Can We Learn to Beat the Best Stock

Allan Borodin, Ran El-Yaniv, Vincent Gogan

JAIR 2004 pp. 579-594

doi:10.1613/JAIR.1336 /jair/2004/borodin2004jair-we/

Abstract

A novel algorithm for actively trading stocks is presented. While traditional 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 technical 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.

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Text

Borodin et al. "Can We Learn to Beat the Best Stock." Journal of Artificial Intelligence Research, 2004. doi:10.1613/JAIR.1336

Markdown

[Borodin et al. "Can We Learn to Beat the Best Stock." Journal of Artificial Intelligence Research, 2004.](https://mlanthology.org/jair/2004/borodin2004jair-we/) doi:10.1613/JAIR.1336

BibTeX

@article{borodin2004jair-we,
  title     = {{Can We Learn to Beat the Best Stock}},
  author    = {Borodin, Allan and El-Yaniv, Ran and Gogan, Vincent},
  journal   = {Journal of Artificial Intelligence Research},
  year      = {2004},
  pages     = {579-594},
  doi       = {10.1613/JAIR.1336},
  volume    = {21},
  url       = {https://mlanthology.org/jair/2004/borodin2004jair-we/}
}