How to Run an Event Study in Excel
Build a market-model event study in Excel from the ARC sample files: log returns, abnormal returns, CAR and a single-event t statistic.
This worked example uses Event 10001, Adobe Systems against the SP500 on 30 April 1997, from the downloadable ARC sample. Every displayed result below is calculated from the three files with log returns, a 120-trading-day estimation period from relative days -130 through -11, and a 21-day event window from -10 through +10. They come from the steps described here; ARC can differ in implementation details and reports a broader result set.
1. Open and arrange the sample files
Download 01_RequestFile.csv, 02_FirmData.csv and 03_MarketData.csv. In Excel's text-import dialog choose a semicolon delimiter, no header row, and dd.mm.yyyy for dates. The request file identifies Event 10001, Adobe Systems, SP500 and the event date. Filter the firm and market files to those names, then join them by their matching date.
On an Analysis worksheet, use columns A:J: Date, Firm price, Market price, Firm log return, Market log return, Relative day, Estimation flag, Expected return, AR and Running CAR. Include one prior trading day in row 2. Put relative day -130 in row 3, -11 in row 122, -10 in row 123 and +10 in row 143. That makes the exact ranges in the formulas below visible and auditable.
2. Enter the calculation formulas
In D3 enter =LN(B3/B2) and in E3 enter =LN(C3/C2), then fill both down through row 143. In F3 write -130 and increase by one per row through F143. Put =AND(F3>=-130,F3<=-11) in G3 and fill down. This uses the log-return convention of the ARC sample documentation and puts a non-trading event date on the later trading day.
Place the summary labels in A147:A152 and the following formulas in B147:B152:
- Alpha, B147:
=INTERCEPT(D$3:D$122,E$3:E$122) - Beta, B148:
=SLOPE(D$3:D$122,E$3:E$122) - S_AR, B149:
=STEYX(D$3:D$122,E$3:E$122) - CAR(-10,10), B150:
=SUM(I123:I143) - CAR(-1,1), B151:
=SUM(I132:I134) - t statistic, B152:
=B150/(SQRT(21)*B149)
In H123 enter =$B$147+$B$148*E123, in I123 enter =D123-H123, and in J123 enter =I123. Fill H and I through row 143; in J124 enter =J123+I124 and fill through J143. If you want a two-sided p-value, use =T.DIST.2T(ABS(B152),118); this tutorial deliberately gives the formula only, not an unverified displayed p-value.
3. Check the 21 event-window results
Format AR and CAR as percentages with four decimal places. The check script rounds the following values in the same way.
| Relative day | Date | AR | Running CAR |
|---|---|---|---|
| -10 | 16 Apr 1997 | -2.4107% | -2.4107% |
| -9 | 17 Apr 1997 | 0.1072% | -2.3035% |
| -8 | 18 Apr 1997 | -3.7377% | -6.0412% |
| -7 | 21 Apr 1997 | -0.3057% | -6.3469% |
| -6 | 22 Apr 1997 | -2.1865% | -8.5334% |
| -5 | 23 Apr 1997 | 1.2571% | -7.2763% |
| -4 | 24 Apr 1997 | 1.1178% | -6.1585% |
| -3 | 25 Apr 1997 | -6.4034% | -12.5619% |
| -2 | 28 Apr 1997 | 0.9126% | -11.6493% |
| -1 | 29 Apr 1997 | -2.9904% | -14.6397% |
| 0 | 30 Apr 1997 | 0.6313% | -14.0084% |
| +1 | 1 May 1997 | 6.7154% | -7.2930% |
| +2 | 2 May 1997 | 3.4366% | -3.8565% |
| +3 | 5 May 1997 | 3.4198% | -0.4367% |
| +4 | 6 May 1997 | -2.2770% | -2.7137% |
| +5 | 7 May 1997 | 3.4701% | 0.7564% |
| +6 | 8 May 1997 | -5.9072% | -5.1508% |
| +7 | 9 May 1997 | -2.3525% | -7.5032% |
| +8 | 12 May 1997 | -0.2900% | -7.7932% |
| +9 | 13 May 1997 | 0.2600% | -7.5332% |
| +10 | 14 May 1997 | -2.1718% | -9.7050% |
| Measure | Calculated value |
|---|---|
| Alpha | 0.1118% |
| Beta | 0.9489 |
| S_AR | 2.8094% |
| CAR(-10,10) | -9.7050% |
| CAR(-1,1) | 4.3562% |
| t statistic | -0.7538 |
The residual standard deviation uses the 120 estimation-period residuals with M - 2 degrees of freedom. For this single event, the displayed t statistic divides CAR by SQRT(21)*S_AR. It is a teaching calculation, not a replacement for a full cross-sectional event-study test.
Limits of hand calculations
Excel makes the mechanics transparent, but a hand-built workbook is easy to misalign when dates, firms or windows multiply. Keep source data, formulas and the event-date rule together; do not silently replace missing trading dates. For the concepts behind AR and CAR, see How to calculate abnormal returns. For model choice and inference, see Expected-return models and Significance tests.
Ready to analyse more than one event? Upload the request, firm and market files to ARC for a reproducible multi-event workflow.
Open ARC →FAQ
Why use log returns here?
The bundled ARC example documents log-return calculation. Using the same convention for the firm, market model and abnormal return keeps this workbook internally consistent.
What if the event date is not a trading day?
Use the later trading day, the non-trading-day convention documented for the sample workflow, and record that choice with the event data.
Can I use this workbook for several firms?
You can repeat the layout, but cross-firm aggregation and its test statistics need careful handling. ARC is designed for that multi-event workflow.
Does a single-event t statistic prove an effect?
No. It is a model-based teaching diagnostic. Interpret the event definition, benchmark model and possible confounding news before making a causal claim.