural networks. Journal of applied econometrics, 10(4), 347-364.
Kurzweil, R. (2005). The singularity is near: When humans transcend biology. Penguin.
Lin, C. W. (2015). A Decision Support Mechanism for Outlier Detection in the Concept Drifting Environment (Master`s thesis). Retrieved from http://thesis.lib.nccu.edu.tw/cgi-bin/gs32/gsweb.cgi?o=dallcdr&s=id=%22G0102356002%22.&searchmode=basic
Lustig, H., & Verdelhan, A. (2007). The cross section of foreign currency risk premia and consumption growth risk. The American Economic Review, 97(1), 89-117.
Masud, M. M., Chen, Q., Khan, L., Aggarwal, C., Gao, J., Han, J., & Thuraisingham, B. (2010). Addressing concept-evolution in concept-drifting data streams. In Data Mining (ICDM), 2010 IEEE 10th International Conference on. 929-934.
Masud, M. M., Gao, J., Khan, L., Han, J., & Thuraisingham, B. (2011). Classification and novel class detection in concept-drifting data streams under time constraints. Knowledge and Data Engineering, IEEE Transactions on, 23(6), 859-874.
McCorduck, P. (2004). Machines Who Think, Natick, MA: A. K. Peters, Ltd.
McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics, 5(4), 115-133.
Meese, R. A., & Rogoff, K. (1983). Empirical exchange rate models of the seventies. Journal of International Economics, 14(1-2), 3-24.
Obstfeld, M., & Taylor, A. M. (2004). Global Capital Markets: Integration, Crisis, and Growth. Cambridge University Press, Cambridge.
Owens, J. D., Houston, M., Luebke, D., Green, S., Stone, J. E., & Phillips, J. C. (2008). GPU computing. Proceedings of the IEEE, 96(5), 879-899.
Plantin, G., & Shin, H. S. (2006). Carry trades and speculative dynamics. Retrieved from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=898412
Poterba, J. M., & Summers, L. H., 1986. The persistence of volatility and stock market fluctuations. The American Economic Review, 76(5), 1142-1151.
Rousseeuw, P. J., & Van Driessen, K. (2006). Computing LTS regression for large data sets. Data mining and knowledge discovery, 12(1), 29-45.
Sinclair, P. J. N. (2005). How policy rates affect output, prices, and labour, open economy issues, and inflation and disinflation. In Mahadeva, Lavan, Sinclair, Peter (Eds.), How Monetary Policy Works (pp. 53-81). London: Routledge.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., ... & Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1-9).
Tolvi, J. U. S. S. I. (2002). Outliers and Predictability in Monthly Stock Market Index Returns. Liiketaloudellinen aikakauskirja, 369-380.
Triennial Central Bank Survey. (2016). Triennial Central Bank Survey Foreign exchange turnover in April 2016. Retrieved from http://www.bis.org/publ/rpfx16fx.pdf
Trippi, R. R., & Turban, E. (1992). Neural networks in finance and investing: Using artificial intelligence to improve real world performance. McGraw-Hill, Inc.
Tsaih, R. H., & Cheng, T. C. (2009). A resistant learning procedure for coping with outliers. Annals of Mathematics and Artificial Intelligence, 57(2), 161-180.
Tsaih, R., Hsu, Y., & Lai, C. C. (1998). Forecasting S&P 500 stock index futures with a hybrid AI system. Decision Support Systems, 23(2), 161-174.
Tsymbal, A. (2004). The problem of concept drift: definitions and related work. Computer Science Department, Trinity College Dublin, 106(2).
Verma, A., Pedrosa, L., Korupolu, M., Oppenheimer, D., Tune, E., & Wilkes, J. (2015, April). Large-scale cluster management at Google with Borg. In Proceedings of the Tenth European Conference on Computer Systems (p. 18). ACM.
Walczak, S. (2001). An empirical analysis of data requirements for financial forecasting with neural networks. Journal of management information systems, 17(4), 203-222.
Widmer, G., & Kubat, M. (1996). Learning in the presence of concept drift and hidden contexts. Machine learning, 23(1), 69-101.
描述 碩士
國立政治大學
資訊管理學系
104356020
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0104356020
資料類型 thesis
dc.contributor.advisor 蔡瑞煌zh_TW
dc.contributor.author (Authors) 吳佳真zh_TW
dc.creator (作者) 吳佳真zh_TW
dc.date (日期) 2017en_US
dc.date.accessioned 10-Aug-2017 09:46:03 (UTC+8)-
dc.date.available 10-Aug-2017 09:46:03 (UTC+8)-
dc.date.issued (上傳時間) 10-Aug-2017 09:46:03 (UTC+8)-
dc.identifier (Other Identifiers) G0104356020en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/111742-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊管理學系zh_TW
dc.description (描述) 104356020zh_TW
dc.description.abstract (摘要) 本研究提出了一個類神經網路機制，可以及時有效的預測利差交易(carry trade)的收益。為了實現及時性，我們將通過Tensorflow和圖形處理單元(GPU)來實作這個機制。此外，類神經網路機制需要處理具有概念飄移和異常值的時間序列數據。而我們將透過設計的實驗來驗證這個機制的及時性與有效性。
在實驗過程中，我們發現在演算法設置不同的參數將影響類神經網路的性能。本研究將討論不同參數下所產生的不同結果。實驗結果表明，我們所提出的類神經網路機制可以預測出利差交易的收益的動向。希望這個研究將對機器學習和金融領域皆有所貢獻。		zh_TW
dc.description.abstract (摘要) This research derives an artificial neural networks (ANN) mechanism for timely and effectively predicting the return of carry trade. To achieve the timeliness, the ANN mechanism is implemented via the infrastructure of TensorFlow and graphic processing unit (GPU). Furthermore, the ANN mechanism needs to cope with the time series data that may have concept-drifting phenomenon and outliers. An experiment is also designed to verify the timeliness and effectiveness of the proposed mechanism.
During the experiment, we find that different parameters we set in the algorithm will affect the performance of the neural network. And this research will discuss the different results in different parameters. Our experiment result represents that the proposed ANN mechanism can predict movement of the returns of carry trade well. Hope this research would contribute for both machine learning and finance field.		en_US
dc.description.tableofcontents Abstract 3
Figure Index 5
Table Index 6
1. Introduction 7
1.1 Background 7
1.2 Motivation 8
1.3 Purpose 12
2. Literature Review 14
2.1 Carry Trade Background 14
2.2 Carry Trade Strategies 19
2.3 TensorFlow and GPU 22
2.4 A Mechanism for Effectively Detecting Outlier in the Concept Drifting Environment 27
3. Experiment 37
3.1 Variable Description 37
3.2 Experiment Design 39
4. Experiment Result 45
5. Conclusion and Future Work 57
Reference 60
Appendix 68		zh_TW
dc.format.extent 2104452 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0104356020en_US
dc.subject (關鍵詞) 機器學習zh_TW
dc.subject (關鍵詞) 利差交易zh_TW
dc.subject (關鍵詞) 類神經網路zh_TW
dc.subject (關鍵詞) TensorFlowzh_TW
dc.subject (關鍵詞) 圖形處理單元zh_TW
dc.subject (關鍵詞) Machine learningen_US
dc.subject (關鍵詞) Carry tradeen_US
dc.subject (關鍵詞) Artificial neural networks (ANN)en_US
dc.subject (關鍵詞) TensorFlowen_US
dc.subject (關鍵詞) Graphic processing unit (GPU)en_US
dc.title (題名) 應用機器學習預測利差交易的收益zh_TW
dc.title (題名) Application of machine learning to predicting the returns of carry tradeen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., ... & Ghemawat, S. (2016). TensorFlow: Large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv: 1603.04467.
Alexius, A. (2001). Uncovered interest parity revisited. Review of International Economics, 9(3), 505-517.
Babaeizadeh, M., Frosio, I., Tyree, S., Clemons, J., & Kautz, J. (2016). Reinforcement learning through asynchronous advantage actor-critic on a gpu. arXiv preprint arXiv:1611.06256.
Barzdins, G., Renals, S., & Gosko, D. (2016). Character-Level Neural Translation for Multilingual Media Monitoring in the SUMMA Project. arXiv preprint arXiv:1604.01221.
Basu, S., & Meckesheimer, M. (2007). Automatic outlier detection for time series: an application to sensor data. Knowledge and Information Systems, 11(2), 137-154.
Bekaert, G., & Hodrick, R. J. (1993). On biases in the measurement of foreign exchange risk premiums. Journal of International Money and Finance, 12(2), 115-138.
Bernardo, A., & Ledoit, O. (1999). Approximate arbitrage. Finance. Retrieved from http://www.anderson.ucla.edu/documents/areas/fac/finance/18-99.pdf
Bilson, J. F. O. (2013). Adventures in the Carry Trade. Retrieved from http://www.cmegroup.com/education/files/bilson-adventures-in-the-carry-trade.pdf
Brunnermeier, M. K., Nagel, S., & Pedersen, L. H. (2009). Carry trades and currency crashes. In Daron Acemoglu, Kenneth Rogoff, Michael Woodford (Eds.), NBER Macroeconomics Annual 2008 (Vol. 3), (pp. 313-347). Chicago: University of Chicago Press.
Burnside, C. (2011). The cross-section of foreign currency risk premia and consumption growth risk: comment. The American Economic Review, 101(7), 3456-3476.
Burnside, C., Eichenbaum, M., Kleshchelski, I., & Rebelo, S. (2006). The returns to currency speculation. NBER Working Papers, 12489.
Burnside, C., Eichenbaum, M., Kleshchelski, I., & Rebelo, S., (2011). Do peso problems explain the returns to the carry trade? The Review of Financial Studies, 24(3), 853-891.
Clinton, K. (1998). Transactions costs and covered interest arbitrage: theory and evidence. Journal of Political Economy, 96(2), 358-370.
Elwell, R., & Polikar, R. (2011). Incremental learning of concept drift in nonstationary environments. Neural networks, IEEE Transactions on, 22(10), 1517-1531.
Fama, E. F. (1984). Forward and spot exchange rates. Journal of Monetary Economics, 14(3), 319-338.
Frankel, J. A. (1980). Tests of rational expectations in the forward exchange market. Southern Economic Journal, 46(4), 1083-1101.
Frenkel, J. A., & Levich, R. M., (1975). Covered interest rate arbitrage: unexploited profits? Journal of Political Economy, 83(2), 325-338.
Froot, K. A., & Ramadorai, T. (2008). Institutional portfolio flows and international investments. Review of Financial Studies, 21(2), 937-971.
Froot, K. A., & Thaler, R. H. (1990). Foreign exchange. The Journal of Economic Perspectives, 4(3), 179-192.
Fujii, E., & Chinn, M. D. (2000). Fin de Siècle real interest parity. NBER Working Papers, 7880.
Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., & Bouchachia, A. (2014). A survey on concept drift adaptation. ACM Computing Surveys (CSUR), 46(4), 44.
Hodge, V. J., & Austin, J. (2004). A survey of outlier detection methodologies. Artificial Intelligence Review, 22(2), 85-126.
Hodrick, R. J. (1991) the Empirical Evidence on the Efficiency of Forward and Futures Foreign Exchange Markets, 2nd. edn. London, UK: Routledge.
Huang, S. Y., Lin, J. W., & Tsaih, R. H. (2016, July). Outlier detection in the concept drifting environment. In Neural Networks (IJCNN), 2016 International Joint Conference on (pp. 31-37). IEEE.
Huang, S. Y., Yu, F., Tsaih, R. H., & Huang, Y. (2014). Resistant learning on the envelope bulk for identifying anomalous patterns. In Neural networks (IJCNN), 2014 International Joint Conference on, 3303-3310.
James, J., Marsh, I. W., & Sarno, L. (2012). Handbook of Exchange Rates. New Jersey : Wiley.
Jordà, Ò., & Taylor, A. M. (2012). The carry trade and fundamentals: Nothing to fear but FEER itself. Journal of International Economics, 88, 74-90.
Kuan, C. M., & Liu, T. (1995). Forecasting exchange rates using feedforward and recurrent neural networks. Journal of applied econometrics, 10(4), 347-364.
Kurzweil, R. (2005). The singularity is near: When humans transcend biology. Penguin.
Lin, C. W. (2015). A Decision Support Mechanism for Outlier Detection in the Concept Drifting Environment (Master`s thesis). Retrieved from http://thesis.lib.nccu.edu.tw/cgi-bin/gs32/gsweb.cgi?o=dallcdr&s=id=%22G0102356002%22.&searchmode=basic
Lustig, H., & Verdelhan, A. (2007). The cross section of foreign currency risk premia and consumption growth risk. The American Economic Review, 97(1), 89-117.
Masud, M. M., Chen, Q., Khan, L., Aggarwal, C., Gao, J., Han, J., & Thuraisingham, B. (2010). Addressing concept-evolution in concept-drifting data streams. In Data Mining (ICDM), 2010 IEEE 10th International Conference on. 929-934.
Masud, M. M., Gao, J., Khan, L., Han, J., & Thuraisingham, B. (2011). Classification and novel class detection in concept-drifting data streams under time constraints. Knowledge and Data Engineering, IEEE Transactions on, 23(6), 859-874.
McCorduck, P. (2004). Machines Who Think, Natick, MA: A. K. Peters, Ltd.
McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics, 5(4), 115-133.
Meese, R. A., & Rogoff, K. (1983). Empirical exchange rate models of the seventies. Journal of International Economics, 14(1-2), 3-24.
Obstfeld, M., & Taylor, A. M. (2004). Global Capital Markets: Integration, Crisis, and Growth. Cambridge University Press, Cambridge.
Owens, J. D., Houston, M., Luebke, D., Green, S., Stone, J. E., & Phillips, J. C. (2008). GPU computing. Proceedings of the IEEE, 96(5), 879-899.
Plantin, G., & Shin, H. S. (2006). Carry trades and speculative dynamics. Retrieved from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=898412
Poterba, J. M., & Summers, L. H., 1986. The persistence of volatility and stock market fluctuations. The American Economic Review, 76(5), 1142-1151.
Rousseeuw, P. J., & Van Driessen, K. (2006). Computing LTS regression for large data sets. Data mining and knowledge discovery, 12(1), 29-45.
Sinclair, P. J. N. (2005). How policy rates affect output, prices, and labour, open economy issues, and inflation and disinflation. In Mahadeva, Lavan, Sinclair, Peter (Eds.), How Monetary Policy Works (pp. 53-81). London: Routledge.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., ... & Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1-9).
Tolvi, J. U. S. S. I. (2002). Outliers and Predictability in Monthly Stock Market Index Returns. Liiketaloudellinen aikakauskirja, 369-380.
Triennial Central Bank Survey. (2016). Triennial Central Bank Survey Foreign exchange turnover in April 2016. Retrieved from http://www.bis.org/publ/rpfx16fx.pdf
Trippi, R. R., & Turban, E. (1992). Neural networks in finance and investing: Using artificial intelligence to improve real world performance. McGraw-Hill, Inc.
Tsaih, R. H., & Cheng, T. C. (2009). A resistant learning procedure for coping with outliers. Annals of Mathematics and Artificial Intelligence, 57(2), 161-180.
Tsaih, R., Hsu, Y., & Lai, C. C. (1998). Forecasting S&P 500 stock index futures with a hybrid AI system. Decision Support Systems, 23(2), 161-174.
Tsymbal, A. (2004). The problem of concept drift: definitions and related work. Computer Science Department, Trinity College Dublin, 106(2).
Verma, A., Pedrosa, L., Korupolu, M., Oppenheimer, D., Tune, E., & Wilkes, J. (2015, April). Large-scale cluster management at Google with Borg. In Proceedings of the Tenth European Conference on Computer Systems (p. 18). ACM.
Walczak, S. (2001). An empirical analysis of data requirements for financial forecasting with neural networks. Journal of management information systems, 17(4), 203-222.
Widmer, G., & Kubat, M. (1996). Learning in the presence of concept drift and hidden contexts. Machine learning, 23(1), 69-101.		zh_TW