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Sequential asset ranking in nonstationary time series

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  • Gabriel Borrageiro

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.

Suggested Citation

  • Gabriel Borrageiro, 2022. "Sequential asset ranking in nonstationary time series," Papers 2202.12186, arXiv.org, revised Oct 2022.
    • Handle: RePEc:arx:papers:2202.12186
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    References listed on IDEAS

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    3. Jegadeesh, Narasimhan & Titman, Sheridan, 1993. "Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency," Journal of Finance, American Finance Association, vol. 48(1), pages 65-91, March.
    4. Saejoon Kim, 2019. "Enhancing the momentum strategy through deep regression," Quantitative Finance, Taylor & Francis Journals, vol. 19(7), pages 1121-1133, July.
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