IDEAS home Printed from https://ideas.repec.org/a/eee/jrpoli/v81y2023ics0301420723000077.html
   My bibliography  Save this article

Does happy Twitter forecast gold price?

Author

Listed:
  • Swamy, Vighneswara
  • Lagesh, M.A.

Abstract

This study explores the relationship between Twitter happiness and gold price in the US using wavelet analysis covering daily data from September 2008 to April 2019. We test our main hypothesis that investor attention from Twitter as a news and social medium has a nexus with the gold price. The results suggest that (i) Twitter happiness and gold price exhibit a strong correlation in both time and frequency domains; (ii) Twitter happiness leads the gold price suggesting the direction of causality from Twitter sentiment to gold price; (iii) in the post-crisis period, the gold price has experienced a stable rise and the Twitter sentiment is letting the gold price. Thus, we indicate that Twitter's mood can forecast the gold price. Our findings imply that investors can take a cue from Twitter sentiment in strategizing their gold investment decisions.

Suggested Citation

  • Swamy, Vighneswara & Lagesh, M.A., 2023. "Does happy Twitter forecast gold price?," Resources Policy, Elsevier, vol. 81(C).
    • Handle: RePEc:eee:jrpoli:v:81:y:2023:i:c:s0301420723000077
    DOI: 10.1016/j.resourpol.2023.103299
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0301420723000077
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.resourpol.2023.103299?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
    ---><---

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

    References listed on IDEAS

    as
    1. Zhang, Wei & Wang, Pengfei & Li, Xiao & Shen, Dehua, 2018. "Twitter’s daily happiness sentiment and international stock returns: Evidence from linear and nonlinear causality tests," Journal of Behavioral and Experimental Finance, Elsevier, vol. 18(C), pages 50-53.
    2. Schmidbauer, Harald & Rösch, Angi, 2018. "The impact of festivities on gold price expectation and volatility," International Review of Financial Analysis, Elsevier, vol. 58(C), pages 117-131.
    3. Zhang, Wei & Li, Xiao & Shen, Dehua & Teglio, Andrea, 2016. "Daily happiness and stock returns: Some international evidence," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 460(C), pages 201-209.
    4. Bouri, Elie & Jain, Anshul & Biswal, P.C. & Roubaud, David, 2017. "Cointegration and nonlinear causality amongst gold, oil, and the Indian stock market: Evidence from implied volatility indices," Resources Policy, Elsevier, vol. 52(C), pages 201-206.
    5. You, Wanhai & Guo, Yawei & Peng, Cheng, 2017. "Twitter's daily happiness sentiment and the predictability of stock returns," Finance Research Letters, Elsevier, vol. 23(C), pages 58-64.
    6. Fan, Yanqin & Gençay, Ramazan, 2010. "Unit Root Tests With Wavelets," Econometric Theory, Cambridge University Press, vol. 26(5), pages 1305-1331, October.
    7. Zheludev, Ilya & Smith, Robert & Aste, Tomaso, 2014. "When can social media lead financial markets?," LSE Research Online Documents on Economics 57376, London School of Economics and Political Science, LSE Library.
    8. Connor Jeff & Rossiter Rosemary, 2005. "Wavelet Transforms and Commodity Prices," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 9(1), pages 1-22, March.
    9. Irandoust, Manuchehr, 2017. "Metal prices and stock market performance: Is there an empirical link?," Resources Policy, Elsevier, vol. 52(C), pages 389-392.
    10. Shen, Dehua & Liu, Lanbiao & Zhang, Yongjie, 2018. "Quantifying the cross-sectional relationship between online sentiment and the skewness of stock returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 928-934.
    11. Li, Xiao & Shen, Dehua & Xue, Mei & Zhang, Wei, 2017. "Daily happiness and stock returns: The case of Chinese company listed in the United States," Economic Modelling, Elsevier, vol. 64(C), pages 496-501.
    12. Beckmann, Joscha & Czudaj, Robert & Pilbeam, Keith, 2015. "Causality and volatility patterns between gold prices and exchange rates," The North American Journal of Economics and Finance, Elsevier, vol. 34(C), pages 292-300.
    13. Dutta, Anupam, 2018. "Impacts of oil volatility shocks on metal markets: A research note," Resources Policy, Elsevier, vol. 55(C), pages 9-19.
    14. Zhu, Yanhui & Fan, Jingwen & Tucker, Jon, 2018. "The impact of monetary policy on gold price dynamics," Research in International Business and Finance, Elsevier, vol. 44(C), pages 319-331.
    15. Bhatia, Vaneet & Das, Debojyoti & Tiwari, Aviral Kumar & Shahbaz, Muhammad & Hasim, Haslifah M., 2018. "Do precious metal spot prices influence each other? Evidence from a nonparametric causality-in-quantiles approach," Resources Policy, Elsevier, vol. 55(C), pages 244-252.
    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. Cai, Yi & Tang, Zhenpeng & Chen, Ying, 2024. "Can real-time investor sentiment help predict the high-frequency stock returns? Evidence from a mixed-frequency-rolling decomposition forecasting method," The North American Journal of Economics and Finance, Elsevier, vol. 72(C).

    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. Zhong, Wanxing & Kong, Rui & Chen, Guang, 2019. "Gold prices fluctuation of co-movement forecast between China and Russia," Resources Policy, Elsevier, vol. 62(C), pages 218-230.
    2. Văn, Lê & Bảo, Nguyễn Khắc Quốc, 2022. "The relationship between global stock and precious metals under Covid-19 and happiness perspectives," Resources Policy, Elsevier, vol. 77(C).
    3. Chen, Wen-Yi & Chen, Mei-Ping, 2022. "Twitter’s daily happiness sentiment, economic policy uncertainty, and stock index fluctuations," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
    4. Lee, Chien-Chiang & Chen, Mei-Ping, 2020. "Happiness sentiments and the prediction of cross-border country exchange-traded fund returns," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    5. Qadan, Mahmoud & Aharon, David Y. & Cohen, Gil, 2020. "Everybody likes shopping, including the US capital market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
    6. Bonato, Matteo & Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2021. "A note on investor happiness and the predictability of realized volatility of gold," Finance Research Letters, Elsevier, vol. 39(C).
    7. Naeem, Muhammad Abubakr & Farid, Saqib & Faruk, Balli & Shahzad, Syed Jawad Hussain, 2020. "Can happiness predict future volatility in stock markets?," Research in International Business and Finance, Elsevier, vol. 54(C).
    8. Zhang, Wei & Wang, Pengfei & Li, Xiao & Shen, Dehua, 2018. "Twitter’s daily happiness sentiment and international stock returns: Evidence from linear and nonlinear causality tests," Journal of Behavioral and Experimental Finance, Elsevier, vol. 18(C), pages 50-53.
    9. Li, Yue & W. Goodell, John & Shen, Dehua, 2021. "Does happiness forecast implied volatility? Evidence from nonparametric wave-based Granger causality testing," The Quarterly Review of Economics and Finance, Elsevier, vol. 81(C), pages 113-122.
    10. Yousaf, Imran & Youssef, Manel & Goodell, John W., 2022. "Quantile connectedness between sentiment and financial markets: Evidence from the S&P 500 twitter sentiment index," International Review of Financial Analysis, Elsevier, vol. 83(C).
    11. Zhao, Ruwei, 2020. "Quantifying the cross sectional relation of daily happiness sentiment and stock return: Evidence from US," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 538(C).
    12. Zhang, Tonghui & Yuan, Ying & Wu, Xi, 2020. "Is microblogging data reflected in stock market volatility? Evidence from Sina Weibo," Finance Research Letters, Elsevier, vol. 32(C).
    13. Umar, Zaghum & Nasreen, Samia & Solarin, Sakiru Adebola & Tiwari, Aviral Kumar, 2019. "Exploring the time and frequency domain connectedness of oil prices and metal prices," Resources Policy, Elsevier, vol. 64(C).
    14. Matteo Bonato & Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2020. "Investor Happiness and Predictability of the Realized Volatility of Oil Price," Sustainability, MDPI, vol. 12(10), pages 1-11, May.
    15. Bouri, Elie & Demirer, Riza & Gabauer, David & Gupta, Rangan, 2022. "Financial market connectedness: The role of investors’ happiness," Finance Research Letters, Elsevier, vol. 44(C).
    16. Na, Haejung & Kim, Soonho, 2021. "Predicting stock prices based on informed traders’ activities using deep neural networks," Economics Letters, Elsevier, vol. 204(C).
    17. Li, Xiao, 2020. "When financial literacy meets textual analysis: A conceptual review," Journal of Behavioral and Experimental Finance, Elsevier, vol. 28(C).
    18. Zhang, Zuochao & Shen, Dehua, 2024. "Firm-specific new media sentiment and price synchronicity," Research in International Business and Finance, Elsevier, vol. 69(C).
    19. Pandey, Dharen Kumar & Kumari, Vineeta & Palma, Alessia & Goodell, John W., 2024. "Are markets in happier countries less affected by tragic events? Evidence from market reaction to the Israel–Hamas conflict," Finance Research Letters, Elsevier, vol. 60(C).
    20. Byström, Hans, 2020. "Happiness and Gold Prices," Finance Research Letters, Elsevier, vol. 35(C).

    More about this item

    Keywords

    Twitter; Sentiment; Gold price; Wavelet analysis; Causality;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market

    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:eee:jrpoli:v:81:y:2023:i:c:s0301420723000077. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/30467 .

    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.