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.

翻译：专注于金融时间序列预测的机器学习算法已引起广泛关注。然而，由于不同算法的估计精度可能随时间不稳定，在多个算法之间进行选择颇具挑战性。专家在线聚合方法无需对模型做任何假设，即可将有限模型集合的预测结果融合于统一框架中。本文采用伯恩斯坦在线聚合（BOA）方法，基于不同机器学习模型生成的个股收益率预测构建多空策略。即使面对非平稳环境，该在线专家混合方法仍能产生具有吸引力的投资组合表现。该聚合方法优于单个算法，在保持相似换手率的同时，实现了更高的投资组合夏普比率与更低的下行风险。此外，本文还提出了专家与聚合策略的专业化拓展方案，以优化投资组合评估指标族中的整体混合效果。