Archives for posts with tag: event study

An August 9th, 2021 Wall Street Journal article by Karen Langley stated, “Shares of Colgate-Palmolive Co. fell 4.8% after the consumer-products company said it expects costs to chip away at its margins in the second half of the year.” A graph of the market’s reaction is available here and links to the filings are here and here.

Despite the most recent seasonal quarterly earnings per share increasing by 12% (from $0.74 to $0.83), and Net Sales increasing by 9.5%, the company also provided the following forward guidance: “On a GAAP basis, the Company now expects a decline in gross profit margin, increased advertising investment and earnings-per-share growth at the lower end of its low to mid-single-digit rate.”

Prices then promptly dropped the 4.8% noted above. If the market was expecting much larger increases then this earnings announcement could still be seen as disappointing. But it could also be the case that the reported results were in line with expectations but what was deemed more important was the information about the future rather than how the last quarter shook out.

What does the academic literature say about how much the market actually cares about accounting performance measures that are always (mostly) backward looking and historical in nature? I think that depends on which pieces of the published evidence you look at, and that’s what I’m going to discuss today.

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NGVC announced their most recent earnings after markets closed on 5/6/2021 and promptly fell by 20% the next day. I was curious about how the market typically reacts to grocers reporting earnings that are less than the prior seasonally lagged quarter. Using data from Sharadar and obtained via Quandl, the steps to answer this question are:

Step 1: Pull the Tickers dataset conditional on the ticker == NGVC. This dataset has the four-digit SIC code along with other useful information.

Step 2: Filter this dataset based on the SIC Code== 5411 and Table == SF1.

Step 3: Initialize a Python list object, iterate through the tickers that meet the conditions in Step 2, and append each ticker to the list. [Note: these steps are not shown in the posted code. This post starts with Steps 6 and 7 as they are the most complicated.]

Step 4: Pull the Sharadar SF1 data, which contains financial statement variables, via feeding the list in Step 3 into the query and conditioning on the “ARQ” time dimension.

Step 5: Pull the Sharadar daily price data from the SEP table.

Step 6: Merge the earnings and accounting data in Step 4 with the stock price data in Step 5. This step requires consideration of firms reporting earnings on non-trading days (i.e., the earnings announcement date in the SF1 data does not exist in the SEP data since markets were closed on that day). My approach to this was to identify earnings announcement dates that were not connected to trading days, and then to “shift” these dates forward by one or two days and also backward by one or two days to see trading occurred on the shifted dates.

Step 7: Write a function that will take an input for the number of trading days around the earnings announcement to calculate the returns. Note that the day before the earnings announcement is used for the beginning price in the returns calculation. This accounts for firms issuing earnings during or after a given trading day.

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