Expert Aggregation for Financial Forecasting

Carl Remlinger, Bri\`ere Marie, Alasseur Cl\'emence and Joseph Mikael

Papers from arXiv.org

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.

Date: 2021-11, Revised 2023-07
New Economics Papers: this item is included in nep-big, nep-cmp, nep-cwa and nep-for
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