IDEAS home Printed from https://ideas.repec.org/a/eee/eneeco/v114y2022ics0140988322004017.html
   My bibliography  Save this article

Twitter and market efficiency in energy markets: Evidence using LDA clustered topic extraction

Author

Listed:
  • Polyzos, Efstathios
  • Wang, Fang

Abstract

We use an extended sample of tweets relating to energy markets in order to examine and quantify the existence of market efficiency. The tweets are used as a proxy for publicly available information and we examine the degree to which this information determines market movements on the next trading day for nine energy market indices. We mine the topics of increasing and decreasing days using latent Dirichlet allocation and find that the topics of tweets in increasing and decreasing days differ. We validate our approach by feeding the extracted topics into three classifier machines and find that the classifiers provide forecasts on market movements with accuracy 57.83% (39.02%) in bull (bear) markets. Our findings support the presence of semi-strong efficiency, since we find evidence of price movements not reflecting public information, while the asymmetry of forecast accuracy over increasing and decreasing markets suggests a different rate of information propagation across market regimes. Our findings can provide useful input to valuation models linked to market efficiency.

Suggested Citation

  • Polyzos, Efstathios & Wang, Fang, 2022. "Twitter and market efficiency in energy markets: Evidence using LDA clustered topic extraction," Energy Economics, Elsevier, vol. 114(C).
    • Handle: RePEc:eee:eneeco:v:114:y:2022:i:c:s0140988322004017
    DOI: 10.1016/j.eneco.2022.106264
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0140988322004017
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.eneco.2022.106264?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. Steve Y. Yang & Sheung Yin Kevin Mo & Anqi Liu, 2015. "Twitter financial community sentiment and its predictive relationship to stock market movement," Quantitative Finance, Taylor & Francis Journals, vol. 15(10), pages 1637-1656, October.
    2. Kristoufek, Ladislav & Vosvrda, Miloslav, 2014. "Commodity futures and market efficiency," Energy Economics, Elsevier, vol. 42(C), pages 50-57.
    3. Coakley, Jerry & Fuertes, Ana-Maria, 2006. "Valuation ratios and price deviations from fundamentals," Journal of Banking & Finance, Elsevier, vol. 30(8), pages 2325-2346, August.
    4. Schwert, G. William, 2003. "Anomalies and market efficiency," Handbook of the Economics of Finance, in: G.M. Constantinides & M. Harris & R. M. Stulz (ed.), Handbook of the Economics of Finance, edition 1, volume 1, chapter 15, pages 939-974, Elsevier.
    5. Alvarez-Ramirez, Jose & Alvarez, Jesus & Solis, Ricardo, 2010. "Crude oil market efficiency and modeling: Insights from the multiscaling autocorrelation pattern," Energy Economics, Elsevier, vol. 32(5), pages 993-1000, September.
    6. Grün, Bettina & Hornik, Kurt, 2011. "topicmodels: An R Package for Fitting Topic Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i13).
    7. Tiwari, Aviral Kumar & Umar, Zaghum & Alqahtani, Faisal, 2021. "Existence of long memory in crude oil and petroleum products: Generalised Hurst exponent approach," Research in International Business and Finance, Elsevier, vol. 57(C).
    8. Abdulnasser Hatemi-J, 2012. "Asymmetric causality tests with an application," Empirical Economics, Springer, vol. 43(1), pages 447-456, August.
    9. Dominik M. Rösch & Avanidhar Subrahmanyam & Mathijs A. van Dijk, 2017. "The Dynamics of Market Efficiency," Review of Financial Studies, Society for Financial Studies, vol. 30(4), pages 1151-1187.
    10. Feinerer, Ingo & Hornik, Kurt & Meyer, David, 2008. "Text Mining Infrastructure in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(i05).
    11. Arturo Bris & William N. Goetzmann & Ning Zhu, 2007. "Efficiency and the Bear: Short Sales and Markets Around the World," Journal of Finance, American Finance Association, vol. 62(3), pages 1029-1079, June.
    12. Chen, Shiu-Sheng, 2009. "Predicting the bear stock market: Macroeconomic variables as leading indicators," Journal of Banking & Finance, Elsevier, vol. 33(2), pages 211-223, February.
    13. Bruce N. Lehmann, 1990. "Fads, Martingales, and Market Efficiency," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 105(1), pages 1-28.
    14. Basse, Tobias & Klein, Tony & Vigne, Samuel A. & Wegener, Christoph, 2021. "U.S. stock prices and the dot.com-bubble: Can dividend policy rescue the efficient market hypothesis?," Journal of Corporate Finance, Elsevier, vol. 67(C).
    15. Basu, S, 1977. "Investment Performance of Common Stocks in Relation to Their Price-Earnings Ratios: A Test of the Efficient Market Hypothesis," Journal of Finance, American Finance Association, vol. 32(3), pages 663-682, June.
    16. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    17. Navratil, Robert & Taylor, Stephen & Vecer, Jan, 2021. "On equity market inefficiency during the COVID-19 pandemic," International Review of Financial Analysis, Elsevier, vol. 77(C).
    18. Reboredo, Juan C. & Ugolini, Andrea, 2018. "The impact of Twitter sentiment on renewable energy stocks," Energy Economics, Elsevier, vol. 76(C), pages 153-169.
    19. Zhang, Wei & Li, Jing & Li, Guoxiang & Guo, Shucen, 2020. "Emission reduction effect and carbon market efficiency of carbon emissions trading policy in China," Energy, Elsevier, vol. 196(C).
    20. Okorie, David Iheke & Lin, Boqiang, 2021. "Adaptive market hypothesis: The story of the stock markets and COVID-19 pandemic," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    21. Kraaijeveld, Olivier & De Smedt, Johannes, 2020. "The predictive power of public Twitter sentiment for forecasting cryptocurrency prices," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 65(C).
    22. Chordia, Tarun & Roll, Richard & Subrahmanyam, Avanidhar, 2008. "Liquidity and market efficiency," Journal of Financial Economics, Elsevier, vol. 87(2), pages 249-268, February.
    23. Robert S. Ludwig & Michael J. Piovoso, 2005. "A comparison of machine‐learning classifiers for selecting money managers," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 13(3), pages 151-164, July.
    24. Elroy Dimson & Massoud Mussavian, 1998. "A brief history of market efficiency," European Financial Management, European Financial Management Association, vol. 4(1), pages 91-103.
    25. Mensi, Walid & Lee, Yun-Jung & Vinh Vo, Xuan & Yoon, Seong-Min, 2021. "Does oil price variability affect the long memory and weak form efficiency of stock markets in top oil producers and oil Consumers? Evidence from an asymmetric MF-DFA approach," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    26. Montagnoli, Alberto & de Vries, Frans P., 2010. "Carbon trading thickness and market efficiency," Energy Economics, Elsevier, vol. 32(6), pages 1331-1336, November.
    27. Choi, Sun-Yong, 2021. "Analysis of stock market efficiency during crisis periods in the US stock market: Differences between the global financial crisis and COVID-19 pandemic," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 574(C).
    28. Michael Lachanski & Steven Pav, 2017. "Shy of the Character Limit: "Twitter Mood Predicts the Stock Market" Revisited," Econ Journal Watch, Econ Journal Watch, vol. 14(3), pages 302–345-3, September.
    29. Kian‐Ping Lim & Robert Brooks, 2011. "The Evolution Of Stock Market Efficiency Over Time: A Survey Of The Empirical Literature," Journal of Economic Surveys, Wiley Blackwell, vol. 25(1), pages 69-108, February.
    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. Li, Yiying & Ren, Xiaohang & Taghizadeh-Hesary, Farhad, 2023. "Vulnerability of sustainable markets to fossil energy shocks," Resources Policy, Elsevier, vol. 85(PB).
    2. Wang, Fang & Gacesa, Marko, 2023. "Semi-strong efficient market of Bitcoin and Twitter: An analysis of semantic vector spaces of extracted keywords and light gradient boosting machine models," International Review of Financial Analysis, Elsevier, vol. 88(C).
    3. Polyzos, Efstathios & Fotiadis, Anestis & Huan, Tzung-Cheng, 2023. "From Heroes to Scoundrels: Exploring the effects of online campaigns celebrating frontline workers on COVID-19 outcomes," Technology in Society, Elsevier, vol. 72(C).
    4. Düsterhöft, Maximilian & Schiemann, Frank & Walther, Thomas, 2023. "Let’s talk about risk! Stock market effects of risk disclosure for European energy utilities," Energy Economics, Elsevier, vol. 125(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, Meirui & Zhang, Rui & Ren, Xiaohang, 2023. "The time-varying effects of liquidity and market efficiency of the European Union carbon market: Evidence from the TVP-SVAR-SV approach," Energy Economics, Elsevier, vol. 123(C).
    2. Deniz Erer & Elif Erer & Selim Güngör, 2023. "The aggregate and sectoral time-varying market efficiency during crisis periods in Turkey: a comparative analysis with COVID-19 outbreak and the global financial crisis," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-25, December.
    3. Sattarhoff, Cristina & Gronwald, Marc, 2022. "Measuring informational efficiency of the European carbon market — A quantitative evaluation of higher order dependence," International Review of Financial Analysis, Elsevier, vol. 84(C).
    4. Wang, Fang & Gacesa, Marko, 2023. "Semi-strong efficient market of Bitcoin and Twitter: An analysis of semantic vector spaces of extracted keywords and light gradient boosting machine models," International Review of Financial Analysis, Elsevier, vol. 88(C).
    5. Thanh Huong Nguyen, 2019. "Information and Noise in Stock Markets: Evidence on the Determinants and Effects Using New Empirical Measures," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 7-2019.
    6. Jennifer N. Carpenter & Fangzhou Lu & Robert F. Whitelaw, 2015. "The Real Value of China's Stock Market," NBER Working Papers 20957, National Bureau of Economic Research, Inc.
    7. Espinosa-Paredes, G. & Rodriguez, E. & Alvarez-Ramirez, J., 2022. "A singular value decomposition entropy approach to assess the impact of Covid-19 on the informational efficiency of the WTI crude oil market," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    8. Ashok Chanabasangouda Patil & Shailesh Rastogi, 2019. "Time-Varying Price–Volume Relationship and Adaptive Market Efficiency: A Survey of the Empirical Literature," JRFM, MDPI, vol. 12(2), pages 1-18, June.
    9. Auer, Benjamin R. & Rottmann, Horst, 2019. "Have capital market anomalies worldwide attenuated in the recent era of high liquidity and trading activity?," Journal of Economics and Business, Elsevier, vol. 103(C), pages 61-79.
    10. Wang, Xiao-Qing & Su, Chi-Wei & Lobonţ, Oana-Ramona & Li, Hao & Nicoleta-Claudia, Moldovan, 2022. "Is China's carbon trading market efficient? Evidence from emissions trading scheme pilots," Energy, Elsevier, vol. 245(C).
    11. Leković Miljan, 2018. "Evidence for and Against the Validity of Efficient Market Hypothesis," Economic Themes, Sciendo, vol. 56(3), pages 369-387, September.
    12. Carpenter, Jennifer N. & Lu, Fangzhou & Whitelaw, Robert F., 2018. "The real value of China's stock market," BOFIT Discussion Papers 2/2018, Bank of Finland Institute for Emerging Economies (BOFIT).
    13. Carpenter, Jennifer N. & Lu, Fangzhou & Whitelaw, Robert F., 2018. "The real value of China’s stock market," BOFIT Discussion Papers 2/2018, Bank of Finland, Institute for Economies in Transition.
    14. repec:zbw:bofitp:2018_002 is not listed on IDEAS
    15. Carpenter, Jennifer N. & Lu, Fangzhou & Whitelaw, Robert F., 2018. "The real value of China’s stock market," BOFIT Discussion Papers 2, Bank of Finland, Institute for Economies in Transition.
    16. Chordia, Tarun & Subrahmanyam, Avanidhar & Tong, Qing, 2014. "Have capital market anomalies attenuated in the recent era of high liquidity and trading activity?," Journal of Accounting and Economics, Elsevier, vol. 58(1), pages 41-58.
    17. repec:zbw:bofitp:002 is not listed on IDEAS
    18. Gaio, Luiz Eduardo & Stefanelli, Nelson Oliveira & Pimenta, Tabajara & Bonacim, Carlos Alberto Grespan & Gatsios, Rafael Confetti, 2022. "The impact of the Russia-Ukraine conflict on market efficiency: Evidence for the developed stock market," Finance Research Letters, Elsevier, vol. 50(C).
    19. Ortiz-Cruz, Alejandro & Rodriguez, Eduardo & Ibarra-Valdez, Carlos & Alvarez-Ramirez, Jose, 2012. "Efficiency of crude oil markets: Evidences from informational entropy analysis," Energy Policy, Elsevier, vol. 41(C), pages 365-373.
    20. Cakici, Nusret & Zaremba, Adam, 2022. "Salience theory and the cross-section of stock returns: International and further evidence," Journal of Financial Economics, Elsevier, vol. 146(2), pages 689-725.
    21. Mohrschladt, Hannes, 2021. "The ordering of historical returns and the cross-section of subsequent returns," Journal of Banking & Finance, Elsevier, vol. 125(C).
    22. Abul Shamsuddin & Jae H. Kim, 2010. "Short‐Horizon Return Predictability in International Equity Markets," The Financial Review, Eastern Finance Association, vol. 45(2), pages 469-484, May.

    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:eneeco:v:114:y:2022:i:c:s0140988322004017. 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/eneco .

    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.