An alternative title for this post could easily be “Merging CRSP and COMPUSTAT: Date Considerations.” Say your research question involves how the stock market reacts to new information about particular financial statement data or disclosures. Linking accounting variables to stock returns, stock prices, or perhaps trading volume is necessary for this examination. Multiple date variables are included in various COMPUSTAT tables, but which date should be used to construct the merges and event windows? The short answer is that it depends on your research question and the assumptions behind the research design. I am not advocating for any one date variable. The goal here is to discuss possible choices, where these variables are, and the differences between them. A detailed example is given using an earnings announcement from AAPL.

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This post is motivated by a lecture I gave in ACCT 3100: Financial Statement Analysis. The first half of the semester covers basic journal entries, drafting financial statements, and the articulation among various financial statements. The second half of the semester covers the behavior of returns, fundamentals of bonds, and equity valuation.

The textbook I use is Stephen Penman’s Financial Statement Analysis and Security Valuation (5th ed). On page 51, Figure 2.3 shows various percentiles of Price-Earnings (P/E) ratios from 1963 to 2010. The students in the course told me at the beginning of the semester that they wanted more practice working with data, and I decided recreating Figure 2.3 would be a good way to accomplish this while also bringing the textbook to life. During a lecture the class compared historical prices per Yahoo! Finance and Google Finance to price data from COMPUSTAT and CRSP. At first glance, there appear to be discrepancies between the various providers of historical price data, but this is often explained by prices that are retroactively adjusted for certain corporate actions (e.g., stock splits). This is shown using CME Group as a detailed example.

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Everyone makes mistakes. The same blunders I made on my first few research papers I saw repeated in the PhD student cohorts that followed me. As a committee member on MS Economics and Finance theses, I have noticed that strikingly similar problems tend to surface. These mistakes are not limited to students. I think anyone that has worked with data for a substantial amount of time has had that sinking feeling in the pit of the stomach when realizing that a programming, data management, or organization problem exists. An old adage in boxing is that the punch a boxer doesn’t see coming is the one that does the most damage, and my opinion is that this also applies to working with data.

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Behavioral researchers in accounting regularly design experiments with two primary independent variables of interest. The researcher creates four separate written cases with various combinations of these two independent variables, and each participant in the study receives one of the four cases for the experiment.

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Recent papers (e.g., Bills, Jeter, Stein (2015); Reichelt and Wang (2010)) have included measures which identify some audit firms as “specialist” or “dominant” auditors. This post includes an example of how to calculate these indicator variables using data that was obtained from the Audit Analytics- Audit Fees online menu query within WRDS.

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This post manually calculates standard errors under a variety of assumptions. The example dataset purposely includes a cluster where a dummy variable is nonzero in only one cluster. This causes a “.” to be reported for the model’s F statistic. The post concludes with manual calculation of the outer product of gradients (OPG) variance estimator.

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Event studies in finance and accounting typically accumulate abnormal returns over a particular “event window.” Further, a non-overlapping “estimation window” is often used for parameter estimation.
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There are some great resources online for merging CRSP and Compustat. These include:

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This post walks through two alternative ways to calculate CFO tenure using panel data from Execucomp.  The first method uses the -bysort- command and relies on the sorted, relative observation number of each executive within each firm.  The second method uses a user-written .ado file, -tsspell-, which was written by Nick Cox.  If you have not previously installed this Read the rest of this entry »