Sequential asset ranking in nonstationary time series

Abstract

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.

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• The Recurrent Reinforcement Learning Crypto Agent
• Reinforcement Learning for Systematic FX Trading