Sequential asset ranking in nonstationary time series

Gabriel Borrageiro

Papers from arXiv.org

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.

Date: 2022-02, Revised 2022-10
New Economics Papers: this item is included in nep-ban and nep-ets
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