Comparing Rating Announcement Effects Across Economic Periods
Summary
The document outlines a way to compare stock market reactions to corporate credit rating news during normal and crisis periods. It proposes a linear regression of returns on indicators for news events, then separates the indicators by period: one for announcements in normal times and another for announcements during a financial crisis. Comparing the estimated coefficients can indicate whether average reactions differ across those periods.
The response also points to event study methods as an alternative framework and cites prior research on rating news and financial markets. Its suggested regression is a starting point rather than a full event study design: it does not detail controls, standard errors, event windows, overlapping announcements, or how to account for broader market movements. The claim that reactions are more extreme in crises is asserted and linked to a cited study, but the document provides no underlying estimates or evidence to assess that claim directly.
Key ideas
- Use return regressions with indicators for rating news to estimate announcement effects.
- Separate event indicators for normal and crisis periods to compare their estimated effects.
- A larger crisis-period coefficient would suggest a stronger average reaction under the proposed model.
- Event study frameworks are another option for analyzing announcement effects.
- The suggested regression leaves controls and statistical inference details unspecified.
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Full text
# Compare events effect on stock prices from different time periods
# Compare events effect on stock prices from different time periods
I’m going to test for the effect corporate credit rating announcements have on stock prices through different economic climates (good times vs. bad times). I want to research whether or not the stock price reaction to a corporate credit rating upgrade/downgrade is more extreme in a tough economic climate (financial crisis).
A lot of research has been done on corporate credit rating announcements (upgrade, downgrade, positive and negative watch) effect on stock prices but not a lot of research has compared the results from different time periods (before/after vs. during financial crisis, before vs. after regulation). Joo & Pruitt (2006) studied the Korean financial crisis, and Jorion et al. (2006) studied the effects before and after Regulation Fair Disclosure. However, these papers are fairly short and don’t explain the statistical framework in detail (At least to me it’s not clear what they are doing).
I have done some previous research using the event study methodology presented by Brown & Warner (1985) and MacKinlay (1997) but I’m not sure if it’s applicable to this problem. And if it is, I’m not completely sure how to compare the results from different periods.
I'd be forever grateful if anyone could point me in the right direction.
- Brown, S. J., & Warner, J. B. (1985). Using daily stock returns: The case of event studies. Journal of financial economics, 14(1), 3-31.
- Joo, S. L., & Pruitt, S. W. (2006). Corporate bond ratings changes and economic instability: Evidence from the Korean financial crisis. Economics Letters, 90(1), 12-20.
- Jorion, P., Liu, Z., & Shi, C. (2005). Informational effects of regulation FD: evidence from rating agencies. Journal of financial economics, 76(2), 309-330.
- MacKinlay, A. C. (1997). Event studies in economics and finance. Journal of economic literature, 13-39.
## Answer by Quantopik (score 1, accepted)
https://quant.stackexchange.com/a/18821
The rating downgrade/upgrade effect is definitely more extreme during financial crisis, because of several effects (among all, flight-to quality, flight-to-liquidity and news effects itself), as shown by:
> Arezki, Rabah, Bertrand Candelon, and Amadou Nicolas Racine Sy. "Sovereign rating news and financial markets spillovers: Evidence from the European debt crisis." IMF working papers (2011): 1-27.
The paper analyzes the news effects on financial markets by using simply linear regression model, as follows:
$r_{i,t}$ = $\alpha$ + $\sum\limits_{i=1}^n \beta_i*D_{i,t}$ + $\epsilon_t$
where $r_{i,t}$ is the market returns relative to the country you're going to analyze and $D_{i,t}$ is the variable relative to the presence of news; if $D_{i,t}$ results to be statistically significant and different from $0$, then news will have been affected the market.
This could be a solution to your question, but, alternatively, you could take into consideration:
> Brown, S. J., & Warner, J. B. (1985). Using daily stock returns: The case of event studies. Journal of financial economics, 14(1), 3-31.
that you cited in the question, or, better:
> Kothari, S. P., and Jerold B. Warner. "The econometrics of event studies." Available at SSRN 608601 (2004).
that is more recent and used in event study methodology.
If you want to carry with linear regression model on, I suggest you to implement the following model:
$r_{i,t}$ = $\alpha$ + $\sum\limits_{i=1}^n \beta_i*D^1_{i,t}$ + $\sum\limits_{i=1}^n \delta_i*D^2_{i,t}$ + $\epsilon_t$
where, in this case, $D^1_{i,t}$ is the news presence during normal times and $D^2_{i,t}$ is the news presence during financial crisis time periods. If $\delta$ $>$ $\beta$, you showed that, on average, news affects financial markets much more during financial crisis than during normal times.
Hope this helps.Shown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.