Trading in Markovian Price Models
Abstract
We examine a Markovian model for the price evolution of a stock, in which the probability of local upward or downward movement is arbitrarily dependent on the current price itself (and perhaps some auxiliary state information). This model directly and considerably generalizes many of the most well-studied price evolution models in classical finance, including a variety of random walk, drift and diffusion models. Our main result is a “universally profitable” trading strategy — a single fixed strategy whose profitability competes with the optimal strategy (which knows all of the underlying parameters of the infinite and possibly nonstationary Markov process).
Cite
Text
Kakade and Kearns. "Trading in Markovian Price Models." Annual Conference on Computational Learning Theory, 2005. doi:10.1007/11503415_41Markdown
[Kakade and Kearns. "Trading in Markovian Price Models." Annual Conference on Computational Learning Theory, 2005.](https://mlanthology.org/colt/2005/kakade2005colt-trading/) doi:10.1007/11503415_41BibTeX
@inproceedings{kakade2005colt-trading,
title = {{Trading in Markovian Price Models}},
author = {Kakade, Sham M. and Kearns, Michael J.},
booktitle = {Annual Conference on Computational Learning Theory},
year = {2005},
pages = {606-620},
doi = {10.1007/11503415_41},
url = {https://mlanthology.org/colt/2005/kakade2005colt-trading/}
}