Ever since 22 October, the fund’s volatility skyrocketed, and since March it has been on a massive drawdown. I’m guessing the increased volatility is from the addition of daily predictions (and trades?).
Could someone explain this situation?
Richard said in one interview that they increased the metamodels exposure to more volatility right before the big drawdown. It would have fell either way, but the higher volatility certainly didn’t help.
2 Likes
Thanks, that’s quite reassuring.
Do you know if there is any available historical data on the corr. scores of the meta-model? I’m trying to see if there is a “performance degradation” of the models submitted or if it’s just a “below EV” period.
Do you mean corr meta-model with target? You can calculate it on your own if you download the historical meta_model predictions here https://numer.ai/data/v4.2 napi.download_dataset("v4.2/meta_model.parquet", "meta_model.parquet")
2 Likes