This is the final article of the series “ Predicting Interest Rate with Classification Models”. Here are the links if you didn’t read the First or the Second articles of the series where I explain the challenge I had when started at M2X Investments. As I mentioned before, I will try my best to make this article understandable per se. I will skip the explanation of assumptions regarding the data for “article-length” reasons. Nevertheless, you can check them in previous posts of the series. Let’s do it!

Fast Recap

In previous articles, I applied a couple of classification models to the problem of predicting up movements of the Fed Fund Effective Rate. In short, it is a binary classification problem where 1 represents up movement and 0, neutral or negative movement. The models applied were Logistic Regression, Naive Bayes, and Random Forest. Random Forest was the one that yielded the best results so far, without hyperparameter optimization, with an F1-score of 0.76.

#machine-learning #support-vector-machine #interest-rates #ai #catboost