Yu Wei

Purpose: This study aims to examine whether the adoption of artificial intelligence (AI) can alleviate financial mismatches and enhance capital allocation efficiency through firms' investment decisions, particularly in the context of rapid digitalization and structural financial reforms. Design/methodology/approach: The study develops a “technology–efficiency–allocation” analytical framework to explain how AI promotes investment efficiency and mitigates financial mismatches. Using the Skip-gram model in Word2vec, firm-level indicators of AI application are constructed to overcome the biases of patent-based or industry-level measures. Inefficient investment is introduced as an instrumental variable, and a double machine learning (DML) framework is employed to control for high-dimensional covariates and specification errors, ensuring credible causal identification. Findings: The study develops a “technology–efficiency–allocation” analytical framework to explain how AI promotes investment efficiency and mitigates financial mismatches. Using the Skip-gram model in Word2vec, firm-level indicators of AI application are constructed to overcome the biases of patent-based or industry-level measures. Inefficient investment is introduced as an instrumental variable, and a DML framework is employed to control for high-dimensional covariates and specification errors, ensuring credible causal identification. Originality/value: This study provides firm-level empirical evidence clarifying AI's role in reducing financial mismatches and improving capital allocation efficiency. It extends the literature by integrating advanced textual measurement of AI application with causal machine learning methods and offers practical implications for promoting technological integration and enhancing financial efficiency in emerging economies.

Crude oil markets are characterized by noise and multifractal features, which undermine the reliability of traditional hedge ratio estimation models. Therefore, this study develops a denoising-multifractal dual intelligent integration framework for estimating optimal hedge ratios in the Shanghai crude oil futures on the Shanghai International Energy Exchange (INE) and crude oil spot markets, with a primary focus on minimizing spot risk while enhancing hedging returns. The framework begins with an innovative use of the Complementary Ensemble Empirical Mode Decomposition (CEEMD) approach to remove high-frequency noise, improving data quality. It then employs the Multifractal Detrended Cross-Correlation Analysis (MF-DCCA) method to capture the multifractal structure of crude oil futures and spot markets. Building on these results, the Flower Pollination Algorithm (FPA) is employed to integrate hedge ratios in a two-stage manner. First, local hedge ratios are aggregated across time scales within each fluctuation level. Second, hedge ratios are further integrated across different fluctuation amplitudes. This unique design allows the model to fully exploit the multi-scale and multi-fluctuation information for deriving the optimal hedge ratios. Empirical analysis confirms the existence of significant noise and multifractal properties in crude oil markets. Moreover, the hedging results show that the proposed model outperforms all competing methods, achieving higher accumulated returns, Hedging Effectiveness (HE), and Sharpe ratios in most cases. The study offers an effective hedge ratio estimation method for investors in the INE crude oil futures and spot markets.

This paper investigates the diversification effects of China's new photovoltaic weather index (PVWI) on commodity portfolios. Using a TVP‐VAR spillover analysis, we find the PVWI is highly insulated from the commodity system, acting as a net shock receiver and confirming its diversification potential. We then construct novel “minimum‐connectedness portfolios” (MCoP). Results show that portfolios incorporating the PVWI, particularly the MCoP, significantly enhance risk‐adjusted returns and demonstrably outperform traditional minimum‐variance benchmarks. This study validates the practical benefits of well‐designed weather derivatives and the superiority of spillover‐aware asset allocation frameworks.

Purpose: This study aims to explore the impacts of U.S. debt ceiling uncertainty on crude oil markets and further reveal the specific influence mechanisms. Design/methodology/approach: This paper introduces a debt ceiling uncertainty index based on news reports and selects six representative crude oil futures and spot markets to investigate the heterogeneous impacts of U.S. debt ceiling uncertainty on crude oil markets. More specifically, on the one hand, the nonparametric causality-in-quantiles test method is used to discuss the asymmetric impacts of debt ceiling uncertainty on the different conditional distributions of crude oil series. On the other hand, the dynamic effects of debt ceiling uncertainty on crude oil markets are analyzed, combining with the time-varying parameter vector autoregressive model. Findings: The conclusions of this paper are as follows: First, the U.S. debt ceiling uncertainty has obvious nonlinear impacts on each crude oil market, and the effects are greater under the normal condition of crude oil markets rather than under their extreme conditions. Second, the shocks from debt ceiling uncertainty to crude oil markets are mainly illustrated as negative and can be significantly enhanced by important debt-related events. Over time, the reactions of crude oil markets turn to weak positive and gradually dissipate after 6 months. Finally, enterprise production, investor sentiment and government shutdown play important roles as transmission intermediaries for the influence of debt ceiling uncertainty on the crude oil market. Originality/value: The findings are beneficial for investors to accurately judge oil price trends and prevent investment losses caused by debt risks and also help producers prevent the impacts of crude oil price changes on production and operation activities. Moreover, it is conducive to the management department to maintain the stability of the crude oil market, thus enabling the crude oil financial market to better serve the real economy.

The relationship between climate risks and commodity markets remains insufficiently explored, especially when analyzed through the lens of high-frequency data. This study seeks to address this gap by investigating the spillover effects of global climate risks, both physical and transitional, on key commodity markets and employs a novel analytical framework. By utilizing newly developed climate risk indices alongside the innovative mixed-frequency spillover measure, this research combines high-frequency climate risk data with the responses of low-frequency commodity prices. Our results highlight notable spillover effects, demonstrating that climate risks serve as the primary drivers of spillovers to commodity markets in a mixed-frequency data context, whereas such effects are not observed within a common-frequency data environment. These findings have important implications for policy-makers and investors, indicating that current market analyses may not capture the influence of climate risk adequately.

As China plays a pivotal role in shaping global energy trends, the healthy development of energy markets faces formidable challenges, making the achievement of carbon neutrality, particularly, crucial. Unfortunately, geopolitical risk (GPR) introduces significant uncertainties for the interactions between China's carbon neutrality process and energy markets. To this end, we explore the connectedness between China's carbon neutrality index and energy futures prices from both return and risk perspectives using the TVP‐VAR method. Additionally, we analyse the impact mechanisms and predictive effects of GPR on the volatility of this connectedness applying the GARCH‐MIDAS method. Our empirical findings reveal several key insights. First, the GPR drives the volatility in connectedness between China's carbon neutrality index and energy futures prices, particularly, within the petrochemical industry, while the opposite impact is observed in certain coal‐related sectors. Moreover, the models indicate that poor GPR predictive effects are primarily evident in industries with a close link to coal energy in their production processes. Secondly, the risk connectedness of China's carbon neutrality index and energy futures prices contains more implicit information and is more responsive to GPR shocks compared to return connectedness. This explains why the GPR is less effective in forecasting the volatility of risk connectedness. Finally, the global GPR is more helpful than the China‐specific GPR in forecasting the volatility of connectedness in China's carbon neutrality index and energy futures prices. These insights reveal the potential vulnerability of China's carbon neutrality and energy markets to GPR, offering actionable guidance for strengthening risk management strategies.