On-Line Portfolio Selection Using Multiplicative Updates
Abstract
We present an on‐line investment algorithm that achieves almost the same wealth as the best constant‐rebalanced portfolio determined in hindsight from the actual market outcomes. The algorithm employs a multiplicative update rule derived using a framework introduced by Kivinen and Warmuth. Our algorithm is very simple to implement and requires only constant storage and computing time per stock in each trading period. We tested the performance of our algorithm on real stock data from the New York Stock Exchange accumulated during a 22‐year period. On these data, our algorithm clearly outperforms the best single stock as well as Cover's universal portfolio selection algorithm. We also present results for the situation in which the investor has access to additional “side information.”
Cite
Text
Helmbold et al. "On-Line Portfolio Selection Using Multiplicative Updates." International Conference on Machine Learning, 1996. doi:10.1111/1467-9965.00058Markdown
[Helmbold et al. "On-Line Portfolio Selection Using Multiplicative Updates." International Conference on Machine Learning, 1996.](https://mlanthology.org/icml/1996/helmbold1996icml-line/) doi:10.1111/1467-9965.00058BibTeX
@inproceedings{helmbold1996icml-line,
title = {{On-Line Portfolio Selection Using Multiplicative Updates}},
author = {Helmbold, David P. and Schapire, Robert E. and Singer, Yoram and Warmuth, Manfred K.},
booktitle = {International Conference on Machine Learning},
year = {1996},
pages = {243-251},
doi = {10.1111/1467-9965.00058},
url = {https://mlanthology.org/icml/1996/helmbold1996icml-line/}
}