Expert Aggregation for Financial Forecasting
Abstract
Machine learning algorithms dedicated to financial time series forecasting have gained a lot of interest. But choosing between several algorithms can be challenging, as their estimation accuracy may be unstable over time. Online aggregation of experts combine the forecasts of a finite set of models in a single approach without making any assumption about the models. In this paper, a Bernstein Online Aggregation (BOA) procedure is applied to the construction of long-short strategies built from individual stock return forecasts coming from different machine learning models. The online mixture of experts leads to attractive portfolio performances even in environments characterised by non-stationarity. The aggregation outperforms individual algorithms, offering a higher portfolio Sharpe Ratio, lower shortfall, with a similar turnover. Extensions to expert and aggregation specialisations are also proposed to improve the overall mixture on a family of portfolio evaluation metrics.
Keywords
Cite
@article{arxiv.2111.15365,
title = {Expert Aggregation for Financial Forecasting},
author = {Carl Remlinger and Brière Marie and Alasseur Clémence and Joseph Mikael},
journal= {arXiv preprint arXiv:2111.15365},
year = {2023}
}