# Sequential asset ranking in nonstationary time series

arXiv: https://arxiv.org/abs/2202.12186 · HTML: https://mlsanity.com/paper/2202.12186/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 62 references that are in the map.
- First posted on arXiv 24 Feb 2022.

## Builds on (most cited first, 7 of 7)

- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Learning to reinforcement learn](https://mlsanity.com/paper/1611.05763/) (2016)
- [Building Cross-Sectional Systematic Strategies By Learning to Rank](https://mlsanity.com/paper/2012.07149/) (2020)
- [Enhancing Cross-Sectional Currency Strategies by Context-Aware Learning to Rank with Self-Attention](https://mlsanity.com/paper/2105.10019/) (2021)
- [The Recurrent Reinforcement Learning Crypto Agent](https://mlsanity.com/paper/2201.04699/) (2022)
- [Switching Portfolios](https://mlsanity.com/paper/1301.7413/) (2013)
- [Reinforcement Learning for Systematic FX Trading](https://mlsanity.com/paper/2110.04745/) (2021)

## Abstract (arXiv)

We create a ranking algorithm, the naive Bayes asset ranker. Our algorithm computes the posterior probability that individual assets will be ranked higher than other portfolio constituents. Unlike earlier algorithms, such as the weighted majority, our algorithm allows poor-performing experts to have increased weight when they start performing well. We outperform the long-only holding of the S&P 500 index and a regress-then-rank baseline.