Recent content by drchaos

Agreed. I'm a skeptical of this because of the high noise level and very high dimensionality. I mean you can already do something dumb, and just put all your observations of a big length M vector into a PCA and extract out the principal components. They'll probably look like Lagrange...

Maybe, but it depends on how well the model was trained, and how confident I am in its robustness, e.g. did I do significant regularization & cross validation? How big a space did I search in blindly to estimate a model---the bigger the space the more skepticism. Also, given a trained model...

Perhaps something like a short term Sharpe, or moments of positive returns over some period minus moments of the negative returns? Here is my bias---without experimental experience. I think it would be more stable to attempt to predict some functional of the future price---and also be able to...

SVM with linear kernels are typically used for binary classification problems (target is +1/-1, instead of a continuous value). (Linear kernel is "no kernel", really). It is virtually no different from "logistic regression" which is the natural analog of linear regression with categorical...

Hi, I saw this thread referenced somewhere else and was intrigued because I'm interested in some of the mathematically interesting quantitative methods. However, I'm quite skeptical of the utility (at least at first) or wisdom of the more sophisticated machine learning methods for trading...