Does deep learning really work for regression tasks?  I mean, I know it could in theory.  But I’ve never seen anyone do it (at least in quant trading).  Everyone wants to convert regression tasks into classification tasks first, which seems odd to me.  At a deep learning summer school I attended years ago, I was chatting with Hugo Larochelle, and I shared with him my observation.  Surprisingly, he didn't jump to the defense of DL regression and instead made some comment like maybe back propagation struggles with the more strongly varying y variable ... (I forget exactly what he said).I really want DL regression to work.  My own experimentation hasn’t gone well, so I’m hoping to get some help.  I’m currently trading in my personal account, and I have one model in particular that is pretty good.  If anyone is willing, I’d like to see if you can beat it or match it with a neural network.So, I’d like to hold my own little (Kaggle-like) competition.  Here’s my plan:* The prize is $599 (any more and I’d need to mess with IRS 1099 forms).
* It will be paid out through Zelle to a US bank account to the winner on February 20, 2021.
* Only one submission per person.
* I will choose one winner entirely subjectively, but basically you want to correctly predict strong positive signals.
* Please email me for the data: deep_learning_regression@fastmail.com
* I will send you two Numpy npz files. The first has a design matrix for training, with 31 unnormalized and non-orthogonal features.  It also has weights and y-values.  There are 43,266 rows, sorted chronologically.
* The test data will only have the design matrix, in the same format.  It will have 19,588 rows representing the out-of-sample time period following the training period.
* For your submission, please email me back your predictions on the training set and on the test set in an npz file.
* I’m asking the winner to walk me through their code, so I can learn.I appreciate your help!