Boyer, Mitton, and Vorkink (2010) developed a model of expected skewness that incorporates past returns and trading volume as well as known ﬁrm characteristics. In the first step, the find expected skewness. Then the asset pricing model, they try to explain excess stock returns using trading volume, lagged skewness, and a set of control variables such as firm size, exchange dummy, stock momentum, and industry dummies as explanatory variables.
What is included in our code?
- Using factors from Fama and French Library, first we regress stock excess returns on these factors to find residuals.
- Using these residuals, we create the volatility and skewness variables.
- Using lagged values of skewness, volatility, and other firm-specific variables, the expected value of skewness is calculated.
- Table 1 and Table are then created.
- If clients show interest, we can further develop the remaining tables in the paper.
Our Stata Code
We have developed easy to use yet robust codes for the above steps. The codes need just a basic understanding of Stata. Further, our comments on each line of code will surely help you in running the code as well as in understanding the process more clearly. We normally share all Stata files, the raw data files, and Stata codes with comments. The purpose is to help researchers to learn and apply these codes on their own. We also try to answer questions that might arise at a later stage when the researcher applies these codes.
The code is available for $ $199 with some example data. The Fama and French factors are downloaded from Fama and French libarary. Incase the Fama and French Factors need to be developed from scratch, there is an additional fee of $100.
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Project tags: Initial Public offerings, IPO, Fama and French, BHAR, CAR, cumulative abnormal returns, market-adjusted returns, event study, Stata, FinTechprofessor