economics

The absence of standardized frameworks for data valuation and monetization impedes organizations’ systematic assessment and realization of data value. Method: Through a systematic literature review (N=162) integrating thematic analysis and taxonomy development, this study proposes— for the first time—the Balanced Scorecard–based Comprehensive Data Valuation and Monetization Framework. Contribution/Results: The framework spans strategic, business, technical, and governance dimensions, offering a fine-grained, four-tiered indicator taxonomy. Concurrently, an open-source indicator repository comprising 162 validated metrics is established. The study identifies critical implementation challenges—including cross-departmental coordination, value attribution, and dynamic adaptability—and provides both a practical classification tool and theoretical foundations to advance data asset management practices and evidence-based decision-making.

Organizations face significant challenges in AI investment decision-making: conventional ROI models fail to simultaneously capture AI’s cost-reduction and efficiency-gain benefits and its novel risk exposures—including algorithmic failure, bias-related litigation, model drift, and regulatory noncompliance. This paper introduces the first risk-adjusted financial evaluation framework explicitly aligned with regulatory standards such as ISO/IEC 42001 and the EU AI Act. Methodologically, it innovatively incorporates control effectiveness, failure contingency reserves, and ongoing operational costs into benefit quantification, and employs annualized loss expectancy analysis, Monte Carlo simulation, and risk exposure gap modeling for rigorous risk-adjusted valuation. The framework enables precise calculation of AI project net benefits, thereby supporting evidence-based capital allocation and investment decisions. It further fulfills dual objectives: upholding fiduciary duty and ensuring regulatory compliance.

Cyber threat intelligence (CTI) investments face justification challenges within conventional cost-benefit frameworks due to the “negative evidence problem”—the difficulty of quantifying value derived from prevented, rather than observed, incidents. Method: This paper proposes a data-driven CTI return-on-investment (ROI) quantification framework that integrates an extended Gordon-Loeb model with the Factor Analysis of Information Risk (FAIR) methodology. It introduces the Threat Intelligence Effectiveness Index (TIEI)—a weighted geometric mean of quality, enrichment level, integration maturity, and operational impact—thereby systematically transforming negative evidence into interpretable ROI metrics. The framework further incorporates empirical parameters—including mean time to detect (MTTD), mean time to respond (MTTR), and attacker dwell time—to enable multidimensional assessment across financial loss reduction, adversary coverage, and business enablement. Results: Validated across financial services, healthcare, and retail sectors, the framework supports CTI’s strategic repositioning from a cost center to a value-generating investment, demonstrating cross-industry replicability and capacity for continuous refinement.

Existing performance measurement frameworks struggle to simultaneously satisfy customizability, interpretability, and mathematical tractability in interdisciplinary contexts. Method: This paper proposes a goal-oriented, customizable metric construction framework featuring a novel “base metric–auxiliary metric” dichotomy. Integrating utility theory and multi-criteria decision analysis, it introduces an uncertainty-aware utility function and establishes a systematic metric decomposition–synthesis workflow. Contributions: (1) It reduces reliance on complex mathematical formalisms, enhancing applicability under resource constraints or high uncertainty; (2) it ensures metric transparency, traceability, and domain adaptability; and (3) it enables quantitative assessment of goal attainment, real-time progress monitoring, and downstream statistical modeling and decision optimization. The framework has been empirically validated across diverse disciplines, demonstrating generality and extensibility.

This study addresses the long-standing lack of systematic measurement of “implementation risk” in quantitative investment backtesting—the performance discrepancies arising from differences in backtesting engine implementations. The work formally defines this risk for the first time and proposes four metrological metrics alongside a taxonomy of five failure modes. These are derived from parallel execution of 15 benchmark strategies across five open-source backtesting engines, incorporating transaction cost modeling, non-overlapping stratified asset buckets, and source code defect analysis. Experiments reveal that while engine outputs converge under zero-cost assumptions, performance divergence can reach up to 3.71% when transaction costs are introduced. Crucially, however, the relative ranking of strategy efficacy remains unchanged across engines (conclusion stability index = 1), indicating that implementation risk affects performance attribution but does not alter investment decisions.

For projects with non-conventional cash flows—exhibiting multiple sign changes in cumulative cash flows—the payback period lacks a rigorous, universally accepted definition, leading to conceptual ambiguity and inconsistent practice. Method: This paper establishes, for the first time, an axiomatic framework formalizing the economic meaning of the payback period; it rigorously derives and proves that the *last* breakeven point of the project balance is the unique definition satisfying three fundamental axioms: time value of money, cost of capital incorporation, and economic rationality. Theoretical consistency is verified by unifying discounted and conventional payback models. Contribution/Results: This definition resolves long-standing academic disputes, providing a theoretically sound yet operationally practical benchmark for investment appraisal, performance evaluation, and pedagogy—thereby advancing both financial theory and managerial practice.

This study addresses the limitation of conventional e-commerce A/B tests, which often overlook the long-term impact of interventions on profitability across an inventory item’s full lifecycle due to short experimental windows. To overcome this, the authors propose Stock Lifetime Value (SLV), a novel metric that aggregates the expected profit of current inventory over its entire sales horizon within short-term experiments, thereby enabling more accurate assessment of long-term profitability. SLV uniquely integrates inventory constraints and seasonal lifecycle dynamics into the A/B testing framework, combining causal inference with financial mapping to support both item-level and user-level experimentation while aligning with annual financial reporting. Empirical validation at Zalando demonstrates that SLV effectively predicts actual profits over an 18-month horizon, enhances pricing algorithm performance, and delivers interpretable estimates of annual financial impact.

This study addresses the challenge of effectively integrating heterogeneous risks across multiple scenarios in financial markets by proposing a Weighted Generalized Risk Measure (WGRM) and its associated Weighted Risk Quadrangle (WRQ), thereby extending the generalized risk measure and risk quadrangle framework to a weighted setting for the first time. Theoretically, the work establishes analytical characterizations of WGRM under both discrete and continuous settings, proving that its structural properties remain invariant and revealing intrinsic connections among risk, deviation, regret, and error under weighting. Computationally, it leverages convex analysis, stochastic optimization, and linear programming reformulation techniques to transform complex risk optimization problems into tractable linear programs. Empirical results demonstrate that portfolios constructed using WGRM significantly improve risk-adjusted returns, enhance downside resilience, and mitigate losses caused by misjudgments in individual scenarios on NASDAQ 100 and S&P 500 constituents.

This study addresses the limitations of traditional risk-adjusted performance measures—such as the Sharpe ratio—in effectively ranking financial or insurance positions. The authors develop an axiomatic framework that introduces monotonicity and cash quasi-concavity to define a novel class of ranking measures, which map positions directly to performance levels rather than standardized returns. This approach establishes theoretical connections with acceptance sets and risk measures, encompassing classical ratio-based metrics while extending to new ranking methodologies grounded in expected shortfall, Lambda quantiles, and bibliometric-inspired constructions. Empirical analyses involving portfolio rankings and climate risk insurance demonstrate both the theoretical coherence and practical applicability of the proposed framework.

Traditional portfolio optimization relies excessively on low-order statistics (e.g., mean–variance), limiting its ability to capture tail risks, asymmetry, and higher-order distributional features critical for robust decision-making. Method: This paper proposes a unified optimization framework centered on the gain probability density function (PDF) as the fundamental modeling unit. It is the first to treat one-dimensional PDFs as primary optimization objects, enabling direct target-PDF matching, multi-criteria optimization (e.g., CVaR, higher-order moments), and a budget-unit-based suboptimality cost quantification mechanism that bridges classical objectives with manager-specified goals. Contribution/Results: Validated via PDF modeling, numerical PDE and Monte Carlo estimation, and empirical analysis on energy asset portfolios, the framework significantly improves distributional fidelity, decision controllability, and practical interpretability. It uniquely supports novel objectives such as high-profit control, thereby enhancing transparency and operational feasibility in investment decisions.

This study addresses the limitations of existing approaches in simultaneously accounting for the time value of money and the integrated effects of multidimensional decision criteria on financial risk in manufacturing firms, while also overlooking the interactions among economic, operational, and managerial factors. To bridge this gap, we propose an evaluation framework that integrates a compound discounting model with multicriteria linear regression. For the first time, a time-discounting mechanism is incorporated into multicriteria decision analysis, enabling unified treatment of one-time expenditures, proportional costs, and complex cost structures. The method effectively quantifies the present value of costs and benefits across different time points and reveals how synergistic interactions among multiple factors influence discounted performance. This approach significantly enhances the systematicity and accuracy of financial risk assessment, offering manufacturing enterprises quantifiable decision support for optimizing the economic efficiency of control systems.