cost decomposition analysis

This study addresses the challenge of quantifying the complexity and cost induced by external requirement changes when detailed knowledge of a system’s internal logic is unavailable. To this end, the authors propose a black-box assessment method based on a directed graph of component coupling. By analyzing component interfaces and integrating multi-view modeling—graphical, algebraic, and tabular—the approach uniquely links interface characteristics to cost factors, enabling computable bounded estimates of change-induced complexity and associated costs. The method was validated through a large-scale integration case in a retail banking platform, demonstrating its effectiveness and providing architects and operations teams with actionable, quantitative insights for system design and maintenance.

This study addresses the challenge of accurately estimating firm-level price markups—the ratio of price to marginal cost—in the absence of explicit demand or market structure models. Treating markup estimates derived from production-based approaches as production residuals akin to Solow residuals, the paper leverages large-scale firm-level panel data to systematically examine their variation across industries and over time. It demonstrates that existing markup estimates are highly sensitive to model specification and measurement error, and that much of the apparent disagreement in the literature regarding trends in markups stems from misattributing technical misspecifications to shifts in market power. By reframing estimation discrepancies through a residual-based perspective, the analysis underscores the critical need to disentangle technological factors from genuine changes in market power and calls for greater methodological transparency in empirical practice.

Existing correlation–complexity mapping approaches for identifying economic diversification lack theoretical optimality guarantees and empirical validation. Method: This paper formalizes diversification as an optimization problem subject to specialization constraints and proposes a novel Economic Complexity Optimization (ECO) algorithm that minimizes a cost function integrating the Economic Complexity Index (ECI), the Product Space network, and gradient-driven neighborhood search. Contribution: The approach overcomes the empirically grounded but theoretically ad hoc limitations of graph-based methods, shifting the paradigm from descriptive analysis to normative, actionable strategy generation. The resulting diversification pathways exhibit superior theoretical coherence and empirical interpretability compared to conventional recommendations. By bridging theory and practice, this work advances economic complexity methodologies toward operational, policy-ready tools. (149 words)

This study addresses the distortion in traditional weighted composite indices, where the actual variance contributions of constituent indicators deviate from pre-specified weights due to inter-indicator variances and correlations. To resolve this issue, the authors propose a purely analytical composite index method that reconstructs the constituent indicators such that their variance contributions in the final composite strictly match the priori assigned weights. Grounded in variance decomposition and covariance structure analysis, this approach is the first to achieve exact alignment between empirical variance contributions and prescribed weights, thereby eliminating weight distortion inherent in conventional aggregation schemes. Simulation experiments confirm the method’s validity and demonstrate its practical applicability, for instance, in constructing exchange-traded funds. Accompanying R code is provided to facilitate implementation.

Financial cluster analysis faces three key challenges: difficulty in modeling dynamic temporal dependencies, high ambiguity in heterogeneous business knowledge sources, and poor interpretability due to exhaustive pairwise comparisons. To address these in the context of thematic stock investment, this paper proposes a three-stage collaborative clustering paradigm—“dynamic generation, knowledge exploration, and correlation validation.” It integrates time-series similarity metrics with domain-specific knowledge graph embeddings to construct a multi-view interactive clustering framework that jointly quantifies performance and qualifies semantic relationships. The framework incorporates heatmap-, relational-graph-, and trajectory-based visualizations and supports user-driven iterative refinement. Experiments demonstrate significant improvements in clustering validity and interpretability; domain experts highly endorse its effectiveness in thematic stock construction, risk-hedging portfolio identification, and emerging investment theme discovery.

This study addresses the challenge of accurately attributing aggregate prediction bias to individual components within complex modeling frameworks. Within an expected loss framework, it formalizes traditional walk-through analysis, exposing its inherent sequential dependency limitations, and proposes two order-agnostic attribution methods: an extension of the Logarithmic Mean Divisia Index (LMDI) tailored to the expected loss structure, and a Shapley-value-based approach that averages marginal contributions. For the first time, both methods are systematically applied to a comprehensive suite of financial risk models incorporating probability of default (PD), loss given default (LGD), exposure at default (EAD), and survival model multiplier (SMM) components, along with Monte Carlo simulation layers. The authors derive efficient vectorized computation formulas, enabling empirical attribution on real-world portfolio scales in mere seconds of additional runtime, thereby substantially enhancing computational efficiency and result consistency while providing robust support for model validation and regulatory compliance.

Traditional revenue forecasting approaches struggle to uncover the underlying customer behavioral drivers—such as customer acquisition, repeat purchase rates, and average transaction value—that influence revenue dynamics. To address this limitation, this work proposes the Customer-Based Multi-Task Transformer (CBMT), which uniquely integrates multi-task learning with a Transformer architecture to jointly model customer behavioral metrics and total revenue through shared representations. Furthermore, CBMT incorporates a downstream alignment mechanism to enhance both interpretability and predictive accuracy. Empirical evaluation on real-world customer transaction panel data demonstrates that CBMT outperforms existing methods across 23 out of 24 evaluation metrics, achieving a 30% reduction in total sales prediction error compared to the strongest baseline and significantly surpassing single-task models employed by 74.3% of firms.