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題名 Google 的搜尋熱度是否有助於預測匯率？
Does Google Trends-ruled Exchange Rate Predictions Beat Random Walk?作者 張力尹
Chang, Li-Yin貢獻者 郭炳伸
Kuo, Biing-Shen
張力尹
Chang, Li-Yin關鍵詞 匯率
Google趨勢
異質信仰
有限注意力
樣本外預測
短期預測
方向性預測
Exchange rates
Google Trends
Heterogeneous belief
Limited attention
Out‐of‐sample prediction
Short horizon
Directional prediction日期 2019 上傳時間 7-Aug-2019 15:51:14 (UTC+8) 摘要 在匯率預測的研究上，Meese and Rogoff（1983）發現基本面模型的預測力甚至不及隨機漫步模型，經歷了多方研究的驗證後，隨機漫步模型逐漸立於不敗之地。Rossi（2013）從這些研究歸結出兩點：只有在長天期預測時，基本面模型才能打敗隨機漫步，且其預測能力因時而變。本文以創新的方式，試圖捕捉因時而變的市場訊號。我們選擇使用Google Trends搜尋熱度（下稱GTI），每日提取外匯市場的訊號，進行匯率變動的方向性預測。不同於過去研究時常採用的中至長天期預測，本文憑藉著GTI能被即時觀察及取得的性質，進行短天期預測。我們最大的創新在於，透過非線性的雙層預測方法及簡單的加總預測，捕捉並統合外匯市場因時而變的特質。此雙層方法係以GTI在第一層篩選預測模型，並透過模型於第二層預測匯率變動方向。儘管雙層預測的結果再次確認了預測能力因時而變的性質，最終在簡單的加總預測中，本文的預測在統計上取得了相對隨機漫步更高的勝出率。這確認了GTI在追蹤此變動性質的總體力量，並證實以GTI在短期捕捉此變動性質的合理性。
Since Meese and Rogoff (1983), fundamental models’ inabilities to beat random walk in exchange rate predictions have been widely documented. It is concluded by Rossi (2013) that the fundamental models can only beat random walk at long horizons, and the predictive ability at most be time-varying and occasional. Our research invokes another new attempt that deals directly with the varying predictive ability for fundamental models across time.We ask whether the time-varying nature can be tracked straightforward, when signaling to market. If the signals can be detected and received, there is a higher chance to improve the exchange rate predictability. Tapping into the information extracted from Google Trends, we check if the signals are captured and reflected. Unlike past studies where predictions were usually conducted at medium-to-long horizons, we focus on out-of-sample daily predictions at short horizons. Given the observable and obtainable real time Google Trends index (GTI), we justify the high forecast frequency. Aside from that, our predictions greatly differentiate from past studies with an easy yet novel 2-layer approach following an aggregated result. For the 2-layer approach, we have GTI-ruled model selection in the first layer and predictive models in the second layer. Rather than evaluating the performance in statistical sense, our study places an emphasis on that of the directions of change.The results for the 2-layer predictions though reaffirm the time-varying nature, by counting the statistical success and failure, a higher rate of beating the random walk confirms GTI’s aggregate power in tracking the such nature. The aggregated predictions further legitimate the use of Google Trends search intensity in capturing such time-varying nature at short horizons.參考文獻 Bacchetta, P. and Van Wincoop, E. (2003), “Can Information Heterogeneity Explain the Exchange Rate Determination Puzzle?” National Bureau of Economic Research Working Paper No. 9498.Bacchetta, P. and Van Wincoop, E. (2004), “A Scapegoat Model of Exchange-Rate Fluctuations,” American Economic Review, vol. 94 (2), pages 114-118.Brock, W., Lakonishok, J. and LeBaron, B. (1992), “Simple Technical Trading Rules and the Stochastic Properties of Stock Returns,” The Journal of Finance, 47(5), 1731-1764.Bulut, L. (2018), “Google Trends and the Forecasting Performance of Exchange Rate Models,” Journal of Forecasting, 37(3), 303-315.Chinn, M. D. and Meese, R. A. (1995), “Banking on Currency Forecasts: How Predictable Is Change in Money?” Journal of International Economics, vol. 38(1–2), pages 161-178.Clark, T. E. and West, K. D. (2007), “Approximately Normal Tests for Equal Predictive Accuracy in Nested Models,” Journal of Econometrics, vol. 138(1), pages 291-311.Da, Z., Engelberg, J. and Gao, P. (2011), “In Search of Attention,” Journal of Finance, vol. 66, issue 5, 1461-1499.De Grauwe, P. and Grimaldi, M. (2006), “Exchange Rate Puzzles: A Tale of Switching Attractors,” European Economic Review, vol. 50, issue 1, 1-33Dick, C. D. and Menkhoff, L. (2013), “Exchange Rate Expectations of Chartists and Fundamentalists,” Journal of Economic Dynamics and Control, vol. 37, issue 7, pages 1362-1383.Engel, C. and Hamilton, J. (1990), “Long Swings in the Dollar: Are They in the Data and Do Markets Know It?” American Economic Review, vol. 80(4), pages 689-713.Frankel, J. A. and Froot, K. (1990), “Chartists, Fundamentalists, and Trading in the Foreign Exchange Market,” American Economic Review, 80(2), 181-85.Kahneman, D. (1973), “Attention and Effort,” Prentice-Hall, Englewood Cliffs, NJ.Kuo, B.-S., Lan, C.-Y. and Yeh, B.-H. (2018), “Carry Trade Strategy in the Presence of Central Bank Interventions: The Economic Value of Fundamentals,” Taiwan Economic Review, 46, 363-399. (in Chinese)Mark, N. C. (1995), “Exchange Rates and Fundamentals: Evidence on Long-Horizon Predictability,” American Economic Review, vol. 85(1), pages 201-218.Mark, N. C. and Sul, D. (2001), “Nominal Exchange Rates and Monetary Fundamentals: Evidence from a Small Post-Bretton Woods Panel,” Journal of International Economics, vol. 53(1), pages 29-52.Markiewicz, A., Verhoeks, R., Verschoor, W. and Zwinkels, R. (2017), “The Winner Takes it All: Predicting Exchange Rates with Google Trend,” SSRN Electronic Journal, 10.2139/ssrn.3020932.Meese, R. and Rogoff, K. (1983), “Empirical Exchange Rate Models of the Seventies: Do They Fit Out of Sample?” Journal of International Economics, vol. 14(1-2), pages 3-24.Molodtsova, T. and Papell, D. H. (2009), “Out-of-sample Exchange Rate Predictability with Taylor Rule Fundamentals,” Journal of International Economics, vol. 77(2), pages 167-180.Rime, D., Sarno, L. and Sojli, E. (2010), “Exchange Rate Forecasting, Order Flow and Macroeconomic Information,” Journal of International Economics, vol. 80(1), pages 72-88.Rossi, B. (2013), “Exchange Rate Predictability,” CAFE Research Paper No. 13.16.Spronk, R., Verschoor, W. F. C. and Zwinkels, R. C. J. (2013), “Carry trade and foreign exchange rate puzzles,” European Economic Review, vol. 60(C), pages 17-31. 描述 碩士
國立政治大學
國際經營與貿易學系
107351005資料來源 http://thesis.lib.nccu.edu.tw/record/#G0107351005 資料類型 thesis dc.contributor.advisor 郭炳伸 zh_TW dc.contributor.advisor Kuo, Biing-Shen en_US dc.contributor.author (Authors) 張力尹 zh_TW dc.contributor.author (Authors) Chang, Li-Yin en_US dc.creator (作者) 張力尹 zh_TW dc.creator (作者) Chang, Li-Yin en_US dc.date (日期) 2019 en_US dc.date.accessioned 7-Aug-2019 15:51:14 (UTC+8) - dc.date.available 7-Aug-2019 15:51:14 (UTC+8) - dc.date.issued (上傳時間) 7-Aug-2019 15:51:14 (UTC+8) - dc.identifier (Other Identifiers) G0107351005 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/124644 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 國際經營與貿易學系 zh_TW dc.description (描述) 107351005 zh_TW dc.description.abstract (摘要) 在匯率預測的研究上，Meese and Rogoff（1983）發現基本面模型的預測力甚至不及隨機漫步模型，經歷了多方研究的驗證後，隨機漫步模型逐漸立於不敗之地。Rossi（2013）從這些研究歸結出兩點：只有在長天期預測時，基本面模型才能打敗隨機漫步，且其預測能力因時而變。本文以創新的方式，試圖捕捉因時而變的市場訊號。我們選擇使用Google Trends搜尋熱度（下稱GTI），每日提取外匯市場的訊號，進行匯率變動的方向性預測。不同於過去研究時常採用的中至長天期預測，本文憑藉著GTI能被即時觀察及取得的性質，進行短天期預測。我們最大的創新在於，透過非線性的雙層預測方法及簡單的加總預測，捕捉並統合外匯市場因時而變的特質。此雙層方法係以GTI在第一層篩選預測模型，並透過模型於第二層預測匯率變動方向。儘管雙層預測的結果再次確認了預測能力因時而變的性質，最終在簡單的加總預測中，本文的預測在統計上取得了相對隨機漫步更高的勝出率。這確認了GTI在追蹤此變動性質的總體力量，並證實以GTI在短期捕捉此變動性質的合理性。 zh_TW dc.description.abstract (摘要) Since Meese and Rogoff (1983), fundamental models’ inabilities to beat random walk in exchange rate predictions have been widely documented. It is concluded by Rossi (2013) that the fundamental models can only beat random walk at long horizons, and the predictive ability at most be time-varying and occasional. Our research invokes another new attempt that deals directly with the varying predictive ability for fundamental models across time.We ask whether the time-varying nature can be tracked straightforward, when signaling to market. If the signals can be detected and received, there is a higher chance to improve the exchange rate predictability. Tapping into the information extracted from Google Trends, we check if the signals are captured and reflected. Unlike past studies where predictions were usually conducted at medium-to-long horizons, we focus on out-of-sample daily predictions at short horizons. Given the observable and obtainable real time Google Trends index (GTI), we justify the high forecast frequency. Aside from that, our predictions greatly differentiate from past studies with an easy yet novel 2-layer approach following an aggregated result. For the 2-layer approach, we have GTI-ruled model selection in the first layer and predictive models in the second layer. Rather than evaluating the performance in statistical sense, our study places an emphasis on that of the directions of change.The results for the 2-layer predictions though reaffirm the time-varying nature, by counting the statistical success and failure, a higher rate of beating the random walk confirms GTI’s aggregate power in tracking the such nature. The aggregated predictions further legitimate the use of Google Trends search intensity in capturing such time-varying nature at short horizons. en_US dc.description.tableofcontents CHAPTER 1 INTRODUCTION AND MOTIVATION 1CHAPTER 2 PREDICTIVE MODELS 52.1 Predictive Models Review 52.2 Fundamental Model – Uncovered Interest Rate Parity 62.3 Technical Model – Moving Average Convergence-Divergence 72.4 Benchmark Model 82.5 Statistical Test 9CHAPTER 3 GTI-RULED MODEL SELECTION 103.1 What Is GTI 103.2 The GTI Queries 133.3 The GTI-ruled Model Selection 163.4 Complete Picture of the Two-layer Exchange Rate Prediction 17CHAPTER 4 DATA AND EMPIRICAL RESULTS 194.1 Data Description 194.2 Analysis on Empirical Results 21CHAPTER 5 AGGREGATED RESULTS 255.1 A Simple Count 255.2 Aggregated Prediction 27CHAPTER 6 CONCLUSIONS 29REFERENCES 30 zh_TW dc.format.extent 2164756 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0107351005 en_US dc.subject (關鍵詞) 匯率 zh_TW dc.subject (關鍵詞) Google趨勢 zh_TW dc.subject (關鍵詞) 異質信仰 zh_TW dc.subject (關鍵詞) 有限注意力 zh_TW dc.subject (關鍵詞) 樣本外預測 zh_TW dc.subject (關鍵詞) 短期預測 zh_TW dc.subject (關鍵詞) 方向性預測 zh_TW dc.subject (關鍵詞) Exchange rates en_US dc.subject (關鍵詞) Google Trends en_US dc.subject (關鍵詞) Heterogeneous belief en_US dc.subject (關鍵詞) Limited attention en_US dc.subject (關鍵詞) Out‐of‐sample prediction en_US dc.subject (關鍵詞) Short horizon en_US dc.subject (關鍵詞) Directional prediction en_US dc.title (題名) Google 的搜尋熱度是否有助於預測匯率？ zh_TW dc.title (題名) Does Google Trends-ruled Exchange Rate Predictions Beat Random Walk? en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) Bacchetta, P. and Van Wincoop, E. (2003), “Can Information Heterogeneity Explain the Exchange Rate Determination Puzzle?” National Bureau of Economic Research Working Paper No. 9498.Bacchetta, P. and Van Wincoop, E. (2004), “A Scapegoat Model of Exchange-Rate Fluctuations,” American Economic Review, vol. 94 (2), pages 114-118.Brock, W., Lakonishok, J. and LeBaron, B. (1992), “Simple Technical Trading Rules and the Stochastic Properties of Stock Returns,” The Journal of Finance, 47(5), 1731-1764.Bulut, L. (2018), “Google Trends and the Forecasting Performance of Exchange Rate Models,” Journal of Forecasting, 37(3), 303-315.Chinn, M. D. and Meese, R. A. (1995), “Banking on Currency Forecasts: How Predictable Is Change in Money?” Journal of International Economics, vol. 38(1–2), pages 161-178.Clark, T. E. and West, K. D. (2007), “Approximately Normal Tests for Equal Predictive Accuracy in Nested Models,” Journal of Econometrics, vol. 138(1), pages 291-311.Da, Z., Engelberg, J. and Gao, P. (2011), “In Search of Attention,” Journal of Finance, vol. 66, issue 5, 1461-1499.De Grauwe, P. and Grimaldi, M. (2006), “Exchange Rate Puzzles: A Tale of Switching Attractors,” European Economic Review, vol. 50, issue 1, 1-33Dick, C. D. and Menkhoff, L. (2013), “Exchange Rate Expectations of Chartists and Fundamentalists,” Journal of Economic Dynamics and Control, vol. 37, issue 7, pages 1362-1383.Engel, C. and Hamilton, J. (1990), “Long Swings in the Dollar: Are They in the Data and Do Markets Know It?” American Economic Review, vol. 80(4), pages 689-713.Frankel, J. A. and Froot, K. (1990), “Chartists, Fundamentalists, and Trading in the Foreign Exchange Market,” American Economic Review, 80(2), 181-85.Kahneman, D. (1973), “Attention and Effort,” Prentice-Hall, Englewood Cliffs, NJ.Kuo, B.-S., Lan, C.-Y. and Yeh, B.-H. (2018), “Carry Trade Strategy in the Presence of Central Bank Interventions: The Economic Value of Fundamentals,” Taiwan Economic Review, 46, 363-399. (in Chinese)Mark, N. C. (1995), “Exchange Rates and Fundamentals: Evidence on Long-Horizon Predictability,” American Economic Review, vol. 85(1), pages 201-218.Mark, N. C. and Sul, D. (2001), “Nominal Exchange Rates and Monetary Fundamentals: Evidence from a Small Post-Bretton Woods Panel,” Journal of International Economics, vol. 53(1), pages 29-52.Markiewicz, A., Verhoeks, R., Verschoor, W. and Zwinkels, R. (2017), “The Winner Takes it All: Predicting Exchange Rates with Google Trend,” SSRN Electronic Journal, 10.2139/ssrn.3020932.Meese, R. and Rogoff, K. (1983), “Empirical Exchange Rate Models of the Seventies: Do They Fit Out of Sample?” Journal of International Economics, vol. 14(1-2), pages 3-24.Molodtsova, T. and Papell, D. H. (2009), “Out-of-sample Exchange Rate Predictability with Taylor Rule Fundamentals,” Journal of International Economics, vol. 77(2), pages 167-180.Rime, D., Sarno, L. and Sojli, E. (2010), “Exchange Rate Forecasting, Order Flow and Macroeconomic Information,” Journal of International Economics, vol. 80(1), pages 72-88.Rossi, B. (2013), “Exchange Rate Predictability,” CAFE Research Paper No. 13.16.Spronk, R., Verschoor, W. F. C. and Zwinkels, R. C. J. (2013), “Carry trade and foreign exchange rate puzzles,” European Economic Review, vol. 60(C), pages 17-31. zh_TW dc.identifier.doi (DOI) 10.6814/NCCU201900612 en_US
