This paper examines whether subjective macroeconomic expectations predict equity risk premia in China. Using professional macro forecasts from the Wind database over 2011- 2023, we construct a composite macroeconomic expectation index via PLS. We find that the index significantly and positively predicts future equity premia both in-sample and out-ofsample, implying a countercyclical equity premium. The predictive power is robust to a wide range of macro-financial controls and delivers economically meaningful gains in portfolio allocation. Further analysis shows that controlling for forecast errors does not attenuate predictability, and the subjective index largely subsumes the information in realized macro variables, suggesting that it captures forward-looking macroeconomic conditions rather than belief bias. Cross-sectional and long-horizon results reinforce this interpretation.

On October 10, 2025, Bitcoin fell 9.1% in thirty minutes on Binance following a geopolitical shock, contributing to a 16.0% peak-to-trough drawdown over 33 hours. Using minute-level Binance and Bybit data across spot, perpetual futures, and mark price feeds for BTC, ETH, and SOL, this paper situates the event within an empirical distribution of 62 BTC drawdowns exceeding 3% over 30 minutes during 2024–2026. October 10 ranks first of 62 on every microstructure metric simultaneously: a SOL futures-spot drawdown gap of 11.0 percentage points (z = +7.65), a BTC basis swing of $1,367 (z = +6.18), and a mark to-spot undershoot of $2,507 (z = −4.21). It also ranks first on multivariate anomaly measures (Mahalanobis distance 7.70; sum of absolute z-scores 45.9, 2.4 times the runner-up). A parsimonious conceptual cascade model identifies the mark price oracle as the crypto-specific amplifier.

This study examines the role of artificial intelligence (AI) adoption in the market for corporate control using a sample of Chinese listed firms from 2011 to 2021. We construct a novel firm-level AI Index through textual analysis of annual reports and find that AI adoption significantly enhances both the likelihood of becoming an acquisition target and the valuation premiums commanded in M&A transactions. Specifically, a one-standard-deviation increase in the AI Index is associated with a significant increase in the probability of being acquired and higher deal premiums measured by price-to-earnings multiples. We identify two channels through which AI adoption creates value recognized by the M&A market: an efficiency channel, whereby AI reduces agency costs and improves profitability, and an innovation channel, evidenced by increased high-quality patent output. The persistence of these effects over time further suggests that AI adoption generates substantive improvements in firm fundamentals rather than serving as a transitory informational signal. Importantly, we document significant heterogeneity across ownership structures: the positive effects of AI adoption are substantially weaker for State-Owned Enterprises (SOEs) than for non-SOEs. Our findings contribute to the literature on digital transformation and corporate finance by demonstrating that AI adoption serves as a value-relevant firm attribute that shapes outcomes in the market for corporate control.

Whether risk aversion is time varying and countercyclical is central to modern asset pricing, yet evidence remains limited and is based mainly on experimental, survey, or aggregate stock market data. We provide individual-level evidence from 10 million Chinese auto insurance contracts from 2011 to 2017, estimating policyholders’ risk aversion from deductible choices. We find that risk aversion is time varying and countercyclical. The estimates are negatively related to lottery and stock trading, positively related to insurance sales and bond trading, and vary with psychological factors, including seasonal mood, “zodiac year,” and calendar events.

Financial crises rarely erupt without warning; they are preceded by long periods of hidden fragility. Yet traditional market indicators such as the VIX capture only realized volatility, offering little foresight. This paper introduces the Stability Gap Model (SGM), developed iteratively from a simple intuition: fragility arises when risk-taking diverges from fundamentals and systemic buffers are insufficient. We trace the evolution of the model from its original formulation, through corrections and extensions, to its present form. The Classic SGM captures instantaneous imbalance, while the Beta SGM incorporates memory of past shocks. Empirical analysis demonstrates that the SGM provided clear early warnings ahead of the 2008 Global Financial Crisis, the 2011 Eurozone debt episode, the 2015 China/oil slowdown, and the 2018 tightening cycle, while also trending upward in 2019 before the COVID-19 crash. Furthermore, the paper demonstrates the model's utility in assessing fragility in hedge funds and proposes its application as a universal framework for stability analysis across diverse systems, from corporate finance to supply chains.

This paper quantifies the impact of rural–urban migration on agricultural fires, a major source of air pollution in China. Using satellite records and census data, we exploit an exogenous trade shock that raised manufacturing labor demand in destination prefec-tures, drawing workers from origin counties through established migration networks. A one-percentage-point increase in rural labor emigration in 2010 led to a 5% rise in agricultural fires from 2011 to 2017. The effect stems from reduced agricultural labor, which incentivizes fire use as a labor-saving clearing method. Consistent with this mech-anism, we find no effect from non-agricultural emigration or from migration to nearby areas where return farming remains feasible, with the strongest impact during harvest seasons. Institutional reforms, particularly land titling programs, significantly mitigate the problem by enhancing tenure security, enabling farmland reallocation, and reducing reliance on fire—proving more effective than nationwide administrative bans.

Investors’ prior experiences with a stock substantially affect their willingness to repurchase it. This paper explores the repurchase effect, a psychological bias in which investors are reluctant to repurchase stocks that have appreciated after a prior sale. To quantify this bias, we develop a novel stock-level measure, termed Repur, and investigate its implications for cross-sectional asset pricing. Our findings show that stocks with higher Repur tend to experience reduced future buying pressure from investors, which in turn results in lower subsequent returns. Economically, long-short portfolios based on Repur yield annualized abnormal returns exceeding 23% for equal-weighted and 11% for value-weighted risk-adjusted returns. Further analyses show that the pricing effect of Repur is more pronounced following periods of high investor sentiment, for stocks with greater arbitrage constraints, and for firms with smaller investor bases. Out-of-sample evidence from China confirms the significant pricing impact of the repurchase effect.

Data-factor marketization (DFM) serves as a critical driver for cultivating manufacturing-enterprise new-quality productive forces (ME-NQPF), fundamentally supporting China's transition toward high-quality economic development. Integrating matched panel data from A-share listed Chinese manufacturing firms (2011–2022) with the staggered establishment of regional data trading platforms as a quasi-natural experiment, this study employs a multi-period difference-in-differences (DID) framework to identify the causal impact of DFM on ME-NQPF. Empirical results demonstrate that DFM significantly enhances ME-NQPF, a finding that remains robust across alternative specifications and endogeneity treatments. Mechanism analysis identifies enterprise digital transformation as a pivotal mediator in this relationship, while competitive intensity is found to positively moderate the productivity gains from data marketization. Heterogeneity analysis further indicates that these effects are most pronounced among non-state-owned enterprises, technology-intensive sectors, and firms situated in China's eastern and central regions. These findings suggest that institutionalizing data-factor markets and accelerating digital integration are effective mechanisms for optimizing resource allocation and sustaining advanced industrial productivity.

货币结构研究长期存在一个核心困惑：1999-2019 年M1/QM存量比始终稳定在 1:2，2020 年后却出现持续性失锚。现有研究大多聚焦M1/M2增速剪刀差的短期周期特征，普遍忽视存量资金收支的底层约束，同时资本收益率与融资成本的核算口径混乱，导致不同研究结论缺乏可比性。本文构建居民-商业银行-实体企业三部门时变参数 DSGE 模型，采用白重恩（2006）国民核算法范式，采用MPK与全实体WACC对偶口径，提炼得到M1,tre,t=QMtrd,t这一收益等价核心恒等式。依托1999Q1-2026Q1跨国时序、全国年度、中国31省份季度面板三层数据，综合多套识别策略，分国别测算资本收益倍数Kt的破裂临界值：中国 1.801、日本 1.785、德国1.922，混合样本临界值1.803可作为类似制度特征工业化经济体的参考基准。当Kt跌破临界阈值时，存量货币持续从经营性活期向定期沉淀。纠正“单纯依靠放宽银行信贷供给就能改善货币结构、降息万能、紧盯历史比值调控”的政策误区。量化测算结果显示，累计新增 11.23 万亿元实体利润可修复收益闭环，经济体将自发形成适配现行制度的全新货币均衡比例。

兴业消费金融2025年在银登中心挂牌转让不良资产401.06亿元，占行业总额的34%，覆盖借款人超31.3万户。2025年净利润12.00亿元，同比增长179.07%，而营收下滑11.71%。基于公开数据的时序分析显示，利润高增与大规模不良资产出清高度吻合，不良出清是会计报表层面利润修复的重要来源，经营效率改善的贡献有待年报明细科目进一步分离。本文基于监管处罚公告、债券募集说明书、银登网转让公告及裁判文书等全公开数据，构建”监管套利—风控外包—资产质量恶化—经营挤出”的分析框架，提出”融资挤压指数”“钱的质地”与”存量吸血效应”等分析工具，测算助贷模式对M1流动性的抽离效应约1941亿元。研究发现，2020年利率窗口指导后样本机构不良率呈现结构性加速趋势；挤压指数达+9至+54个百分点。基于公开数据的结构性分析显示，兴业消金在业务实践中呈现出与母行”利润-风险转换器”职能高度相似的特征，温和的”纠偏式”监管约束效果有限，需向”止血式”干预过渡。