I'd like to write my master's thesis in quantitative finance. Specifically, I am interested in the application of machine learning methods to problems in finance. I also enjoy coding in C++ and R in my free time, so I would enjoy to put those skills to use.
I would imagine (correct me if I'm wrong) that one way to structure such a thesis would be to simply introduce a problem X and then explore traditional methods to solving problem X, and then introducing some novel machine learning methods to solve the same problem, and then apply and compare the various methods under various settings and present the numeric results.
My problem is, I don't have a clue what the problem "X" could be, and what novel new machine learning ideas have been introduced that I could talk about.
So I would appreciate it if I could be given some ideas or pushed in the right direction.
My current strategy is just reading some papers on Arxiv under "Computational Finance", right now I'm trying to digest this one: https://arxiv.org/pdf/1905.09474.pdf (Machine Learning Tree and Exact Integration for Pricing American Options in High Dimensions)