We investigate the capabilities of Large Language Models (LLMs) in generating novel alpha factors for option returns. Utilizing a structured prompt-engineering approach, LLMs like GPT-5 can directly create factors for two distinct options markets: the mature U.S. market and the emerging Chinese market. Empirical analysis further reveals that the LLM-generated factors exhibit remarkable and robust performance, delivering statistically signifcant returns in both all-sample and extensive out-of-sample tests. Beyond their statistical signifcance, such factors are economically meaningful. They display low self-correlation, indicating genuine innovation, and are grounded in sound economic rationale derived from market microstructure and behavioral fnance principles, showcasing a key advantage over traditional machine learning models.

We document “foreign bias” in AI financial predictions, reversing the classic home bias. U.S.-based ChatGPT is systematically more optimistic than China-based DeepSeek about Chinese firms—in price predictions and directional forecasts—yet significantly less accurate. Evidence supports an information-availability mechanism: bias is strongest when U.S. media coverage of Chinese firms is limited and attenuates for cross-listed firms. Crucially, injecting Chinese news eliminates the prediction gap. Both models produce similar forecasts for U.S. firms, consistent with broader worldwide coverage. LLMs trained in different information environments can create divergent signals, with implications for investors and policymakers as AI increasingly intermediates global markets.

This paper examines how AI investment and data assets affect corporatecredit risk. Using Chinese listed firms, we construct four complementary measures ofAI investment, asset-based, labor-based, LLM-based, and text-based, and link them tofirms’ distance-to-default. We find that benchmark-level AI investment reduces defaultrisk, while excessive ffrm-speciffc investment increases it by eroding profitability andreffecting risk-taking and competitive pressure. The dominance of this adverse effectyields a negative overall relation between AI investment and credit risk. Cash flow riskis the transmission channel: benchmark-level AI improves cash ffow quality, whereasexcessive investment worsens it. High-quality data assets complement benchmark-levelAI by stabilizing cash ffow, but this benefit fades once investment becomes excessive.Overall, the impact of AI on credit risk depends on both investment intensity and dataquality, operating primarily through cash flow dynamics.

This paper develops an autonomous framework for systematic factor investing via agentic AI. Rather than relying on sequential manual prompts, our approach operationalizes the model as a self-directed engine that endogenously formulates interpretable trading signals. To mitigate data snooping biases, this closed-loop system imposes strict empirical discipline through out-of-sample validation and economic rationale requirements. Applying this methodology to the U.S. equity market, we document that long-short portfolios formed on the simple linear combination of signals deliver an annualized Sharpe ratio of 2.75 and a return of 54.81%. Finally, our empirics demonstrate that self-evolving AI offers a scalable and interpretable paradigm.

This study applies large language models (LLMs) to measure technological links and examines its predictive power in the Chinese stock market. Using the BAAI General Embedding (BGE) model, we extract semantic information from patent textual data to construct the technological momentum measure. As a comparison, the measure based on traditional International Patent Classification (IPC) is also considered. Empirical analysis shows that both measures significantly predict stock returns and they capture complementary dimensions of technological links. Further investigation through stratified analysis reveals the critical role of investor inattention in explaining their differential performance: in stocks with low investor inattention, IPC-based measure loses its predictive power while BGE-based measure remains significant, indicating that straightforward information is fully priced in while complex semantic relationships require greater cognitive processing; in stocks with high investor inattention, both measures exhibit predictability, with BGE-based measure showing stronger effects. These findings support behavioral finance theories suggesting that complex information diffuses more slowly in markets, especially under significant cognitive constraints, and demonstrate LLMs’ advantage in uncovering subtle technological connections that traditional methods overlook.

Enterprise digital transformation has become an inevitable trend in the digital economy era that can significantly impact enterprises. This paper takes the data of A-share listed companies from 2006 to 2022 as a sample to explore the effect of enterprise digital transformation on listed companies' corporate social responsibility and the mechanism of its role. It was found that corporate digital transformation can significantly enhance Csr(Corporate social responsibility), and enterprise digital transformation has a noticeable enabling effect on Csr, which can dramatically improve Csr. The relationship between the two still holds after the robustness test. It has been found that digital transformation can affect Csr by enhancing the green innovation capability of enterprises, the fairness of internal compensation distribution, and the sustainable development capability of enterprises. Heterogeneity analysis reveals that corporate digital transformation's impact on Csr fulfillment performance is more significant for non-state-owned firms and firms in the central and eastern regions. In addition, corporate financing constraints and government innovation subsidies influence Csr.

We examine whether large language models (LLMs) can extract contextualized representation of Chinese news articles and predict stock returns. The LLMs we examine include BERT, RoBERTa, FinBERT, Baichuan, ChatGLM and their ensemble model. We find that tones and return forecasts extracted by LLMs from news significantly predict future returns. The equal- and value-weighted long minus short portfolios yield annualized returns of 90% and 69% on average for the ensemble model. Given that these news articles are public information, the predictive power lasts about two days. More interestingly, the signals extracted by LLMs contain information about firm fundamentals, and can predict the aggressiveness of future trades. The predictive power is noticeably stronger for firms with less efficient information environment, such as firms with lower market cap, shorting volume, institutional and state ownership. These results suggest that LLMs are helpful in capturing under-processed information in public news, for firms with less efficient information environment, and thus contribute to overall market efficiency.

A strong reputation is a cornerstone of corporate finance theory, widely believed to relax financial constraints and lower capital costs. We challenge this view by identifying an ‘reputation paradox’: under modern risk-sensitive regulation, for firms with long-term liabilities, a better reputation may paradoxically increase capital strain. We argue that the improvement of firm’s reputation alters customer behavior , , which extends liability duration and amplifies measured risk. By using the life insurance industry as an ideal laboratory, we develop an innovative framework that integrates LLMs with actuarial cash flow models, which confirms that the improved reputation increases regulatory capital demands. A comparative analysis across major regulatory regimes—C-ROSS, Solvency II, and RBC—and two insurance products, we further demonstrate that improvements in reputation affect capital requirements unevenly across product types and regulatory frameworks. Our findings challenge the conventional view that reputation uniformly alleviates capital pressure, emphasizing the necessity for insurers to strategically align reputation management with solvency planning.

在我国资本市场中，政策作为宏观调控的重要工具，对行业资产价格具有显著影响。本文尝试将政策文本纳入金融文本分析框架，构建政策——行业相似度指标体系，识别政策支持导向，并探讨其在行业定价中的作用机制。文章构建了涵盖多层级政策的文本数据库，分别采用传统模型（LDA和LSA）与大语言模型（LLM）识别政策中的行业提及频次，测算政策——行业相似度指数，并结合行业收益数据构建策略。文章进一步引入支持向量回归（SVR）识别不同行业的最优政策滞后期，提升策略表现。实证结果表明：LLM模型在政策主题提取上明显优于传统方法，基于政策相似度构建的行业策略在多阶段均展现出稳健的超额收益，且政策对行业的影响有长期滞后效应，行业反应通常在政策发布半年后。考虑现实市场约束，基于最优滞后窗口构建的单边多头策略也表现优秀，具备良好实用性，特别是在政策密集期（如2015、2020年）表现突出。本文的研究为政策信号的量化研究与行业资产配置提供了新的方法与实证支持。

Reputation is widely regarded as a stabilizing factor in financial institutions, reducing capital constraints and enhancing firm resilience. However, in the insurance industry, where capital requirements are shaped by solvency regulations and policyholder behavior, the effects of reputation on capital management remain unclear. This paper examines the unintended consequences of reputation in insurance asset-liability management, focusing on its impact on capital allocation. Using a novel reputation risk measure based on large language models (LLMs) and actuarial models, we show that reputation shifts influence surrender rates, altering capital requirements. While higher reputation reduces surrender risk, it increases capital demand for investment-oriented insurance products, whereas protection products remain largely unaffected. These findings challenge the conventional wisdom that reputation always eases capital constraints, highlighting the need for insurers to integrate reputation management with capital planning to avoid unintended capital strain.