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Year 2020, Volume: 9 Issue: 1, 52 - 61, 30.03.2020
https://doi.org/10.17261/Pressacademia.2020.1192

Abstract

References

  • Cappiello, L., Engle, Sheppard, K., (2006), Asymmetric Dynamics in the Correlations of Global Equity and Bond Returns, Journal of Financial Econometrics, Vol. 4, No. 4, pp. 537–572
  • Chen, X., Ghysels, E., (2010), News-Good or Bad-and Its Impact on Volatility Predictions over Multiple Horizons, The Society for Financial Studies, Oxford University Press.
  • Cove, S., College, R., and Sardy, M., (2019), How Presidential Tweets Affect Financial Markets
  • Fama, E. (1970. Efficient capital markets: A review of theory and empirical work, Journal of Finance, 25(2), 383-417
  • Forbes, K., J., and Rigobon, F. (2001), Measuring Contagion: Conceptual and Empirical Issues in International Financial Contagion (Eds) Claessens, S., and Forbes, K., New York: Springer, 43-66
  • Goldstein, J., and Martin, L., L., (2000), Legalization, trade liberalization, and domestic politics: A cautionary note, International Organization, 54(3), 603-632
  • Jensen, M., Nathan (2007), International institutions and market expectations: Stock price responses to the WTO ruling on the 2002 US steel tariffs, Springer Science+ Business Media, 2: 261-280
  • Kaminsky, G., L., Reinhart, C., M., Vegh, C., A., (2003), The unholy trinity of financial contagion, The Journal of Economic Perspectives, Vol. 17, No. 4 (Autumn, 2003), pp. 51-74
  • King, M., A, Wadhwani, S., (1990), Transmission of volatility between stock markets, The Review of Financial Studies, 57(2), 769-799
  • Kodres, L., E., and Pritsker, M., (1998), A rational expectations model of financial contagion, Finance and Economics Discussion Series 1998-48, Board of Governors of the Federal Reserve System (US).
  • Lo, A. W., and MacKinlay, A., C., (1999), A non-random walk down wall street, Princeton, NJ: Princeton University Press
  • M. Guidolin et al. (2019), Cross-asset contagion in the financial crisis: A Bayesian timevarying parameter approach,Journal of Financial Markets 45 (2019) 83-114
  • Nelson, B., D, (1991), Conditional Heteroskedasticity in Asset Returns: A New Approach, Econometrica, Vol. 59, No. 2., pp. 347-370.
  • Sensier, M., Van Dijk, D., (2004). Testing for volatility changes in US macroeconomic time series, The Review of Economics and Statistics, 86 (3), 833-839
  • Stock, J.H., Watson, M.W., (1996). Evidence on structural instability in macroeconomic time series relations, Journal of Business & Economic Statistics, 14 (1), 11-30.
  • Tafti, A., Zotti, R., Jank, W., (2016), Real-Time Diffusion of Information on Twitter and the Financial Markets, PLos ONE, Vol. 11(8)

COMPANY STOCK REACTIONS TO BLACK NOISE TWEETS: EVIDENCE FROM STEEL INDUSTRY

Year 2020, Volume: 9 Issue: 1, 52 - 61, 30.03.2020
https://doi.org/10.17261/Pressacademia.2020.1192

Abstract

Purpose – The purpose of this study is to test the validity of super-fast development of social media and its wide range of use by even professional investors as the new financial contagion which is carried with “Black Noise” tweets. Newly established robotic modern finance environment and various news channels provide the necessary infrastructure to utilize a focused and directed market noise. Measuring the impact of this noise in the financial market volatility is a crucial and important issue.
Methodology - In this study, we investigate the news impact of trade wars and monetary policy news on steel industry of US and its reflection on Turkish markets utilizing 30 minutes high frequency return data. The novelty is this study is the interaction terms that we generated and embedded in the E-GARCH models to test the reactions of steel major listed US steel industry companies such as US Steel, AK Steel, Nucor and the pioneer Turkish company Ereğli in this sector.
Findings- Findings of this study highlights that specific news about trade war and monetary policy have a significant impact on steel company returns. For further research papers testing the speculation strength of such tweet can be a beneficial topic for the other researchers.
Conclusion- As a result of this study, being one the major market makers, Trump’s direct messages to the market via Twitter and such, about sanctions, interest rates and monetary policy creates “Black Noise” in financial markets. Even in a durable production industry like steel sector this leads to speculation.

References

  • Cappiello, L., Engle, Sheppard, K., (2006), Asymmetric Dynamics in the Correlations of Global Equity and Bond Returns, Journal of Financial Econometrics, Vol. 4, No. 4, pp. 537–572
  • Chen, X., Ghysels, E., (2010), News-Good or Bad-and Its Impact on Volatility Predictions over Multiple Horizons, The Society for Financial Studies, Oxford University Press.
  • Cove, S., College, R., and Sardy, M., (2019), How Presidential Tweets Affect Financial Markets
  • Fama, E. (1970. Efficient capital markets: A review of theory and empirical work, Journal of Finance, 25(2), 383-417
  • Forbes, K., J., and Rigobon, F. (2001), Measuring Contagion: Conceptual and Empirical Issues in International Financial Contagion (Eds) Claessens, S., and Forbes, K., New York: Springer, 43-66
  • Goldstein, J., and Martin, L., L., (2000), Legalization, trade liberalization, and domestic politics: A cautionary note, International Organization, 54(3), 603-632
  • Jensen, M., Nathan (2007), International institutions and market expectations: Stock price responses to the WTO ruling on the 2002 US steel tariffs, Springer Science+ Business Media, 2: 261-280
  • Kaminsky, G., L., Reinhart, C., M., Vegh, C., A., (2003), The unholy trinity of financial contagion, The Journal of Economic Perspectives, Vol. 17, No. 4 (Autumn, 2003), pp. 51-74
  • King, M., A, Wadhwani, S., (1990), Transmission of volatility between stock markets, The Review of Financial Studies, 57(2), 769-799
  • Kodres, L., E., and Pritsker, M., (1998), A rational expectations model of financial contagion, Finance and Economics Discussion Series 1998-48, Board of Governors of the Federal Reserve System (US).
  • Lo, A. W., and MacKinlay, A., C., (1999), A non-random walk down wall street, Princeton, NJ: Princeton University Press
  • M. Guidolin et al. (2019), Cross-asset contagion in the financial crisis: A Bayesian timevarying parameter approach,Journal of Financial Markets 45 (2019) 83-114
  • Nelson, B., D, (1991), Conditional Heteroskedasticity in Asset Returns: A New Approach, Econometrica, Vol. 59, No. 2., pp. 347-370.
  • Sensier, M., Van Dijk, D., (2004). Testing for volatility changes in US macroeconomic time series, The Review of Economics and Statistics, 86 (3), 833-839
  • Stock, J.H., Watson, M.W., (1996). Evidence on structural instability in macroeconomic time series relations, Journal of Business & Economic Statistics, 14 (1), 11-30.
  • Tafti, A., Zotti, R., Jank, W., (2016), Real-Time Diffusion of Information on Twitter and the Financial Markets, PLos ONE, Vol. 11(8)

Details

Primary Language English
Subjects Finance, Business Administration
Journal Section Articles
Authors

Caner OZDURAK This is me 0000-0003-0793-7480

Veysel ULUSOY This is me 0000-0001-7227-894X

Publication Date March 30, 2020
Published in Issue Year 2020 Volume: 9 Issue: 1

Cite

APA OZDURAK, C., & ULUSOY, V. (2020). COMPANY STOCK REACTIONS TO BLACK NOISE TWEETS: EVIDENCE FROM STEEL INDUSTRY. Journal of Business Economics and Finance, 9(1), 52-61. https://doi.org/10.17261/Pressacademia.2020.1192

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