IDEAS home Printed from https://ideas.repec.org/a/eee/reveco/v42y2016icp291-312.html
   My bibliography  Save this article

Public information arrival and stock return volatility: Evidence from news sentiment and Markov Regime-Switching Approach

Author

Listed:
  • Shi, Yanlin
  • Ho, Kin-Yip
  • Liu, Wai-Man

Abstract

Using computational linguistic analysis of intraday firm-level news releases, this study models the relation between public information flows and stock volatility under different regimes. We analyze how the hourly return volatility of S&P100 stocks from 2000 to 2010 are linked to the various linguistics-based sentiment scores of the news releases, which are obtained from the RavenPack News Analytics Database. Results from the Markov Regime-Switching GARCH (MRS-GARCH) model indicate that firm-specific news sentiment is more significant in quantifying intraday volatility persistence in the calm (low-volatility) state than the turbulent (high-volatility) state. Furthermore, the impact of news sentiment differs across industries and firm size.

Suggested Citation

  • Shi, Yanlin & Ho, Kin-Yip & Liu, Wai-Man, 2016. "Public information arrival and stock return volatility: Evidence from news sentiment and Markov Regime-Switching Approach," International Review of Economics & Finance, Elsevier, vol. 42(C), pages 291-312.
    • Handle: RePEc:eee:reveco:v:42:y:2016:i:c:p:291-312
    DOI: 10.1016/j.iref.2015.12.003
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1059056015002294
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.iref.2015.12.003?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. Cheng, Louis T.W. & Leung, T.Y. & Yu, Wayne, 2014. "Information arrival, changes in R-square and pricing asymmetry of corporate news," International Review of Economics & Finance, Elsevier, vol. 33(C), pages 67-81.
    2. Mullen, Katharine M. & Ardia, David & Gil, David L. & Windover, Donald & Cline, James, 2011. "DEoptim: An R Package for Global Optimization by Differential Evolution," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i06).
    3. Susmel, Raul & Engle, Robert F., 1994. "Hourly volatility spillovers between international equity markets," Journal of International Money and Finance, Elsevier, vol. 13(1), pages 3-25, February.
    4. Fostel, Ana & Geanakoplos, John, 2012. "Why does bad news increase volatility and decrease leverage?," Journal of Economic Theory, Elsevier, vol. 147(2), pages 501-525.
    5. Jones, Charles M. & Lamont, Owen & Lumsdaine, Robin L., 1998. "Macroeconomic news and bond market volatility," Journal of Financial Economics, Elsevier, vol. 47(3), pages 315-337, March.
    6. Franc Klaassen, 2002. "Improving GARCH volatility forecasts with regime-switching GARCH," Empirical Economics, Springer, vol. 27(2), pages 363-394.
    7. Chang, Chia-Lin & Hsu, Hui-Kuang & McAleer, Michael, 2013. "Is small beautiful? Size effects of volatility spillovers for firm performance and exchange rates in tourism," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 519-534.
    8. Dueker, Michael J, 1997. "Markov Switching in GARCH Processes and Mean-Reverting Stock-Market Volatility," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(1), pages 26-34, January.
    9. Laakkonen Helinä & Lanne Markku, 2009. "Asymmetric News Effects on Exchange Rate Volatility: Good vs. Bad News in Good vs. Bad Times," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(1), pages 1-38, December.
    10. Xilong Chen & Eric Ghysels, 2011. "News--Good or Bad--and Its Impact on Volatility Predictions over Multiple Horizons," The Review of Financial Studies, Society for Financial Studies, vol. 24(1), pages 46-81, October.
    11. Markus Haas, 2004. "A New Approach to Markov-Switching GARCH Models," Journal of Financial Econometrics, Oxford University Press, vol. 2(4), pages 493-530.
    12. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    13. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Clara Vega, 2003. "Micro Effects of Macro Announcements: Real-Time Price Discovery in Foreign Exchange," American Economic Review, American Economic Association, vol. 93(1), pages 38-62, March.
    14. Berger, David & Chaboud, Alain & Hjalmarsson, Erik, 2009. "What drives volatility persistence in the foreign exchange market?," Journal of Financial Economics, Elsevier, vol. 94(2), pages 192-213, November.
    15. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    16. García-Vega, María & Guariglia, Alessandra & Spaliara, Marina-Eliza, 2012. "Volatility, financial constraints, and trade," International Review of Economics & Finance, Elsevier, vol. 21(1), pages 57-76.
    17. Lamoureux, Christopher G & Lastrapes, William D, 1990. "Heteroskedasticity in Stock Return Data: Volume versus GARCH Effects," Journal of Finance, American Finance Association, vol. 45(1), pages 221-229, March.
    18. Marcucci Juri, 2005. "Forecasting Stock Market Volatility with Regime-Switching GARCH Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 9(4), pages 1-55, December.
    19. Kari Harju & Syed Mujahid Hussain, 2011. "Intraday Seasonalities and Macroeconomic News Announcements," European Financial Management, European Financial Management Association, vol. 17(2), pages 367-390, March.
    20. Andrew Ang & Allan Timmermann, 2012. "Regime Changes and Financial Markets," Annual Review of Financial Economics, Annual Reviews, vol. 4(1), pages 313-337, October.
    21. Clark, Peter K, 1973. "A Subordinated Stochastic Process Model with Finite Variance for Speculative Prices," Econometrica, Econometric Society, vol. 41(1), pages 135-155, January.
    22. Hamilton, James D., 1988. "Rational-expectations econometric analysis of changes in regime : An investigation of the term structure of interest rates," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 385-423.
    23. Ho, Kin-Yip & Shi, Yanlin & Zhang, Zhaoyong, 2013. "How does news sentiment impact asset volatility? Evidence from long memory and regime-switching approaches," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 436-456.
    24. Chen, Carl R. & Su, Yuli & Huang, Ying, 2008. "Hourly index return autocorrelation and conditional volatility in an EAR-GJR-GARCH model with generalized error distribution," Journal of Empirical Finance, Elsevier, vol. 15(4), pages 789-798, September.
    25. Fama, Eugene F & French, Kenneth R, 1996. "Multifactor Explanations of Asset Pricing Anomalies," Journal of Finance, American Finance Association, vol. 51(1), pages 55-84, March.
    26. Fu, Fangjian, 2009. "Idiosyncratic risk and the cross-section of expected stock returns," Journal of Financial Economics, Elsevier, vol. 91(1), pages 24-37, January.
    27. Harris, Lawrence, 1986. "A transaction data study of weekly and intradaily patterns in stock returns," Journal of Financial Economics, Elsevier, vol. 16(1), pages 99-117, May.
    28. Sharma, Susan Sunila & Narayan, Paresh Kumar & Zheng, Xinwei, 2014. "An analysis of firm and market volatility," Economic Systems, Elsevier, vol. 38(2), pages 205-220.
    29. Kalev, Petko S. & Liu, Wai-Man & Pham, Peter K. & Jarnecic, Elvis, 2004. "Public information arrival and volatility of intraday stock returns," Journal of Banking & Finance, Elsevier, vol. 28(6), pages 1441-1467, June.
    30. Park, Beum-Jo, 2010. "Surprising information, the MDH, and the relationship between volatility and trading volume," Journal of Financial Markets, Elsevier, vol. 13(3), pages 344-366, August.
    31. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    32. Tauchen, George E & Pitts, Mark, 1983. "The Price Variability-Volume Relationship on Speculative Markets," Econometrica, Econometric Society, vol. 51(2), pages 485-505, March.
    33. Smith, L. Vanessa & Yamagata, Takashi, 2011. "Firm level return–volatility analysis using dynamic panels," Journal of Empirical Finance, Elsevier, vol. 18(5), pages 847-867.
    34. Andersen, Torben G. & Bollerslev, Tim, 1997. "Intraday periodicity and volatility persistence in financial markets," Journal of Empirical Finance, Elsevier, vol. 4(2-3), pages 115-158, June.
    35. Veronesi, Pietro, 1999. "Stock Market Overreaction to Bad News in Good Times: A Rational Expectations Equilibrium Model," The Review of Financial Studies, Society for Financial Studies, vol. 12(5), pages 975-1007.
    36. Hammoudeh, Shawkat & Yuan, Yuan & Chiang, Thomas & Nandha, Mohan, 2010. "Symmetric and asymmetric US sector return volatilities in presence of oil, financial and economic risks," Energy Policy, Elsevier, vol. 38(8), pages 3922-3932, August.
    37. Guo, Feng & Chen, Carl R. & Huang, Ying Sophie, 2011. "Markets contagion during financial crisis: A regime-switching approach," International Review of Economics & Finance, Elsevier, vol. 20(1), pages 95-109, January.
    38. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
    39. Cai, Jun, 1994. "A Markov Model of Switching-Regime ARCH," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(3), pages 309-316, July.
    40. Bollerslev, Tim, 1987. "A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return," The Review of Economics and Statistics, MIT Press, vol. 69(3), pages 542-547, August.
    41. Hamilton, James D. & Susmel, Raul, 1994. "Autoregressive conditional heteroskedasticity and changes in regime," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 307-333.
    42. Torben G. Andersen & Tim Bollerslev, 1998. "Deutsche Mark-Dollar Volatility: Intraday Activity Patterns, Macroeconomic Announcements, and Longer Run Dependencies," Journal of Finance, American Finance Association, vol. 53(1), pages 219-265, February.
    43. Harris, Lawrence, 1987. "Transaction Data Tests of the Mixture of Distributions Hypothesis," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 22(2), pages 127-141, June.
    44. Andersen, Torben G, 1996. "Return Volatility and Trading Volume: An Information Flow Interpretation of Stochastic Volatility," Journal of Finance, American Finance Association, vol. 51(1), pages 169-204, March.
    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. Seok, Sang Ik & Cho, Hoon & Ryu, Doojin, 2019. "Firm-specific investor sentiment and daily stock returns," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    2. Roland Füss & Massimo Guidolin & Christian Koeppel, 2019. "Sentiment Risk Premia In The Cross-Section of Global Equity," Working Papers on Finance 1913, University of St. Gallen, School of Finance, revised May 2020.
    3. Jiang, Xiandeng & Shi, Yanlin, 2020. "Does US partisan conflict affect US–China bilateral trade?," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 1117-1131.
    4. Chuliá, Helena & Guillén, Montserrat & Uribe, Jorge M., 2017. "Measuring uncertainty in the stock market," International Review of Economics & Finance, Elsevier, vol. 48(C), pages 18-33.
    5. Gong, Yuting & Li, Kevin X. & Chen, Shu-Ling & Shi, Wenming, 2020. "Contagion risk between the shipping freight and stock markets: Evidence from the recent US-China trade war," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 136(C).
    6. Ho, Kin-Yip & An, Jiyoun, 2020. "Decomposing the value premium: The role of intangible information in the Chinese stock market," Emerging Markets Review, Elsevier, vol. 44(C).
    7. Wang, Gaoshan & Yu, Guangjin & Shen, Xiaohong, 2021. "The effect of online environmental news on green industry stocks: The mediating role of investor sentiment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 573(C).
    8. Peng Ye & Yong Li & Abu Bakkar Siddik, 2023. "Forecasting the Return of Carbon Price in the Chinese Market Based on an Improved Stacking Ensemble Algorithm," Energies, MDPI, vol. 16(11), pages 1-39, June.
    9. Gianluca Anese & Marco Corazza & Michele Costola & Loriana Pelizzon, 2023. "Impact of public news sentiment on stock market index return and volatility," Computational Management Science, Springer, vol. 20(1), pages 1-36, December.
    10. Zhou, Xinmiao & Zhang, Junru & Zhang, Zhaoyong, 2021. "How does news flow affect cross-market volatility spillovers? Evidence from China’s stock index futures and spot markets," International Review of Economics & Finance, Elsevier, vol. 73(C), pages 196-213.
    11. Roland Fuess & Massimo Guidolin & Christian Koeppel, 2019. "Sentiment Risk Premia in the Cross-Section of Global Equity and Currency Returns," BAFFI CAREFIN Working Papers 19116, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    12. Feng, Lingbing & Fu, Tong & Shi, Yanlin, 2022. "How does news sentiment affect the states of Japanese stock return volatility?," International Review of Financial Analysis, Elsevier, vol. 84(C).
    13. Chou, Ke-Hsin & Day, Min-Yuh & Chiu, Chien-Liang, 2023. "Do bitcoin news information flow and return volatility fit the sequential information arrival hypothesis and the mixture of distribution hypothesis?," International Review of Economics & Finance, Elsevier, vol. 88(C), pages 365-385.

    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. Ho, Kin-Yip & Shi, Yanlin & Zhang, Zhaoyong, 2013. "How does news sentiment impact asset volatility? Evidence from long memory and regime-switching approaches," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 436-456.
    2. Shi, Yanlin & Feng, Lingbing, 2016. "A discussion on the innovation distribution of the Markov regime-switching GARCH model," Economic Modelling, Elsevier, vol. 53(C), pages 278-288.
    3. Gao, Guangyuan & Ho, Kin-Yip & Shi, Yanlin, 2020. "Long memory or regime switching in volatility? Evidence from high-frequency returns on the U.S. stock indices," Pacific-Basin Finance Journal, Elsevier, vol. 61(C).
    4. Shi, Yanlin & Liu, Wai-Man & Ho, Kin-Yip, 2016. "Public news arrival and the idiosyncratic volatility puzzle," Journal of Empirical Finance, Elsevier, vol. 37(C), pages 159-172.
    5. Degiannakis, Stavros & Xekalaki, Evdokia, 2004. "Autoregressive Conditional Heteroskedasticity (ARCH) Models: A Review," MPRA Paper 80487, University Library of Munich, Germany.
    6. Feng, Lingbing & Fu, Tong & Shi, Yanlin, 2022. "How does news sentiment affect the states of Japanese stock return volatility?," International Review of Financial Analysis, Elsevier, vol. 84(C).
    7. Halkos, George & Tzirivis, Apostolos, 2018. "Effective energy commodities’ risk management: Econometric modeling of price volatility," MPRA Paper 90781, University Library of Munich, Germany.
    8. Ho, Kin-Yip & Shi, Yanlin & Zhang, Zhaoyong, 2020. "News and return volatility of Chinese bank stocks," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 1095-1105.
    9. Ataurima Arellano, Miguel & Rodríguez, Gabriel, 2020. "Empirical modeling of high-income and emerging stock and Forex market return volatility using Markov-switching GARCH models," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    10. Zhang, Yue-Jun & Yao, Ting & He, Ling-Yun & Ripple, Ronald, 2019. "Volatility forecasting of crude oil market: Can the regime switching GARCH model beat the single-regime GARCH models?," International Review of Economics & Finance, Elsevier, vol. 59(C), pages 302-317.
    11. Ardia, David & Bluteau, Keven & Boudt, Kris & Catania, Leopoldo, 2018. "Forecasting risk with Markov-switching GARCH models:A large-scale performance study," International Journal of Forecasting, Elsevier, vol. 34(4), pages 733-747.
    12. Yanlin Shi, 2023. "Long memory and regime switching in the stochastic volatility modelling," Annals of Operations Research, Springer, vol. 320(2), pages 999-1020, January.
    13. King, Daniel & Botha, Ferdi, 2015. "Modelling stock return volatility dynamics in selected African markets," Economic Modelling, Elsevier, vol. 45(C), pages 50-73.
    14. Carol Alexander & Emese Lazar, 2009. "Modelling Regime‐Specific Stock Price Volatility," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(6), pages 761-797, December.
    15. Taylor, Nicholas, 2008. "Can idiosyncratic volatility help forecast stock market volatility?," International Journal of Forecasting, Elsevier, vol. 24(3), pages 462-479.
    16. BAUWENS, Luc & HAFNER, Christian & LAURENT, Sébastien, 2011. "Volatility models," LIDAM Discussion Papers CORE 2011058, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
      • Bauwens, L. & Hafner, C. & Laurent, S., 2012. "Volatility Models," LIDAM Reprints ISBA 2012028, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
      • Bauwens, L. & Hafner C. & Laurent, S., 2011. "Volatility Models," LIDAM Discussion Papers ISBA 2011044, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    17. Abounoori, Esmaiel & Elmi, Zahra (Mila) & Nademi, Younes, 2016. "Forecasting Tehran stock exchange volatility; Markov switching GARCH approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 445(C), pages 264-282.
    18. Richard D. F. Harris & Murat Mazibas, 2022. "A component Markov regime‐switching autoregressive conditional range model," Bulletin of Economic Research, Wiley Blackwell, vol. 74(2), pages 650-683, April.
    19. Shi, Yanlin & Ho, Kin-Yip, 2015. "Long memory and regime switching: A simulation study on the Markov regime-switching ARFIMA model," Journal of Banking & Finance, Elsevier, vol. 61(S2), pages 189-204.
    20. repec:uts:finphd:39 is not listed on IDEAS
    21. Szabolcs Blazsek & Anna Downarowicz, 2013. "Forecasting hedge fund volatility: a Markov regime-switching approach," The European Journal of Finance, Taylor & Francis Journals, vol. 19(4), pages 243-275, April.

    More about this item

    Keywords

    Public information arrival; Stock return volatility; News sentiment; Markov Regime-Switching GARCH;
    All these keywords.

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

    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:reveco:v:42:y:2016:i:c:p:291-312. 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/620165 .

    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.