Iranian Journal of Finance

Abstract This study investigates the causal relationship between uncertainty shocks and factor premia in the US equity market. While traditional asset pricing models rely on correlation-based factor structures, this research argues that identifying true alpha necessitates a causal framework. Employing a novel combination of Double Machine Learning (DML) and Causal Forest algorithms on Kenneth French's five-factor data and VIX-based uncertainty shocks from 2001 to 2025, robust evidence is provided demonstrating that uncertainty shocks exert significant causal effects on factor returns. The findings reveal that the market factor (MKT-RF) and the size factor (SMB) exhibit strong negative causal responses to uncertainty shocks. In contrast, the profitability factor (RMW) demonstrates a positive and significant effect. The value (HML) and investment (CMA) factors, however, show no statistically detectable causal effect. Substantial heterogeneity in these effects is documented across high- and low-volatility regimes, with the market's negative response being substantially larger in high-uncertainty periods. From an economic significance perspective, a long-only trading strategy based on causal signals delivers a competitive Sharpe ratio of 0.437, with a substantially lower maximum drawdown (-32.5%) compared to the market portfolio (-51.4%). The results survive rigorous robustness checks and statistical tests, providing evidence consistent with a causal effect of uncertainty shocks on factor premia. This study contributes to the growing literature on causal machine learning in finance and offers practical implications for risk management and portfolio construction.

Abstract 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.