Predictive modeling in turbulent times – What Twitter reveals about the EUR/USD exchange rate

NETNOMICS: Economic Research and Electronic Networking

Volumn 15, Issue 2, 2014, Pages 69-106

  Janetzko, Dietmar ^a  

^a Université libre de Bruxelles   (Germany)

Author keywords

Exchange rate modeling; regARIMA; Social networks; Twitter

Indexed keywords

EID: 84914666292     PISSN: 13859587     EISSN: 15737071     Source Type: Journal    
DOI: 10.1007/s11066-014-9087-y     Document Type: Article

References (48)

1. Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716–723.
2. Arlot, S., & Celisse, A. (2010). A survey of cross-validation procedures for model selection. Statistics Surveys, 4, 40–79.
3. Asur, S., & Huberman, B.A. (2010). Predicting the future with social media. In: Web Intelligence and Intelligent Agent Technology (WI-IAT), 2010 IEEE/WIC/ACM (vol. 1, pp. 492–499). IEEE.
4. Bod, R., Fisseni, B., Kurji, A., Löwe, B. (2012). Objectivity and reproducibility of Proppian narrative annotations. In: Workshop on Computational Models of Narrative 2012 at the International Conference on Language Resources and Evaluation (LREC) Istanbul.
5. Bollen, J., Mao, H., Zeng, X. (2010). Twitter mood predicts the stock market. Journal of Computational Science, 2, 1–8.
6. Box, G.E.P., & Jenkins, G.M. (1976). In Time series analysis: Forecasting and control. San Francisco: Holden-Dag.
7. Bracke, T., Skala, M., Thimann, C. (2008). Thirty years of exchange rate communication: How, when and why does the G7 speak
8. Bruegger, U., & Knorr-Cetina, K. (2002). Global microstructures: The virtual societies of financial markets. American Journal of Sociology, 107(4), 905–950.
9. Bryden, J., Funk, S., Jansen, V.A.A. (2013). Word usage mirrors community structure in an online social network. EPJ Data Science, 2(3), 1–9.
10. Chen, J.-H. (2008). Variance ratio tests of random walk hypothesis of the Euro exchange rate. International Business & Economics Research Journal (IBER), 7(12), 97–106.
11. Davenport, T.H., & Beck, J.C. (2001). The attention economy: Understanding the new currency of business. Boston: Harvard Business Press.
12. De Choudhury, M.D., Sundaram, H., John, A., Seligmann, D.D. (2010). Analyzing the dynamics of communication in online social networks. In: Handbook of Social Networks Technologies and Applications. Springer.
13. Falkinger, J. (2008). Limited attention as a scarce resource in information-rich economies. The Economic Journal, 118(532), 1596–1620.
14. Fama, E.F. (1970). Efficient capital markets: a review of theory and empirical work. Journal of Finance, 25, 383–417.
15. Fama, E.F. (1991). Efficient capital markets: II. Journal of Finance, 46(5), 1575–1617.
16. Flack, V.F., & Chang, P.C. (1987). Frequency of selecting noise variables in subset regression analysis: A simulation study. The American Statistician, 7(1), 84–86.
17. Fox, J. (1997). In Applied Regression Analysis Linear Models and Related Methods. Thousand Oaks, CA: Sage.
18. Geweke, J., & Feige, E. (1979). Some joint tests of the efficiency of markets for forward foreign exchange. The Review of Economics and Statistics, 61(3), 334–341.
19. Giacomini, R., & Rossi, B. (2013). Forecasting in macroeconomics. In Hashimzade, N., & Thornton, M. (Eds.), Handbook of research methods and applications on empirical macroeconomics, (pp. 618–658). Cheltenham: Edward Elgar Publishing. chapter 7.
20. Goel, V. (2013). Twitter introduces tool to make collecting and sharing tweets easier. New York Times.
21. Grossman, S.J., & Stiglitz, J.E. (1980). On the impossibility of informationally efficient markets. The American economic review, 70(3), 393–408.
22. Hong, Y., Li, H., Zhao, F. (2007). Can the random walk model be beaten in out-of-sample density forecasts? evidence from intraday foreign exchange rates. Journal of Econometrics, 141(2), 736–776.
23. Hyndman, R.J. (2010). Why every statistician should know about cross-validation. http://robjhyndman.com/researchtips/crossvalidation/ Accessed: 2014-07-14.
24. Hyndman, R.J., & Athanasopoulos, G. (2012). Forecasting: Principles and practice. an online textbook. http://otexts.com/fpp/. Accessed:2014-07-14
25. Hyndman, R.J., & Khandakar, Y. (2008). Automatic time series forecasting: The forecast package for R. Journal of Statistical Software, 27(3).
26. Kilian, L., & Taylor, M.P. (2003). Why is it so difficult to beat the random walk forecast of exchange rates Journal of International Economics, 60(1), 85–107.
27. King, M.R., Osler, L., Rime, D. (2011). Foreign exchange market structure, players and evolution. Technical report, Norges Bank.
28. Kuhn, M. (2013). caret: Classification and Regression Training. R package version 5.15-61.
29. Lee, H.-Y., & Sodoikhuu, K. (2012). Efficiency tests in foreign exchange market. International Journal of Economics And Financial Issues, 2, 216–224.
30. Lee, T.-H., White, H., Granger, C.W. (1993). Testing for neglected nonlinearity in time series models: A comparison of neural network methods and alternative tests. Journal of Econometrics, 56(3), 269–290.
31. Lisi, F., & Medio, A. (1997). Is a random walk the best exchange rate predictor? International Journal of Forecasting, 13(2), 255–267.
32. Macdonald, R., & Taylor, M.P. (1994). The monetary model of the exchange rate: Long-run relationships, short-run dynamics and how to beat a random walk. Journal of International Money And Finance, 13(3), 276–290.
33. Meese, R.A., & Rogoff, K.S. (1983). Empirical exchange rate models of the seventies: Do they fit out of sample? Journal of International Economics (pp. 3-24).
34. Newbold, P., Rayner, T., Kellar, N., Ennew, C. (1998). Is the Dollar/ECU exchange rate a random walk Applied Financial Economics, 8, 553–558.
35. Papaioannou, P., Russo, L., Papaioannou, G., Siettos, C.I. (2013). Can social microblogging be used to forecast intraday exchange rates Netnomics: Economic Research And Electronic Networking, 14(1-2), 47–68.
36. Pierce, R. (2008). In Research methods in politics. London: Sage.
37. Propp, V. (1968). In Morphology of the folktale, 2nd edition. Austin: University of Texas Press.
38. R Core Team (2013). R: A Language And Environment For Statistical Computing. Vienna: R Foundation For Statistical Computing.
39. Richard, S. (2013). Terms of crisis. European Issues (278). http://www.robert-schuman.eu/en/european-issues/0278-terms-of-crisis. Accessed 14 Jul 2014.
40. Rossi, B. (2013). Exchange rate predictability. Journal of Economic Literature, 51(4), 1063–1119.
41. Sato, A.-H., & Takayasu, H. (2013). Segmentation procedure based on fisher’s exact test and its application to foreign exchange rates. arXiv:1309.0602.
42. Schwarz, G. (1978). Estimating the dimension of a model. The annals of statistics, 6(2), 461–464.
43. Signorini, A., Segre, A.M., Polgreen, P.M. (2011). The use of twitter to track levels of disease activity and public concern in the us during the influenza A H1N1 pandemic. PloS one, 6(5), e19467.
44. Stracca, L. (2013). Our currency, your problem? The global effects of the Euro debt crisis. University of St. Gallen, Switzerland, Finance Research Seminar.
45. Theiler, J., Eubank, S., Longtin, A., Galdrikian, B., Doyne Farmer, J. (1992). Testing for nonlinearity in time series: the method of surrogate data. Physica D: Nonlinear Phenomena, 58(1), 77–94.
46. Thompson, P. (2009). Market manipulation? Applying the propaganda model to financial media reporting. Westminster Papers on Communication and Culture, 6(2), 73–96.
47. Tumasjan, A., Sprenger, T.O., Sandner, P.G., Welpe, I.M. (2010). Predicting elections with Twitter: What 140 characters reveal about political sentiment. In: Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media, (pp. 178–185).
48. Weiss, J., & Kemper, S. (2011). In Analysis of exchange rate communication. Munich: GRIN Verlag.