Guillaume Coqueret, Martial Laguerre
arXiv 30 Jun 2025 · Finance — Statistical Finance
arXiv:2506.23619 · PDF · DOI · OpenAlex · Extracted main text
This paper investigates the impact of posterior drift on out-of-sample forecasting accuracy in overparametrized machine learning models. We document the loss in performance when the loadings of the data generating process change between the training and testing samples. This matters crucially in settings in which regime changes are likely to occur, for instance, in financial markets. Applied to equity premium forecasting, our results underline the sensitivity of a market timing strategy to sub-periods and to the bandwidth parameters that control the complexity of the model. For the average investor, we find that focusing on holding periods of 15 years can generate very heterogeneous returns, especially for small bandwidths. Large bandwidths yield much more consistent outcomes, but are far less appealing from a risk-adjusted return standpoint. All in all, our findings tend to recommend cautiousness when resorting to large linear models for stock market predictions.
appendix boundary found by appendix_command · 37% of the source is main text. Read the extracted text to check this.
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 | Kelly, B. T., S. Malamud, and K. Zhou (2024) The virtue of complexity in return prediction | 1.000 | 24 | 4 | 100% |
| 2 | Hastie, T., A. Montanari, S. Rosset, and R. J. Tibshirani (2022) Surprises in high-dimensional ridgeless least squares interpolation | 0.899 | 11 | 3 | 73% |
| 3 | Bartlett, P. L., P. M. Long, G. Lugosi, and A. Tsigler (2020) Benign overfitting in linear regression | 0.644 | 2 | 2 | 100% |
| 4 | Shen, Z. and D. Xiu (2025) Can machines learn weak signals? | 0.644 | 2 | 2 | 100% |
| 5 | Hastie, T., A. Montanari, S. Rosset, and R. J. Tibshirani (2020) Surprises in high-dimensional ridgeless least squares interpolation | 0.511 | 5 | 2 | 20% |
| 6 | Fallahgoul, H (2025) High-dimensional learning in finance | 0.511 | 2 | 1 | 100% |
| 7 | Goyal, A., I. Welch, and A. Zafirov (2024) A comprehensive 2022 look at the empirical performance of equity premium prediction | 0.511 | 2 | 1 | 100% |
| 8 | Londschien, M., P. Bühlmann, and S. Kovács (2023) Random forests for change point detection | 0.511 | 2 | 1 | 100% |
| 9 | Enikeeva, F. and Z. Harchaoui (2019) High-dimensional change-point detection under sparse alternatives | 0.405 | 1 | 1 | 100% |
| 10 | Frick, K., A. Munk, and H. Sieling (2014, 05) (2014) Multiscale Change Point Inference | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 43 scored citations.