Significance Tests

Event studies are generally the first step in a two-step analysis process that aims at identifying the determinants of stock market repsonses to distinct event types. They produce as an outcome abnormal returns (ARs), which are cumulated over time to cumulative abnormal returns (CARs) and then 'averaged' - in the case of so called sample studies - over several observations of identical events to AARs and CAARs - where the second 'A' stands for 'average'. These event study results are then oftentimes used as dependent variables in regression analyses.

Explaining abnormal returns by means of regression analysis, however, is only meaningful if the abnormal returns are significantly different from zero, and thus not the result of pure chance. This assessment wil be made by hypothesis testing. Following general principles of inferential statistics, the null hypothesis ($H_0$) thus maintains that there are no abnormal returns within the event window, whereas the alternative hypothesis ($H_1$) suggests the presence of ARs within the event window. Formally, the testing framework reads as follows:

$$H_0: μ = 0                                              (1)$$

$$H_1: μ \neq 0                                              (2)$$

Event studies may imply a hierarchy of calculations, with ARs being compounded to CARs, which can again be 'averaged' to CAARs in cross-sectional studies (sometimes also called 'sample studies'). There is a need for significance testing at each of these levels. μ in the abovementioned equations may thus represent ARs, CARs, and CAARs. Let's shortly revisit these three different forms of abnormal return calcuations, as presented in the introduction:

$$AR_{i,t}=R_{i,t}-E(R_{i,t})               (3)$$

$$AAR_{t}= \frac{1}{N} \sum\limits_{i=1}^{N}AR_{i,t}                           (4)$$

$$CAR(t_1,t_2)_{i}=\sum\limits_{i=t_1}^{t_2} AR_{i,t}                    (5)$$

$$CAAR(t_1,t_2)=\frac{1}{N}\sum\limits_{i=1}^{N}CAR(t_1,t_2)      (6)$$


The literature on event study test statistics is very rich, as is the range of significance tests. Generally, significance tests can be grouped in parametric and nonparametric tests (NPTs). Parametric tests assume that individual firm's abnormal returns are normally distributed, whereas nonparametric tests do not rely on any such assumptions. In research, scholars commonly complement a parametric test with a nonparametric tests to verify that the research findings are not due to eg. an outlier (see Schipper and Smith (1983) for an example). Table 1 provides an overview and links to the formulas of the different test statistics.

Table 1: 'Recommended' Significance Tests per Test Level (note: work in progress/ draft)

Null hypothesis tested Parametric tests Nonparametric tests Test level
$H_0: AR = 0$ AR t-test*   Individual Event
$H_0: AAR = 0$ AAR t-test, Patell-test, BMP-test, J-test GRANK Sample of Events
$H_0: CAR = 0$

CAR t-test*

GRANK, SIGN Individual Event
$H_0: CAAR = 0$

CAAR t-test*, Patell-test, BMP-test, J-test


Sample of Events

N.B.: *-labeled test results are included in the output of EventStudyTools' abnormal return calculator

Nonparametric test statistics ground on the classic t-test. Yet, scholars have further developed the test to correct for the t-test's prediction error. The most widely used of these 'scaled' tests are those developed by Patell (1976) and Boehmer, Musumeci and Poulsen (1991). Among the nonparametric tests, the rank-test of Corrado (1989), and the sign-based of Cowan (1992) are very popular. EST provides these test statistics (soon) in its analysis results reports.

Why different test statistics are needed

The choice of test statistic should be informed by the research setting and the statistical issues the analyzed data holds. Specifically, event-date clustering poses a problem leading to (1) cross-sectional correlation of abnormal returns, and (2) distortions from event-induced volatility changes. Cross-sectional correlation arises when sample studies focus on (an) event(s) which happened for multiple firms at the same day(s). Event-induced volatility changes, instead, is a phenomenon common to many event types (e.g., M&A transactions) that becomes problematic when events are clustered. As consequence, both issues introduce a downward bias in the standard deviation and thus overstate the t-statistic, leading to an over-rejection of the null hypothesis.

Comparison of test statistics

There have been several attempts to address these statistical issues. Patell (1976, 1979), for example, tried to overcome the t-test's proneness to event-induced volatility by standardizing the event window's ARs. He used the dispersion of the estimation interval's ARs to limit the impact of stocks with high return standard deviations. Yet, the test too often rejects the true null hypothesis, particularly when samples are characterized by non-normal returns, low prices or little liquidity. Also, the test has been found to be still affected by event-induced volatility changes (Campbell and Wasley, 1993; Cowan and Sergeant, 1996; Maynes and Rumsey, 1993, Kolari and Pynnonen, 2010). Boehmer, Musumeci and Poulsen (1991) resolved this latter issue and developed a test statistic robust against volatility-changing events.

The nonparametric rank test of Corrado and Zivney (1992) (RANK) applies re-standardized event window returns and has proven robust against induced volatility and cross-correlation. Sign tests are another category of tests. One advantage the tests’ authors stress over the common t-test is that they are apt to also identify small levels of abnormal returns. Moreover, scholars have recommend the used of nonparametric sign and rank tests for applications that require robustness against non-normally distributed data. Past research (e.g. Fama, 1976) has argued that daily return distributions are more fat-tailed (exhibit very large skewness or kurtosis) than normal distributions, what suggests the use of nonparametric tests.

Several authors have further advanced the sign and ranked tests pioneered by Cowan (1992) and Corrado and Zivney (1992). Campbell and Wasley (1993), for example, improved the RANK test by introducing an incremental bias into the standard error for longer CARs, creating the Campbell-Wasley test statistic (CUM-RANK). Another NPT is the generalized rank test (GRANK) test with a Student t-distribution with T-2 degrees of freedom (T is the number of observations). It seems that GRANK is one of the most powerful instruments for both shorter and longer CAR-windows.

The Cowan (1992) sign test (SIGN) is also used for testing CARs by comparing the share of positive ARs close to an event to the proportion from a normal period. SIGN's null hypothesis includes the possibility of asymmetric return distribution. Because this test considers only the sign of the difference between abnormal returns, associated volatility does not influence in any way its rejection rates. Thus, in the presence of induced volatility scholars recommend the use of BMP, GRANK, SIGN.

Most studies have shown that if the focus is only on single day ARs, the means of all tests stick close to zero. In the case of longer event windows, however, the mean values deviate from zero. Compared to their nonparametric counterparts, the Patell and the BMP-tests produce means that deviate quite fast from zero, whereas the standard deviations of all tests gravitate towards zero. For longer event windows, academics recommend nonparametric over parametric tests.

Therefore, the main idea is that in case of longer event-windows, the conclusions on the tests power should be very carefully drawn because of the many over- or under-rejections of the null hypothesis. Overall, comparing the different test statistics yields the following insights:

  1. Parametric tests based on scaled abnormal returns perform better than those based on non-standardized returns
  2. Generally, nonparametric tests tend to be more powerful than parametric tests
  3. The generalized rank test (GRANK) is one of the most powerful test for both shorter CAR-windows and longer periods

Table 2 provides a short summary of the individual test statistics discussed above.


Table 2: Summary Overview of Main Test Statistics

# Name
Key Reference Type
Antecedent Strengths Weaknesses
1 t-test
  • Simplicity
  • Prone to cross-sectional correlation and volatility changes
2 Standardized residual test
Patell (1976) P ORDIN
  • Immune to the way in which ARs are distributed across the (cumulated) event window.

Standardized cross-sectional test

Boehmer, Musumeci and Poulsen (1991) P Patell-test
  • Immune to the way in which ARs are distributed across the (cumulated) event window.
4 Adjusted BMP-test
Kolari and Pynnönen (2010) P BMP-test
  • Accounts for cross-correlation and event-induced volatility.
  • Prone to changes in cross-correlation during event time.
5 Generalized sign test
Cowan (1992) NP  
  • Accounts for skewness in security returns
  • Poor performance for longer event windows

Rank test

Corrado and Zivney (1992) NP    
  • Loses power for longer CARs (e.g., [-10,10]).
7 Campbell-Wasley test statistic
Campell and Wasley (1993) NP RANK  
  • Loses power for longer CARs (e.g., [-10,10]).
8 Generalized rank test
Kolari and Pynnonen (2010) NP RANK
  • Overcomes the issues extant nonparametric tests have with cumulative abnormal returns
  • Immune to the way in which ARs are distributed across the (cumulated) event window
9 Generalized sign test
  NP SIGN    
10 Wilcoxon signed-rank test Wilcoxon (1945) NP  
  • Considers that both the sign and the magnitude of ARs are important.

Notes: P = parametric, NP = nonparametric; Insights about strenghts and weaknesses were compiled from Kolari and Pynnonen (2011)


Formulas, acronyms, and the decision rule applicable to all test statistics

$T= t_2- t_1+1$ (days in the event window), with $t_1$ denoting the 'earliest' day of the event window, and $t_2$ the 'latest' day of the event window; $N$ = sample size (i.e., number of events/ observations); $EW$ = Estimation Window, with $EW_{min}$ denoting the 'earliest' day of the estimation window, and $EW_{max}$ the 'latest' day of the estimation window; $\hat{\sigma}^2_{AR_i}$, resp. $\hat{\sigma}_{AR_i}$ represent the variance, resp. the standard deviation as produced by the regression analysis over the estimation window according to the following formula.

$$\hat{\sigma}^2_{AR_i} = \frac{1}{M_i-dF} \sum\limits_{t=EW_{min}}^{EW_{max}}(AR_{i,t})^2$$

$M_{i}$ refers to the number of non-missing (i.e., matched) returns and $dF$ to the degrees of freedom (for the market model, $dF$ = N-2); Please note: If you use the ARC of this website, the 'analysis report'-CSV provides you with $\hat{\sigma}_{AR_i}$ for each event/ observation.

The decision rule for all test statistics mandates the rejection of the null hypothesis with a confidence level of $1-\alpha$ when the test statistic is larger than the critical value from the t-table (i.e., if $|t(AR_{i,t})|>t_c(\alpha)$).

[1] t-test

t statistic $t_{AR_{i,t}}=\frac{AR_{i,t}}{\hat{\sigma}_{AR_i}}      $   $t_{CAR(t_1,t_2)}=\frac{CAR_i}{\sqrt{T\hat{\sigma}_{AR_i}^2}}    $ $t_{CAAR(t_1,t_2)}=\frac{CAAR(t_1,t_2)}{\hat{\sigma}_{CAAR(t_1,t_2)}}      $
Standard deviation $\hat{\sigma}_{AR_i} = \sqrt{\frac{1}{M_i-dF} \sum\limits_{t=EW_{min}}^{EW_{max}}(AR_{i,t})^2}$   $\hat{\sigma}_{CAR(t_1,t_2)} = \sqrt{T\hat{\sigma}_{AR_i}^2}$ $\hat{\sigma}_{CAAR(t_1,t_2)} = \sqrt{\frac{1}{N(N-dF)} \sum\limits_{i=1}^{N}(CAR_i(t_1,t_2)-CAAR(t_1,t_2))^2}$

Please note: There are alternative aproaches to calculate the standard deviations for CARs and CAARs (see, for example, Campbell, Lo and MacKinleay (1997)).

[2] Patell-test 

The cross-sectional t-test does not account for event-induced variance and thus overstates significance levels. Patell (1976, 1979) suggested to correct for this overstatement by first standardizing each $AR_i$ before calculating the test statistic using the standardized $AR_i$.

$$SAR_{i,t} = \frac{AR_{i,t}}{S(AR_i)} $$

As the event-window abnormal returns are out-of-sample predictions, Patell adjusts the standard error by the forecast-error:

$$S(AR_{i,t}) = \hat{\sigma}_{AR_i} \sqrt{1+\frac{1}{M_i}+\frac{(R_{m,t}-R_{m,EW})^2} {\sum\limits_{t=EW_{min}}^{EW_{max}}(R_{m,t}-R_m)^2}}$$

'Cumulating' these standardized abnormal returns over time gives us:

$$CSAR_{i,(t_1, t_2)} = \sum\limits_{t=t1}^{t2} \frac{AR_{i,t}}{S(AR_i)} $$

Assuming a Student's t-distribution wit ${M_i-d}$ degrees of freedom (Campbell, Lo, MacKinlay (1997), the expected value of $CSAR_i(t_1,t_2)$ is zero and the standard deviation assumes the following value:

$$\hat{\sigma}_{CSAR_i} = \sqrt{T\frac{M_i-d}{M_i-2d}}$$

The t-statistic reads as:



[3] BMP-test

Similarly, Boehmer, Musumeci and Poulsen (1991) proposed a stadardized cross-sectional method which is robust to the variance induced by the event. It grounds on the the standardized residual tests

$$\overline{CSAR(t_1,t_2)} = \frac{1}{N}\sum\limits_{i=1}^{N}CSAR(t_1,t_2)_i$$

$$\hat{\sigma}(\overline{CSAR(t_1,t_2)}) = \sqrt{\frac{1}{N(N-1)}\sum\limits_{i=1}^{N}(CSAR(t_1,t_2)-(\overline{CSAR(t_1,t_2)})^2)}$$

$$t_{BMP}= \frac{\overline{CSAR(t_1,t_2)}}{\hat{\sigma}(\overline{CSAR(t_1,t_2)}}$$


[4] J-test (adjusted BMP-test)

Kolari and Pynnönen (2010) propose a modification to the BMP-test to account for cross-correlation of the abnormal returns. Using the standardized abnormal returns ($SAR_{i,t}$) defined as in the previous section, and defining $\bar r$as the average of the sample cross-correlation of the estimation period residuals, the J-test can be written as:

$$t_{J}=\frac{\overline{SAR}_{i,0}\sqrt N}{\hat{\sigma}_{SAR} \sqrt{1+(N-1)\bar r}}$$

Where $\overline{SAR}_{i,0}$ is the mean of the $SAR$ at the event date, $N$ the number of firms, and the estimated standard deviation $\hat{\sigma}_{SAR}$ is defined as $\hat{\sigma}_{SAR}=\sqrt{\frac{1}{N-1}\sum\limits_{i=1}^{N}(SAR_{i,0}-\overline{SAR}_{i,0})^2}$

Assuming the square-root rule holds for the standard deviation of different return periods, this test can be used when considering Cumulated Abnormal Returns. While the average cross-correlation remains unchanged, the $SAR_{i,0}$ should be replaced by $CSAR_{i}(t_1,t_2)$ in the estimation.


[5] SIGN-test

This sign test has been proposed by Cowan (1991) and builds on the ratio of positive cumulative abnormal returns $p^{+}_0$ present in the event window. Under the null hypothesis, this ratio should not significantly differ from 0.5.

$$t_{SIGN}= \frac{p^+_0-0.5}{\sqrt{0.5(1-0.5)/N}}$$


[6] RANK-test

In a first step, the Corrado's (1989) rank test transforms abnormal returns into ranks. This ranking is done for each event and stock combination and for all abnormal returns of both the event and the estimation window ('tied ranks').

$$K_{i, t}=rank(AR_{i, t})$$

Thereafter, the average rank is calculated as 0.5 plus half the number of returns observed in the event ($L_2$) and the estimation window ($L_1$).

$$AK_{i, L_1+L_2}=0.5+\frac{(L_1+L_2)}{2}$$

The t-statistic then denotes as:

$$T_{Corrado}=\frac{1}{\sqrt{N}}\sum\limits_{i=1}^{N}(K_{i, t}-AK_{i, L_1+L_2})/\hat{\sigma_U}$$

The standard deviation is calculated as follow. $l_{1b}$ denotes the first day in the estimation and $l_ {2e}$ the last day of the event window.

$$\hat{\sigma_U}=\sqrt{\frac{1}{L_1+L_2}\sum\limits_{i=l_{1b}}^{l_{2e}}(\frac{1}{\sqrt{N}}\sum\limits_{i=1}^{N}(K_{i, t}-AK_{i, L_1+L_2}))^2}$$


[7] CUM-RANK-test

When analyzing multiday event periods, Campell and Wasley (1993) define the RANK-test considering the sum of the mean excess rank for the event window as follows:


In this equation, $t$ is the starting date of the event period and $L_2$ is the number of days in the event window as before. $\overline{K}_\tau$ is the mean excess rank on day $\tau$, defined as $\overline{K}_\tau=\frac{1}{N}\sum\limits_{i=1}^{N}(K_{i,\tau}-AK_i)$, where $K_{i,\tau}$ is the rank of the abnormal return of firm $i$ on period $\tau$ and $AK_i$ is the average rank of $i$ as defined in the RANK-test. Finally, $\hat{\sigma}^2(\overline{K}_{\tau})$ is defined as the variance used in the RANK-test.

[8] GRANK-test

The GRANK test squeezes the whole event window into one observation, the so-called 'cumulative event day'. Thus, the demeaned standardized abnormal ranks of the generalized abnormal returns read as below. For the definition of $L_1$, see the RANK test.

$$K_{i, t}=\frac{rank(GSAR_{i, t})}{L_1+1}-0.5$$

The generalized rank t-statistic is then defined as:




$$\sigma_{\overline{K}}=\sqrt{\frac{1}{L_1}\sum\limits_{t \in CW}\frac{n_t}{n}\overline{K}_t^2}$$

with CW representing the combined window consisting of estimation window and the cumulative event day, and



[9] GSIGN-test

Under the Null Hypothesis of no abnormal returns, the number of stocks with positive abnormal cumulative returns ($CAR$) is expected to be in line with the fraction ($\hat{p}_{EW}^{+}$) of positive $CAR$ from the estimation period. When the number of positive $CAR$ is significantly higher than the number expected from the estimated fraction, it is suggested to reject the Null Hypothesis.

The fraction $\hat{p}_{EW}^{+}$ is estimated as $\hat{p}_{EW}^{+}=\frac{1}{N}\sum\limits_{i=1}^{N}\frac{1}{T_i}\sum\limits_{t=1}^{T_i}\varphi_{i,t}$, where $\varphi_{i,t}$ is $1$ if the sign is positive and $0$ otherwise.

The Generalized sign test statistic is


Where $W$ is the number of stocks with positive $CAR$ during the event period.

Comment: The GSIGN test is based on the traditional SIGN test where the null hypothesis assumes a binomial distribution with parameter $p=0.5$ for the sign of the $N$ cumulative abnormal returns.



References and further readings

Boehmer, E., Musumeci, J. and Poulsen, A. B. 1991. 'Event-study methodology under conditions of event-induced variance'. Journal of Financial Economics, 30(2): 253-272.

Campbell, C. J. and Wasley, C. E. 1993. 'Measuring security performance using daily NASDAQ returns'. Journal of Financial Economics, 33(1): 73-92.

Campbell, J., Lo, A., MacKinlay, A.C. 1997. 'The econometrics of financial markets'. Princeton: Princeton University Press.

Corrado, C. J. and Zivney, T. L. 1992. 'The specification and power of the sign test in event study hypothesis test using daily stock returns'. Journal of Financial and Quantitative Analysis, 27(3): 465-478.

Cowan, A. R. (1992). 'Nonparametric event study tests'. Review of Quantitative Finance and Accounting, 2: 343-358.

Cowan, A. R. and Sergeant, A. M. A. 1996. 'Trading frequency and event study test specification'. Journal of Banking and Finance, 20(10): 1731-1757.

Fama, E. F. 1976. Foundations of Finance. New York: Basic Books.

Kolari, J. W. and Pynnonen, S. 2010. 'Event study testing with cross-sectional correlation of abnormal returns'. Review of Financial Studies, 23(11): 3996-4025.

Maynes, E. and Rumsey, J. 1993. 'Conducting event studies with thinly traded stocks'. Journal of Banking and Finance, 17(1): 145-157.

Patell, J. A. 1976. 'Corporate forecasts of earnings per share and stock price behavior: Empirical test'. Journal of Accounting Research, 14(2): 246-276.

Schipper, K. and Smith, A. 1983. 'Effects of recontracting on shareholder wealth: The case of voluntary spin-offs.' Journal of Financial Economics, 12(4): 437-467.

Wilcoxon, F. (1945). 'Individual comparison by ranking methods'. Biometrics Bulletin, 1(6): 80-83.