Employing Google Trends and Deep Learning in Forecasting Financial Market Turbulence

References

• Aouadi, A., M. Arouri, and D. Roubaud. 2018. “Information Demand and Stock Market Liquidity: International Evidence.” Economic Modelling 70:194–202. doi:10.1016/j.econmod.2017.11.005
   Web of Science ®Google Scholar
• Bekkar, M., H. Kheliouane, and A. Taklit. 2013. “Evaluation Measures for Models Assessment over Imbalanced Data Sets.” Journal of Information Engineering and Applications 3 (10):27–38.
   Google Scholar
• Breiman, L. 2001. “Random Forests.” Machine Learning 45 (1):5–32. doi:10.1023/A:1010933404324
   Web of Science ®Google Scholar
• Bulut, L. 2018. “Google Trends and the Forecasting Performance of Exchange Rate Models.” Journal of Forecasting 37 (3):303–15. doi:10.1002/for.2500
   Web of Science ®Google Scholar
• Chen, T., E. P. K. So, L. Wu, and I. K. M. Yan. 2015. “The 2007–2008 US Recession: What Did the Real‐Time Google Trends Data Tell the United States?” Contemporary Economic Policy 33 (2):395–403. doi:10.1111/coep.12074
   Web of Science ®Google Scholar
• D’Amuri, F., and J. Marcucci. 2017. “The Predictive Power of Google Searches in Forecasting US Unemployment.” International Journal of Forecasting 33 (4):801–16. doi:10.1016/j.ijforecast.2017.03.004
   Web of Science ®Google Scholar
• Dergiades, T., C. Milas, and T. Panagiotidis. 2015. “Tweets, Google Trends, and Sovereign Spreads in the GIIPS.” Oxford Economic Papers 67 (2):406–32. doi:10.1093/oep/gpu046
   Web of Science ®Google Scholar
• Dietzel, M. A. 2016. “Sentiment-Based Predictions of Housing Market Turning Points with Google Trends.” International Journal of Housing Markets and Analysis 9 (1):108–36. doi:10.1108/IJHMA-12-2014-0058
   Web of Science ®Google Scholar
• Friedman, J. H. 2001. “Greedy Function Approximation: A Gradient Boosting Machine.” Annals of Statistics 29 (5):1189–232. doi:10.1214/aos/101320345
   Google Scholar
• González-Fernández, M., and G.-V. Carmen. 2018. “Do Google Trends Help to Forecast Sovereign Risk in Europe?” In Mathematical and Statistical Methods for Actuarial Sciences and Finance, edited by M. Corazza, M. Durban, A. Grane, C. Perna, and M. Sibillo, 413–7. Cham: Springer. 10.1007/978-3-319-89824-7_74
   Google Scholar
• Hamid, A., and M. Heiden. 2015. “Forecasting Volatility with Empirical Similarity and Google Trends.” Journal of Economic Behavior & Organization 117:62–81. doi:10.1016/j.jebo.2015.06.005
   Web of Science ®Google Scholar
• Heiberger, R. H. 2015. “Collective Attention and Stock Prices: Evidence from Google Trends Data on Standard and Poor's 100.” PLoS One 10 (8):e0135311. doi:10.1371/journal.pone.0135311
   PubMed Web of Science ®Google Scholar
• Hu, H., L. Tang, S. Zhang, and H. Wang. 2018. “Predicting the Direction of Stock Markets Using Optimized Neural Networks with Google Trends.” Neurocomputing 285:188–95. doi:10.1016/j.neucom.2018.01.038
   Web of Science ®Google Scholar
• Jun, S.-P., H. S. Yoo, and S. Choi. 2018. “Ten Years of Research Change Using Google Trends: From the Perspective of Big Data Utilizations and Applications.” Technological Forecasting and Social Change 130:69–87. doi:10.1016/j.techfore.2017.11.009
   Web of Science ®Google Scholar
• Kohns, D., and A. Bhattacharjee. 2019. “Interpreting Big Data in the Macro Economy: A Bayesian Mixed Frequency Estimator.” Working paper, CEERP Series 010, Centre for Energy Economics Research and Policy, Heriot-Watt University.
   Google Scholar
• Kostopoulos, D., M. Steffen, and U. Charline. 2020. “Google Search Volume and Individual Investor Trading.” Journal of Financial Markets 49C:100544. doi:10.1016/j.finmar.2020.100544
   Google Scholar
• Kristoufek, L. 2013. “Can Google Trends Search Queries Contribute to Risk Diversification?” Scientific Reports 3:2713. doi:10.1038/srep02713
   PubMed Web of Science ®Google Scholar
• Lu, Q., Y. Li, J. Chai, and S. Wang. 2020. “Crude Oil Price Analysis and Forecasting: A Perspective of “New Triangle.” Energy Economics 87:104721. doi:10.1016/j.eneco.2020.104721
   Web of Science ®Google Scholar
• Masuda, M., and F. Takeda. 2019. “Application of Google Trends Data in Exchange Rate Prediction.” International Symposium on Forecasting 2019.
   Google Scholar
• Naccarato, A., S. Falorsi, S. Loriga, and A. Pierini. 2018. “Combining Official and Google Trends Data to Forecast the Italian Youth Unemployment Rate.” Technological Forecasting and Social Change 130:114–22. doi:10.1016/j.techfore.2017.11.022
   Web of Science ®Google Scholar
• Nagao, S., F. Takeda, and R. Tanaka. 2019. “Nowcasting of the US Unemployment Rate Using Google Trends.” Finance Research Letters 30:103–9. doi:10.1016/j.frl.2019.04.005
   Web of Science ®Google Scholar
• Niesert, R. F., J. Oorschot, C. Veldhuisen, K. Brons, and R. Lange. 2020. “Can Google Search Data Help Predict Macroeconomic Series?” International Journal of Forecasting 36 (3):1163–72. doi:10.1016/j.ijforecast.2018.12.006
   Web of Science ®Google Scholar
• Preis, T., H. S. Moat, and H. E. Stanley. 2013. “Quantifying Trading Behavior in Financial Markets Using Google Trends.” Scientific Reports 3:1684. doi:10.1038/srep01684
   PubMed Web of Science ®Google Scholar
• Simionescu, M., D. Streimikiene, and W. Strielkowski. 2020. “What Does Google Trends Tell Us about the Impact of Brexit on the Unemployment Rate in the UK?” Sustainability 12:1011. doi:10.3390/su12031011
   Google Scholar
• Strasser, H., and C. Weber. 1999. “On the Asymptotic Theory of Permutation Statistics.” Mathematical Methods of Statistics 8:220–50.
   Google Scholar
• Vapnik, V. N. 1998. Statistical Learning Theory, Vol. 1. New York: Wiley.
   Google Scholar
• Vlastakis, N., and R. N. Markellos. 2012. “Information Demand and Stock Market Volatility.” Journal of Banking & Finance 36 (6) :1808–21. doi:10.1016/j.jbankfin.2012.02.007
   Web of Science ®Google Scholar
• Vozlyublennaia, N. 2014. “Investor Attention, Index Performance, and Return Predictability.” Journal of Banking & Finance 41:17–35. doi:10.1016/j.jbankfin.2013.12.010
   Web of Science ®Google Scholar
• Wilcoxson, J., L. Follett, and S. Severe. 2020. “Forecasting Foreign Exchange Markets Using Google Trends: Prediction Performance of Competing Models.” Journal of Behavioral Finance 21 (4) :412–22. doi:10.1080/15427560.2020.1716233
   Web of Science ®Google Scholar
• Yilmazkuday, H. 2020. “Coronavirus Effects on the US Unemployment: Evidence from Google Trends.” Available at SSRN 3559860.
   Google Scholar
• Yu, L., Y. Zhao, L. Tang, and Z. Yang. 2019. “Online Big Data-Driven Oil Consumption Forecasting with Google Trends.” International Journal of Forecasting 35 (1) :213–23. doi:10.1016/j.ijforecast.2017.11.005
   Web of Science ®Google Scholar
• Yung, K., and N. Nafar. 2017. “Investor Attention and the Expected Returns of REITs.” International Review of Economics & Finance 48:423–39. doi:10.1016/j.iref.2016.12.009
   Web of Science ®Google Scholar