Predicting Stock Market Dips, Crashes and Corrections with Light Gradient Boosting Machines

Here’s the deal — as an occasional swing/day/options trader, I want to know when there’s a chance that a big dip might happen. By “big dip”, I mean taking the percentage change in the closing price, normalizing (z-scoring) it and then defining a “big” drop as anything more than 4 standard deviations under the mean. Depending on the data you use, this comes out to ~5.4% drop from the day before. I wanted to make a model that was generalizable, aspiring towards universal — so rather than focusing on one…