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題名 機器學習匯率訂價投資組合
Machine Learning for Foreign Exchange Pricing Investment Portfolio作者 林庭陞
Lin, Ting-Sheng貢獻者 林建秀
林庭陞
Lin, Ting-Sheng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
機器學習策略
超參數
FX trade
Carry trade
Momentum strategy
Value strategy
Machine learning strategy
Hyper-parameter日期 2020 上傳時間 3-Aug-2020 17:38:46 (UTC+8) 摘要 本研究主要是以總經因子(x_t)以及外匯個別因子(c_(i,t))經由寇雷克乘積運算得到的預測因子(z_(i,t))為基礎，進行機器學習模型的訓練，其中包括隨機森林(Random Forest, RF)、梯度提升樹(Gradient Boosted Trees, GBRT)、神經網路(Neural Network, NN)模型。接著，再從驗證集選擇超參數使得外匯超額報酬的預測準確度最高，即驗證集R^2。但實際要追求的是測試集外匯超額報酬的準確度，即測試集R^2。故在訓練期間(1997/01至2015/12)，將共19國貨幣外匯超額報酬，即應變數，及預測因子(z_(i,t))，即自變數做參數估計。旨在探索機器學習模型的測試集R^2與二因子模型(市場因子及利差策略因子)及四因子模型(市場因子、利差、動能及價值策略因子)的高低。最終發現機器學習模型的測試集R^2皆較二因子及四因子模型高。接著，使用已經訓練好的機器學習模型對測試集的19國貨幣做外匯超額報酬預測，預測為最高的前25%的國家貨幣進行買入，同時預測為最低的後25%的國家貨幣進行賣出。目的就是要對價值、動能及利差策略測度所構建出的買入前25%的國家貨幣，賣出後25%的國家貨幣策略做比較。同時，進行平均值(Avg)、標準差(Std)、夏普比率(Sharpe Ratio)及最大虧損(Max DD)的比較。可發現大抵上機器學習模型的夏普比率較價值、動能及利差策略來的佳。而在驗證集中選擇的超參數可能對R^2帶來的影響也可能是一大重點。整體而言，機器學習模型策略的累積報酬優於價值、動能及利差策略。
This paper mainly trained machine learning models including Random Forest, Gradient Boosted Trees, and Neural Network models based on prediction factors(z_(i,t)) calculated by Kronecker product of macro-economical factors(x_t) and separated foreign exchange factors(c_(i,t)).Then selected the hyper-parameters from the validation set to make prediction accuracy of foreign exchange excess return, namely R^2 of validation set, highest. In reality, we pursued the highest foreign exchange excess return R^2 of test set, namely R^2 of test set. So we used foreign exchange excess return of 19 kinds of currencies(dependent variable) and prediction factors(independent variables) during train set period from January 1997 to December 2015 to estimate parameters of different models. We want to see whether R^2 of test set of machine learning models is higher than that of two-factors and four-factors models. Finally, machine learning models performed better than two-factors and four-factors models indeed.Next used the well-trained machine learning models to predict foreign exchange excess return of 19 kinds of currencies in the test set. We buy the currencies predicted the top quarter by the models, selling the currencies predicted the bottom quarter. The purpose is to compare with the strategies constructed by different measures of Value, Momentum, and Carry strategies. Meanwhile, we do the comparison of Avg, Std, Sharpe Ratio, and Max DD. Generally, we can find Sharpe Ratio of machine learning models is better than that of Value, Momentum, and Carry strategies. And the hyper-parameters chosen from validation set might be another key point. To sum up, the cumulative return of machine learning models is better than that of Value, Momentum, and Carry strategies.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] Adlar, J.K., Amir, E.K., & Andrew, W.L. (2010). Consumer credit-risk models via machine learning algorithms. Journal of Banking & Finance, 34, 2767-2787.[3] Akhtar, S., Andrew, W.L., Brian, C., Florentin, B., Qingqing, C. & Sanmay, D. (2016). Risk and risk management in the credit card industry. Journal of Banking & Finance, 72, 218-239.[4] Andrew, W.L., James, M.H. & Tomaso, P. (1994). A nonparametric approach to pricing and hedging derivative securities via learning networks. The Journal of Finance, 49, 851-889.[5] Apaar, S., Justin, S. & Kay G. (2016). Deep learning for mortgage risk. Available at SSRN 2799443.[6] Asness, C.S., Moskowitz, T.J., & Pedersen, L. (2013). Value and Momentum Everywhere. Journal of Finance, 68(3), 929-985.[7] Baberies, N., Shleifer, A., & Vishny, R. (1998). A model of investor sentiment. Journal of Finance, 49(3), 307-343.[8] Barroso, P., & Santa-Clara, P. (2014). Beyond the carry trade: Optimal currency portfolios. Journal of Financial and Quantitative Analysis, 50(5), 1037-1056.[9] Benjamin, M. & Tom, Z. (2016). Tree-based conditional portfolio sorts: The relation between past and future stock returns. Available at SSRN 2740751.[10] Bilson, J.F.O. (1981). The "Speculative Efficiency" Hypothesis. Journal of Business, 54(3), 435-451.[11] Brunnermeier, M.K., Nagel, S., & Pedersen, L.H. (2009). Carry Trades and Currency Crashes. NBER Macroeconomics Annual, 23, 313-347.[12] Bryan, K., Dacheng, X. & Shihao, G. (2019). Empirical Asset Pricing via Machine Learning. The Review of Financial Studies , 33(5), 2223-2273.[13] Burnside, C., Eichenbaum, M., & Rebelo, S. (2011). Carry Trade and Momentum in Currency Markets. Annual Review of Financial Economics, 3(1), 511-535.[14] Campbell, R.H., Heqing, Z., & Yan, L. (2016). … and the cross-section of expected returns. Review of Financial Studies, 29, 5-68.[15] Campbell, R.H., & Wayne, E.F. (1999). Conditioning variables and the cross-section of stock returns. Journal of Finance, 54, 1325-1360.[16] Chaboud, A.P., & Wright, J.H. (2005). Uncovered interest parity: it works, but not for long. Journal of International Economics, 66(2), 349-362.[17] Chew, L.T., Jingtao, Y. & Yili, L. (2000). Option price forecasting using neural networks. Omega, 28, 455-466.[18] Chordia, T., & Shivakumar, L. (2002). Momentum, Business Cycle, and Time- varying Expected Returns. Journal of Finance, 57(2), 985-1019.[19] Daniel, K., Hirshleifer, D., & Subrahmanyam, A. (1998). Investor Psychology and Security Market Under- and Overreactions. Journal of Finance, 53(6), 1839-1885.[20] Fama, E.F. (1970). Efficient Capital Market: A Review of Theory and Empirical Work. Journal of Finance, 25(2), 383-417.[21] Fama, E.F. (1984). Forward and Spot Exchange Rates. Journal of Monetary Economics, 14(3), 319-338.[22] Fama, E.F., & French, K.R. (1988). Dividend yields and expected stock returns. Journal of Financial Economics, 22(1), 3-25.[23] Fama, E.F., & French, K.R. (1992). The Cross-Section of Expected Stock Returns. Journal of Finance, 47(2), 427-465.[24] 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.[25] Fama, E.F., & French, K.R. (1996). Multifactor Explanation of Asset Pricing Anomalies. Journal of Finance, 51(1), 55-84.[26] Fama, E.F., & MacBeth, J.D. (1973). Risk, Return, and Equilibrium: Empirical Tests. Journal of Political Economy, 81(3), 607–636.[27] Filippou, I., & Taylor, M. P. (2017). Common Macro Factors and Currency Premia. Journal of Financial and Quantitative Analysis, 52(4), 1731-1763.[28] Flood, M.D. (1994). Market structure and inefficiency in the foreign exchange market. Journal of International Money and Finance, 13(2), 131-158.[29] Galati, G., & Melvin, M. (2004). Why has FX trading surged?. BIS Quarterly Review, Dec. 2004, 67-74.[30] Grinblatt, M., & Han, B. (2005). Prospect theory, mental accounting, and momentum. Journal of Financial Economics, 78(2), 311-339.[31] Heaton, J.B., Polson, N.G., & Witte, J.H. (2016). Deep learning in Finance. arXiv preprint arXiv:1602.06561.[32] Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. Journal of Finance, 48(1), 65-91.[33] Jegadeesh, N., & Titman, S. (2002). Profitability of Momentum Strategies: An Evaluation of Alternative Explanations. Journal of Finance, 56(2), 699-720.[34] Jylhä, P., & Suominen, M. (2011). Speculative capital and currency carry trades. Journal of Financial Economics, 99(1), 60-75.[35] Jylhä, P., Rinne, K., & Suominen, M. (2014). Do Hedge Funds Supply or Demand Liquidity?. Review of Finance, 18(4), 1259-1298.[36] Kroencke, T. A., Schindler, F., & Schrimpf, A. (2014). International diversification benefits with foreign exchange investment styles. Review of Finance, 18(5),1847- 1883.[37] Lakonishok, J., Shleifer, A., & Vishny, R. W. (1994). Contrarian Investment, Extrapolation, and Risk. Journal of Finance, 49(5), 1541-1578.[38] 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.[39] Lustig, H., Roussanov, N., & Verdelhan, A. (2011). Common Risk Factor in Currency Markets. Review of Financial Studies, 24(11), 3731-3777.[40] Lustig, H., Roussanov, N., & Verdelhan, A. (2014). Countercyclical currency risk premia. Journal of Financial Economics, 111(3), 527-553.[41] Menkhoff, L., Sarno, L., Shmeling, M., & Schrimpf, A. (2012a). Carry Trades and Global Foreign Exchange Volatility. Journal of Finance, 67(2), 681-718.[42] Menkhoff, L., Sarno, L., Shmeling, M., & Schrimpf, A. (2012b). Currency Momentum Strategies. Journal of Financial Economics, 106(3), 660-684.[43] Menkhoff, L., Sarno, L., Shmeling, M., & Schrimpf, A. (2016). Currency value. Review of Financial Studies, 30(2), 416-441.[44] Moosa, I. A. (2010). The Profitability of Carry Trade - La redditività del carry trade. Economia Internazionale / International Economics, 63(3), 361-380.[45] 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.[46] Paster ,L., & Stambaugh, R. F. (2001). Liquidity Risk and Expected Stock Returns. Journal of Political Economy, 111(3), 642-685.[47] Plantin, G., & Shin, H. S. (2011). Carry Trades, Monetary Policy and Speculative Dynamics. CEPR Discussion Paper (February 2011), DP8224.[48] Raza, A. (2015). Are Value Strategies Profitable in the Foreign Exchange Market.[49] Rouwenhorst, K.G. (1998). International momentum strategies. Journal of Finance, 53(1), 267-284.[50] Rouwenhorst, K.G. (1999). Local return factors and turnover in emerging stock markets. Journal of Finance, 54(4), 1439-1464.[51] Sharpe, W. F. (1964). Capital Asset Prices: A Theory of Market Equilibrium Under Conditions of Risk. Journal of Finance, 19(3), 425-442. 描述 碩士
國立政治大學
金融學系
107352026資料來源 http://thesis.lib.nccu.edu.tw/record/#G0107352026 資料類型 thesis dc.contributor.advisor 林建秀 zh_TW dc.contributor.author (Authors) 林庭陞 zh_TW dc.contributor.author (Authors) Lin, Ting-Sheng en_US dc.creator (作者) 林庭陞 zh_TW dc.creator (作者) Lin, Ting-Sheng en_US dc.date (日期) 2020 en_US dc.date.accessioned 3-Aug-2020 17:38:46 (UTC+8) - dc.date.available 3-Aug-2020 17:38:46 (UTC+8) - dc.date.issued (上傳時間) 3-Aug-2020 17:38:46 (UTC+8) - dc.identifier (Other Identifiers) G0107352026 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/130993 - 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) 107352026 zh_TW dc.description.abstract (摘要) 本研究主要是以總經因子(x_t)以及外匯個別因子(c_(i,t))經由寇雷克乘積運算得到的預測因子(z_(i,t))為基礎，進行機器學習模型的訓練，其中包括隨機森林(Random Forest, RF)、梯度提升樹(Gradient Boosted Trees, GBRT)、神經網路(Neural Network, NN)模型。接著，再從驗證集選擇超參數使得外匯超額報酬的預測準確度最高，即驗證集R^2。但實際要追求的是測試集外匯超額報酬的準確度，即測試集R^2。故在訓練期間(1997/01至2015/12)，將共19國貨幣外匯超額報酬，即應變數，及預測因子(z_(i,t))，即自變數做參數估計。旨在探索機器學習模型的測試集R^2與二因子模型(市場因子及利差策略因子)及四因子模型(市場因子、利差、動能及價值策略因子)的高低。最終發現機器學習模型的測試集R^2皆較二因子及四因子模型高。接著，使用已經訓練好的機器學習模型對測試集的19國貨幣做外匯超額報酬預測，預測為最高的前25%的國家貨幣進行買入，同時預測為最低的後25%的國家貨幣進行賣出。目的就是要對價值、動能及利差策略測度所構建出的買入前25%的國家貨幣，賣出後25%的國家貨幣策略做比較。同時，進行平均值(Avg)、標準差(Std)、夏普比率(Sharpe Ratio)及最大虧損(Max DD)的比較。可發現大抵上機器學習模型的夏普比率較價值、動能及利差策略來的佳。而在驗證集中選擇的超參數可能對R^2帶來的影響也可能是一大重點。整體而言，機器學習模型策略的累積報酬優於價值、動能及利差策略。 zh_TW dc.description.abstract (摘要) This paper mainly trained machine learning models including Random Forest, Gradient Boosted Trees, and Neural Network models based on prediction factors(z_(i,t)) calculated by Kronecker product of macro-economical factors(x_t) and separated foreign exchange factors(c_(i,t)).Then selected the hyper-parameters from the validation set to make prediction accuracy of foreign exchange excess return, namely R^2 of validation set, highest. In reality, we pursued the highest foreign exchange excess return R^2 of test set, namely R^2 of test set. So we used foreign exchange excess return of 19 kinds of currencies(dependent variable) and prediction factors(independent variables) during train set period from January 1997 to December 2015 to estimate parameters of different models. We want to see whether R^2 of test set of machine learning models is higher than that of two-factors and four-factors models. Finally, machine learning models performed better than two-factors and four-factors models indeed.Next used the well-trained machine learning models to predict foreign exchange excess return of 19 kinds of currencies in the test set. We buy the currencies predicted the top quarter by the models, selling the currencies predicted the bottom quarter. The purpose is to compare with the strategies constructed by different measures of Value, Momentum, and Carry strategies. Meanwhile, we do the comparison of Avg, Std, Sharpe Ratio, and Max DD. Generally, we can find Sharpe Ratio of machine learning models is better than that of Value, Momentum, and Carry strategies. And the hyper-parameters chosen from validation set might be another key point. To sum up, the cumulative return of machine learning models is better than that of Value, Momentum, and Carry strategies. 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 3第一節 價值交易文獻探討 3第二節 利差交易文獻探討 4第三節 動能交易文獻探討 5第四節 機器學習文獻探討 6第三章 樣本選擇與研究方法 7第一節 樣本選擇 7第二節 策略因子建構 10第三節 研究方法 14第四章 實證結果分析 27第五章 結論與建議 36參考文獻 37 zh_TW dc.format.extent 2518188 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0107352026 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) 機器學習策略 zh_TW dc.subject (關鍵詞) 超參數 zh_TW dc.subject (關鍵詞) FX trade en_US dc.subject (關鍵詞) Carry trade en_US dc.subject (關鍵詞) Momentum strategy en_US dc.subject (關鍵詞) Value strategy en_US dc.subject (關鍵詞) Machine learning strategy en_US dc.subject (關鍵詞) Hyper-parameter en_US dc.title (題名) 機器學習匯率訂價投資組合 zh_TW dc.title (題名) Machine Learning for Foreign Exchange Pricing Investment Portfolio 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] Adlar, J.K., Amir, E.K., & Andrew, W.L. (2010). Consumer credit-risk models via machine learning algorithms. Journal of Banking & Finance, 34, 2767-2787.[3] Akhtar, S., Andrew, W.L., Brian, C., Florentin, B., Qingqing, C. & Sanmay, D. (2016). Risk and risk management in the credit card industry. Journal of Banking & Finance, 72, 218-239.[4] Andrew, W.L., James, M.H. & Tomaso, P. (1994). A nonparametric approach to pricing and hedging derivative securities via learning networks. The Journal of Finance, 49, 851-889.[5] Apaar, S., Justin, S. & Kay G. (2016). Deep learning for mortgage risk. Available at SSRN 2799443.[6] Asness, C.S., Moskowitz, T.J., & Pedersen, L. (2013). Value and Momentum Everywhere. Journal of Finance, 68(3), 929-985.[7] Baberies, N., Shleifer, A., & Vishny, R. (1998). A model of investor sentiment. Journal of Finance, 49(3), 307-343.[8] Barroso, P., & Santa-Clara, P. (2014). Beyond the carry trade: Optimal currency portfolios. Journal of Financial and Quantitative Analysis, 50(5), 1037-1056.[9] Benjamin, M. & Tom, Z. (2016). Tree-based conditional portfolio sorts: The relation between past and future stock returns. Available at SSRN 2740751.[10] Bilson, J.F.O. (1981). The "Speculative Efficiency" Hypothesis. Journal of Business, 54(3), 435-451.[11] Brunnermeier, M.K., Nagel, S., & Pedersen, L.H. (2009). Carry Trades and Currency Crashes. NBER Macroeconomics Annual, 23, 313-347.[12] Bryan, K., Dacheng, X. & Shihao, G. (2019). Empirical Asset Pricing via Machine Learning. The Review of Financial Studies , 33(5), 2223-2273.[13] Burnside, C., Eichenbaum, M., & Rebelo, S. (2011). Carry Trade and Momentum in Currency Markets. Annual Review of Financial Economics, 3(1), 511-535.[14] Campbell, R.H., Heqing, Z., & Yan, L. (2016). … and the cross-section of expected returns. Review of Financial Studies, 29, 5-68.[15] Campbell, R.H., & Wayne, E.F. (1999). Conditioning variables and the cross-section of stock returns. Journal of Finance, 54, 1325-1360.[16] Chaboud, A.P., & Wright, J.H. (2005). Uncovered interest parity: it works, but not for long. Journal of International Economics, 66(2), 349-362.[17] Chew, L.T., Jingtao, Y. & Yili, L. (2000). Option price forecasting using neural networks. Omega, 28, 455-466.[18] Chordia, T., & Shivakumar, L. (2002). Momentum, Business Cycle, and Time- varying Expected Returns. Journal of Finance, 57(2), 985-1019.[19] Daniel, K., Hirshleifer, D., & Subrahmanyam, A. (1998). Investor Psychology and Security Market Under- and Overreactions. Journal of Finance, 53(6), 1839-1885.[20] Fama, E.F. (1970). Efficient Capital Market: A Review of Theory and Empirical Work. Journal of Finance, 25(2), 383-417.[21] Fama, E.F. (1984). Forward and Spot Exchange Rates. Journal of Monetary Economics, 14(3), 319-338.[22] Fama, E.F., & French, K.R. (1988). Dividend yields and expected stock returns. Journal of Financial Economics, 22(1), 3-25.[23] Fama, E.F., & French, K.R. (1992). The Cross-Section of Expected Stock Returns. Journal of Finance, 47(2), 427-465.[24] 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.[25] Fama, E.F., & French, K.R. (1996). Multifactor Explanation of Asset Pricing Anomalies. Journal of Finance, 51(1), 55-84.[26] Fama, E.F., & MacBeth, J.D. (1973). Risk, Return, and Equilibrium: Empirical Tests. Journal of Political Economy, 81(3), 607–636.[27] Filippou, I., & Taylor, M. P. (2017). Common Macro Factors and Currency Premia. Journal of Financial and Quantitative Analysis, 52(4), 1731-1763.[28] Flood, M.D. (1994). Market structure and inefficiency in the foreign exchange market. Journal of International Money and Finance, 13(2), 131-158.[29] Galati, G., & Melvin, M. (2004). Why has FX trading surged?. BIS Quarterly Review, Dec. 2004, 67-74.[30] Grinblatt, M., & Han, B. (2005). Prospect theory, mental accounting, and momentum. Journal of Financial Economics, 78(2), 311-339.[31] Heaton, J.B., Polson, N.G., & Witte, J.H. (2016). Deep learning in Finance. arXiv preprint arXiv:1602.06561.[32] Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. Journal of Finance, 48(1), 65-91.[33] Jegadeesh, N., & Titman, S. (2002). Profitability of Momentum Strategies: An Evaluation of Alternative Explanations. Journal of Finance, 56(2), 699-720.[34] Jylhä, P., & Suominen, M. (2011). Speculative capital and currency carry trades. Journal of Financial Economics, 99(1), 60-75.[35] Jylhä, P., Rinne, K., & Suominen, M. (2014). Do Hedge Funds Supply or Demand Liquidity?. Review of Finance, 18(4), 1259-1298.[36] Kroencke, T. A., Schindler, F., & Schrimpf, A. (2014). International diversification benefits with foreign exchange investment styles. Review of Finance, 18(5),1847- 1883.[37] Lakonishok, J., Shleifer, A., & Vishny, R. W. (1994). Contrarian Investment, Extrapolation, and Risk. Journal of Finance, 49(5), 1541-1578.[38] 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.[39] Lustig, H., Roussanov, N., & Verdelhan, A. (2011). Common Risk Factor in Currency Markets. Review of Financial Studies, 24(11), 3731-3777.[40] Lustig, H., Roussanov, N., & Verdelhan, A. (2014). Countercyclical currency risk premia. Journal of Financial Economics, 111(3), 527-553.[41] Menkhoff, L., Sarno, L., Shmeling, M., & Schrimpf, A. (2012a). Carry Trades and Global Foreign Exchange Volatility. Journal of Finance, 67(2), 681-718.[42] Menkhoff, L., Sarno, L., Shmeling, M., & Schrimpf, A. (2012b). Currency Momentum Strategies. Journal of Financial Economics, 106(3), 660-684.[43] Menkhoff, L., Sarno, L., Shmeling, M., & Schrimpf, A. (2016). Currency value. Review of Financial Studies, 30(2), 416-441.[44] Moosa, I. A. 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