International Review of Financial Analysis最新文献

Financial information transparency is vital for the various users of financial statements. This study employs the Explainable Artificial Intelligence (XAI) approach, utilizing eXtreme Gradient Boost (XGBoost) to explore management's motivations for voluntary disclosure. By transforming financial data into various plots, we introduce a voluntary disclosure model that enhances interpretability through Shapley Additive exPlanations (SHAP) techniques. These XAI methods aim to clarify different results in the voluntary disclosure literature, addressing the ongoing debate within the financial research community regarding voluntary disclosure. This research marks a significant advancement in voluntary disclosure by merging the transparency of XAI with effective voluntary disclosure prediction, offering a more comprehensive understanding of the determinants of voluntary disclosure.

Banks' responses to regulatory requirements have a direct effect on their balance sheet mix and their business models. The paper introduces the concept of regulatory profiling, which establishes a nexus between banks' operations and their regulatory choices. Regulatory profiling is a process that identifies an optimal number of regulatory peers sharing similar operational characteristics for a bank. We also introduce a novel methodology for identifying the optimal direction of improvement in bank operations through Principal Components Pursuit, thereby overcoming restrictive shortcomings of competing approaches. This methodology identifies the core operations within each regulatory profile, which are effectively projections of the actual operations, and uses the projected points as optimal directions of improvement. Using data from US commercial banks following the Dodd-Frank Act's relaxation, we find empirical evidence of convergence in operations while controlling for banks' regulatory responses. Core banking operations shift towards a safer mode of operations, arguably to improve capital adequacy. Our findings are validated for banks' risk and profitability while carry important policy implications, since regulatory profiling seems to matter the most for smaller banks.

This paper introduces a dynamic portfolio selection approach that integrates the robustness of interval type-2 fuzzy sets (IT2FSs) with the flexibility of the MARCOS (Measurement of Alternatives and Ranking according to COmpromise Solution) method, offering a novel framework for asset evaluation amidst uncertainty. The IT2FSs enhance the adaptability of asset criteria representation, while the innovative application of MARCOS within the IT2FS environment refines the asset selection process. The weighted semi-absolute deviation metric is embraced to capture portfolio risk characteristic, which ingeniously harnesses the utility function values derived from the IT2F-MARCOS framework to delineate the anticipated return profile. On this basis, a dynamic bi-objective portfolio allocation model with realistic constraints and dynamic risk preference is formulated to rebalance portfolio periodically. Empirical evidence demonstrates the robustness and effectiveness of this approach compared to benchmark indexes, different allocation strategies, and the TOPSIS-based selection method, offering significant advancements in portfolio optimization for investors navigating uncertain markets. This study endeavors to contribute to the field of portfolio management, providing a thoughtful approach that enhances both theoretical understanding and practical application.

Firms are the fundamental units driving the development of regional systems, and their enhancement in overall strength determines the effectiveness of industrial collaborative innovation networks, serving as a crucial support for the high-quality development of urban agglomerations. In the digital era, how firms can improve their overall strength to enhance their supporting role in the construction of regional industrial networks is a matter of significant interest. Therefore, as a new driving force for firm development, exploring the impact of digital technology on firm development quality is of great importance. From the perspective of the enabling effects of digital technology, this paper takes A-share listed firms within China's five major urban agglomerations as the research subjects, utilizing panel data from 2012 to 2022. Through theoretical analysis and empirical testing using various econometric methods—including the fixed effects model, two-stage least squares regression model, and mediation effect model—the study investigates the impact of digital technology application on firm development quality and its underlying mechanisms. This study finds that the application of digital technology significantly enhances the quality of firm development, and as the level of digital technology application deepens, firms' development continues to improve. From the perspective of enabling factors, digital technology application can promote the improvement of firm development quality by activating innovation vitality, alleviating financing constraints, and reducing human resource management costs. The heterogeneity analysis results reveal that the impact of digital technology application on firm development quality exhibits various heterogeneous characteristics, including differences in urban agglomeration, ownership structure, and firm size.