درخت حوزه‌های تخصصی

Although previous research has examined the application of artificial intelligence (AI) across various areas of finance, there remains limited empirical evidence regarding its impact on corporate financial asset allocation. This gap is particularly evident when considering the organisational capabilities that enable firms to utilise AI technologies effectively. In the rapidly evolving technological landscape, artificial intelligence (AI) has emerged as a pivotal force driving innovation and transformation within corporate financial management. By embedding AI into organisational processes, companies have fundamentally reshaped their financial decision-making frameworks. However, the exact mechanisms through which AI adoption shapes the allocation of financial assets are still not fully understood. This study examines how artificial intelligence (AI) technologies influence the allocation of financial assets within corporations, with a specific focus on the moderating influence of three dynamic organisational capabilities: absorptive capacity, innovation capability, and adaptability. Based on panel data collected from companies listed on the Tehran Stock Exchange between 2020 and 2024, AI adoption is measured through textual analysis of management commentary reports obtained from the Codal system. The dependent variable comprises a set of financial ratios, including the proportion of financial assets relative to a firm’s total assets. The analysis employs multiple regression models with interaction terms to test the proposed hypotheses. Findings indicate that the adoption of AI substantially enhances the effectiveness of distributing financial assets. Moreover, absorptive capacity and innovation capability strengthen the association between AI adoption and the allocation of financial assets within firms' performance, while adaptability shows no statistically significant moderating effect. These results highlight the importance of both technological infrastructure and internal capabilities for leveraging advanced technologies to their fullest potential. This research not only enriches the academic discourse with fresh empirical insights but also offers valuable implications for financial managers, capital market regulators, and policymakers engaged in organisational digital transformation strategies.

This study investigates the role of economic factors such as total annual water withdrawal, total renewable energy consumption, total greenhouse gas emissions, and waste generation in shaping the trajectory of the circular economy. Utilizing a nonlinear multilevel regression model with combined fixed and random effects, the research examines the impact of these variables on the circular economy index in Iran over the period 1993 to 2023. The choice of this model is justified by its ability to incorporate both fixed effects (such as average variable levels) and random effects (to account for unobserved heterogeneity across observations or units). The results indicate that among the factors influencing the circular economy, renewable energy consumption has a positive and statistically significant effect on circular economy index, with a coefficient of 0.004. In other words, higher renewable energy consumption is associated with improvements in the circular economy index, underscoring the positive role of clean and renewable energy in promoting sustainable economic development. Conversely, water withdrawal, greenhouse gas emissions, and waste generation negatively impact the circular economy, with coefficients of -0.091, -0.02, and -0.003, respectively. This implies that increased unsustainable resource use (such as water extraction), higher greenhouse gas emissions, and greater waste generation hinder the development of a circular economy. Strategic and targeted investment in water-efficient technologies, renewable energy development, greenhouse gas mitigation, and comprehensive waste management can serve as fundamental pillars for advancing the circular economy.

For forty years, LIBOR was the dominant benchmark interest rate for various financial products, playing a central role in global financial markets. LIBOR was available in 10 currencies and 15 maturities, fulfilling two primary roles: acting as a reference rate for financial contracts and serving as a benchmark for evaluating financing costs. However, these rates were not based on actual transaction data but were instead calculated from surveys of participating panel banks. Thomson Reuters computed the rates using a trimmed mean. The LIBOR scandal involved three forms of manipulation by panel banks: under-reporting, over-reporting, and maintaining artificial stability in rates. The Wheatley Review recommended reforming LIBOR instead of replacing it. In March 2021, the UK FCA officially confirmed the gradual elimination of all LIBOR settings. By June 30, 2023, the remaining US dollar LIBOR settings were discontinued. Fines related to the LIBOR scandal surpassed $10 billion. In July 2021, the World Bank announced the transition from LIBOR to new reference rates for both new and existing loans. The main alternatives to LIBOR include SOFR, SONIA, €STR, SARON, TONAR, and AONIA. This paper explores the debate between using a single or multiple reference rates, considering the benefits of a multi-reference system to mitigate systemic risk.