IDEAS home Printed from https://ideas.repec.org/a/wly/jforec/v39y2020i2p281-295.html
   My bibliography  Save this article

Filtering and prediction of noisy and unstable signals: The case of Google Trends data

Author

Listed:
  • Livio Fenga

Abstract

Google Trends data is a dataset increasingly employed for many statistical investigations. However, care should be placed in handling this tool, especially when applied for quantitative prediction purposes. Being by design Internet user dependent, estimators based on Google Trends data embody many sources of uncertainty and instability. They are related, for example, to technical (e.g., cross‐regional disparities in the degree of computer alphabetization, time dependency of Internet users), psychological (e.g., emotionally driven spikes and other form of data perturbations), linguistic (e.g., noise generated by double‐meaning words). Despite the stimulating literature available today on how to use Google Trends data as a forecasting tool, surprisingly, to the best of the author's knowledge, it appears that to date no articles specifically devoted to the prediction of these data have been published. In this paper, a novel forecasting method, based on a denoiser of the wavelet type employed in conjunction with a forecasting model of the class SARIMA (seasonal autoregressive integrated moving average), is presented. The wavelet filter is iteratively calibrated according to a bounded search algorithm, until a minimum of a suitable loss function is reached. Finally, empirical evidence is presented to support the validity of the proposed method.

Suggested Citation

  • Livio Fenga, 2020. "Filtering and prediction of noisy and unstable signals: The case of Google Trends data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 281-295, March.
    • Handle: RePEc:wly:jforec:v:39:y:2020:i:2:p:281-295
    DOI: 10.1002/for.2626
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/for.2626
    Download Restriction: no
    File URL: https://libkey.io/10.1002/for.2626?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Ulrich S Tran & Rita Andel & Thomas Niederkrotenthaler & Benedikt Till & Vladeta Ajdacic-Gross & Martin Voracek, 2017. "Low validity of Google Trends for behavioral forecasting of national suicide rates," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-26, August.
    2. Goldfarb, Avi & Greenstein, Shane M. & Tucker, Catherine E. (ed.), 2015. "Economic Analysis of the Digital Economy," National Bureau of Economic Research Books, University of Chicago Press, number 9780226206981, December.
    3. Andrea Freyer Dugas & Mehdi Jalalpour & Yulia Gel & Scott Levin & Fred Torcaso & Takeru Igusa & Richard E Rothman, 2013. "Influenza Forecasting with Google Flu Trends," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-7, February.
    4. Avi Goldfarb & Shane M. Greenstein & Catherine E. Tucker, 2015. "Economic Analysis of the Digital Economy," NBER Books, National Bureau of Economic Research, Inc, number gree13-1, March.
    5. Jennifer L. Castle & Nicholas W.P. Fawcett & David F. Hendry, 2009. "Nowcasting Is Not Just Contemporaneous Forecasting," National Institute Economic Review, National Institute of Economic and Social Research, vol. 210(1), pages 71-89, October.
    6. Zhi Da & Joseph Engelberg & Pengjie Gao, 2011. "In Search of Attention," Journal of Finance, American Finance Association, vol. 66(5), pages 1461-1499, October.
    7. Lynn Wu & Erik Brynjolfsson, 2015. "The Future of Prediction: How Google Searches Foreshadow Housing Prices and Sales," NBER Chapters, in: Economic Analysis of the Digital Economy, pages 89-118, National Bureau of Economic Research, Inc.
    8. Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
    9. Cerqueti, Roy & Fenga, Livio & Ventura, Marco, 2018. "Does the U.S. exercise contagion on Italy? A theoretical model and empirical evidence," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 499(C), pages 436-442.
    10. Simeon Vosen & Torsten Schmidt, 2011. "Forecasting private consumption: survey‐based indicators vs. Google trends," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(6), pages 565-578, September.
    11. Jeremy Ginsberg & Matthew H. Mohebbi & Rajan S. Patel & Lynnette Brammer & Mark S. Smolinski & Larry Brilliant, 2009. "Detecting influenza epidemics using search engine query data," Nature, Nature, vol. 457(7232), pages 1012-1014, February.
    12. T. Ozaki, 1977. "On the Order Determination of Arima Models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 26(3), pages 290-301, November.
    13. Vlastakis, Nikolaos & Markellos, Raphael N., 2012. "Information demand and stock market volatility," Journal of Banking & Finance, Elsevier, vol. 36(6), pages 1808-1821.
    14. Jun, Seung-Pyo & Yoo, Hyoung Sun & Choi, San, 2018. "Ten years of research change using Google Trends: From the perspective of big data utilizations and applications," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 69-87.
    15. Joseph, Kissan & Babajide Wintoki, M. & Zhang, Zelin, 2011. "Forecasting abnormal stock returns and trading volume using investor sentiment: Evidence from online search," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1116-1127, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tariq Aziz & Valeed Ahmad Ansari, 2021. "How Does Google Search Affect the Stock Market? Evidence from Indian Companies," Vision, , vol. 25(2), pages 224-232, June.
    2. Ramya Rajajagadeesan Aroul & Sanjiv Sabherwal & Sergiy Saydometov, 2022. "FEAR Index, city characteristics, and housing returns," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 50(1), pages 173-205, March.
    3. Coble, David & Pincheira, Pablo, 2017. "Nowcasting Building Permits with Google Trends," MPRA Paper 76514, University Library of Munich, Germany.
    4. Serhan Cevik, 2022. "Where should we go? Internet searches and tourist arrivals," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 4048-4057, October.
    5. Tsoyu Calvin Lin & Shih-Hsun Hsu, 2020. "Forecasting Housing Markets from Number of Visits to Actual Price Registration System," International Real Estate Review, Global Social Science Institute, vol. 23(4), pages 505-536.
    6. Konstantinos N. Konstantakis & Despoina Paraskeuopoulou & Panayotis G. Michaelides & Efthymios G. Tsionas, 2021. "Bank deposits and Google searches in a crisis economy: Bayesian non‐linear evidence for Greece (2009–2015)," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 5408-5424, October.
    7. 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.
    8. Abay,Kibrom A. & Hirfrfot,Kibrom Tafere & Woldemichael,Andinet, 2020. "Winners and Losers from COVID-19 : Global Evidence from Google Search," Policy Research Working Paper Series 9268, The World Bank.
    9. Hamid, Alain & Heiden, Moritz, 2015. "Forecasting volatility with empirical similarity and Google Trends," Journal of Economic Behavior & Organization, Elsevier, vol. 117(C), pages 62-81.
    10. David Coble & Pablo Pincheira, 2021. "Forecasting building permits with Google Trends," Empirical Economics, Springer, vol. 61(6), pages 3315-3345, December.
    11. Bangwayo-Skeete, Prosper F. & Skeete, Ryan W., 2015. "Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach," Tourism Management, Elsevier, vol. 46(C), pages 454-464.
    12. Chauvet, Marcelle & Gabriel, Stuart & Lutz, Chandler, 2016. "Mortgage default risk: New evidence from internet search queries," Journal of Urban Economics, Elsevier, vol. 96(C), pages 91-111.
    13. Imene Ben El Hadj Said & Skander Slim, 2022. "The Dynamic Relationship between Investor Attention and Stock Market Volatility: International Evidence," JRFM, MDPI, vol. 15(2), pages 1-25, February.
    14. Sergiy Saydometov & Sanjiv Sabherwal & Ramya Rajajagadeesan Aroul, 2020. "Sentiment and its asymmetric effect on housing returns," Review of Financial Economics, John Wiley & Sons, vol. 38(4), pages 580-600, October.
    15. 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.
    16. Symitsi, Efthymia & Markellos, Raphael N. & Mantrala, Murali K., 2022. "Keyword portfolio optimization in paid search advertising," European Journal of Operational Research, Elsevier, vol. 303(2), pages 767-778.
    17. 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).
    18. Mingming Hu & Haiyan Song, 2020. "Data source combination for tourism demand forecasting," Tourism Economics, , vol. 26(7), pages 1248-1265, November.
    19. Kim, Neri & Lučivjanská, Katarína & Molnár, Peter & Villa, Roviel, 2019. "Google searches and stock market activity: Evidence from Norway," Finance Research Letters, Elsevier, vol. 28(C), pages 208-220.
    20. Jolana Stejskalova, 2023. "We investigated the link between stock returns of automobile companies, Fama French factors, and behavioral attention, represented by demand for a selected car brand belonging to an automobile company," Journal of Economics / Ekonomicky casopis, Institute of Economic Research, Slovak Academy of Sciences, vol. 71(3), pages 202-221, March.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:jforec:v:39:y:2020:i:2:p:281-295. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www3.interscience.wiley.com/cgi-bin/jhome/2966 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.