The role of Market-incentive environmental regulation (MIER) within the framework of environmental governance is patently evident. While extant literature lauds the advantageous outcomes attributed to the environmental protection tax (EPT) which as a representative of MIER, our empirical inquiry presents a contrasting narrative. By employing the sophisticated Difference-in-Difference-in-Difference (DDD) methodology and utilizing data from A-share listed firms in Shanghai and Shenzhen from 2015-2022, our investigation reveals a significant decrease in firms’ total factor productivity (TFP) following the implementation of EPT. Our core assertion is fortified through the discernment of two plausible mechanisms, namely, the production downsizing effect and the production capital crowding-out effect. Building upon this revelation, we delve into the nuanced pathways through which firms can strategically mitigate the impacts of EPT, encompassing the enhancement of human capital, amplification of research and development (R&D) investments, and fortification of overall firm resilience. Heterogeneity analysis discloses a notably heightened impact of EPT on TFP of state-owned enterprises (SOEs), larger enterprises and enterprises located in eastern regions. Ultimately, an approximately cost-benefit analysis conclusively demonstrates that the benefits derived from EPT far surpass the costs incurred by the concomitant industrial output reduction, which further illustrates the rationale for the implementation of EPT.

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.

This paper examines the impact of asset bubbles on innovation and long-run economic growth within a semi-endogenous growth framework, incorporating idiosyncratic productivity shocks and endogenous credit constraints in the R&D sector. It demonstrates that pure bubbles tied to intrinsically useless assets and equity bubbles linked to intermediate goods firms can coexist, relaxing credit constraints and boosting entrepreneurs’ total factor productivity (TFP), which stimulates R&D and enhances growth along the transitional path. However, these bubbles generally do not influence the long-run economic growth rate. The model’s mechanisms and predictions are supported by aggregate and firm-level evidence, showing a positive correlation between equity bubbles and R&D investment, with stronger effects during periods of tightened financial constraints.

We study how financial markets integrate news about pollution abatement costs into firm values. Using China’s automation of pollution monitoring, we find that firms with factories in bad-news cities---cities that used to report much lower pollution than the automated reading---see significant declines in stock prices. This is consistent with the view that investors expect firms in high-pollution cities to pay significant adjustment and abatement costs to become “greener.” However, the efficiency with which such information is incorporated into prices varies widely---while the market reaction is quick in the Hong Kong stock market, it is considerably delayed in the mainland ones, resulting in a drift. The equity markets expect most of these abatement costs to be paid by private firms and not by state-owned enterprises, and by brown firms and not by green firms.

Banks have a significant funding-cost advantage if their liabilities are protected by bailout guarantees. We construct a corporate finance-style model showing that banks can exploit this funding-cost advantage by just intermediating funds between investors and ultimate borrowers, thereby earning the spread between their reduced funding rate and the competitive market rate. This mechanism leads to a crowding-out of direct market finance and real effects for bank borrowers at the intensive margin: banks protected by bailout guarantees induce their borrowers to leverage excessively, to overinvest, and to conduct inferior high-risk projects. We confirm our model predictions using U.S. panel data, exploiting exogenous changes in banks' political connections, which cause variation in bailout expectations. At the bank level, we find that higher bailout probabilities are associated with more wholesale debt funding and lending. Controlling for loan demand, we confirm this effect on bank lending at the bank-firm level and find evidence on loan pricing consistent with a shift towards riskier borrower real investments. Finally, at the firm level, we find that firms linked to banks that experience an expansion in their bailout guarantees show an increase in their leverage, higher investment levels with indications of overinvestment, and lower productivity.

This study shows how price limit rules, intended to stabilize markets, inadvertently distort price dynamics by fostering strategic speculation. Through a dynamic rational expectations model, we demonstrate that price limits induce post limit-up price jumps by impeding full information incorporation, enabling speculators to artificially push prices to upper bounds and exploit uninformed traders. The model predicts two distinct patterns: (1) stocks closing at price limits exhibit positive overnight returns followed by long-term reversals, and (2) stocks retreating from upper bounds suffer sharp reversals with partial recovery. Empirical analysis confirms these predictions. A natural experiment from China’s 2020 GEM reform —- which widened the price limit -— further provides causal evidence that relaxed limits mitigate speculative distortions.

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.

This paper provides evidence that some seasoned equity offerings are motivated by public information. We test this channel in the supply chain setting, where supplier managers are more attentive than outside investors to customer news. We find that supplier firms are more likely to issue seasoned equity when their customer firms have negative earnings surprises. The results are mitigated when there is common scrutiny on the customer-supplier firm pairs by outside investors and analysts. Furthermore, long-run stock market performance appears to be worse for firms that issue seasoned equity following the negative earnings surprise of their customer firms.

This study examines the economic consequences of China’s Three-Red-Lines policy—introduced in 2021 to cap real estate developers’ leverage by imposing strict thresholds on debt ratios and liquidity. Developers breaching these thresholds experienced sharp declines in financing, land acquisitions, and financial performance, with privately-owned developers disproportionately affected relative to state-owned firms. Using granular project-level data, we document significant drops in sales and a demand shift from private to state-owned developers. The policy also reduced local governments’ land sale revenues, prompting greater reliance on hidden local government financing vehicles for land purchases. The policy induced broad structural changes in China’s housing and land markets.

We construct a novel risk-based similarity peer network by applying machine learning techniques to extract a comprehensive set of disclosed risk factors from firms' annual reports. We find that a firm's future returns can be significantly predicted by the past returns of its risk-similar peers, even after excluding firms within the same industry. A long-short portfolio, formed based on the returns of these risk-similar peers, generates an alpha of 84 basis points per month. This return predictability is particularly pronounced for negative-return stocks and those with limited investor attention, suggesting that the effect is driven by slow information diffusion across firms with similar risk exposures. Our findings highlight that the risk factors disclosed in 10-K filings contain valuable information that is often overlooked by investors.