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Good News, Bad News and Garch Effects in Stock Return Data

Author

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  • Craig A. Depken

Abstract

It is shown that the volume of trade can be decomposed into proportional proxies for stochastic flows of good news and bad news into the market. Positive (good) information flows are assumed to increase the price of a financial vehicle while negative (bad) information flows decrease the price. For the majority of a sample of ten split-stocks it is shown that the proposed decomposition explains more GARCH than volume itself. Using the proposed decomposition, the variance of returns for younger split stocks reacts asymmetrically to good news flowing into the market, while the variance for older split-stocks reacts symmetrically to good news and bad news.

Suggested Citation

  • Craig A. Depken, 2001. "Good News, Bad News and Garch Effects in Stock Return Data," Journal of Applied Economics, Taylor & Francis Journals, vol. 4(2), pages 313-327, November.
    • Handle: RePEc:taf:recsxx:v:4:y:2001:i:2:p:313-327
    DOI: 10.1080/15140326.2001.12040567
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    Cited by:

    1. Aigbe Akhigbe & Melinda Newman & Ann Marie Whyte, 2021. "Is There a Differential Market Size Effect in U.S. Free Agent Signings? Evidence From Localized Sentiment Trading," Journal of Sports Economics, , vol. 22(6), pages 678-721, August.
    2. Jason P. Berkowitz & Craig A. Depken, 2018. "A rational asymmetric reaction to news: evidence from English football clubs," Review of Quantitative Finance and Accounting, Springer, vol. 51(2), pages 347-374, August.
    3. ROUSAN, Raya & AL-KHOURI, Ritab, 2005. "Modeling Market Volatility in Emerging Markets: The case of Daily Data in Amman Stock Exchange 1992-2004," International Journal of Applied Econometrics and Quantitative Studies, Euro-American Association of Economic Development, vol. 2(4), pages 99-118.
    4. Ding, Xinpeng & He, Jiayi & Zhang, Yali & Yin, Yi, 2025. "Asymmetric autocorrelation in the crude oil market at multiple scales based on a hybrid approach of variational mode decomposition and quantile autoregression," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 660(C).
    5. Kamal, Mona, 2014. "Studying the Validity of the Efficient Market Hypothesis (EMH) in the Egyptian Exchange (EGX) after the 25th of January Revolution," MPRA Paper 54708, University Library of Munich, Germany.
    6. Muhammad Ateeq ur REHMAN & Syed Ghulam Meran SHAH & Lucian-Ionel CIOCA & Alin ARTENE, 2021. "Accentuating the Impacts of Political News on the Stock Price, Working Capital and Performance: An Empirical Review of Emerging Economy," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 55-73, June.
    7. Semen Son-Turan, 2016. "The Impact of Investor Sentiment on the "Leverage Effect"," International Econometric Review (IER), Econometric Research Association, vol. 8(1), pages 4-18, April.
    8. Muhammad Ateeq ur REHMAN & Furman ALI & Shang XIE, 2022. "Impact of Foreign Investment News on the Return, Cost of Equity and Cash Flow Activities," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 112-127, December.
    9. Kalu O. Emenike & Omweno N. Enock, 2020. "How Does News Affect Stock Return Volatility in a Frontier Market?," Management and Labour Studies, XLRI Jamshedpur, School of Business Management & Human Resources, vol. 45(4), pages 433-443, November.

    More about this item

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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