Can We Learn to Beat the Best Stock
Abstract
A novel algorithm for actively trading stocks is presented. While tradi- tional universal algorithms (and technical trading heuristics) attempt to predict winners or trends, our approach relies on predictable statistical relations between all pairs of stocks in the market. Our empirical results on historical markets provide strong evidence that this type of techni- cal trading can “beat the market” and moreover, can beat the best stock in the market. In doing so we utilize a new idea for smoothing critical parameters in the context of expert learning.
Cite
Text
Borodin et al. "Can We Learn to Beat the Best Stock." Neural Information Processing Systems, 2003.Markdown
[Borodin et al. "Can We Learn to Beat the Best Stock." Neural Information Processing Systems, 2003.](https://mlanthology.org/neurips/2003/borodin2003neurips-we/)BibTeX
@inproceedings{borodin2003neurips-we,
title = {{Can We Learn to Beat the Best Stock}},
author = {Borodin, Allan and El-Yaniv, Ran and Gogan, Vincent},
booktitle = {Neural Information Processing Systems},
year = {2003},
pages = {345-352},
url = {https://mlanthology.org/neurips/2003/borodin2003neurips-we/}
}