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Nested dynamic network data envelopment analysis models with infinitely many decision making units for portfolio

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European Journal of Operational Research
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Summary
This summary is machine-generated.

This study introduces novel nested dynamic network DEA models for multi-period portfolio performance evaluation. These models assess individual asset and overall portfolio efficiency, incorporating valuable historical data for better financial decision-making.

Keywords:
BayesianData envelopment analysisInfinitely many dmusNestedPortfolio

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Area of Science:

  • Finance
  • Operations Research
  • Econometrics

Background:

  • Traditional Data Envelopment Analysis (DEA) models often aggregate historical data, losing valuable time-period specific information for portfolio performance evaluation.
  • Existing DEA approaches treat financial assets as a 'black box,' failing to provide insights into individual asset performance.
  • Real-world investment decisions are inherently multi-period, necessitating evaluation methods that account for temporal dynamics.

Purpose of the Study:

  • To develop advanced nested dynamic network DEA models (NDN DEA) for comprehensive portfolio performance assessment.
  • To simultaneously evaluate the multi-period efficiency of both the portfolio and its constituent financial assets.
  • To integrate conditional value-at-risk (CVaR) constraints and address the need for full diversification comparisons.

Main Methods:

  • Development of nested dynamic network DEA models, integrating an additive model within a slacks-based measure (SBM) DEA framework.
  • Formulation of these models as linear programs with CVaR constraints, allowing for an infinite number of decision-making units (DMUs).
  • Application of Markov chain Monte Carlo Bayesian algorithms for future performance forecasting in volatile markets.

Main Results:

  • The proposed NDN DEA models effectively utilize individual time-period data for a more accurate multi-period efficiency assessment.
  • The models provide simultaneous evaluation of portfolio and individual financial asset performance.
  • Empirical application demonstrates the models' utility in a real-world investment scenario with future performance forecasts.

Conclusions:

  • The NDN DEA models offer a significant advancement in portfolio performance evaluation by capturing dynamic, multi-period, and individual asset efficiencies.
  • These models provide a more holistic and informative approach compared to traditional single-period or aggregated data methods.
  • The integration of CVaR and forecasting techniques enhances their applicability in complex and volatile financial environments.