Carl Remlinger, Brière Marie, Alasseur Clémence, Joseph Mikael
arXiv 25 Nov 2021 · Finance — Statistical Finance · publishedThe Journal of Finance and Data Science (2023) · 6 citations (OpenAlex)
arXiv:2111.15365 · PDF · DOI · OpenAlex · Extracted main text
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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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | S. Gu, B. Kelly, and D. Xiu (2020) Empirical asset pricing via machine learning | 0.920 | 9 | 4 | 78% |
| 2 | P. Gaillard and Y. Goude (2014) Forecasting electricity consumption by aggregating experts; how to design a good set of experts | 0.843 | 3 | 3 | 100% |
| 3 | N. Cesa-Bianchi and G. Lugosi (2006) Prediction, Learning, and Games | 0.811 | 4 | 2 | 100% |
| 4 | O. Wintenberger (2017) Optimal learning with bernstein online aggregation | 0.811 | 4 | 2 | 100% |
| 5 | M. Devaine, P. Gaillard, Y. Goude, and G. Stoltz (2013) Forecasting electricity consumption by aggregating specialized experts | 0.737 | 3 | 2 | 100% |
| 6 | K. S. Azoury and M. K. Warmuth (2001) Relative loss bounds for on-line density estimation with the exponential family of distributions | 0.644 | 2 | 2 | 100% |
| 7 | Y. Freund, R. E. Schapire, Y. Singer, and M. K. Warmuth (1997) Using and combining predictors that specialize | 0.644 | 2 | 2 | 100% |
| 8 | J. Freyberger, A. Neuhierl, and M. Weber (2020) Dissecting characteristics nonparametrically | 0.644 | 2 | 2 | 100% |
| 9 | N. Littlestone and M. K. Warmuth (1994) The weighted majority algorithm | 0.644 | 2 | 2 | 100% |
| 10 | V. G. Vovk (1990) Aggregating strategies | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 57 scored citations.