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Is it possible to write an algorithm to find similar companies for each firm in the given sample?
Hello!
I am writing a Thesis where I exploit news shocks to see the effect on the stock prices of the companies. Now I have a sample with several hundred companies and there characteristics downloaded from Eikon (treated sample). It is necessary to find a control companies to each firm in the sample, basing on different characteristics such as industry, size, country, etc. It will be very painful to do it manually, i.e. using criteria search.
I know that i is possible to retrieve all peers for a company using Python, but I am not sure whether they will be good as a control companies. Ideally, I want to write a function that has some firms characteristics in input and gives me list of companies which satisfies these criteria (+- some allowed deviation).
Could you, please, advice me some way how to do it algorithmically?
Thank you very much!
Nikita
@i-sergeeich you need to take a look at the screener app that does exactly that. A screener query/expression can then be exported into either Excel or Python. So:
- Go to Eikon Excel > Thomson Reuters > Screener
- Select criteria and preview the output
- Once you are done click on 'Insert Screen' which is going to paste the screener query into your spreadsheet
- Copy the 'Universe' parameter of the =TR function into your Python code and remove double quotes
- Use the query as an input to your get_data function
Hope this helps!
@Zhenya Kovalyov, I am so so sorry for the long answer. I did a mistake, so I am collecting the sample again and haven't tried the solution yet. Therefore, I couldn't answer about the positive results. Could I leave the question open just in case something will go wrong? Thank you very much for the help!
@i-sergeeich sure, it is a community forum, it's going to stay open until you accept it as the correct answer.
@Zhenya Kovalyov, thank you very much! It works and I received excellent for my thesis..)