Price Clustering Asymmetries in Limit Order Flows

Abstract

We explore the relation between limit order price clustering and price efficiency. We find that executed sell limit orders cluster more frequently on round increments than buy This paper examines how the clustering of limit orders on round pricing increments affects the execution prices of marketable orders and, thereby, contributes to temporary deviations from price efficiency. We define price efficiency to be the degree to which security prices reflect all available information, both timely and accurately.1 Prior research shows that upon the arrival of material firm-specific or macroeconomic information, liquidity-demanding traders improve price efficiency by quickly submitting marketable orders in the direction of permanent price changes and in the opposite direction of temporary pricing errors (Kyle, 1985;Brogaard, Hendershott, and Riordan, 2014;Chaboud et al., 2014). However, when those marketable orders execute against less-precise clustered limit orders, stock prices are slower to reflect permanent price changes.Prior literature offers three explanations for why limit orders might execute more frequently on discrete price sets: First, traders might simply wish to reduce cognitive processing costs (Wyckoff, 1963). Second, investors may desire to increase order execution probability or lower negotiation costs (Harris, 1991;Cooney, Van Ness, and Van Ness, 2003). Third, liquidity providers might be uncertain about the underlying value of a security (Ball, Torous, and Tschoegl, 1985). In any of these scenarios, the clustered order price represents a lower precision estimate of the security's fundamental value. Thus, the predictable clustering patterns in limit orders might delay the incorporation of new information into asset prices.Research shows that markets react differently to buy and sell orders, and that the price impact of purchases exceeds that of sales (Kraus and Stoll, 1972; Mayers, 1987, 1990;Gemmill, 1996). Saar (2001) provides a theoretical justification for this impact differential based on information effects. If informed traders are active market participants, and their strategy results in buy orders conveying more information than sell orders on average, then equilibrium prices are expected to adjust more for buys than for sells. Similarly, Chan and Lakonishok (1993) argue that institutional investors do not typically hold the market portfolio, and the decision of which stock to sell does not necessarily convey negative information. The choice to buy a

Modeling Price Clustering in High-Frequency Prices

“…Figure 2 shows fitted lines from panel regression of price clustering on volatility for two stocks traded on NASDAQ -Apple Inc. (AAPL) and Microsoft (MSFT) -and two stocks traded on NYSE -Boeing (BA) and Visa Inc. (V). The positive dependence between price clustering and volatility is in line with the existing literature (see, ap Gwilym et al, 1998b;Davis et al, 2014;Box and Griffith, 2016;Hu et al, 2017;Blau, 2019;Lien et al, 2019).…”

“…Using a panel regression with fixed effects, we find a positive effect of daily volatility measured by realized kernels of Barndorff-Nielsen et al (2008) on price clustering. This finding is in line with the results of ap Gwilym et al (1998b); Davis et al (2014); Box and Griffith (2016); Hu et al (2017); Blau (2019); Lien et al (2019). Next, we estimate the proposed high-frequency price model and arrive at different conclusion -the instantaneous volatility obtained by the model has a negative effect on price clustering.…”

“…However, as shown e.g. by ap Gwilym et al (1998b); Davis et al (2014); Box and Griffith (2016); Hu et al (2017); Blau (2019); Lien et al (2019), a variety of other factors such as trade size, price value, spread, and investor sentiment is also related to price clustering. In the context of the proposed high-frequency price model, these variables could be straightforwardly included in the price clustering dynamics.…”

“…Mbanga (2019)). Another studies analyzed the panel data by pooled regression (see, Chung et al, 2005;Liu and Witte, 2013;Blau and Griffith, 2016), models with fixed effects (see, Box and Griffith, 2016;Blau, 2019;Das and Kadapakkam, 2020), or random effects (see, Ohta, 2006). Part of the literature models price clustering as a binary variable using logit (see, Aitken et al, 1996;Brown and Mitchell, 2008;Bharati et al, 2012), or probit models (see, Kahn et al, 1999;Sopranzetti and Datar, 2002;Palmon et al, 2004;Ohta, 2006;Alexander and Peterson, 2007;Capelle-Blancard and Chaudhury, 2007;Liu, 2011;Narayan and Smyth, 2013;Lien et al, 2019).…”

ETF Competition and Market Quality

“…The additional control variables, , appearing in Equation , account for a variety of other factors that we believe may impact ETF liquidity. First, Box and Griffith () demonstrate that extreme price movements lead to transitory deviations from price efficiency. To control for any reduction in liquidity provision brought on by these deviations, we include the absolute value of fund i ’s cumulative return, , during year t .…”