Expert Aggregation for Financial Forecasting

• URL: http://arxiv.org/abs/2111.15365v4
• Date: Thu, 6 Jul 2023 09:38:40 GMT
• Title: Expert Aggregation for Financial Forecasting
• Authors: Carl Remlinger, Bri\`ere Marie, Alasseur Cl\'emence, Joseph Mikael
• Abstract summary: Online aggregation of experts combine the forecasts of a finite set of models in a single approach. Online mixture of experts leads to attractive portfolio performances even in environments characterised by non-stationarity. Extensions to expert and aggregation specialisations are also proposed to improve the overall mixture on a family of portfolio evaluation metrics.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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