  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.
