Using 4724 A-share listed firms in the Chinese stock market from 2009 to 2024, we investigate how CPU, an index constructed from textual analysis of major Chinese newspapers, affects investors' behavior, measured by turnover ratios and abnormal returns. The baseline regression shows that higher CPU significantly reduces stock turnover ratio and abnormal returns, indicating heightened investor caution and negative sentiment. Extended regression with provincial-level CPU further supports these findings. Heterogeneity analysis reveal that low-carbon firms, non-state-owned enterprises (non-SOEs), and firms in Central and Western China are more negatively affected by CPU, while high-carbon firms and SOEs are relatively resilient when facing higher CPU, mainly due to expected policy support and institutional investor dominance. Further mechanism analysis uncovers that ESG performance moderates CPU's effects. Firms with higher ESG ratings experience smaller valuation declines but lower trading volumes. In addition, consumer sentiment mediates CPU's effects. CPU suppresses public confidence, which in turn reduces investor participation and firm valuations. These findings highlight the importance of improving policy transparency, promoting standardized ESG disclosure, and mitigating retail investor overreaction. The study contributes to behavioral and climate finance literature by linking policy-driven uncertainty with micro-level investor responses in a key emerging market.

In view of the divergence of existing research results on the relationship between ESG and investment returns, this paper constructs an S-score metric, which comprehensively measures corporate sustainability performance. It further tests the applicability of a sustainability-based investment strategy using this metric in Chinas A-share market. Using Shanghai and Shenzhen A-shares from May 2016 to April 2024 as the research sample, the S-score is constructed across five dimensions: Profitability, Growth Opportunities, Investment Efficiency, Risk Mitigation, and ESG Performance. The S-score is calculated using Z-score standardization and entropy weighted. Strategy effectiveness was tested through univariate grouping, bivariate grouping, and Fama-Macbeth regression, further examining strategy performance under varying market conditions, holding periods, and information environments. The study finds that the Sscore demonstrates significant discriminative power for cross-sectional stock returns. The hedge portfolio based on this metric achieved an annualized excess return of 7.943% after adjusting for the China three-factor (CH-3) model. Its predictive power remains robust after controlling for variables such as market capitalization and bookto-market ratio, delivering significant positive returns across bull and bear markets, extreme pandemic conditions, and holding periods of up to eight years. From a behavioral finance perspective, this paper reveals that explanations such as the gradual diffusion of information and investors limited attention span help elucidate the profitability of the S-score strategy. The findings demonstrate the effectiveness of Sustainable Investing strategies in Chinas A-share market, indicating that ESGintegrated factor investing can optimize resource allocation. This research contributes empirical evidence on Sustainable Investing in emerging markets, providing insights for policy formulation and practical implementation while supporting the virtuous cycle between Sustainable Investing and long-termism.

This study investigates a novel calendar anomaly in the Shanghai Composite Index (SSE Composite) based on the Nayin (纳音) Five Elements system, a cyclical classification embedded in the traditional Chinese sexagenary calendar (Heavenly Stems and Earthly Branches). Each two-year period within the sixty-year cycle is assigned one of five elemental attributes: Earth(土), Metal (金), Fire (火), Wood (木), or Water (水). Using daily trading data from December 1990 to February 2025 spanning 18 complete Nayin cycles, the study finds that Earth-element periods exhibit a 100% positive return rate (mean: +123.4%), while Metal-element periods universally display late-cycle declines (4/4 periods, mean late-segment return: −30.4%). Wood element periods tend toward negative returns (mean: −11.4%). Fire-element periods contain structural bottoms, and Water-element periods mirror the directional momentum of their predecessors (3/3 accuracy). A binomial test for Earth-period returns yields p = 0.095 (one tailed), with Cohen’s d = 1.50 versus non-Earth periods. These patterns are not replicated in the S&P 500 Index under the same calendar framework, suggesting a China-specific cultural behavioral channel consistent with theories of culturally embedded market sentiment. This paper contributes to the calendar anomalies literature by documenting a previously unexplored two year cyclical pattern linked to traditional Chinese metaphysical calendrics.

This article examines the Chinese financial system as an institutional arrangement in which finance operates in alignment with industrial productivity. Drawing on the theoretical foundations of Friedrich List and Gustav Schmoller, the study revisits the German Historical School to interpret contemporary financial development in China. While China hosts one of the largest capital market systems in the world, the article argues that the defining feature of its financial architecture lies in the structural role of the banking sector. Large-scale credit allocation toward manufacturing, infrastructure, and strategic industries suggests that financial institutions function as instruments of long-term capability accumulation and technological upgrading. The findings challenge the assumption that financial liberalization and autonomous capital markets are necessary conditions for industrial growth. Instead, the Chinese case illustrates how finance, when embedded within coordinated institutional structures, can serve as a complementary mechanism supporting productive development.

Against a backdrop of economic transformation and industrial upgrading, financing constraints have become a significant barrier to the growth of microeconomic entities. Investor sentiment, a recognized source of irrationality in capital markets, is understood to influence the financing environment through various channels. Concurrently, advances in generative artificial intelligence (AI) are generating novel methods for the precise measurement of this sentiment. Leveraging data from Chinese A-share listed corporations from 2011 to 2023, this study constructs a double machine learning framework to examine the impact of investor sentiment on financing constraints and its underlying transmission mechanisms. Our findings indicate that elevated investor sentiment significantly reduces these constraints. This effect operates primarily through an enhanced capacity for debt financing, a reduction in market scrutiny, and a stimulation of consumer demand that boosts corporate revenue. Conversely, periods of subdued sentiment tend to trigger risk-averse behavior, intensify liquidity pressures, and further restrict access to capital. A heterogeneity analysis reveals that the effects of sentiment vary across industries, ownership structures, and regional levels of information infrastructure. The impact is particularly pronounced within the real estate sector and in more digitally advanced regions. Interestingly, the effect does not fundamentally differ across ownership types, suggesting a degree of market neutrality. In sum, this study offers fresh empirical evidence on the mechanisms through which sentiment drives financing behavior. It also expands the application of generative AI, demonstrating its value as a tool for financial research.

This paper focuses on China's green financial development context, selects carbon taxes and green credit policies as adjustable fiscal instruments, constructs a four-sector dynamic stochastic general equilibrium (DSGE) model, and innovatively divides the production sector into traditional and clean firms. We then separately investigate the impacts of these two structural policies on green economic development. The impact of structural policies on the development of green economy and emission reduction is investigated individually in terms of each policy. The results of the study show that the positive impacts of green credit and pollution tax rate have a significant inhibiting effect on pollution emissions; for total output, the policy of adjusting the carbon tax rate has a negative impact on economic development, while the negative impacts of targeted subsidy on economic development in the long run are not obvious. Based on this, this paper combines the green development practice to put forward the corresponding policy recommendations for the construction of a green low-carbon development policy system to provide references.

In this paper, we investigate the availability of external finance for state-owned enterprises (SOEs) and private enterprises (PEs) across all manufacturing industries in China. Employing a disequilibrium model, we find that SOEs are generally less financially constrained than PEs, but the difference is of small magnitude and has been steadily declining over time. We also reveal a positive relation between the advantage of SOEs in accessing external funds and the level of SOE monopoly at the industry level. In general, while SOEs and PEs are equally constrained in competitive downstream industries, we find a significant comparative advantage in accessing external finance for SOEs in monopolized mid- and upstream industries. The policy implication is straightforward: to finance PEs better, the Chinese government should consider breaking the existing monopolies or near monopolies of SOEs in certain industries further, rather than encouraging banks to favor PEs in their lending.

We investigate how investors assess transition risks arising from the discretionary regulatory mandates for nature conservation. We focus on the UN Biodiversity Conference in Kunming, China, a policy shock signaling heightened political commitment to protection. During the conference, firms exhibited negative abnormal stock returns on average. Using a machine learning approach, we identify historical nature-related subsidies to capture firm-level variations in expectations of mandated conservation engagement, beyond measuring corporate environmental (ecological) impacts. This metric negatively correlates with returns during the conference, particularly for firms with high operational volatility, and those in regions with limited fiscal flexibility and stringent conservation pressures. Investor concerns primarily stem from policy uncertainty, without positive earning expectations from conservation. Our findings underscore the role of authoritative discretion in shaping nature transition risks.

China’s green-finance expansion shifts attention from how much firms disclose to whether transition claims remain credible when verification tightens. Using a 2007–2022 panel of Chinese A-share listed firms (crash-risk measures available through 2021), we measure transition credibility by decomposing firms’ transition signals into verifiable actions, narrative disclosure, and realized environmental outcomes, and we propose a transparent narrative–action gap measure (WordsMinusActions = WordsIndex − ActionIndex) to capture ‘words without deeds.’ Exploiting the staggered rollout of China’s Central Environmental Protection Inspections (CEPI) as a plausibly exogenous increase in verification and enforcement intensity, we find that firms with high pre-CEPI narrative–action gaps experience a sharp increase in stock price crash risk around inspections: NCSKEW rises by about 0.09 in the inspection year and 0.20 in the following year (≈0.24 SD), and DUVOL rises by about 0.04 and 0.10 (≈0.27 SD) relative to low-gap firms. Consistent with enforcement discipline, these high-gap firms subsequently reduce disclosure intensity and increase verifiable green actions, narrowing the narrative–action gap after CEPI. The evidence supports governance that rewards verifiable action and consistency over disclosure volume in markets.

In recent years, despite China’s sustained expansion of public investment in education, pronounced regional disparities in educational development persist, revealing the practical constraints of the existing fiscal sharing structure in advancing equity objectives. In particular, the mechanism through which the allocation of fiscal responsibilities between central and local governments influences educational equity performance remains insufficiently examined in a systematic and empirical manner. From the perspective of fiscal structure, this study seeks to identify and explicate the evolutionary trajectory of educational fiscal equity in China, as well as its underlying institutional determinants. To this end, the study constructs a fiscal performance evaluation framework comprising seven core indicators. Objective weights are assigned using the entropy method, and the TOPSIS model is subsequently employed to conduct a comparative ranking analysis of national education fiscal performance over the period from 2013 to 2023. The results indicate that educational fiscal equity has exhibited an overall upward trend during the study period; however, its evolution displays clear stage-specific characteristics. In the initial phase, improvements in equity were primarily driven by fiscal expansion, leading to rapid gains. In the subsequent period, progress became increasingly constrained by structural optimization bottlenecks, with fiscal performance gradually stabilizing at a plateau in more recent years. Further analysis reveals that the persistently heavy fiscal burden borne by local governments, coupled with the relatively limited regulatory and equalization capacity of the central government, constitutes a key structural impediment to the sustained improvement of educational equity. By elucidating the internal mechanism through which the allocation of fiscal responsibilities shapes educational equity performance, this study provides both theoretical grounding and quantitative support for deepening fiscal system reform and optimizing policies related to the allocation of educational resources.