Smart decision framework for financial planning and investment optimization

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.

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Publication Details

Journal
Scientific Reports
Published
2026-03-11
DOI
https://doi.org/10.1038/s41598-026-43270-9
Primary Topic
Capital Investment and Risk Analysis
Type
article
Field-Weighted Citation Impact
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article

Smart decision framework for financial planning and investment optimization

Ao Shen, Xueqian Chen
Scientific Reports
Capital Investment and Risk Analysis
article

Smart decision framework for financial planning and investment optimization

Ao Shen, Xueqian Chen
article en

Abstract

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.

Scientific Reports
Qiqihar University (CN)
Openalex Percentile: Top 29%
Capital Investment and Risk Analysis
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