IDEAS home Printed from https://ideas.repec.org/a/oup/jfinec/v19y2021i5p910-933..html
   My bibliography  Save this article

Does High-Frequency Social Media Data Improve Forecasts of Low-Frequency Consumer Confidence Measures?
[Regression Models with Mixed Sampling Frequencies]

Author

Listed:
  • Steven Lehrer
  • Tian Xie
  • Tao Zeng

Abstract

Social media data present challenges for forecasters since one must convert text into data and deal with issues related to these measures being collected at different frequencies and volumes than traditional financial data. In this article, we use a deep learning algorithm to measure sentiment within Twitter messages on an hourly basis and introduce a new method to undertake mixed data sampling (MIDAS) that allows for a weaker discounting of historical data that is well-suited for this new data source. To evaluate the performance of approach relative to alternative MIDAS strategies, we conduct an out of sample forecasting exercise for the consumer confidence index with both traditional econometric strategies and machine learning algorithms. Irrespective of the estimator used to conduct forecasts, our results show that (i) including consumer sentiment measures from Twitter greatly improves forecast accuracy and (ii) there are substantial gains from our proposed MIDAS procedure relative to common alternatives.

Suggested Citation

  • Steven Lehrer & Tian Xie & Tao Zeng, 2021. "Does High-Frequency Social Media Data Improve Forecasts of Low-Frequency Consumer Confidence Measures? [Regression Models with Mixed Sampling Frequencies]," Journal of Financial Econometrics, Oxford University Press, vol. 19(5), pages 910-933.
    • Handle: RePEc:oup:jfinec:v:19:y:2021:i:5:p:910-933.
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/jjfinec/nbz037
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Steven Lehrer & Tian Xie, 2017. "Box Office Buzz: Does Social Media Data Steal the Show from Model Uncertainty When Forecasting for Hollywood?," The Review of Economics and Statistics, MIT Press, vol. 99(5), pages 749-755, December.
    2. Andreou, Elena & Ghysels, Eric & Kourtellos, Andros, 2010. "Regression models with mixed sampling frequencies," Journal of Econometrics, Elsevier, vol. 158(2), pages 246-261, October.
    3. Lieven Baele, 2010. "The Determinants of Stock and Bond Return Comovements," The Review of Financial Studies, Society for Financial Studies, vol. 23(6), pages 2374-2428, June.
    4. Fulvio Corsi, 2009. "A Simple Approximate Long-Memory Model of Realized Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 7(2), pages 174-196, Spring.
    5. Patrick Bajari & Denis Nekipelov & Stephen P. Ryan & Miaoyu Yang, 2015. "Machine Learning Methods for Demand Estimation," American Economic Review, American Economic Association, vol. 105(5), pages 481-485, May.
    6. Felix Chan & Robert B. Durand & Joyce Khuu & Lee A. Smales, 2017. "The Validity of Investor Sentiment Proxies," International Review of Finance, International Review of Finance Ltd., vol. 17(3), pages 473-477, September.
    7. Malcolm Baker & Jeffrey Wurgler, 2012. "Comovement and Predictability Relationships Between Bonds and the Cross-section of Stocks," The Review of Asset Pricing Studies, Society for Financial Studies, vol. 2(1), pages 57-87.
    8. Susan Athey & Guido W. Imbens, 2017. "The State of Applied Econometrics: Causality and Policy Evaluation," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 3-32, Spring.
    9. Brown, Gregory W. & Cliff, Michael T., 2004. "Investor sentiment and the near-term stock market," Journal of Empirical Finance, Elsevier, vol. 11(1), pages 1-27, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Steven F. Lehrer & Tian Xie, 2022. "The Bigger Picture: Combining Econometrics with Analytics Improves Forecasts of Movie Success," Management Science, INFORMS, vol. 68(1), pages 189-210, January.
    2. Lahiri, Kajal & Yang, Cheng, 2022. "Boosting tax revenues with mixed-frequency data in the aftermath of COVID-19: The case of New York," International Journal of Forecasting, Elsevier, vol. 38(2), pages 545-566.
    3. Qiu, Yue, 2020. "Forecasting the Consumer Confidence Index with tree-based MIDAS regressions," Economic Modelling, Elsevier, vol. 91(C), pages 247-256.
    4. Algaba, Andres & Borms, Samuel & Boudt, Kris & Verbeken, Brecht, 2023. "Daily news sentiment and monthly surveys: A mixed-frequency dynamic factor model for nowcasting consumer confidence," International Journal of Forecasting, Elsevier, vol. 39(1), pages 266-278.
    5. Lehrer, Steven & Xie, Tian & Zhang, Xinyu, 2021. "Social media sentiment, model uncertainty, and volatility forecasting," Economic Modelling, Elsevier, vol. 102(C).
    6. Daniele Ballinari & Simon Behrendt, 2021. "How to gauge investor behavior? A comparison of online investor sentiment measures," Digital Finance, Springer, vol. 3(2), pages 169-204, June.

    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. Seo, Sung Won & Kim, Jun Sik, 2015. "The information content of option-implied information for volatility forecasting with investor sentiment," Journal of Banking & Finance, Elsevier, vol. 50(C), pages 106-120.
    2. Kim, Jun Sik & Ryu, Doojin & Seo, Sung Won, 2014. "Investor sentiment and return predictability of disagreement," Journal of Banking & Finance, Elsevier, vol. 42(C), pages 166-178.
    3. Ghysels, Eric & Qian, Hang, 2019. "Estimating MIDAS regressions via OLS with polynomial parameter profiling," Econometrics and Statistics, Elsevier, vol. 9(C), pages 1-16.
    4. Bethke, Sebastian & Gehde-Trapp, Monika & Kempf, Alexander, 2015. "Investor sentiment, flight-to-quality, and corporate bond comovement," CFR Working Papers 13-06 [rev.3], University of Cologne, Centre for Financial Research (CFR).
    5. Bethke, Sebastian & Gehde-Trapp, Monika & Kempf, Alexander, 2017. "Investor sentiment, flight-to-quality, and corporate bond comovement," Journal of Banking & Finance, Elsevier, vol. 82(C), pages 112-132.
    6. Wang, Wenzhao & Duxbury, Darren, 2021. "Institutional investor sentiment and the mean-variance relationship: Global evidence," Journal of Economic Behavior & Organization, Elsevier, vol. 191(C), pages 415-441.
    7. Yang, Jianlei & Yang, Chunpeng, 2021. "The impact of mixed-frequency geopolitical risk on stock market returns," Economic Analysis and Policy, Elsevier, vol. 72(C), pages 226-240.
    8. Andreou, Elena, 2016. "On the use of high frequency measures of volatility in MIDAS regressions," Journal of Econometrics, Elsevier, vol. 193(2), pages 367-389.
    9. Demirovic, Amer & Guermat, Cherif & Tucker, Jon, 2017. "The relationship between equity and bond returns: An empirical investigation," Journal of Financial Markets, Elsevier, vol. 35(C), pages 47-64.
    10. Abrell, Jan & Kosch, Mirjam & Rausch, Sebastian, 2022. "How effective is carbon pricing?—A machine learning approach to policy evaluation," Journal of Environmental Economics and Management, Elsevier, vol. 112(C).
    11. Baker, Malcolm & Wurgler, Jeffrey & Yuan, Yu, 2012. "Global, local, and contagious investor sentiment," Journal of Financial Economics, Elsevier, vol. 104(2), pages 272-287.
    12. Carla Fernandes & Paulo M. Gama & Elisabete Vieira, 2016. "Does local and Euro area sentiment matter for sovereign debt markets? Evidence from a bailout country," Applied Economics, Taylor & Francis Journals, vol. 48(9), pages 816-834, February.
    13. Madadkhani, Shiva & Ikonnikova, Svetlana, 2024. "Toward high-resolution projection of electricity prices: A machine learning approach to quantifying the effects of high fuel and CO2 prices," Energy Economics, Elsevier, vol. 129(C).
    14. Lehrer, Steven & Xie, Tian & Zhang, Xinyu, 2021. "Social media sentiment, model uncertainty, and volatility forecasting," Economic Modelling, Elsevier, vol. 102(C).
    15. Chevallier, Julien, 2011. "Evaluating the carbon-macroeconomy relationship: Evidence from threshold vector error-correction and Markov-switching VAR models," Economic Modelling, Elsevier, vol. 28(6), pages 2634-2656.
    16. Fabrizio Casalin & Enzo Dia, 2016. "The dynamic interrelation between external finance and bank credit," Applied Economics, Taylor & Francis Journals, vol. 48(3), pages 243-259, January.
    17. Philippe Mueller & Andrea Vedolin & Hao Zhou, 2019. "Short-Run Bond Risk Premia," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 9(03), pages 1-34, September.
    18. Juan M. Londono & Nancy R. Xu, 2021. "The Global Determinants of International Equity Risk Premiums," International Finance Discussion Papers 1318, Board of Governors of the Federal Reserve System (U.S.).
    19. Bams, Dennis & Blanchard, Gildas & Honarvar, Iman & Lehnert, Thorsten, 2017. "Does oil and gold price uncertainty matter for the stock market?," Journal of Empirical Finance, Elsevier, vol. 44(C), pages 270-285.
    20. Philippe Mueller & Andrea Vedolin & Yu-min Yen, 2012. "Bond Variance Risk Premia," FMG Discussion Papers dp699, Financial Markets Group.

    More about this item

    Keywords

    big data; forecasting; machine learning; MIDAS; social media;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

    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:oup:jfinec:v:19:y:2021:i:5:p:910-933.. 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: Oxford University Press (email available below). General contact details of provider: https://edirc.repec.org/data/sofieea.html .

    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.