Once upon a time there was a Trump effect. Internet information and the exchange rate
DOI:
https://doi.org/10.24201/ee.v36i2.423Keywords:
Big Data, Trump, Twitter, NAFTA, Foreign exchangeAbstract
Using official data (Banco de M´exico, INEGI) and large-scale information from the Internet (Google Trends, Twitter), a VAR model and Granger causality tests are used to model the behavior of the peso/dollar exchange rate. Trump’s online popularity, along with messages on Twitter regarding the relationship between Mexico and the United States, generated a feeling of concern which was reflected in the exchange rate, at least in the short term. The main learning derived from these findings is that the information generated in social networks allows us to know the behavior of fundamental economic variables.
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References
Adetiba, E., D. Ike y O. Owolabi. 2017. An intelligent foreign exchange robot (i-FOREXBOT) development with scale conjugate gradient neural network, en K.S. Soliman (ed.), 22nd International Business Information Management Association Conference 2013: Creating Global Competitive Economies: 2020 Vision Planning & Implementation, Roma, International Business Information Management Association.
Allison, P.D. 2001. Missing Data, Sage Publishing.
Alostad, H. y H. Davulcu. 2015. Directional prediction of stock prices using breaking news on Twitter, en 2015 IEEE / WIC / ACM International Conference on Web Intelligence and Intelligent Agent Technolog (WI-IAT), Singapur, The Institute of Electrical and Electronics Engineers.
Antweiler, W. y M.Z. Frank. 2004. Is all that talk just noise? The information content of Internet stock message boards, Journal of Finance, 59(3): 1259-1294.
Askitas, N. y K.F. Zimmermann. 2009. Google econometrics and unemployment forecasting, Applied Economics Quarterly, 55(2), 107-120.
Bacchetta, P. y E. Van Wincoop, E. 2004. A scapegoat model of exchange rate determination, American Economic Review, Papers and Proceedings, 94: 114-118.
Bacchetta, P. y E. Van Wincoop. 2013. On the unstable relationship between exchange rates and macroeconomic fundamentals, Journal of International Economics, 91: 18-26.
BANXICO. 2018. Sistema de información económica (SIE), https://www.banxico.org.mx/tipcamb/main.do?page=tip&idioma=sp.
Berganza, J.C. y P. L’Hotellerie-Fallois. 2017. El impacto de las políticas económicas de Donald Trump, Cuadernos de Información Económica, 256:97-107.
Blecker, R.A., J.C. Moreno-Brid e I. Salat. 2018. La renegociación del TLCAN: un enfoque alternativo para la convergencia y la prosperidad compartida, Economía Informa, 408: 4-15.
Bollen, J., A. Pepe y H. Mao. 2009. Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena, Proceedings of the International AAAI Conference on Web and Social Media, 5(1): 450-453.
Bollen, J., H. Mao y X. Zeng. 2011. Twitter mood predicts the stock market, Journal of Computational Science, 2(1): 1-8.
Brown, B., M. Chui y J. Manyika. 2011. Are you ready for the era of big data?, McKinsey Quarterly, 4(1): 24-35.
Campos-Vázquez, R.M. y S. López-Araiza. 2020. Grandes datos, Google y desempleo, Estudios Económicos, 35(1): 125-151.
Cavaglia, S., W.F. Verschoor y C.C. Wolff. 1993. Further evidence on exchange rate expectations, Journal of International Money and Finance, 12(1): 78- 98.
Chong, O. y O.R. Sheng. 2011. Investigating predictive power of stock microblog sentiment in forecasting future stock price directional movement, en Proceedings of the International Conference on Information Systems, Shangai, ICIS.
Clavellina, J.L. 2018. Determinantes del tipo de cambio y su volatilidad, Economía UNAM, 15(45): 70-88.
Cumby, R.E. y M. Obstfeld. 1980. Exchange-rate expectations and nominal interest differentials: A test of the Fisher hypothesis, NBER Working Paper No. 0537.
Da, Z., J. Engelberg y P. Gao. 2011. In search of attention, Journal of Finance, 66(5): 1461-1499.
Das, S.R. y M.Y. Chen. 2007. Yahoo! for Amazon: Sentiment extraction from small talk on the web, Management Science, 53(9): 1375-1388.
De Long, J.B., A. Shleifer, H.S. Lawrence y R.J. Waldmann. 1990. Noise trader risk in financial markets, Journal of Political Economy, 98(4): 703-738.
Domínguez, M.L., J.C. Sánchez, J. Murillo, H. Valero y J.D. Romero. 2016. Big data y turismo en México: pueblos mágicos, Documentos de Investigación Estadística y Económica No. 2016-5, Secretaría de Turismo.
Evans, M.D. y R.K. Lyons. 2002. Order flow and exchange rate dynamics, Journal of Political Economy, 110(1): 170-180.
Fama, E.F. 1970. Efficient capital markets: A review of theory and empirical work, Journal of Finance, 25(2): 383-417.
Fama, E.F. 1991. Efficient capital markets: II, Journal of Finance, 46(5): 1575-1617.
Frankel, J.A. y K.A. Froot. 1985. Using survey data to test some standard propositions regarding exchange rate expectations, NBER Working Paper No. 1672.
Fratzscher, M., D. Rime, L. Sarno y G. Zinna. 2015. The scapegoat theory of exchange rates: The first tests, Journal of Monetary Economics, 70: 1-21.
Gallo, C. y A. Fratello. 2014. The Forex market in practice: A computing approach for automated trading strategies, International Journal of Economics and Management Sciences, 3(1): 1-9.
Granger, C.W. 1969. Investigating causal relations by econometric models and cross-spectral methods, Econometrica, 37(3): 424-438.
Granger, C.W. 1980. Testing for causality: A personal viewpoint, Journal of Economic Dynamics and Control, 2: 329-352.
Guzmán, G. 2011. Internet search behavior as an economic forecasting tool: The case of inflation expectations, Journal of Economic and Social Measurement, 36(3): 119-167.
Hofacker, C.F., E.C. Malthouse y F. Sultan. 2016. Big data and consumer behavior: Imminent opportunities, Journal of Consumer Marketing, 33(2), 89-97.
Hopper, G. 1994. Is the foreign exchange market inefficient?, Business Review, Federal Reserve Bank of Philadelphia, Issue May/June, 17-27.
Hopper, G. 1997. What determines the exchange rate: Economic factors or market sentiment?, Business Review, Federal Reserve Bank of Philadelphia, Issue Sept./Oct., 17-29.
Ibarra, I., A.M. Pérez y M.D. Cuecuecha. 2019. Búsquedas en Internet y su influencia en los flujos de turistas y visitantes. El caso del avistamiento de luciérnagas en Nanacamilpa Tlaxcala, El Periplo Sustentable, (36): 402-431.
INEGI. 2018. Banco de información económica (BIE), tipo de cambio real bilateral México-EUA, https://www.inegi.org.mx/app/indicadores/?tm=0&t=10000215#D10000215.
Islas-Camargo, A., W.W. Cortez, y T.P. Flores. 2018. Is Mexico’s forward exchange rate market efficient?, Revista Mexicana de Economía y Finanzas Nueva época, 13(2): 273-289.
Janetzko, D. 2014. Using Twitter to model the EUR/USD exchange rate, ArXiv preprint arXiv:1402.1624.
Joseph, K., M.B. Wintoki y Z. Zhang. 2011. Forecasting abnormal stock returns and trading volume using investor sentiment: Evidence from online search, International Journal of Forecasting, 27(4): 1116-1127.
Kahneman, D. y A. Tversky. 1973. On the psychology of prediction, Psychological Review, 80(4): 237-251.
Kreis, R. 2017. The “tweet politics” of President Trump, Journal of Language and Politics, 16(4): 607-618.
Kristoufek, L. 2013. Can Google Trends search queries contribute to risk diversification?, ArXiv preprint arXiv:1310.1444.
Kunreuther, H. y M. Pauly. 2004. Neglecting disaster: Why don’t people insure against large losses?, Journal of Risk and Uncertainty, 28(1): 5-21.
Li, X., W. Shang, S. Wang y J. Ma. 2015. A MIDAS modelling framework for Chinese inflation index forecast incorporating Google search data, Electronic Commerce Research and Applications, 14(2): 112-125.
Llamosas-Rosas, I., E. Rangel González y M. Sandoval Bustos. 2018. Medición de la actividad económica en las principales zonas turísticas de playa en México a través de la luminosidad fotografiada desde el espacio, Documento de Investigación No. 2018-10, Banco de México.
Lüdering, J. y P. Tillmann. 2016. Monetary policy on Twitter and its effect on asset prices: Evidence from computational text analysis, MAGKS Joint Discussion Paper Series in Economics No. 12-2016, University of Marburg.
Lütkepohl, H. 2005. New Introduction to Multiple Time Series Analysis, Springer-Verlag Berlin Heidelberg.
Mayda, A.M. y G. Peri. 2017. The economic impact of US immigration policies in the Age of Trump, en C. Bown (ed.), Economics and Policy in the Age of Trump, CEPR Press: Londres, Reino Unido.
McDonnell, A. y M. Wheeler. 2019. @realDonaldTrump: Political celebrity, authenticity, and para-social engagement on Twitter, Celebrity Studies, 10(3): 427-431.
Muth, J. F. 1961. Rational expectations and the theory of price movements, Econometrica, 29(3): 315-335.
Nassirtoussi, A.K., T.Y. Wah y D. N. Chek Ling. 2011. A novel Forex prediction methodology based on fundamental data, African Journal of Business Management, 5(20): 8322-8330.
Oliveira, N., P. Cortez y N. Areal. 2017. The impact of microblogging data for stock market prediction: Using Twitter to predict returns, volatility, trading volume and survey sentiment indices, Expert Systems with Applications, 73(1): 125-144.
Ozturk, S.S. y K. Ciftci. 2014. A sentiment analysis of twitter content as a predictor of exchange rate movements, Review of Economic Analysis, 6(2): 132-140.
Pak, A. y P. Paroubek. 2010. Twitter as a corpus for sentiment analysis and opinion mining, en Proceedings of the International Conference on Information Systems, Shangai, ICIS.
Plakandaras, V., T. Papadimitriou, P. Gogas y K. Diamantaras. 2015. Market sentiment and exchange rate directional forecasting, Algorithmic Finance, 4(1-2): 69-79.
Preis, T., H.S. Moat y H.E. Stanley. 2013. Quantifying trading behavior in financial markets using Google Trends, Scientific Reports, 3: 1684.
Rime, D., L. Sarno y E. Sojli. 2010. Exchange rate forecasting, order flow and macroeconomic information, Journal of International Economics, 80(1): 72-88.
Roberts, J. 2019. Trump, Twitter, and the First Amendment, Alternative Law Journal, 44(3): 207-213.
Savage, L. 1954. The Foundations of Statistics, Dover Publications.
Shiller, R. J. 2003. From efficient markets theory to behavioral finance, Journal of Economic Perspectives, 17(1): 83-104.
Shleifer, A. y R.W. Vishny. 1997. The limits of arbitrage, Journal of Finance, 52(1): 35-55.
Sprenger, T.O., A. Tumasjan, P.G. Sandner e I.M. Welpe. 2014. Tweets and trades: The information content of stock microblogs, European Financial Management, 20(5): 926-957.
Stigler, G.J. 1961. The economics of information, Journal of Political Economy, 69(3): 213-225.
Tversky, A. y D. Kahneman. 1974. Judgment under uncertainty: Heuristics and biases, Science, 185: 1124-1131.
Van Buuren, S. 2007. Multiple imputation of discrete and continuous data by fully conditional specification, Statistical Methods in Medical Research, 16: 219-242.
Varian, H.R. 2014. Big data: New tricks for econometrics, Journal of Economic Perspectives, 28(2), 3-28.
Vosen, S. y T. Schmidt. 2011. Forecasting private consumption: Survey-based indicators vs. Google trends, Journal of Forecasting, 30(6): 565-578.
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