p&l management

This study investigates core challenges confronting corporate financial management under remote and hybrid work models—specifically, weakened budgetary control, diminished financial transparency, and inefficient cross-departmental collaboration. Employing a mixed-methods approach, it integrates quantitative surveys of managers, HR professionals, and finance staff with ERP system log analysis, digital workflow assessment, and organizational practice framework modeling. It delivers the first empirical evidence on financial process performance in flexible work environments. Results indicate that ERP integration and digitized workflows significantly enhance budget execution controllability and procedural transparency; however, reduced demand forecasting accuracy and suboptimal interdepartmental communication persist as critical bottlenecks. Notably, improved employee stress mitigation and work–life balance yield positive spillover effects on financial operations. The study advances both theoretical understanding and practical guidance for reconfiguring finance functions to align with emerging work paradigms.

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.

Conventional financial ratio analysis at the industry level suffers from statistical deficiencies—including skewness, non-normality, direction dependence (sensitivity to numerator/denominator selection), and outlier susceptibility. Method: This paper pioneers the systematic application of Compositional Data Analysis (CoDA) to finance, introducing clr/alr/ilr transformations for geometric mean aggregation, compositional principal component biplots, compositional k-means clustering, and compositional linear regression, alongside a CoDA-based DuPont decomposition framework. Validation employs the CoDaPack toolkit on Spanish winery financial statements. Contribution/Results: The approach enables unbiased industry-level mean estimation, visualizes structural heterogeneity in financial composition, identifies robust performance clusters, and supports direct modeling of ratios with interpretable, statistically coherent regression. This work establishes a theoretically consistent, robust, and reliable analytical framework for financial ratio modeling.

This study investigates whether large language models (LLMs) can accurately forecast the direction of future corporate earnings—upward or downward—solely from standardized, anonymized financial statements, without domain-specific knowledge or narrative context. Using GPT-4 in a zero-shot setting, we design a two-stage task: financial metric extraction followed by binary directional classification, augmented with interpretable, economically grounded reasoning chains. Our empirical analysis provides the first evidence that LLMs significantly outperform professional human analysts—especially in cases where analyst accuracy is low—and that this superiority stems not from memorization of training data but from generating forward-looking insights aligned with economic principles. Moreover, LLM predictions achieve accuracy comparable to state-of-the-art task-specific machine learning models; a trading strategy built upon these predictions delivers superior Sharpe ratio and alpha, confirming their practical efficacy in real-world investment decision-making.

This paper addresses the challenge of accurately identifying demand under substantial temporal fluctuations and absent cost-variation information, focusing on the French railway industry. We systematically evaluate the economic performance of revenue management (RM) strategies using a novel identification framework that integrates time-series relative price changes, consumer rational expectations, and firms’ weak optimality conditions in pricing. Our methodology combines structural econometric modeling, counterfactual demand estimation, endogenous price treatment, and censoring-handling techniques to overcome identification issues arising from sales cutoffs and the lack of exogenous price variation. Results show that current RM practices significantly outperform uniform pricing but still incur a 16.7% revenue loss relative to theoretically optimal dynamic pricing. This study provides the first empirical quantification of RM’s net economic value in a real-world industrial setting and reveals its critical role in aggregating and processing information under demand uncertainty.

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.

This study addresses the challenge of disentangling execution performance from audit scores in the evaluation of LLM-based financial agents. To isolate the effects of model responses from execution rules, we propose a fixed-tape replay mechanism and construct a multi-defect audit task suite with explicit multi-label prompting to refine answer keys. By integrating synthetic scenario simulation, seed clustering, and Holm correction, this work quantifies the significant negative impact of stressed execution on returns and demonstrates that single-objective recall fails to accurately reflect audit quality. Ultimately, this research delineates the valid boundaries for evaluating LLM financial agents and provides methodological support for reliable scoring frameworks.

研究通过FinVision系统，利用多模态大语言模型处理异构财务数据，提高估值准确性和决策效率。

本文提出AnalysisBank，通过从专家报告中提炼分析模式库来生成财务报告，提高了数据驱动的洞见比例。

研究揭示了商业盈利目标如何导致大型语言模型在处理模糊信号时忽视潜在安全问题，通过3600次对照试验展示了利润导向对风险评估的影响。