# Online Ensemble Learning for Sector Rotation: A Gradient-Free Framework

arXiv: https://arxiv.org/abs/2304.09947 · HTML: https://mlsanity.com/paper/2304.09947/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 33 references that are in the map.
- First posted on arXiv 30 Mar 2023.

## Builds on (most cited first, 3 of 3)

- [Deep Learning in Asset Pricing](https://mlsanity.com/paper/1904.00745/) (2019)
- [AdaNet: A Scalable and Flexible Framework for Automatically Learning Ensembles](https://mlsanity.com/paper/1905.00080/) (2019)
- [Machine Learning Algorithms for Financial Asset Price Forecasting](https://mlsanity.com/paper/2004.01504/) (2020)

## Built on by (most cited first, 2 of 2)

- [Ensemble of Precision-Recall Curve (PRC) Classification Trees with Autoencoders](https://mlsanity.com/paper/2509.05766/) (2025)
- [Hedging Memory Horizons for Non-Stationary Prediction via Online Aggregation](https://mlsanity.com/paper/2605.06541/) (2026)

## Abstract (arXiv)

We propose a gradient-free online ensemble learning algorithm that dynamically combines forecasts from a heterogeneous set of machine learning models based on their recent predictive performance, measured by out-of-sample R-squared. The ensemble is model-agnostic, requires no gradient access, and is designed for sequential forecasting under nonstationarity. It adaptively reweights 16 constituent models-three linear benchmarks (OLS, PCR, LASSO) and thirteen nonlinear learners including Random Forests, Gradient-Boosted Trees, and a hierarchy of neural networks (NN1-NN12). We apply the framework to sector rotation, using sector-level features aggregated from firm characteristics. Empirically, sector returns are more predictable and stable than individual asset returns, making them suitable for cross-sectional forecasting. The algorithm constructs sector-specific ensembles that assign adaptive weights in a rolling-window fashion, guided by forecast accuracy. Our key theoretical result bounds the online forecast regret directly in terms of realized out-of-sample R-squared, providing an interpretable guarantee that the ensemble performs nearly as well as the best model in hindsight. Empirically, the ensemble consistently outperforms individual models, equal-weighted averages, and traditional offline ensembles, delivering higher predictive accuracy, stronger risk-adjusted returns, and robustness across macroeconomic regimes, including during the COVID-19 crisis.