AISTATS 2020poster6 citations
Long-and Short-Term Forecasting for Portfolio Selection with Transaction Costs
Abstract
In this paper we focus on the problem of online portfolio selection with transaction costs. We tackle this problem using a novel approach for combining the predictions of long-term experts with those of short-term experts so as to effectively reduce transaction costs. We prove that the new strategy maintains bounded regret relative to the performance of the best possible combination (switching times) of the long-and short-term experts. We empirically validate our approach on several standard benchmark datasets. These studies indicate that the proposed approach achieves state-of-the-art performance.
BibTeX
@InProceedings{pmlr-v108-uziel20a,
title = {Long-and Short-Term Forecasting for Portfolio Selection with Transaction Costs},
author = {Uziel, Guy and El-Yaniv, Ran},
booktitle = {Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics},
pages = {100--110},
year = {2020},
editor = {Chiappa, Silvia and Calandra, Roberto},
volume = {108},
series = {Proceedings of Machine Learning Research},
month = {26--28 Aug},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v108/uziel20a/uziel20a.pdf},
url = {https://proceedings.mlr.press/v108/uziel20a.html},
abstract = { In this paper we focus on the problem of online portfolio selection with transaction costs. We tackle this problem using a novel approach for combining the predictions of long-term experts with those of short-term experts so as to effectively reduce transaction costs. We prove that the new strategy maintains bounded regret relative to the performance of the best possible combination (switching times) of the long-and short-term experts. We empirically validate our approach on several standard benchmark datasets. These studies indicate that the proposed approach achieves state-of-the-art performance.}
}