arXiv subjects

Congluo Xu

Publications and source records attributed to Congluo Xu.

3 recordsLinked to original sources

arXiv2026

Dissipation of Debt Financing Privilege on Corporate AI Washing: Evidence from China

The rapid development of artificial intelligence motivates firms to engage in AI washing. This study examines whether strategic policy shocks increase debt financing costs for such firms. Leveraging China's 14th Five Year Plan as a quasi natural experiment, we identify AI washing through the residual between AI narrative intensity and patent output. External validation confirms this decoupling reflects strategic deception evidenced by subsidy extraction and future regulatory violations rather than benign ambition, supporting its validity as an AI washing proxy. Difference in differences estimations reveal that AI washing firms experience a 12.5 basis point relative increase in debt financing cost afterward. Joint estimation confirms simultaneous adjustments across financing and innovation margins. Management shareholding and analyst attention amplify the penalty while supply chain concentration and bank proximity attenuate it. Results remain robust across checks. Our findings illuminate how macro level policy shocks activate market discipline in emerging market debt markets.

econ.GN↗

arXiv2025

DeepGreen: Effective LLM-Driven Greenwashing Monitoring System Designed for Empirical Testing -- Evidence from China

Motivated by the emerging adoption of Large Language Models (LLMs) in economics and management research, this paper investigates whether LLMs can reliably identify corporate greenwashing narratives and, more importantly, whether and how the greenwashing signals extracted from textual disclosures can be used to empirically identify causal effects. To this end, this paper proposes DeepGreen, a dual-stage LLM-Driven system for detecting potential corporate greenwashing in annual reports. Applied to 9369 A-share annual reports published between 2021 and 2023, DeepGreen attains high reliability in random-sample validation at both stages. Ablation experiment shows that Retrieval-Augmented Generation (RAG) reduces hallucinations, as compared to simply lengthening the input window. Empirical tests indicate that "greenwashing" captured by DeepGreen can effectively reveal a positive relationship between greenwashing and environmental penalties, and IV, PSM, Placebo test, which enhance the robustness and causal effects of the empirical evidence. Further study suggests that the presence and number of green investors can weaken the positive correlation between greenwashing and penalties. Heterogeneity analysis shows that the positive relationship between "greenwashing - penalty" is less significant in large-sized corporations and corporations that have accumulated green assets, indicating that these green assets may be exploited as a credibility shield for greenwashing. Our findings demonstrate that LLMs can standardize ESG oversight by early warning and direct regulators' scarce attention toward the subsets of corporations where monitoring is more warranted.

cs.CL↗

arXiv2025

FinArena: A Human-Agent Collaboration Framework for Financial Market Analysis and Forecasting

To improve stock trend predictions and support personalized investment decisions, this paper proposes FinArena, a novel Human-Agent collaboration framework. Inspired by the mixture of experts (MoE) approach, FinArena combines multimodal financial data analysis with user interaction. The human module features an interactive interface that captures individual risk preferences, allowing personalized investment strategies. The machine module utilizes a Large Language Model-based (LLM-based) multi-agent system to integrate diverse data sources, such as stock prices, news articles, and financial statements. To address hallucinations in LLMs, FinArena employs the adaptive Retrieval-Augmented Generative (RAG) method for processing unstructured news data. Finally, a universal expert agent makes investment decisions based on the features extracted from multimodal data and investors' individual risk preferences. Extensive experiments show that FinArena surpasses both traditional and state-of-the-art benchmarks in stock trend prediction and yields promising results in trading simulations across various risk profiles. These findings highlight FinArena's potential to enhance investment outcomes by aligning strategic insights with personalized risk considerations.

cs.CE↗