Oct 2026· Advances in Economics, Management and Political Sciences· 0 citations
Financial Markets and Investment Strategies
Abstract
Using a panel of 45,385 firm-year observations of Chinese A-share listed firms over 2010-2025, this paper examines whether firm-level artificial intelligence (AI) development affects stock price crash risk. Two-way fixed effects estimates show that a higher level of corporate AI development significantly reduces both the negative conditional skewness and the down-to-up volatility of firm-specific returns. The finding survives an instrumental variable strategy, the exclusion of turbulent years, alternative measures of the dependent and the explanatory variables, and alternative specifications. Mechanism tests indicate that AI eases financing constraints and raises total factor productivity, while financing constraints amplify and productivity dampens crash risk. Heterogeneity tests indicate that the mitigating effect is concentrated in non-high-tech firms and in firms located in economically developed regions. The study extends the assessment of the economic consequences of AI from real-economy outcomes to extreme downside risk in capital markets.
This paper investigates developing real-world perception for active agents, proposes Gibson Environment for this purpose, and showcases a set of perceptual tasks learned therein.
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GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations...
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An algorithm is proposed which leverages disentangled semantic factors to generate adversarial perturbation by altering controlled semantic attributes to fool the learner towards various "adversarial" targets.
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This work proposes a HALO that directly maximizes the utility of generations instead of maximizing the log-likelihood of preferences, and suggests that the best loss depends on the inductive biases most appropriate for a given setting, an oft-overlooked consideration.
This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.
This work shows that orders of magnitude enhancement in performance could be obtained by a combination of hardware improvements and tight quantum-HPC integration and introduces high-performance architectures for quantum-probabilistic computing with custom-designed accelerators to tackle today's industry-scale classical...
Masoud Mohseni, Artur Scherer, K. Johnson et al.· arXiv.org· 121 citations· ⚡9
