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<p>The article discusses static and dynamic investments in a set of assets, where, at each moment in time <i>t</i> an investor selects one asset from the available options to invest the capital. The choice strategy employed is a mixed strategy, which means it is represented as a probabilistic measure <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(P_{t}\)</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>P</mi> <mi>t</mi> </msub> </math></EquationSource> </InlineEquation> over the set of assets. This approach differs from a standard investment portfolio; however, as it will be demonstrated in the introduction, there are notable commonalities. Two assessments of investment quality are considered in this analysis. When selecting a strategy, machine learning methods, along with value at risk (VaR) and conditional value at risk (CVaR) for empirical distribution, are utilized.</p>

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MACHINE LEARNING METHODS IN A RANDOM INVESTMENTS PROBLEM

  • 期刊论文

  • 发表日期：

    2025年5月7日

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  • Grigory Beliavsky,
  • Natalia Danilova,
  • Guennady Ougolnitsky,
  • Keyu Yao

摘要

The article discusses static and dynamic investments in a set of assets, where, at each moment in time t an investor selects one asset from the available options to invest the capital. The choice strategy employed is a mixed strategy, which means it is represented as a probabilistic measure \(P_{t}\) P t over the set of assets. This approach differs from a standard investment portfolio; however, as it will be demonstrated in the introduction, there are notable commonalities. Two assessments of investment quality are considered in this analysis. When selecting a strategy, machine learning methods, along with value at risk (VaR) and conditional value at risk (CVaR) for empirical distribution, are utilized.