This research develops valuation methodology of digital companies which exploit network effects. The main asset of companies of this type is their user base. The business model design makes the user base of many of the companies to be directly observable and measurable by any user in the network (wholly or at least partially through sampling). This creates an opportunity for market participants to get an in-depth understanding of the state of the business of the company. This research starts with a brief recap of the key characteristics of networks and dynamic processes on them. After that the most common business model patterns of network companies are mapped and analyzed using business model canvas. Having obtained the mechanics of the business and the qualities of networks of users the DCF valuations are conducted. The baseline DCF simulations use top-down approaches for projecting cash-flows, growth and risks and the test case simulations use network science based approaches. The last part of the research is devoted to empirical testing of the influence of the network effects on company pricing using cluster analysis and multiple regression techniques. The findings of this research are of a value to valuation practitioners, standard setters and IR departments.
Even though LinkedIn tries to protect itself from web scrapers there are ways to extract information using Python. In this example we will gather info about all the positions we have applied to for as long as LinkedIn allows us to see (for me it is 2 years).
If you need to be the first one to know when any changes have been made on a website you can use this Python script. It compares the code of webpages every 5 minutes and tells you whether there are any differences have been made.
Python code to merge images from a specified folder into a gif animation.