1011 points by quant_prophet 6 months ago flag hide 14 comments
finance_ml 6 months ago next
Fascinating topic! I recently came across some research on XGBoost and Random Forest models applied to stock market volatility predictions. Anyone have experience with these in the finance domain?
algorithmtrader 6 months ago next
Yes, indeed! XGBoost and Random Forest models are quite popular for stock market prediction. I've seen Large Ensemble methods being employed too, aggregating multiple base estimators. #fintech #AI
quantdeveloper 6 months ago next
I can confirm, I've used XGBoost for volatility predictions before as well. Feature engineering was tricky but worth the effort. #quant #data
techstockobserver 6 months ago prev next
Do you find that tree-based ML models perform better than NNs (Neural Networks) in volatile markets? #ml #trading
finance_ml 6 months ago next
@TechStockObserver While I can't say for certain, the peer-reviewed studies I've gone through suggest that tree-based models tend to generalize better than vanilla NNs.
neuralexplorer 6 months ago next
@finance_ML I've been using a 1D convolution layer to extract features from time-series data before feeding them into RNNs. It's a hybrid method, improving my model's ability to memorize relevant patterns.
deeplearningfan 6 months ago prev next
Been working on LSTM-based RNNs for predicting stock trends. Haven't specifically tested it for volatility, though.
dataengineer 6 months ago next
Seems interesting - I've attended a talk about Echo State Networks, a type of RNN, for stock market forecasting.
dataengineer 6 months ago next
@DataEngineer I'd be curious to learn more about Echo State Networks. They're supposed to overcome issues of vanishing or exploding gradients, aren't they?
mlresearcher 6 months ago prev next
Saw an intriguing volatility prediction model using Gaussian Copulas. Unfortunately, hadn't got a chance to explore it thoroughly.
statarb 6 months ago prev next
Have you considered data leakage when evaluating your models? It's crucial to never train or test with future information.
statarb 6 months ago next
@StatArb Absolutely! Had to implement Lagged Variables technique with walk-forward validation to resolve this.
aiforthewin 6 months ago prev next
I believe using an adversarial approach could spruce up the robustness of our predictions. Thoughts?
randomwanderlust 6 months ago prev next
AI in economics paper presents an interesting model using DL-SVRs for stock trend predictions.