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.

在信息过载的数字经济时代，平台算法驱动的注意力配置机制如何影响资本市场效率，是关乎市场信息生态与平台治理的重要问题。本文基于2020—2025年手工搜集的3693条上市公司相关微博热搜数据及同期全量微博话题数据，系统考察平台注意力配置对资本市场信息效率的影响及其作用机制。研究发现，上市公司登上微博热搜后股价同步性显著下降，股价信息含量得到提升。利用热搜竞争排名特征构造的准热搜对照组表明，话题热度相近但未上榜的公司股价同步性未发生显著变化，说明驱动结果的是榜单带来的公共注意力提升，而非事件本身的信息内容或自然讨论热度。基于清朗行动的双重差分检验表明，平台治理能够通过改变平台注意力配置，对资本市场信息效率产生溢出影响。渠道分析表明，热搜通过激发投资者主动信息搜寻、提高股票流动性和降低信息不对称提升股价信息含量。进一步研究发现，热搜主要影响散户投资者交易行为，且非负面内容的热搜能够显著提升价格效率，而负面热搜的作用有限，表明负面情绪冲击可能削弱公共注意力向价格效率的转化。此外，热搜与传统信息中介存在替代关系，其增量价值在传统信息中介覆盖不足的公司中更为显著。本文为理解数字平台注意力配置与市场效率的关系提供了新的经验证据，对完善平台信息治理机制具有政策启示。

依托2000Q1-2025Q4中国省级季度面板数据，传统线性货币传导逻辑无法解释总量宽松与实体流动性紧缩并存的结构性矛盾。结合小微企业家企一体的产权特征，构建行为视角下的货币分层分析框架，从企业主避险逐利、居民资产配置、金融中介摩擦三个维度，拆解M1与M2结构性分流的内在机制。实证结果显示，实体综合收益率与白重恩HJ口径资本机会成本的相对变动，是驱动资金跨圈层流转的核心动因；实体收益率下行对M1收缩的贡献度约为65.0%，影响幅度是资本成本抬升的1.86倍。M1萎缩由企业主公转私抽资的主观撤离、金融扩张挤出实体的客观约束共同驱动。小微家企全口径综合HJ收益率4.90%为核心行为阈值，规上工业主营业务HJ收益率5.20%为宏观同步表征，二者0.3个百分点差值对应规上企业社保合规成本；盈利跌破临界值后，资金双向流动由弹性互通转为塑性固化。实体收益率低于金融业收益时将形成恶性资金虹吸，抹平理财与实体收益差距、提升信贷投放落地效率，是盘活存量储蓄、修复M2向经营资金转化的关键。研究结合前景理论完善资产定价分析，修正经典货币线性传导假设，所有结论均经过多轮稳健性检验。

本文聚焦房地产收益率偏离合理区间引发资金脱实向虚、实体经济融资成本高企的现实问题，重新识别房地产与实体经济的均衡边界。研究以无套利均衡与资本机会成本原理为基础，结合土地财政本土特征展开分析，采用全国 50 城住宅 2.2% 公允租金收益率核算收益指标，通过面板门槛模型测得房地产综合收益率黄金阈值为 3.30%，该值与 2025 年实体经济加权平均融资成本匹配。即收益率低于 3.30% 时，房地产可通过财富效应与抵押品渠道支撑实体经济增长；高于该水平则会形成资金虹吸与成本挤压，对实体经济产生显著挤出作用。3.30% 对应的稳健运行区间为 3.1%-3.5%，是兼顾市场稳定与实体发展的最优区间。本文选取 2000-2025 年省级面板数据，经多重内生性处理与跨国检验验证结论稳健，提出分层运行与深水静流增长模式，为稳楼市、防风险、促实体转型提供可量化的调控锚点。

近年来，宽货币与紧信用并存的结构性矛盾凸显，货币政策传导效率不高、金融资源区域分化、新质生产力领域融资匹配度不足等问题较为突出。地方土地抵押主导的 货币派生模式，与新质生产力跨区域、非属地、全国统一配置的资产属性存在系统性错配。本文从货币派生机制与现代中央银行制度视角，对比发达经济体主权信用背书的货币创造模式，揭示我国央行以流动性调节为主、缺乏全国统一信用锚的制度特征，以及地方抵押品主导带来的信用分割与传导阻滞。研究表明，新质生产力资产具有天然去地方化属性，地方难以沿用土地抵押路径实现信用扩张，货币体系向国家主权信用统一驱动转型具有必然性。本文从金融稳定、传导畅通、央行制度完善层面提出职能优化路径，为弱化地产金融依赖、防范系统性金融风险、支持新质生产力发展提供参考。

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。

摘要 资金脱离实体经济在金融与债务体系内部空转，制约中国产业转型升级。传统GDP单一口径长期误读费雪1911年原始交易方程的全口径内涵，无法解释"宏观货币宽松、微观产业缺钱"的现实悖论。本文采用T1-T2-T3交易层级三分法框架，基于2000Q1—2024Q4经季节调整的官方季度数据（100个观测值），辅以年度及31省省级面板数据交叉验证（2025年为模拟值，不纳入基准样本），运用门槛回归等计量方法检验实体流动性弹塑性断裂机制。 2000—2025年全域货币流通速度累计增长8.34%，但T1实体交易层仅增长6.81%，T2、T3层分别达13.34%、17.35%；债务流速16.92%为系统性突变门槛，样本期内持续被突破，流动性边际冲击放大3.08倍，实体产业进入不可逆塑性区间；传统T=GDP范式存在8.34%的系统性测算缺口。依托国家统计局收入法GDP、财政部财税官方原始数据，严格遵循白重恩（2006）基准、白重恩与张琼（2014）迭代核算规则重新测算税后实体资本收益率，全样本均值为4.82%，长期低于5%临界安全线。结合姊妹文献M1灭失等结果形成存量-流量两重视证链条，本文构建的量化测算工具具备全流程可复现特征，能够常态化监测资金空转现象，佐证T1-T2-T3框架的科学性与适用性。

宏观经济学长期面临主流模型测算自然利率与实体真实回报显著背离的 "利率鸿沟" 困境，传统 HLW 滤波、DSGE 等方法在 2020 年代低增长、高债务环境中已普遍失效。基于无套利均衡原理，构建包含家庭、企业、金融中介的三部门一般均衡模型，推导出自然利率白箱测算公式，提出 "自然利率即实体经济长期盈利盈亏平衡线" 核心命题，构建与 BIS、IMF 等国际标准全面衔接的五维债务健康度评价体系。经多轮参数校准与 2017Q1-2026Q1 中美日德韩跨国面板数据实证检验，中国自然利率中枢稳定在 4.8%-6.1% 区间，显著高于传统测算值；该指标对固定资产投资具有显著预测力，比 HLW 滤波平均提前 2-3 个季度预警经济波动。研究表明，中国债务核心指标处于发达国家合理区间，产业利润可稳定覆盖利息支出，不存在系统性风险。以持续付息保障新质生产力培育时间窗口的发展逻辑，可为宏观调控提供可落地的实操工具。