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The problem of managing an investment portfolio in assets with random returns is considered. In solving the problem, a mixed investment strategy called random investment is used. This strategy differs from the commonly accepted Markowitz diversification, while being in no way inferior to it in the context of the Mean Variance (MV) criterion. Random investment allows for a more natural use of modern data analysis methods, namely, statistical machine learning, in the context of the Value at Risk (VaR) criterion, compared to diversification.

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Statistical Machine Learning in Risk Management for Random Investing

  • 会议论文

  • 发表日期：

    2026年

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  • N. Danilova,
  • G. Belyavsky,
  • E. Kamchatnaya

摘要

The problem of managing an investment portfolio in assets with random returns is considered. In solving the problem, a mixed investment strategy called random investment is used. This strategy differs from the commonly accepted Markowitz diversification, while being in no way inferior to it in the context of the Mean Variance (MV) criterion. Random investment allows for a more natural use of modern data analysis methods, namely, statistical machine learning, in the context of the Value at Risk (VaR) criterion, compared to diversification.