Econ Papers Daily / 工作论文
当现金苏醒：机构货币市场基金的惯性及AI颠覆的潜在影响
When Cash Wakes Up: Inertia in Institutional Money Market Funds and the Potential Effects of AI Disruption
摘要
We study institutional cash management by constructing a novel dataset that records the specific money market funds (MMFs) held by each mutual fund in each month from 2010 to 2024. We document remarkably high inertia: more than half of mutual funds never switched their MMF choice throughout the sample period, despite non-negligible yield dispersion. To decompose the sources of this inertia, we develop a two-stage structural model that separately identifies the determinants of portfolio managers’ attentiveness (“waking up”) and their conditional preferences once attentive. We estimate the model using a methodology that combines BLP-style contraction mapping with maximum likelihood. We establish three main findings. First, MMFs with less attentive investor bases engage in greater liquidity transformation while offering lower yields. As a result, they earn higher profits and exhibit significantly higher survival rates, especially during periods of low interest rates. Second, we find that aggregate flows appear more yield-sensitive during low interest rate periods (the well-documented “reaching for yield” phenomenon) not because individual investors become more yield-seeking, but because a larger fraction of investors becomes attentive—a composition effect rather than a preference shift. Third, counterfactual analysis reveals a potential tension between market efficiency and financial stability if AI-driven automation reduces investor inattention. In the absence of inattention, fees fall by approximately half, yield dispersion collapses to near zero, and market concentration declines by roughly 20%, but monthly fund flows increase by about three times and the incidence of fund-months in which redemptions exceed a fund’s weekly liquid assets rises by about six times, raising the risk of fire sales and MMF runs.
中文摘要
我们通过构建一个新颖的数据集来研究机构现金管理，该数据集记录了2010年至2024年期间每个月每只共同基金所持有的具体货币市场基金（MMF）。我们记录了极高的惯性：尽管存在不可忽视的收益率离散度，超过一半的共同基金在整个样本期内从未更换过其MMF选择。为了分解这种惯性的来源，我们构建了一个两阶段结构模型，分别识别投资组合经理注意力（“苏醒”）的决定因素以及他们在关注后的条件偏好。我们使用一种结合BLP风格收缩映射与最大似然的方法来估计该模型。我们得出三个主要发现。第一，投资者基础注意力较低的MMF进行更大程度的流动性转换，同时提供较低的收益率。因此，它们赚取更高的利润，并表现出显著更高的存活率，尤其是在低利率时期。第二，我们发现总流量在低利率时期似乎对收益率更敏感（即文献充分记录的“追逐收益”现象），这并不是因为个体投资者变得更加追逐收益，而是因为更大比例的投资者变得关注——这是一种构成效应而非偏好转变。第三，反事实分析揭示了如果AI驱动的自动化减少投资者疏忽，市场效率与金融稳定之间可能存在潜在张力。在不存在疏忽的情况下，费用下降约一半，收益率离散度坍缩至接近零，市场集中度下降约20%，但月度基金流量增加约三倍，赎回超过基金周度流动资产的事件（基金-月）发生率上升约六倍，增加了甩卖和MMF挤兑的风险。