Instructions Abnormal Effect Calculators

Performing an event study is very easy with our research apps. We offer three abnormal effect calculators (AXCs) that provide you with the full range of test statistics common to the three event study types return event study, volume event study, and volatility event study. For investigating abnormal stock returns, use our Abnormal Return Calculator (ARC) - note: our simplified basic ARC is the quickest route to ARs and t-values. For studying stocks' abnormal trading volumes, use our Abnormal Volume Calculator (AVC). And for exploring an event's effects on a stock's volatility, use our Abnormal Volatility Calculator (AVyC).

All calculators work in a similar manner and require you to upload three files that contain all the data and information needed for your event study. Statistical analyses are then performed server-side and you receive the analysis results as attachments to an email (see Figure 1 for the workflow). As for your input, you need to provide an 'analysis request file' that specifies your analysis parameters, a 'firm data file' which holds the firm financial data that is required for your analysis, and a 'market data file' that holds the respective capital market data. You can upload the files in any of the following formats: xls, xlsx, csv. You can also choose to ZIP-compress them prior to your upload

Figure 1: Event Study Workflow of EventStudyTools

event study workflow

You can retrieve the required financial data free-of-charge at any of the major financial news portals, such as Yahoo!Finance. If you want to retrieve data for multiple stocks at once, e.g., as needed for a sample event study, you may use this retrieval makro and save time with your data download. Table 1 describes the data structure of the files you need provide to the AXCs. If you want to use the CSV file format, please use the semicolon as delimiter. Table 2 compares the two abnormal return calculators, and Table 3 describes their outputs. The following example files may assist you in the construction of your files. Similar descriptions of the in- and outputs for AVC and AVyC are provided at the end of this page (Tables 4 and 5).

Table 1: Structure of Input Files to Trigger bARC and aARC1
CSV benchmark model Items to Be Included (Separated by a Semicolon )
Request File Same structure for all models Event ID; Firm ID; Market ID; Event Date; Grouping Variable2; Start Event Window3; End Event Window3; End of Estimation Window3; Estimation Window Length
Firm Data Same structure for all models Firm identifier; Date; Closing price
Market Data

All models but Fama-French models

Market identifier; Date; Closing price

Fama-French 3-Factor Model Market identifier; Date; Closing price; Rf; smb; hml
Fama-French Momentum-4-Factor Model Market identifier; Date; Closing price; Rf; smb; hml; umd

1 Formats of input variables: Please use integer values as firm and market identifiers. The identifiers in the request file must be unique. There is no specific format you have to follow; just make sure that the identifiers you use in the firm and market data CSVs match the ones you use in the analysis request CSV. The dates you provide, however, need to be in a distinct date format. Please use either YYYY-MM-DD (default of Yahoo!Finance) or DD.MM.YYYY (Excel default in many countries). If you should use a date format different from these two, the ARC will prompt an error message in its report.
2 For generating the 'average' values in your analysis (i.e., AAR and CAAR values as produced by aARC), you need to specify the 'grouping variable'. If you use only one value in the grouping variable, which is the default case, AXC will calculate the average values across all events in your request file; if you choose more than one value (e.g., 'acquisition' and 'divestiture' in a boundary choise study), AXC will produce average values across the events grouped by these values.
3 These variables hold figures relative to the event date. ‘Start Event Window’ and ‘End Estimation Window’ typically lie before of the event date therefore must have a negative sign or be zero. Ranges of allowed values: ‘Start Event Window’: [-50, 0]; ‘End Event Window’: [0, 50]; ‘End Estimation Window’: [-unlimited, -1]; you may choose any (positive) length of the estimation window.

Table 2: Comparison of EventStudyTools' bARC and aARC
App Available Return Models Return Calculation Result Levels Test Statistics Non Trading Day Adjustment Results Delivery
bARC market model log-returns AR AR t-test no auto-adjustment prompted to screen
aARC market model

log- or simple returns 

AR, AAR, CAR, CAAR, BHAR

T-tests, Patell-test, Adjusted Patell-test, BMP-test, Adjuted BMP-test, Cowan GSIGN-test, Corrado Rank-test, GRANK-t-test, GRANK-z-test, Skewness adjusted test

optional auto-adjustment (earlier/later) prompted to screen, e-mail delivery

The results of your event studies are delivered to you in CSV files. Depending on the ARC you use, a different set of CSV files is prompted to you (see Table 3). bARC provides you with very basic results at the AR-level only (i.e.,  abnormal returns and their corresponding t-values, as well as the coefficients and values used/generated in the calculation). aARC, instead, will provide you more comprehensive results at the AR-, AAR-, CAR-, and CAAR-level.

Table 3: Structure of bARC and aARC Output Files
App Output file Items Reported (Separated by a Semicolon)
bARC Analysis Results Event ID; ...; AR(-1); AR(0); AR(1); ...; t-value [AR(-1)]; t-value [AR(0)]; t-value [AR(1)]; ...
Analysis Report Event ID; Firm; Reference Market; Event Date; Analysis Report; Estimation Window Length; End of Estimation Window; First Date Estimation Window; Last Date Estimation Window; Actual Stock Return; Actual Market Return; Alpha; Beta; Residual Standard Deviation; Expected Market Return
aARC AR Results Event ID; ...; AR(-1); AR(0); AR(1); ...; t-value [AR(-1)]; t-value [AR(0)]; t-value [AR(1)]; ...
AAR Results

Grouping variable; ...; AAR(-1); AAR(0); AAR(1); ...
N(Grouping variable, AAR(i)); ...; N(AAR(-1)); N(AAR(0)); N(AAR(1)); ...
Pos:Neg(Grouping variable, AAR(i)); ... ; Pos:Neg(Grouping variable, AAR(-1)); Pos:Neg(Grouping variable, AAR(0)); Pos:Neg(Grouping variable, AAR(1)); ...
+ per AAR(i): Patell Z, Generalized Sign Z, Csect T, StdCSect Z, Rank Z, Generalized Rank T, Adjusted Patell Z, Adjusted StdCSect Z, Generalized Rank Z, Skewness Corrected T

CAR Results Event ID; Window; CAR Value; CAR t-test
CAAR Results Grouping Variable; CAAR Type; CAAR Value; Precision Weighted CAAR Value; ABHAR; pos:neg CAR; Number of CARs considered; CAAR Pattell Z; CAAR t-test; CAAR GSIGN-Test; CAAR BMP; CAAR GRANK T; CAAR adjusted Patell; CAAR adjusted BMP; CAAR GRANK Z; CAAR skewness adjusted T; ABHAR T; ABHAR skewness adjusted T
Analysis Report Event ID; Firm; Reference Market; Event Date; Analysis Report; Estimation Window Length; End of Estimation Window; First Date Estimation Window; Last Date Estimation Window; Actual Stock Return; Actual Market Return; Alpha; Beta; Residual Standard Deviation; Expected Market Return

Notes applicable to ARC results:
+ If you encounter very large numbers in your results, open the results CSV in NotePad; Excel sometimes fails in displaying numbers with multiple decimals.

Trigger bARC now -- Trigger aARC now


Table 4: AVC Input Files
CSV Items to Be Included (Separated by a Semicolon )
Request File Event ID; Firm ID; Market ID; Event Date; Grouping Variable2; Start Event Window3, End Event Window3, End of Estimation Window3, Estimation Window Length
Firm Data Firm identifier; Date; number of shares traded; outstanding share of firm
Market Data

Market identifier; Date; mean of log percentage of trading volume of the index

Table 5: AVC Output Files
App Output file Items Reported (Separated by a Semicolon)
AVC AV Results Event ID; ...; AR(-1); AR(0); AR(1); ...; t-value [AR(-1)]; t-value [AR(0)]; t-value [AR(1)]; ...
AAV Results

Grouping variable; ...; AAV(-1); AAV(0); AAV(1); ...
N(Grouping variable, AAV(i)); ...; N(AAV(-1)); N(AAV(0)); N(AAV(1)); ...
Pos:Neg(Grouping variable, AAV(i)); ... ; Pos:Neg(Grouping variable, AAV(-1)); Pos:Neg(Grouping variable, AAV(0)); Pos:Neg(Grouping variable, AAV(1)); ...
+ per AAV(i): Patell Z, Generalized Sign Z, Csect T, StdCSect Z, Rank Z, Generalized Rank T, Adjusted Patell Z, Adjusted StdCSect Z, Generalized Rank Z, Skewness Corrected T

CAV Results Event ID; Window; CAV Value; BHAV Value; CAV t-test
CAAV Results Grouping Variable; CAAV Type; CAAV Value; Precision Weighted CAAV Value; ABHAR; pos:neg CAV; Number of CAVs considered; Patell Z; Csect T; Generalized Sign Z; StdCSect Z; Rank Z; Generalized Rank T; Adjusted Patell Z; Adjusted StdCSect Z; Generalized Rank T; Skewness Corrected T; ABHAR Csect T; ABHAR Skewness Corrected T
Analysis Report Event ID; Firm; Reference Market; Event Date; Analysis Report; Estimation Window Length; End of Estimation Window; First Date Estimation Window; Last Date Estimation Window; Actual Stock Volume; Actual Market volume; Alpha; Beta    Residual Standard Deviation; Expected Market Return; First-order Autocorellation

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Table 6: AVyC Input Files
CSV Items to Be Included (Separated by a Semicolon )
Request File Event ID; Firm ID; Market ID; Event Date; Grouping Variable2; Start Event Window3, End Event Window3, End of Estimation Window3, Estimation Window Length
Firm Data Firm identifier; Date; Closing price
Market Data

Market identifier; Date; Closing price

Table 7: AVyC Output Files
App Output file Items Reported (Separated by a Semicolon)
AVC AVy Results
Event ID; ...; AVy(-1); AVy(0); AVy(1); ...;
AAVy Results

Grouping variable; ...; AAVy(-1); AAVy(0); AAVy(1); ...
N(Grouping variable, AAV(i)); ...; N(AAV(-1)); N(AAV(0)); N(AAV(1)); ...
Pos:Neg(Grouping variable, AAV(i)); ... ; Pos:Neg(Grouping variable, AAV(-1)); Pos:Neg(Grouping variable, AAV(0)); Pos:Neg(Grouping variable, AAV(1)); ...
+ per AAV(i): Cross-Sectional-Vy-t-Test, Cross-Sectional-Corrected-Vy-t-Test, Cross-Sectional-AR-t-Test, Cross-Sectional-Corrected-AR-t-Test

Analysis Report Event ID; Firm ID; Reference Market; Event Date; Analysis Report; Estimation Window Length; End of Estimation Window; First Date Estimation Window; Last Date Estimation Window; Alpha; p-value; Beta; p-value; Gamma; p-value; Delta; p-value; H_i; preLambda; postLambda; Abnormal Return on Event Day; Residual Standard Deviation; Expected Stock Return; Autocorellation

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