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題名 基於特徵選取之LSTM模型應用:外匯超額報酬預測
LSTM Model with Feature Selection for Foreign Exchange Return Prediction作者 黃紹瑋
Huang, Shao-Wei貢獻者 林建秀
Ling, Chien-Hsiu
黃紹瑋
Huang, Shao-Wei關鍵詞 外匯交易
利差交易策略
動能交易策略
價值交易策略
深度學習
特徵篩選
因子重要度
Foreign exchange trading
carry trade
momentum trade
value trade
LSTM
feature selection
feature importance日期 2021 上傳時間 4-Aug-2021 14:49:59 (UTC+8) 摘要 本研究使用總經因子和個別外匯因子之交乘項作為LSTM模型的因子，希望藉由深度學習模型來捕捉總經因子和個別外匯因子的互動，並比較其對於外匯超額報酬之解釋力和傳統四因子(利差、動能、價值、市場因子)在線性模型(OLS)上對外匯超額報酬之解釋力的差異。而在因子的部分，本文做了特徵篩選的處理，希望能提升模型的預測力，最後在比較樣本外R^2時，發現LSTM模型的表現優於OLS模型。接著，將預測力較好的LSTM模型進行策略交易，把LSTM模型預測出的國家超額報酬進行排列，買入預測前25%的國家貨幣，賣出預測後25%的國家貨幣，進而和傳統價值、動能及利差交易策略建構的投資組合做比較，並以夏普比率(Sharpe Ratio)及卡馬比率(Calmar Ratio)當作績效的衡量，最後在結果上發現LSTM模型建立的投資組合績效優於傳統價值、動能及利差因子進行的交易策略。另外，本文最終也探討因子之重要度，發現和利率相關的總經因子對於外匯超額報酬有不錯的預測能力。
This paper used the covariates which are the product of macroeconomic factors and specific foreign exchange factors to train LSTM model, and author hopes to capture the interaction between macroeconomic factors and specific foreign exchange factors through LSTM model. Additionally, author applied feature selection method, trying to enhance the prediction of models. The purpose of using LSTM model with covariates and OLS model with four traditional factors is to compare the prediction of foreign exchange return. Finally, LSTM model performed better than OLS model in the values of coefficient of determination.Furthermore, the paper used the outcomes predicted by LSTM model to trade in currency markets and tried to compare the performance made by value trade, momentum trade and carry trade. All strategies were made to buy the currencies in the top quarter of predictions and to sell currencies in the bottom quarter of prediction. Author used Sharpe ratio and Calmar ratio to measure the performance of all strategies, finding that the strategy made by LSTM model outperformed than other strategies. This paper also explored the importance of factors, and it turned out that the factors related to interests predicted well in foreign exchange return.參考文獻 [1] Asness, C.S., Moskowitz, T.J., & Pedersen, L. (2013). Value and Momentum Everywhere. Journal of Finance, 68(3), 929-985.[2] Bryan, K., Dacheng, X. & Shihao, G. (2019). Empirical Asset Pricing via Machine Learning. The Review of Financial Studies, 33(5), 2223-2273.[3] Burnside, C., Eichenbaum, M., Kleshchelski, I., Rebelo, S. (2006). The returns to currency spec- ulation. NBER Working Paper 12489.[4] Brunnermeier, M.K., Nagel, S., & Pedersen, L.H. (2009). Carry Trades and Currency Crashes. NBER Macroeconomics Annual, 23, 313-347.[5] Burnside, C., Eichenbaum, M., & Rebelo, S. (2011). Carry Trade and Momentum in Currency Markets. Annual Review of Financial Economics, 3(1), 511-535.[6] Chaboud, A.P., & Wright, J.H. (2005). Uncovered interest parity: it works, but not for long. Journal of International Economics, 66(2), 349-362.[7] Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.).[8] Cover, T.M. and Hart, P.E. (1967) Nearest Neighbor Pattern Classification. IEEE. Transactions on Information Theory, 13, 21-27.[9] Fama, E.F., & French, K.R. (1993). Common risk factors in the return on stocks and bonds. Journal of Financial Economics, 33(1), 3-56.[10] Fang G. Liu W. Wang L. (2020). A machine learning approach to select features important to stroke prognosis. Computational Biology and Chemistry, 88, 107316.[11] Filippou, I., & Taylor, M. P. (2017). Common Macro Factors and Currency Premia Journal of Financial and Quantitative Analysis, 52(4), 1731-1763.[12] Batista, G. & Monard, M. C. (2003). An Analysis of Four Missing Data TreatmentMethods for Supervised Learning. Applied Artificial Intelligence 17: 519–533.[13] Huang, K., Qiao, M., Liu, X., Liu, S., Dai, M. (2019). Computer Vision and Metrics Learning for Hypothesis Testing: An Application of Q-Q Plot for Normality Test.[14] Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. Journal of Finance, 48(1), 65-91.[15] Kroencke, T.A., Schindler, F., & Schrimpf,A. (2014). International diversification benefits with foreign exchange investment styles. Review of Finance, 18(5),1847- 1883.[16] Lintner, J. (1965). The Valuation of Risk Assets and the Selection of Risky Investments in Stock Portfolios and Capital Budgets. Review of Economics and Statistics, 47(1), 13-37.[17] Lakonishok, J., Shleifer, A., & Vishny, R. W. (1994). Contrarian Investment, Extrapolation, and Risk. Journal of Finance, 49(5), 1541-1578.[18] Lustig, H., Roussanov, N., & Verdelhan, A. (2011). Common Risk Factor in Currency Markets. Review of Financial Studies, 24(11), 3731-3777.[19] Menkhoff, L., Sarno, L., Shmeling, M., & Schrimpf, A. (2012b). Currency Momentum Strategies. Journal of Financial Economics, 106(3), 660-684.[20] Menkhoff, L., Sarno, L., Shmeling, M., & Schrimpf, A. (2016). Currency value. Review of Financial Studies, 30(2), 416-441.[21] Moosa, I. A. (2010). The Profitability of Carry Trade - La redditività del carry trade. Economia Internazionale / International Economics, 63(3), 361-380.[22] Nelson, M. Q., Pereira, A. C. M., & de Oliveira, R. A. (2017). Stock markets price movement prediction with LSTM neural networks. International Joint Conference on Neural Networks (IJCNN). Piscataway, NJ: IEEE, pp. 1419-1426.[23] 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.[24] Qi, L., Khushi, M., Poon, J. (2020). Event-driven LSTM for forex price prediction. In Proceedings of the 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), Gold Coast, Australia, 16-18.[25] Raza, A. (2015). Are Value Strategies Profitable in the Foreign Exchange Market.[26] Rana, M., Uddin, M., & Hoque, M. (2019). Effects of Activation Functions and Optimizers on Stock Price Prediction using LSTM Recurrent Networks. Proceedings of the 2019 3rd International Conference on Computer Science and Artificial, 354-358.[27] Sharpe, W.F. (1964). Capital Asset Prices: A Theory of Market Equilibrium Under Conditions of Risk. Journal of Finance, 19(3), 425-442.[28] Shapiro, S.S., Wilk, M.B. (1965). An analysis of variance test for normality (Complete samples). Biometrika 52, 591–611. 描述 碩士
國立政治大學
金融學系
108352009資料來源 http://thesis.lib.nccu.edu.tw/record/#G0108352009 資料類型 thesis dc.contributor.advisor 林建秀 zh_TW dc.contributor.advisor Ling, Chien-Hsiu en_US dc.contributor.author (Authors) 黃紹瑋 zh_TW dc.contributor.author (Authors) Huang, Shao-Wei en_US dc.creator (作者) 黃紹瑋 zh_TW dc.creator (作者) Huang, Shao-Wei en_US dc.date (日期) 2021 en_US dc.date.accessioned 4-Aug-2021 14:49:59 (UTC+8) - dc.date.available 4-Aug-2021 14:49:59 (UTC+8) - dc.date.issued (上傳時間) 4-Aug-2021 14:49:59 (UTC+8) - dc.identifier (Other Identifiers) G0108352009 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/136355 - 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) 108352009 zh_TW dc.description.abstract (摘要) 本研究使用總經因子和個別外匯因子之交乘項作為LSTM模型的因子，希望藉由深度學習模型來捕捉總經因子和個別外匯因子的互動，並比較其對於外匯超額報酬之解釋力和傳統四因子(利差、動能、價值、市場因子)在線性模型(OLS)上對外匯超額報酬之解釋力的差異。而在因子的部分，本文做了特徵篩選的處理，希望能提升模型的預測力，最後在比較樣本外R^2時，發現LSTM模型的表現優於OLS模型。接著，將預測力較好的LSTM模型進行策略交易，把LSTM模型預測出的國家超額報酬進行排列，買入預測前25%的國家貨幣，賣出預測後25%的國家貨幣，進而和傳統價值、動能及利差交易策略建構的投資組合做比較，並以夏普比率(Sharpe Ratio)及卡馬比率(Calmar Ratio)當作績效的衡量，最後在結果上發現LSTM模型建立的投資組合績效優於傳統價值、動能及利差因子進行的交易策略。另外，本文最終也探討因子之重要度，發現和利率相關的總經因子對於外匯超額報酬有不錯的預測能力。 zh_TW dc.description.abstract (摘要) This paper used the covariates which are the product of macroeconomic factors and specific foreign exchange factors to train LSTM model, and author hopes to capture the interaction between macroeconomic factors and specific foreign exchange factors through LSTM model. Additionally, author applied feature selection method, trying to enhance the prediction of models. The purpose of using LSTM model with covariates and OLS model with four traditional factors is to compare the prediction of foreign exchange return. Finally, LSTM model performed better than OLS model in the values of coefficient of determination.Furthermore, the paper used the outcomes predicted by LSTM model to trade in currency markets and tried to compare the performance made by value trade, momentum trade and carry trade. All strategies were made to buy the currencies in the top quarter of predictions and to sell currencies in the bottom quarter of prediction. Author used Sharpe ratio and Calmar ratio to measure the performance of all strategies, finding that the strategy made by LSTM model outperformed than other strategies. This paper also explored the importance of factors, and it turned out that the factors related to interests predicted well in foreign exchange return. 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 1第三節 論文架構及章節介紹 2第二章 文獻回顧 3第一節 利差交易策略 3第二節 價值交易策略 4第三節 動能交易策略 5第四節 KNN機器學習 6第五節 LSTM深度學習 6第三章 研究方法 8第一節 研究流程圖 8第二節 研究方法 9第四章 資料 15第一節 資料來源 15第二節 因子建構 17第五章 實證分析 22第一節 國家樣本、因子特徵選取 22第二節 模型比較 31第三節 策略投資組合 32第四節 因子重要度 36第六章 結論與未來建議 38第一節 結論 38第二節 未來建議 38參考文獻 39 zh_TW dc.format.extent 2201484 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0108352009 en_US dc.subject (關鍵詞) 外匯交易 zh_TW dc.subject (關鍵詞) 利差交易策略 zh_TW dc.subject (關鍵詞) 動能交易策略 zh_TW dc.subject (關鍵詞) 價值交易策略 zh_TW dc.subject (關鍵詞) 深度學習 zh_TW dc.subject (關鍵詞) 特徵篩選 zh_TW dc.subject (關鍵詞) 因子重要度 zh_TW dc.subject (關鍵詞) Foreign exchange trading en_US dc.subject (關鍵詞) carry trade en_US dc.subject (關鍵詞) momentum trade en_US dc.subject (關鍵詞) value trade en_US dc.subject (關鍵詞) LSTM en_US dc.subject (關鍵詞) feature selection en_US dc.subject (關鍵詞) feature importance en_US dc.title (題名) 基於特徵選取之LSTM模型應用:外匯超額報酬預測 zh_TW dc.title (題名) LSTM Model with Feature Selection for Foreign Exchange Return Prediction en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) [1] Asness, C.S., Moskowitz, T.J., & Pedersen, L. (2013). Value and Momentum Everywhere. Journal of Finance, 68(3), 929-985.[2] Bryan, K., Dacheng, X. & Shihao, G. (2019). Empirical Asset Pricing via Machine Learning. The Review of Financial Studies, 33(5), 2223-2273.[3] Burnside, C., Eichenbaum, M., Kleshchelski, I., Rebelo, S. (2006). The returns to currency spec- ulation. NBER Working Paper 12489.[4] Brunnermeier, M.K., Nagel, S., & Pedersen, L.H. (2009). Carry Trades and Currency Crashes. NBER Macroeconomics Annual, 23, 313-347.[5] Burnside, C., Eichenbaum, M., & Rebelo, S. (2011). Carry Trade and Momentum in Currency Markets. Annual Review of Financial Economics, 3(1), 511-535.[6] Chaboud, A.P., & Wright, J.H. (2005). Uncovered interest parity: it works, but not for long. Journal of International Economics, 66(2), 349-362.[7] Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.).[8] Cover, T.M. and Hart, P.E. (1967) Nearest Neighbor Pattern Classification. IEEE. Transactions on Information Theory, 13, 21-27.[9] Fama, E.F., & French, K.R. (1993). Common risk factors in the return on stocks and bonds. Journal of Financial Economics, 33(1), 3-56.[10] Fang G. Liu W. Wang L. (2020). A machine learning approach to select features important to stroke prognosis. Computational Biology and Chemistry, 88, 107316.[11] Filippou, I., & Taylor, M. P. (2017). Common Macro Factors and Currency Premia Journal of Financial and Quantitative Analysis, 52(4), 1731-1763.[12] Batista, G. & Monard, M. C. (2003). An Analysis of Four Missing Data TreatmentMethods for Supervised Learning. Applied Artificial Intelligence 17: 519–533.[13] Huang, K., Qiao, M., Liu, X., Liu, S., Dai, M. (2019). Computer Vision and Metrics Learning for Hypothesis Testing: An Application of Q-Q Plot for Normality Test.[14] Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. Journal of Finance, 48(1), 65-91.[15] Kroencke, T.A., Schindler, F., & Schrimpf,A. (2014). International diversification benefits with foreign exchange investment styles. Review of Finance, 18(5),1847- 1883.[16] Lintner, J. (1965). The Valuation of Risk Assets and the Selection of Risky Investments in Stock Portfolios and Capital Budgets. Review of Economics and Statistics, 47(1), 13-37.[17] Lakonishok, J., Shleifer, A., & Vishny, R. W. (1994). Contrarian Investment, Extrapolation, and Risk. Journal of Finance, 49(5), 1541-1578.[18] Lustig, H., Roussanov, N., & Verdelhan, A. (2011). Common Risk Factor in Currency Markets. Review of Financial Studies, 24(11), 3731-3777.[19] Menkhoff, L., Sarno, L., Shmeling, M., & Schrimpf, A. (2012b). Currency Momentum Strategies. Journal of Financial Economics, 106(3), 660-684.[20] Menkhoff, L., Sarno, L., Shmeling, M., & Schrimpf, A. (2016). Currency value. Review of Financial Studies, 30(2), 416-441.[21] Moosa, I. A. (2010). The Profitability of Carry Trade - La redditività del carry trade. Economia Internazionale / International Economics, 63(3), 361-380.[22] Nelson, M. Q., Pereira, A. C. M., & de Oliveira, R. A. (2017). Stock markets price movement prediction with LSTM neural networks. International Joint Conference on Neural Networks (IJCNN). Piscataway, NJ: IEEE, pp. 1419-1426.[23] 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.[24] Qi, L., Khushi, M., Poon, J. (2020). Event-driven LSTM for forex price prediction. In Proceedings of the 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), Gold Coast, Australia, 16-18.[25] Raza, A. (2015). Are Value Strategies Profitable in the Foreign Exchange Market.[26] Rana, M., Uddin, M., & Hoque, M. (2019). Effects of Activation Functions and Optimizers on Stock Price Prediction using LSTM Recurrent Networks. Proceedings of the 2019 3rd International Conference on Computer Science and Artificial, 354-358.[27] Sharpe, W.F. (1964). Capital Asset Prices: A Theory of Market Equilibrium Under Conditions of Risk. Journal of Finance, 19(3), 425-442.[28] Shapiro, S.S., Wilk, M.B. (1965). An analysis of variance test for normality (Complete samples). Biometrika 52, 591–611. zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202100706 en_US
