Sequential asset ranking in nonstationary time series
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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.
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.
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7 of 62 references.
References · 7
- 2016Overcoming catastrophic forgetting in neural networkscited by 4,787
- 2016Learning to reinforcement learncited by 377
- 2020Building Cross-Sectional Systematic Strategies By Learning to Rankcited by 9
- 2021Enhancing Cross-Sectional Currency Strategies by Context-Aware Learning to Rank with Self-Attentioncited by 5
- 2022The Recurrent Reinforcement Learning Crypto Agentcited by 4
- 2013Switching Portfolioscited by 3
- 2021Reinforcement Learning for Systematic FX Tradingcited by 3
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