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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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    1. Merton, Robert C., 1976. "Option pricing when underlying stock returns are discontinuous," Journal of Financial Economics, Elsevier, vol. 3(1-2), pages 125-144.
    2. Leo Breiman & Jerome H. Friedman, 1997. "Predicting Multivariate Responses in Multiple Linear Regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 59(1), pages 3-54.
    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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    Cited by:

    1. Parley R Yang & Alexander Y Shestopaloff, 2024. "Low Volatility Stock Portfolio Through High Dimensional Bayesian Cointegration," Papers 2407.10175, arXiv.org.

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