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<p>In today’s volatile financial landscape, investment planning has become increasingly complex due to inflationary pressures, market instability, and geopolitical uncertainties. Traditional decision-making models, which rely solely on static optimization and quantitative data, often fail to incorporate investors’ subjective judgments, leading to biased evaluations and less adaptive outcomes. To overcome these limitations, this study proposes an intelligent decision-making framework for financial planning and investment optimization that integrates both objective and subjective perspectives. The logarithmic percentage change-driven objective weighting (LOPCOW) method is employed to derive objective weights based on data variability and informational entropy, ensuring transparency and data-driven rigor. Simultaneously, the ranking comparison (RANCOM) technique captures subjective weights from expert evaluations, reflecting strategic insights, behavioral preferences, and market experience. These complementary approaches are combined to create a balanced and holistic weighting structure. The evaluation process is further enhanced through an enhanced preference ranking organization method for enrichment of evaluations (EPROMETHEE-II) that integrates nonlinear distance and similarity measures, facilitating superior modeling of uncertainty, enhanced discrimination among proximate alternatives, and more reliable and precise ranking outcomes compared to the conventional PROMETHEE-II methodology. A real-world case study on financial planning and investment optimization validates the framework, demonstrating its capability to support balanced, expert-informed, and data-driven portfolio decisions, with the ESG-Focused Portfolio (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\({A^{\Psi }}_{8}\)</EquationSource> </InlineEquation>) identified as the top-performing alternative due to its adaptive learning and strong ESG integration. The framework provides superior adaptability, robustness, and interpretability, aiding investors and policymakers in making resilient, informed, and context-aware financial decisions.</p>

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Smart decision framework for financial planning and investment optimization

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月11日

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  • Xueqian Chen,
  • Ao Shen

摘要

In today’s volatile financial landscape, investment planning has become increasingly complex due to inflationary pressures, market instability, and geopolitical uncertainties. Traditional decision-making models, which rely solely on static optimization and quantitative data, often fail to incorporate investors’ subjective judgments, leading to biased evaluations and less adaptive outcomes. To overcome these limitations, this study proposes an intelligent decision-making framework for financial planning and investment optimization that integrates both objective and subjective perspectives. The logarithmic percentage change-driven objective weighting (LOPCOW) method is employed to derive objective weights based on data variability and informational entropy, ensuring transparency and data-driven rigor. Simultaneously, the ranking comparison (RANCOM) technique captures subjective weights from expert evaluations, reflecting strategic insights, behavioral preferences, and market experience. These complementary approaches are combined to create a balanced and holistic weighting structure. The evaluation process is further enhanced through an enhanced preference ranking organization method for enrichment of evaluations (EPROMETHEE-II) that integrates nonlinear distance and similarity measures, facilitating superior modeling of uncertainty, enhanced discrimination among proximate alternatives, and more reliable and precise ranking outcomes compared to the conventional PROMETHEE-II methodology. A real-world case study on financial planning and investment optimization validates the framework, demonstrating its capability to support balanced, expert-informed, and data-driven portfolio decisions, with the ESG-Focused Portfolio ( \({A^{\Psi }}_{8}\) ) identified as the top-performing alternative due to its adaptive learning and strong ESG integration. The framework provides superior adaptability, robustness, and interpretability, aiding investors and policymakers in making resilient, informed, and context-aware financial decisions.