nep-mst New Economics Papers
on Market Microstructure
Issue of 2022‒04‒11
four papers chosen by
Thanos Verousis

  1. News or noise: Mobile internet technology and stock market activity By Brown, Nerissa C.; Elliott, W. Brooke; Wermers, Russ; White, Roger M.
  2. Dynamic Autoregressive Liquidity (DArLiQ) By Hafner, Christian; Linton, Oliver; Wang, Linqi
  3. Nonparametric Estimation of Large Spot Volatility Matrices for High-Frequency Financial Data By Bu, R.; Li, D.; Linton, O.; Wang, H.
  4. Do financial advisors matter for M&A pre-announcement returns? By Betzer, André; Gider, Jasmin; Limbach, Peter

  1. By: Brown, Nerissa C.; Elliott, W. Brooke; Wermers, Russ; White, Roger M.
    Abstract: Mobile internet devices reduce trading frictions and information search costs for investors, but also introduce attention-competing activities,such as social networking. We use exogenous nationwide and city-level outages of the Blackberry Internet Service (BIS) to investigate the effect of mobile internet technology on investors'information-gathering vs. attention-diverting activities. We find that trading volume and trading frequency surge by about 5% on days when mobile internet systems go dark, consistent with a greater role for devices (when not dark) in diverting the limited attention of investors away from information-gathering and trading - even when they are used by presumably more sophisticated investors.
    Keywords: mobile technology,investor activity,stock market liquidity,limited attention,distraction
    JEL: D83 G12 G14 L86
    Date: 2021
  2. By: Hafner, Christian (Université catholique de Louvain, LIDAM/ISBA, Belgium); Linton, Oliver (; Wang, Linqi (Université catholique de Louvain, LIDAM/LFIN, Belgium)
    Abstract: We introduce a new class of semiparametric dynamic autoregressive models forthe Amihud illiquidity measure, which captures both the long-run trend in the illiquidity series with a nonparametric component and the short-run dynamics with an autoregressive component. We develop a GMM estimator based on conditional moment restrictions and an efficient semiparametric ML estimator based on an iid assumption. We derive large sample properties for both estimators. We further develop a methodology to detect the occurrence of permanent and transitory breaks in the illiquidity process. Finally, we demonstrate the model performance and its empirical relevance on two applications. First, we study the impact of stock splits on the illiquidity dynamics of the five largest US technology company stocks. Second, we investigate how the different components of the illiquidity process obtained from our model relate to the stock market risk premium using data on the S&P 500 stock market index.
    Keywords: Nonparametric ; Semiparametric ; Splits ; Structural Change
    JEL: C12 C14
    Date: 2022–02–23
  3. By: Bu, R.; Li, D.; Linton, O.; Wang, H.
    Abstract: In this paper, we consider estimating spot/instantaneous volatility matrices of high-frequency data collected for a large number of assets. We first combine classic nonparametric kernel-based smoothing with a generalised shrinkage technique in the matrix estimation for noise-free data under a uniform sparsity assumption, a natural extension of the approximate sparsity commonly used in the literature. The uniform consistency property is derived for the proposed spot volatility matrix estimator with convergence rates comparable to the optimal minimax one. For the highfrequency data contaminated by the microstructure noise, we introduce a localised pre-averaging estimation method in the high-dimensional setting which first pre-whitens data via a kernel filter and then uses the estimation tool developed in the noise-free scenario, and further derive the uniform convergence rates for the developed spot volatility matrix estimator. In addition, we also combine the kernel smoothing with the shrinkage technique to estimate the time-varying volatility matrix of the high-dimensional noise vector, and establish the relevant uniform consistency result. Numerical studies are provided to examine performance of the proposed estimation methods in finite samples.
    Keywords: Brownian semi-martingale, Kernel smoothing, Microstructure noise, Sparsity, Spot volatility matrix, Uniform consistency
    JEL: C10 C14 C22
    Date: 2022–03–16
  4. By: Betzer, André; Gider, Jasmin; Limbach, Peter
    Abstract: This study documents economically meaningful and persistent financial advisor fixed effects in target firms' abnormal stock returns shortly prior to takeover announcements.Additional difference-in-differences analyses suggest that advisors are associated with lower pre-bid stock returns after their senior staff were defendants in SEC insider trading enforcement actions. Returns are higher for advisors with more previously advised deals and those located in NYC. The evidence helps explain the prevalent phenomenon of pre-bid stock returns. It contributes to the inconclusive literature on banks' exploitation of private information gained via advisory services, which is limited to disclosed, traceable activities indicative of information leakage.
    Keywords: Financial Advisors,Mergers and Acquisitions,Information Leakage,Target Runups
    JEL: G14 G15 G21 G34 K42
    Date: 2022

This nep-mst issue is ©2022 by Thanos Verousis. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.