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Capturing the Impact of Latent Industry-Wide Shocks with Dynamic Panel Model

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    Abstract

    Expanding the panel model of Pesaran (2006) and Bai (2009), we propose a dynamic panel specification with Bayesian approach to capture the impact of unobservable industry-wide shocks to stock price movements. We employ fundamental accounting information to control company specific shocks and equity market index to capture market wide common shocks. Our model is designed to resolve the potential multicollinearity problem that is known to exist when the industry factors are considered by extracting the industry-wide shocks using Bayesian method.

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    File URL: http://www.qfrc.uts.edu.au/research/research_papers/rp347.pdf
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    Bibliographic Info

    Paper provided by Quantitative Finance Research Centre, University of Technology, Sydney in its series Research Paper Series with number 347.

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    Length: 28 pages
    Date of creation: 01 Mar 2014
    Date of revision:
    Handle: RePEc:uts:rpaper:347

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    Keywords: Common Factor Structural Error; Common Shocks; Stock Price Movements; Accounting Fundamentals; Bayesian Gibbs Sampler;

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    1. Geweke, John & Zhou, Guofu, 1996. "Measuring the Pricing Error of the Arbitrage Pricing Theory," Review of Financial Studies, Society for Financial Studies, vol. 9(2), pages 557-87.
    2. JULES H. van BINSBERGEN & RALPH S. J. KOIJEN, 2010. "Predictive Regressions: A Present-Value Approach," Journal of Finance, American Finance Association, vol. 65(4), pages 1439-1471, 08.
    3. Joshua C.C. Chan & Roberto Leon-Gonzalez & Rodney W. Strachan, 2013. "Invariant Inference and Efficient Computation in the Static Factor Model," CAMA Working Papers 2013-32, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    4. Borja Larrain & Motohiro Yogo, 2005. "Does firm value move too much to be justified by subsequent changes in cash flow?," Working Papers, Federal Reserve Bank of Boston 05-18, Federal Reserve Bank of Boston.
    5. Jia Chen & Jiti Gao & Degui Li, 2011. "Semiparametric Trending Panel Data Models with Cross-Sectional Dependence," Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics 15/11, Monash University, Department of Econometrics and Business Statistics.
    6. Harford, Jarrad, 2005. "What drives merger waves?," Journal of Financial Economics, Elsevier, vol. 77(3), pages 529-560, September.
    7. Seung C. Ahn & Young H. Lee & Peter Schmidt, 2006. "Panel Data Models with Multiple Time-Varying Individual Effects," Working Papers 0702, University of Crete, Department of Economics.
    8. 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.
    9. F. Moscone & E. Tosetti, 2010. "Health expenditure and income in the United States," Health Economics, John Wiley & Sons, Ltd., vol. 19(12), pages 1385-1403, December.
    10. M. Hashem Pesaran, 2004. "Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure," CESifo Working Paper Series 1331, CESifo Group Munich.
    11. Lessard, Donald R, 1974. "World, National, and Industry Factors in Equity Returns," Journal of Finance, American Finance Association, vol. 29(2), pages 379-91, May.
    12. 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.
    13. Jushan Bai, 2009. "Panel Data Models With Interactive Fixed Effects," Econometrica, Econometric Society, Econometric Society, vol. 77(4), pages 1229-1279, 07.
    14. Chen, Long, 2009. "On the reversal of return and dividend growth predictability: A tale of two periods," Journal of Financial Economics, Elsevier, vol. 92(1), pages 128-151, April.
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