ML Anthology

Competitive Portfolio Selection Using Stochastic Predictions

Tugkan Batu, Pongphat Taptagaporn
ALT 2016 pp. 288-302
doi:10.1007/978-3-319-46379-7_20 /alt/2016/batu2016alt-competitive/

Abstract

We study a portfolio selection problem where a player attempts to maximise a utility function that represents the growth rate of wealth. We show that, given some stochastic predictions of the asset prices in the next time step, a sublinear expected regret is attainable against an optimal greedy algorithm, subject to tradeoff against the “accuracy” of such predictions that learn (or improve) over time. We also study the effects of introducing transaction costs into the model.

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Cite

Text

Batu and Taptagaporn. "Competitive Portfolio Selection Using Stochastic Predictions." International Conference on Algorithmic Learning Theory, 2016. doi:10.1007/978-3-319-46379-7_20

Markdown

[Batu and Taptagaporn. "Competitive Portfolio Selection Using Stochastic Predictions." International Conference on Algorithmic Learning Theory, 2016.](https://mlanthology.org/alt/2016/batu2016alt-competitive/) doi:10.1007/978-3-319-46379-7_20

BibTeX

@inproceedings{batu2016alt-competitive,
  title     = {{Competitive Portfolio Selection Using Stochastic Predictions}},
  author    = {Batu, Tugkan and Taptagaporn, Pongphat},
  booktitle = {International Conference on Algorithmic Learning Theory},
  year      = {2016},
  pages     = {288-302},
  doi       = {10.1007/978-3-319-46379-7_20},
  url       = {https://mlanthology.org/alt/2016/batu2016alt-competitive/}
}