An event-study is a statistic method applied to the financial markets that has the purpose to measure the relevance/irrelevance of an event on the stock prices ; in other words, we value if an event is or is not price-sensitive, "over the normal returns", calculated by a market model through the following regression :
R(stock) = α + β*R(benchmark)
R are the returns, expressed as a percentage, respectively of a single bond (the subject of the analysis) and of the benchmark (a market index).
α is the intercept.
β is the slope.
An Event-study strategy has the aim to :
- value the relevance of an event (like the earnings releases, like a particular press release, like a macro-event and so on) ;
- value the impact and the statistical significance of the event ;
- measure the market moves of the insiders and the market mood, at the time of the event ;
- by combining the previous aspects, it means to make profitable a trade just buying or just selling the stocks (it depends on the specific situation) a moment before the event (some days, a week or a month before).
The success of the strategy is not absolute but it is very true as the event is significative and repeatable. As known, the market is unpredictable and facing the same news, we could have a different market reaction.
The following case shows the strategy. We speak about the full year 2016 results of Moncler S.p.A. The press release is dated 28, February 2017.
(To read the complete document, see the link https://monclergroup-dunebuggysrl.netdna-ssl.com/wp content/uploads/2016/07/Moncler_Press_Release_FY_2016.pdf).
As the theory says, it needs to create a "window" in three parts :
- Estimation window, useful to build the regression (usually, it is a period of 252 days) ;
- Event window, the window of the event (it is a period of 6 days, 2 days before the event, the day of the event, and 3 days after the event, to cover any delays or news leaks) ;
- Post-event window, useful to value the impact of the event in the following days.
In this way, we can build the regression : we take the historical returns of the stock (Moncler, Y) and of the market index (FTSE-mib, X). The regression is shown in the following image.
The parameters of the regression are the following (see the table).
For the returns (change %), I used the historical data from Investing.com and I put them on a spreadsheet.
The estimation window is the period from March 3, 2016 to February 23, 2017 (in the image, there are some hidden cells, for space requirements).
The event window is the period from February 24, 2017 to March 3, 2017.
The post-event window is the period from March 6, 2017 to March 31, 2017.
With the regression, we measure the abnormal returns (AR).
The formula of the abnormal returns is :
(Effective return of the stock - Estimated return from the market model).
The estimated return is equal to [α + β*(effective return of the market index)].
The AR test t is : AR / STD.ERR.(YX).
To understand if the AR is significative and so if the event is price-sensitive, it needs to measure the statistic significance of the AR : is the absolute value of the AR test t is greater (YES) or less (NO) than 1.96 ?
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| Abnormal returns (AR) in the event window |
We can conclude that the earnings release impacted positively on the stock prices and that the event was significative. Just buying before the event, betting on the publication of good data, it would have been profitable. The following image shows the candlesticks chart of the event.
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| Chart from Investing.com |
As I previously said, the good success is basically based on the repetition of the event and the importance of the event itself. It means also that there is the same market reaction in every occasion (it is not so obvious).
With Moncler, there are many aspects that confirm this, see the following link :
