This study develops a comprehensive methodological framework for identifying and quantifying investor echo chambers in online stock discussion forums. Motivated by a dynamic model of endogenous echo chamber formation, which formalizes how investors optimally allocate attention and update beliefs under cognitive and informational constraints, we construct a two-layer multiplex investor network that integrates common-attention similarity and semantic similarity to jointly capture the informational and cognitive linkages among investors. This framework enables the systematic examination of how shared information sources and convergent opinions emerge within investor communities. We compute both community-level and individual-level (node-level) echo-chamber intensity by integrating measures of social homophily, semantic reinforcement, and community insularity. At the firm level, we further aggregate these micro-level indicators using attention-weighted indices, community concentration (HHI), and semantic polarization metrics to characterize how echo-chamber dynamics manifest in firm-related discussions. In addition, we propose a general empirical panel framework to examine the relationship between investor echo-chamber intensity and firm-level outcomes. Overall, this paper provides a methodological foundation for the broader Investors’ Echo Chamber Project, offering scalable tools for network-based behavioral analysis and laying the groundwork for future research linking online social dynamics, financial market efficiency, and corporate decision-making.

Cross-firm momentum effects via shared analyst coverage are well-documented in de-veloped markets, but their robustness remains unclear in emerging markets, where information diffusion is asymmetric and analyst coverage is highly concentrated. Our work revisits this effect in an environment of extreme informational frictions — the Chinese market. We reconstruct the information transmission channel within the an-alyst coverage network by introducing a novel weighting scheme based on strength centrality (SC). This measure identiffes inffuential leader firms that command dis-proportionate attention from both analysts and the market. Our results demonstrate that SC-weighted connected-firm returns robustly predict cross-sectional stock returns, yielding significant and persistent profits even under a rigorous stock filter. This per-formance cannot be subsumed by strategies based on alternative weighting schemes or by explanations such as intra-industry cross-firm momentum and information discreteness. Further analysis reveals that the superiority of the SC-based approach stems from its ability to effectively identify firms with stronger cross-period fundamental linkages. In addition, high-SC stocks are characterized by higher investor attention, more efficient information processing, lower arbitrage costs, and greater internationa exposures. With this evidence, we further confirm a directional spillover: cross-firm momentum effects flow exclusively from these high-SC leaders to low-SC laggards, and there is no reverse spillover. Our findings suggest that cross-firm momentum may be systematically underestimated in many international markets due to methodological limitations rather than economic irrelevance. The SC-based framework therefore of-fers a portable tool for global investors and researchers operating in environments with asymmetric information.

This study investigates the mean-reversion behavior of trading volume, using China’s A-share market as a representative setting characterized by dispersed retail investors, frequent public disclosures, and active policy interventions. We compare two competing interpretations:the stealth-trading hypothesis, in which persistent volume reflects order-splitting by informed investors, and the informational efficiency hypothesis, which links faster volume reversion to more effective information processing. Using the Ornstein–Uhlenbeck (OU) model, we estimate reversion speeds for over 3,000 stocks and relate these to firm- and industry-level characteristics. We find that trading volume is broadly mean-reverting, with over 98% of stocks exhibiting stationarity. The OU model forecasts reversion speed with less than 7% error. Faster reversion is associated with larger firm size, greater analyst coverage, lower volatility, and higher liquidity. Notably, reversion speed increased after accounting reforms but declined following capital access liberalization, suggesting that regulatory policy can both enhance and impair informational efficiency. These findings position reversion speed as an observable proxy for market responsiveness and highlight trading volume as a central variable in empirical market microstructure research.

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。

中国利率研究长期存在利润率测度不规范、分析框架碎片化、LPR 改革后短样本识别方法缺失、结果不可复现等共性难题。本文经过多轮数据口径校准与计量方法比对，构建覆盖指标测度、理论框架、计量识别、结构分解的标准化研究工具。本文立足马克思主义政治经济学，以加权行业利润率作为社会平均利润率的实证代理，构建内生利润、美元收益、金融刚性三重约束统一框架。基于 2019Q3—2026Q1 官方实测数据，将全社会利润拆分为四大板块，采用国家统计局官方年度总资产动态权重，通过 IMF 标准 Denton 比例插值法完成年度数据季度化转换，引入官方原始季度指标作为非插值对照，开展三层时序实证检验，结合格兰杰因果检验、SVAR 递归识别与 2022Q4 地产周期拐点 Chow 断点检验强化因果识别。研究发现，加权行业利润率构成我国利率体系的本源约束，其持续下行直接挤压利率可调节空间；实体产业刚性利润锚定长期趋势，房地产高弹性利润主导短期波动且周期下行期主导作用进一步强化；市场化利率对利润率变动敏感度更高，政策利率因三重边界锁死呈现显著钝化特征。本文为货币政策提供可量化监测框架与操作抓手，形成的纯白箱可复现工具包可为后续相关研究提供标准化基准。

2014 年我国货币信用体系实现信用创造机制范式转型，正式进入以不动产为核心载体的抵押品经济时代。本文识别出中国不动产抵押品的核心结构参数，将其定义为不动产抵押品非对称乘数（Collateral Asymmetry Multiplier，CAM），其中枢估计值为2.37，95%置信区间为[2.16，2.55]。研究选取2001—2025年宏观数据，构建内嵌时变摩擦的不动产抵押品经济模型，综合采用 Bai-Perron 断点检验、NARDL非对称协整模型与历史地理外生工具变量实证识别。检验结果显示，2014年是信用锚转型的显著结构性断点；不动产抵押品下行收缩效应为上行扩张效应的2.37倍，高市场化区域强度放大至3.02倍；不动产抵押品价值波动通过资产负债表渠道抑制居民可选消费与企业投资，动产融资体系缺失持续放大非对称冲击。基于 CAM 参数的识别，提出差异化区域化宏观审慎方案，为信用周期调控提供量化依据。

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。

In emerging markets characterized by signiffcant information asymmetry, mitigat-ing firm-level risk is paramount for market stability. While the governance role ofinstitutional investors is known, the impact of their direct, on-the-ground engagementremains underexplored. This study’s objective is to investigate how institutionalinvestor site visits, a crucial hands-on governance mechanism, affect stock returnvolatility. Using a sample of Chinese-listed A-share firms from 2012 to 2022, wefind that frequent site visits significantly reduce firm-level stock return volatility.This risk-reduction effect is more pronounced for firms with greater agency problems,poorer ESG performance, and higher expropriation risk. Our analysis, robust toendogeneity concerns, indicates this effect is driven by improved external oversight.We conclude that direct institutional engagement is a vital channel for reducinginformation asymmetry, enhancing corporate governance, and ultimately promotingmarket stability by lowering investment risk.

We examine price discovery after irregularly scheduled macroeconomic announce-ments. Exploiting time variation in Chinese macro announcements released outside regular trading hours, this paper isolates the role of elapsed non-trading time in facilitating investor learning and price discovery upon market reopening. We show that longer non-trading intervals generate more efficient post-announcement price discovery, reduce information asymmetry, and diminish subsequent intraday return reversals. The mechanism operates through enhanced retail investor learning: during non-trading hours, retail investors actively acquire information, subsequently trade more aggressively, earn higher profits, and face reduced informational disadvantages at market opening. Our findings highlight that retail investor learning during non-trading hours levels the informational playing field among heterogeneous investors and improves price quality around irregularly timed macroeconomic announcements. These results have broader implications for emerging markets, which similarly feature irregular announcement timing and large populations of uninformed retail investors.

This study constructs a three-dimensional space model based on fund investment styles, using a sample of open-end equity and mixed funds from 2005 to 2021 to measure the degree of style convergence. The research explores how style convergence impacts fund managers’ window dressing behavior. The results indicate that, after accounting for the effects of fund performance, style convergence exacerbates window dressing behavior among fund managers. Specifically, this is reflected in fund managers increasing their holdings in winning stocks and selling off losing stocks, which indirectly highlights the intense competition within China’s open-end fund industry. The findings remain robust after a series of endogeneity and robustness tests. Further analysis reveals that style convergence contributes to the risk of client attrition, thereby intensifying the agency problem within the fund industry. The window dressing effect due to style convergence is particularly pronounced in funds managed by individuals with lower educational backgrounds, lower investment skills, smaller family sizes, and lower institutional investor ownership. The paper offers valuable insights into the agency problems arising from investment style convergence and provides guidance for mitigating fund managers' self-interested behavior.