Building on what I was working on with my last post, where I was learning Tensorflow probability, I found that it was able to pick up the skew of simulated data pretty well, now I want to try it out on a real financial dataset.
For this, I picked the loan data from Lending Club. This is a nice dataset for this task because there’s a natural skew in the data due to defaults, where a borrower ends up paying less than the full amount they were lent.
Intro Two areas I’ve spent a lot of time in are finance and sports. In these two fields, I often hear the refrain to ‘think probabilistically’, whether that means continuing to go for it on 4th down, even if you were stuffed the last time, or getting back into a trade even though the last one blew up in your face. As Annie Duke lays out in her book Thinking In Bets, all decisions have uncertainty and you have to be able to consider where your eventual outcome fell in the distributuon of possible outcomes.
Using the Tiingo news feed, we can explore which stocks are mentioned in news articles together. This can be an additional technique to identify relationships and correlations between securities. Using the recent case of Apple and Intel, I show how news co-occurences pick up changes.
Tf-idf can be an interesting way to extract keywords from text. In this post I use tidytext’s implementation to extract keywords from ETF descriptions.
