Asset Price Dynamics with Limited Attention

Terrence Hendershott; Albert J. Menkveld; Remy Praz; Mark Seasholes

doi:10.1093/rfs/hhab045

Asset Price Dynamics with Limited Attention

Terrence Hendershott, Albert J. Menkveld^*, Remy Praz, Mark Seasholes

^*Corresponding author for this work

Research output: Contribution to Journal › Article › Academic › peer-review

Abstract

We identify long-lived pricing errors through a model in which inattentive investors arrive stochastically to trade. The model's parameters are structurally estimated using daily NYSE market-maker inventories, retail order flows, and prices. The estimated model fits empirical variances, autocorrelations, and cross-autocorrelations among our three data series from daily to monthly frequencies. Pricing errors for the typical NYSE stock have a standard deviation of 3.2 percentage points and a half-life of 6.2 weeks. These pricing errors account for 9.4%, 7.0%, and 4.5% of the respective daily, monthly, and quarterly idiosyncratic return variances.

Original language	English
Pages (from-to)	962-1008
Number of pages	47
Journal	Review of Financial Studies
Volume	35
Issue number	2
Early online date	15 Apr 2021
DOIs	https://doi.org/10.1093/rfs/hhab045
Publication status	Published - Feb 2022

Bibliographical note

Publisher Copyright:
© 2021 The Authors 2021. Published by Oxford University Press.

Keywords

G12
G14

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10.1093/rfs/hhab045

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title = "Asset Price Dynamics with Limited Attention",

abstract = "We identify long-lived pricing errors through a model in which inattentive investors arrive stochastically to trade. The model's parameters are structurally estimated using daily NYSE market-maker inventories, retail order flows, and prices. The estimated model fits empirical variances, autocorrelations, and cross-autocorrelations among our three data series from daily to monthly frequencies. Pricing errors for the typical NYSE stock have a standard deviation of 3.2 percentage points and a half-life of 6.2 weeks. These pricing errors account for 9.4\%, 7.0\%, and 4.5\% of the respective daily, monthly, and quarterly idiosyncratic return variances.",

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AU - Seasholes, Mark

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N2 - We identify long-lived pricing errors through a model in which inattentive investors arrive stochastically to trade. The model's parameters are structurally estimated using daily NYSE market-maker inventories, retail order flows, and prices. The estimated model fits empirical variances, autocorrelations, and cross-autocorrelations among our three data series from daily to monthly frequencies. Pricing errors for the typical NYSE stock have a standard deviation of 3.2 percentage points and a half-life of 6.2 weeks. These pricing errors account for 9.4%, 7.0%, and 4.5% of the respective daily, monthly, and quarterly idiosyncratic return variances.

AB - We identify long-lived pricing errors through a model in which inattentive investors arrive stochastically to trade. The model's parameters are structurally estimated using daily NYSE market-maker inventories, retail order flows, and prices. The estimated model fits empirical variances, autocorrelations, and cross-autocorrelations among our three data series from daily to monthly frequencies. Pricing errors for the typical NYSE stock have a standard deviation of 3.2 percentage points and a half-life of 6.2 weeks. These pricing errors account for 9.4%, 7.0%, and 4.5% of the respective daily, monthly, and quarterly idiosyncratic return variances.

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JO - Review of Financial Studies

JF - Review of Financial Studies

IS - 2

ER -

Asset Price Dynamics with Limited Attention

Abstract

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Keywords

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