roi modeling

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.

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.

This study addresses the challenge enterprises face in evaluating the true returns of AI initiatives due to uncertain feasibility, a context where traditional ROI methods often fail. To overcome this limitation, the authors propose an expected Return on Investment (eROI) framework that decouples AI project assessment into three independently evaluable business dimensions: value upon success, probability of success, and required investment. This approach enables efficient pre-implementation decision-making and facilitates the construction of diversified AI project portfolios. Notably, the framework requires no complex modeling—only qualitative executive judgments on key dimensions—thereby circumventing the common “build-to-evaluate” dilemma. Empirical application at Compass demonstrated its practical utility: high-value projects such as the Likely-to-Sell recommendation system, which generated nine-figure annual revenue, were successfully prioritized, while low-potential initiatives were terminated early, validating the framework’s discriminative power and operational effectiveness.

In data-driven economies, organizations lack systematic frameworks for evaluating and managing data value within internal business processes. To address this gap, this study develops a comprehensive data value assessment framework grounded in the Balanced Scorecard’s internal process perspective, integrating three interrelated dimensions: data quality, governance compliance, and operational efficiency. It introduces a novel, multi-layered taxonomy of data value—spanning technological, organizational, and regulatory dependencies—that resolves metric redundancy and establishes cross-dimensional conceptual linkages. Through systematic literature review, theoretical modeling, indicator clustering, and taxonomy design, the research produces a scalable, reusable data value metrics system. This system underpins standardized data valuation models and decision-support systems, offering both a methodological foundation and actionable implementation pathways for cross-sectoral data assetization. (149 words)

Business professionals—non-technical domain experts—lack appropriate tools and methodologies for effective what-if analysis (WIA), hindering data-informed decision-making. Method: We conducted a two-phase mixed-methods user study—comprising contextual interviews and in-situ task-based evaluations—to systematically characterize their analytical behaviors for the first time. Contribution/Results: Based on empirical findings, we propose three domain-grounded design principles: business-contextual data preparation, risk-aware assessment, and domain-knowledge integration. We implemented and validated these principles in an interactive visual analytics prototype. The study identifies three critical support gaps, empirically confirms that six classes of what-if techniques significantly improve decision efficiency and confidence, and yields eight actionable design guidelines for commercial business intelligence systems. This work bridges a key theoretical and practical gap in WIA research concerning non-technical users.

Traditional quantitative investment systems typically optimize a single metric—such as the information ratio—and thus struggle to meet professional investors’ multifaceted objectives, including pure alpha generation, style control, drawdown resilience, and turnover and capacity constraints. This work proposes an Objective-Oriented Quantitative Investment (OOQI) framework that formally encodes investment intent as strategy specifications and compiles them into composable, constraint-satisfying strategy assemblies. Key innovations include establishing a dual lattice structure between specifications and assemblies, designing a satisfaction-driven synthesis mechanism, and introducing rolling recertification via e-process-based validation. Empirical results demonstrate that the specification-driven approach satisfies 100% of target constraints across 32 strategies, at the cost of only a 5.5% reduction in information ratio, whereas conventional outcome-oriented methods—despite higher in-sample information ratios—fulfill merely 25% of the specified requirements.

This study addresses the limitations of traditional investment approaches that utilize data solely for asset pricing while neglecting its systematic impact on portfolio construction and performance attribution under real-world constraints, often leading to suboptimal decisions. To overcome this, the paper proposes a novel three-stage knowledge-optimization framework encompassing decision architecture design, portfolio selection, and performance evaluation. For the first time, it integrates data, models, and business insights into actionable knowledge units embedded throughout the investment process. The framework innovatively introduces a knowledge-augmented proxy for the ex-ante Sharpe ratio to enhance knowledge-driven performance attribution. Empirical results across multiple scenarios demonstrate that the proposed method significantly elevates the explicit knowledge value in investment decisions, yielding improved portfolio performance and interpretability under practical constraints.

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.

该研究开发了一种公共数据决策支持工具链，帮助创业者在创业前后通过市场分析、资金伙伴匹配等方法做出更明智的决策。

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.