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Google Internet search activity and volatility prediction in the market for foreign currency

Citations

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Cited by:

  1. Chi, Tsung-Li & Liu, Hung-Tsen & Chang, Chia-Chien, 2023. "Hedging performance using google Trends–Evidence from the indian forex options market," International Review of Economics & Finance, Elsevier, vol. 85(C), pages 107-123.
  2. Falik Shear & Badar Nadeem Ashraf & Mohsin Sadaqat, 2020. "Are Investors’ Attention and Uncertainty Aversion the Risk Factors for Stock Markets? International Evidence from the COVID-19 Crisis," Risks, MDPI, vol. 9(1), pages 1-15, December.
  3. Jichang Dong & Wei Dai & Ying Liu & Lean Yu & Jie Wang, 2019. "Forecasting Chinese Stock Market Prices using Baidu Search Index with a Learning-Based Data Collection Method," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(05), pages 1605-1629, September.
  4. Tihana Škrinjarić, 2019. "Time Varying Spillovers between the Online Search Volume and Stock Returns: Case of CESEE Markets," IJFS, MDPI, vol. 7(4), pages 1-30, October.
  5. De Santis, Roberto A., 2020. "Impact of the Asset Purchase Programme on euro area government bond yields using market news," Economic Modelling, Elsevier, vol. 86(C), pages 192-209.
  6. Theologos Dergiades & Costas Milas & Theodore Panagiotidis, 2015. "Tweets, Google trends, and sovereign spreads in the GIIPS," Oxford Economic Papers, Oxford University Press, vol. 67(2), pages 406-432.
  7. Smales, L.A., 2021. "Investor attention and global market returns during the COVID-19 crisis," International Review of Financial Analysis, Elsevier, vol. 73(C).
  8. Tong Liu & Guojun He & Alexis Lau, 2018. "Avoidance behavior against air pollution: evidence from online search indices for anti-PM2.5 masks and air filters in Chinese cities," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 20(2), pages 325-363, April.
  9. Martina Halouskov'a & Daniel Stav{s}ek & Mat'uv{s} Horv'ath, 2022. "The role of investor attention in global asset price variation during the invasion of Ukraine," Papers 2205.05985, arXiv.org, revised Aug 2022.
  10. D Aromi & A Clements, 2018. "Media attention and crude oil volatility: Is there any 'new' news in the newspaper?," NCER Working Paper Series 118, National Centre for Econometric Research.
  11. Qingjie Zhou & Panpan Zhu & You Wu & Yinpeng Zhang, 2022. "Research on the Volatility of the Cotton Market under Different Term Structures: Perspective from Investor Attention," Sustainability, MDPI, vol. 14(21), pages 1-20, November.
  12. Qadan, Mahmoud & Zoua’bi, Maher, 2019. "Financial attention and the demand for information," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 82(C).
  13. Peltomäki, Jarkko & Vähämaa, Emilia, 2015. "Investor attention to the Eurozone crisis and herding effects in national bank stock indexes," Finance Research Letters, Elsevier, vol. 14(C), pages 111-116.
  14. Halousková, Martina & Stašek, Daniel & Horváth, Matúš, 2022. "The role of investor attention in global asset price variation during the invasion of Ukraine," Finance Research Letters, Elsevier, vol. 50(C).
  15. Gang Chu & John W. Goodell & Dehua Shen & Yongjie Zhang, 2022. "Machine learning to establish proxies for investor attention: evidence of improved stock-return prediction," Annals of Operations Research, Springer, vol. 318(1), pages 103-128, November.
  16. Hervé, Fabrice & Zouaoui, Mohamed & Belvaux, Bertrand, 2019. "Noise traders and smart money: Evidence from online searches," Economic Modelling, Elsevier, vol. 83(C), pages 141-149.
  17. Petrova, Diana & Trunin, Pavel, 2020. "Revealing the mood of economic agents based on search queries," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 59, pages 71-87.
  18. Ana Brochado, 2016. "Investor attention and Portuguese stock market volatility: We’ll google it for you!," EcoMod2016 9345, EcoMod.
  19. Afkhami, Mohamad & Cormack, Lindsey & Ghoddusi, Hamed, 2017. "Google search keywords that best predict energy price volatility," Energy Economics, Elsevier, vol. 67(C), pages 17-27.
  20. Gianna Figà-Talamanca & Marco Patacca, 2020. "Disentangling the relationship between Bitcoin and market attention measures," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 47(1), pages 71-91, March.
  21. Georgios Bampinas & Theodore Panagiotidis & Christina Rouska, 2019. "Volatility persistence and asymmetry under the microscope: the role of information demand for gold and oil," Scottish Journal of Political Economy, Scottish Economic Society, vol. 66(1), pages 180-197, February.
  22. Perroni, Carlo & Scharf, Kimberley & Talavera, Oleksandr & Vi, Linh, 2022. "Does online salience predict charitable giving? Evidence from SMS text donations," Journal of Economic Behavior & Organization, Elsevier, vol. 197(C), pages 134-149.
  23. In Huh & Ju Hyun Pyun, 2018. "Does Nuclear Uncertainty Threaten Financial Markets? The Attention Paid to North Korean Nuclear Threats and Its Impact on South Korea's Financial Markets," Asian Economic Journal, East Asian Economic Association, vol. 32(1), pages 55-82, March.
  24. Svatopluk Kapounek & Zuzana Kučerová & Evžen Kočenda, 2022. "Selective Attention in Exchange Rate Forecasting," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 23(2), pages 210-229, May.
  25. Massimiliano Caporin & Francesco Poli, 2017. "Building News Measures from Textual Data and an Application to Volatility Forecasting," Econometrics, MDPI, vol. 5(3), pages 1-46, August.
  26. Latoeiro, Pedro & Ramos, Sofía B. & Veiga, Helena, 2013. "Predictability of stock market activity using Google search queries," DES - Working Papers. Statistics and Econometrics. WS ws130605, Universidad Carlos III de Madrid. Departamento de Estadística.
  27. Takayuki Morimoto & Yoshinori Kawasaki, 2017. "Forecasting Financial Market Volatility Using a Dynamic Topic Model," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 24(3), pages 149-167, September.
  28. Peri, Massimo & Vandone, Daniela & Baldi, Lucia, 2014. "Internet, noise trading and commodity futures prices," International Review of Economics & Finance, Elsevier, vol. 33(C), pages 82-89.
  29. Zhang, Wei & Wang, Pengfei & Li, Xiao & Shen, Dehua, 2018. "Quantifying the cross-correlations between online searches and Bitcoin market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 657-672.
  30. Perroni, Carlo & Scharf, Kimberley & Talavera, Oleksandr & Vi, Linh, 2021. "Online Salience and Charitable Giving: Evidence from SMS Donations," CAGE Online Working Paper Series 536, Competitive Advantage in the Global Economy (CAGE).
  31. Han, Liyan & Xu, Yang & Yin, Libo, 2018. "Forecasting the CNY-CNH pricing differential: The role of investor attention," Pacific-Basin Finance Journal, Elsevier, vol. 49(C), pages 232-247.
  32. Han, Liyan & Xu, Yang & Yin, Libo, 2018. "Does investor attention matter? The attention-return relationships in FX markets," Economic Modelling, Elsevier, vol. 68(C), pages 644-660.
  33. Vicente, María Rosalía & López-Menéndez, Ana J. & Pérez, Rigoberto, 2015. "Forecasting unemployment with internet search data: Does it help to improve predictions when job destruction is skyrocketing?," Technological Forecasting and Social Change, Elsevier, vol. 92(C), pages 132-139.
  34. Ying Liu & Yibing Chen & Sheng Wu & Geng Peng & Benfu Lv, 2015. "Composite leading search index: a preprocessing method of internet search data for stock trends prediction," Annals of Operations Research, Springer, vol. 234(1), pages 77-94, November.
  35. Kou, Yi & Ye, Qiang & Zhao, Feng & Wang, Xiaolin, 2018. "Effects of investor attention on commodity futures markets," Finance Research Letters, Elsevier, vol. 25(C), pages 190-195.
  36. Basistha, Arabinda & Kurov, Alexander & Wolfe, Marketa Halova, 2019. "Volatility Forecasting: The Role of Internet Search Activity and Implied Volatility," MPRA Paper 111037, University Library of Munich, Germany.
  37. Coble, David & Pincheira, Pablo, 2017. "Nowcasting Building Permits with Google Trends," MPRA Paper 76514, University Library of Munich, Germany.
  38. González-Fernández, Marcos & González-Velasco, Carmen, 2020. "An alternative approach to predicting bank credit risk in Europe with Google data," Finance Research Letters, Elsevier, vol. 35(C).
  39. de Castro, Jessica & Piccoli, Pedro, 2023. "Do online searches actually measure future retail investor trades?," International Review of Financial Analysis, Elsevier, vol. 86(C).
  40. Papadamou, Stephanos & Fassas, Athanasios P. & Kenourgios, Dimitris & Dimitriou, Dimitrios, 2023. "Effects of the first wave of COVID-19 pandemic on implied stock market volatility: International evidence using a google trend measure," The Journal of Economic Asymmetries, Elsevier, vol. 28(C).
  41. ap Gwilym, O. & Kita, A. & Wang, Q., 2014. "Speculate against speculative demand," International Review of Financial Analysis, Elsevier, vol. 34(C), pages 212-221.
  42. Johannes Bock, 2018. "Quantifying macroeconomic expectations in stock markets using Google Trends," Papers 1805.00268, arXiv.org.
  43. Goddard, John & Kita, Arben & Wang, Qingwei, 2015. "Investor attention and FX market volatility," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 38(C), pages 79-96.
  44. Francisca Beer & Fabrice Hervé & Mohamed Zouaoui, 2013. "Is Big Brother Watching Us? Google, Investor Sentiment and the Stock Market," Economics Bulletin, AccessEcon, vol. 33(1), pages 454-466.
  45. Khaskheli, Asadullah & Zhang, Hongyu & Raza, Syed Ali & Khan, Komal Akram, 2022. "Assessing the influence of news indicator on volatility of precious metals prices through GARCH-MIDAS model: A comparative study of pre and during COVID-19 period," Resources Policy, Elsevier, vol. 79(C).
  46. Gulsah Senturk, 2022. "Can Google Search Data Improve the Unemployment Rate Forecasting Model? An Empirical Analysis for Turkey," Journal of Economic Policy Researches, Istanbul University, Faculty of Economics, vol. 9(2), pages 229-244, July.
  47. Salisu, Afees A. & Ogbonna, Ahamuefula E. & Adewuyi, Adeolu, 2020. "Google trends and the predictability of precious metals," Resources Policy, Elsevier, vol. 65(C).
  48. Tripathi, Abhinava & Pandey, Ashish, 2021. "Information dissemination across global markets during the spread of COVID-19 pandemic," International Review of Economics & Finance, Elsevier, vol. 74(C), pages 103-115.
  49. Peltomäki, Jarkko & Graham, Michael & Hasselgren, Anton, 2018. "Investor attention to market categories and market volatility: The case of emerging markets," Research in International Business and Finance, Elsevier, vol. 44(C), pages 532-546.
  50. González-Fernández, Marcos & González-Velasco, Carmen, 2020. "A sentiment index to measure sovereign risk using Google data," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 406-418.
  51. Pham, Linh & Cepni, Oguzhan, 2022. "Extreme directional spillovers between investor attention and green bond markets," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 186-210.
  52. Gao, Lei & Mei, Bin, 2013. "Investor attention and abnormal performance of timberland investments in the United States," Forest Policy and Economics, Elsevier, vol. 28(C), pages 60-65.
  53. Theologos Dergiades & Eleni Mavragani & Bing Pan, 2017. "Arrivals of Tourists in Cyprus: Mind the Web Search Intensity," GreeSE – Hellenic Observatory Papers on Greece and Southeast Europe 107, Hellenic Observatory, LSE.
  54. Papadamou, Stephanos & Fassas, Athanasios & Kenourgios, Dimitris & Dimitriou, Dimitrios, 2020. "Direct and Indirect Effects of COVID-19 Pandemic on Implied Stock Market Volatility: Evidence from Panel Data Analysis," MPRA Paper 100020, University Library of Munich, Germany.
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