IDEAS home Printed from https://ideas.repec.org/a/bla/ecorec/v91y2015i295p495-508.html
   My bibliography  Save this article

Capturing the Impact of Unobserved Sector-Wide Shocks on Stock Returns with Panel Data Model

Author

Listed:
  • KiHoon Jimmy Hong
  • Bin Peng
  • Xiaohui Zhang

Abstract

type="main" xml:id="ecor12208-abs-0001"> Unobserved sector-wide common shocks cause the issue of cross-sectional dependence (CSD) in panel data modelling of stock returns. In this study we apply two econometric techniques: the seemingly unrelated regression approach and a Bayesian estimator for panel data models with factor structural errors, to allow for CSD within a particular sector. By applying these models to monthly stock returns of S&P100 companies from six sectors over 10 years, we can capture and measure the heterogeneous impacts of not only observed individual company accounting fundamentals and market-wide common shocks, but also unobservable sector-wide common shocks. Results from the empirical study show that the impacts from both observed factors and unobserved sector-wide common shocks vary markedly across companies. After controlling for observed accounting fundamentals and market-wide common factors, a considerable proportion of the variation in stock returns can be attributed to unobservable sector-wide common shocks.

Suggested Citation

  • KiHoon Jimmy Hong & Bin Peng & Xiaohui Zhang, 2015. "Capturing the Impact of Unobserved Sector-Wide Shocks on Stock Returns with Panel Data Model," The Economic Record, The Economic Society of Australia, vol. 91(295), pages 495-508, December.
    • Handle: RePEc:bla:ecorec:v:91:y:2015:i:295:p:495-508
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/ecor.2015.91.issue-295
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    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. KiHoon Jimmy Hong & Eliza Wu, 2014. "Can Momentum Factors Be Used to Enhance Accounting Information based Fundamental Analysis in Explaining Stock Price Movements?," Research Paper Series 346, Quantitative Finance Research Centre, University of Technology, Sydney.
    2. Joshua Chan & Roberto Leon-Gonzalez & Rodney W. Strachan, 2018. "Invariant Inference and Efficient Computation in the Static Factor Model," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(522), pages 819-828, April.
    3. Geweke, John & Zhou, Guofu, 1996. "Measuring the Pricing Error of the Arbitrage Pricing Theory," The Review of Financial Studies, Society for Financial Studies, vol. 9(2), pages 557-587.
    4. T. S. Breusch & A. R. Pagan, 1980. "The Lagrange Multiplier Test and its Applications to Model Specification in Econometrics," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 47(1), pages 239-253.
    5. Jenni L. Bettman & Stephen J. Sault & Emma L. Schultz, 2009. "Fundamental and technical analysis: substitutes or complements?," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 49(1), pages 21-36, March.
    6. Clement, Michael B. & Hales, Jeffrey & Xue, Yanfeng, 2011. "Understanding analysts' use of stock returns and other analysts' revisions when forecasting earnings," Journal of Accounting and Economics, Elsevier, vol. 51(3), pages 279-299, April.
    7. Chen, Peter & Zhang, Guochang, 2007. "How do accounting variables explain stock price movements? Theory and evidence," Journal of Accounting and Economics, Elsevier, vol. 43(2-3), pages 219-244, July.
    8. Zhang, GC, 2000. "Accounting information, capital investment decisions, and equity valuation: Theory and empirical implications," Journal of Accounting Research, Wiley Blackwell, vol. 38(2), pages 271-295.
    9. Vasilis Sarafidis & Tom Wansbeek, 2012. "Cross-Sectional Dependence in Panel Data Analysis," Econometric Reviews, Taylor & Francis Journals, vol. 31(5), pages 483-531, September.
    10. M. Hashem Pesaran, 2006. "Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure," Econometrica, Econometric Society, vol. 74(4), pages 967-1012, July.
    11. Jushan Bai, 2009. "Panel Data Models With Interactive Fixed Effects," Econometrica, Econometric Society, vol. 77(4), pages 1229-1279, July.
    12. Lessard, Donald R, 1974. "World, National, and Industry Factors in Equity Returns," Journal of Finance, American Finance Association, vol. 29(2), pages 379-391, May.
    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. Yu, Gun Jea & Hong, KiHoon, 2016. "Patents and R&D expenditure in explaining stock price movements," Finance Research Letters, Elsevier, vol. 19(C), pages 197-203.

    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. KiHoon Jimmy Hong & Bin Peng & Xiaohui Zhang, 2014. "Capturing the Impact of Latent Industry-Wide Shocks with Dynamic Panel Model," Research Paper Series 347, Quantitative Finance Research Centre, University of Technology, Sydney.
    2. Bin Peng & Giovanni Forchini, 2014. "Consistent Estimation of Panel Data Models with a Multifactor Error Structure when the Cross Section Dimension is Large," Working Paper Series 20, Economics Discipline Group, UTS Business School, University of Technology, Sydney.
    3. Antonio Afonso & Jose Alves, 2015. "The Role of Government Debt in Economic Growth," Hacienda Pública Española / Review of Public Economics, IEF, vol. 215(4), pages 9-26, December.
    4. Robertson, Donald & Sarafidis, Vasilis, 2015. "IV estimation of panels with factor residuals," Journal of Econometrics, Elsevier, vol. 185(2), pages 526-541.
    5. Cynthia Fan Yang, 2021. "Common factors and spatial dependence: an application to US house prices," Econometric Reviews, Taylor & Francis Journals, vol. 40(1), pages 14-50, January.
    6. De Vos, Ignace & Stauskas, Ovidijus, 2024. "Cross-section bootstrap for CCE regressions," Journal of Econometrics, Elsevier, vol. 240(1).
    7. Dong, Ziguang & Zhou, Zheng & Ananzeh, Mohammed & Hoang, Khai Nguyen & Shamansurova, Zilola & Luong, Tuan Anh, 2024. "Exploring the asymmetric association between fintech, clean energy, climate policy, natural resource conservations and environmental quality. A post-COVID perspective from Asian countries," Resources Policy, Elsevier, vol. 88(C).
    8. Vasilis Sarafidis & Tom Wansbeek, 2012. "Cross-Sectional Dependence in Panel Data Analysis," Econometric Reviews, Taylor & Francis Journals, vol. 31(5), pages 483-531, September.
    9. Massimiliano Mazzanti & Antonio Musolesi, 2020. "Modeling Green Knowledge Production and Environmental Policies with Semiparametric Panel Data Regression models," SEEDS Working Papers 1420, SEEDS, Sustainability Environmental Economics and Dynamics Studies, revised Sep 2020.
    10. Milda Norkuté & Vasilis Sarafidis & Takashi Yamagata, 2018. "Instrumental Variable Estimation of Dynamic Linear Panel Data Models with Defactored Regressors and a Multifactor Error Structure," ISER Discussion Paper 1019, Institute of Social and Economic Research, Osaka University.
    11. Gioldasis, Georgios & Musolesi, Antonio & Simioni, Michel, 2023. "Interactive R&D spillovers: An estimation strategy based on forecasting-driven model selection," International Journal of Forecasting, Elsevier, vol. 39(1), pages 144-169.
    12. Cheng Hsiao & Qiankun Zhou, 2018. "Panel Parametric, Semi-parametric and Nonparametric Construction of Counterfactuals - California Tobacco Control Revisited," Departmental Working Papers 2018-02, Department of Economics, Louisiana State University.
    13. Afonso, António & Jalles, João Tovar, 2013. "Growth and productivity: The role of government debt," International Review of Economics & Finance, Elsevier, vol. 25(C), pages 384-407.
    14. Wang, Jianxin & Zhu, Guohua & Chang, Tin-Chang, 2024. "Unveiling the relationship between institutional quality, fintech, financial inclusion, human capital development and mineral resource abundance. An Asian perspective," Resources Policy, Elsevier, vol. 89(C).
    15. Markus Eberhardt & Francis Teal, 2008. "Modeling Technology and Technological Change in Manufacturing: How do Countries Differ?," CSAE Working Paper Series 2008-12, Centre for the Study of African Economies, University of Oxford.
    16. Norkutė, Milda & Sarafidis, Vasilis & Yamagata, Takashi & Cui, Guowei, 2021. "Instrumental variable estimation of dynamic linear panel data models with defactored regressors and a multifactor error structure," Journal of Econometrics, Elsevier, vol. 220(2), pages 416-446.
    17. Giovanni Forchini & Bin Peng, 2016. "A Conditional Approach to Panel Data Models with Common Shocks," Econometrics, MDPI, vol. 4(1), pages 1-12, January.
    18. Alexander Chudik & M. Hashem Pesaran, 2013. "Large panel data models with cross-sectional dependence: a survey," Globalization Institute Working Papers 153, Federal Reserve Bank of Dallas.
    19. Jörg Breitung & Philipp Hansen, 2021. "Alternative estimation approaches for the factor augmented panel data model with small T," Empirical Economics, Springer, vol. 60(1), pages 327-351, January.
    20. Khalid, Usman & Shafiullah, Muhammad, 2021. "Financial development and governance: A panel data analysis incorporating cross-sectional dependence," Economic Systems, Elsevier, vol. 45(2).

    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:bla:ecorec:v:91:y:2015:i:295:p:495-508. 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: https://edirc.repec.org/data/esausea.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.