Archives for posts with tag: pandas

Shaw Communications (SJR) has segments that include Wireline customers for businesses and consumers and Wireless customers for prepaid and postpaid plans. The  company has recognized around 1.3  billion (CAD) for recent quarters, of which around 300 million (CAD) is earned from the Wireless segment.

An interesting pattern emerges from the customer segments. Shaw is losing Wireline customers but gaining Wireless customers, and most of the decline in the Wireline segments are from consumers rather than business customers. What does it mean for expectations of future revenue if a company is losing customers in the segments that make up around three quarters of their revenue and gaining customers in the segments that make up around a quarter of their revenue? To attempt to answer this question, I ended up:

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An automated approach to web scraping publicly traded companies’ financial statements is something that I’ve been working on for a while. My first post identified the balance sheet due to a firm-specific characteristic of FCCY’s 12/31/2017 balance sheet- namely that it had a row titled “ASSETS.” Of course, not every firm is going to have this header in all caps to identify which table in the .html file is the balance sheet. But it was a start. The next, currently unpublished, step pulled the annual report and then identified the balance sheet using the summation of the number of times accounts commonly found on balance sheets were present.

Some of the issues I ran into while programming this:  the amount of account variation between balance sheets of companies in different industries, the presence of extra spaces or characters present in the .html file that are not readily apparent to human eyes, the use of capital letters in account titles (or not) by different firms, and variation in how the same account may be called by different companies (e.g., additional paid in capital vs. additional paid-in capital and stockholder’s equity(deficiency) vs. shareholders’ equity vs. stockholders’ equity, etc.), financial statements being split in two across separate pages and identified in the file as two separate tables, notes at the bottom of the page with the financial statements also being tagged as tables by the issuer, substantial variation in the exact titles firms use for the various financial statements, and the actual layout of the tables after they have been scraped (e.g., multiple columns for a given year with data spread across the columns).

All of these are things that can be programmed around, and some of these issues we will see later in the post with FCCY’s 12/31/2017 10-K after we scrape it.

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A common question in business schools is how to best prepare students for the data analysis skills that are increasingly important to employers. Faculty meetings often revolve around what skills are actually needed, which tools should be taught, which departments or professors should do the teaching, and if the optimal approach is to require separate courses or embed analytics in existing courses.

Related discussions include whether teaching students to code is necessary or if GUI-based solutions are sufficient, the extent to which courses should focus on visualizations, how deeply to go into statistics, if the current math prerequisites are doing much of anything, how much math is really even necessary for analytics, and where database management skills fit in to all of this.

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The code provided pulls the 12/31/2017 Balance Sheet for FCCY into a pandas DataFrame. The ultimate goal of this project is to automate this process for all SEC fillings for a given type and time period, but doing this for a given firm and year is the first step.