Machine Learning for Financial Crises

Below are the slides from my discussion of Helene Rey et al., "Answering the Queen: Machine Learning and Financial Crises", which I gave a few days ago at a fine NBER IFM meeting (program and clickable papers here). I also discussed it in June at the BIS annual research meeting in Zurich. The key development since the earlier mid-summer draft is that they actually implemented a real-time financial crisis prediction analysis for France using vintage data, as opposed to quasi-real-time using final-revised data. Moving to real time of course somewhat degrades the quasi-real-time results, but they largely hold up. Very impressive. Therefore I now offer suggestions for improving evaluation credibility in the remaining cases where vintage datasets are not yet available. On the other hand, I also note how subtle but important look-ahead biases can creep in even when vintage data are available and used. I conclude that the only fully-convincing evaluation involves implementing their approach moving forward, recording the results, and building up a true track record.