nep-mst New Economics Papers
on Market Microstructure
Issue of 2020‒12‒21
eight papers chosen by
Thanos Verousis

  1. Deep limit order book events dynamics By Bilodeau, Yann
  2. Wheat Futures Trading Volume Forecasting and the Value of Extended Trading Hours By Joseph Janzen; Nicolas Legrand
  3. Are trading invariants really invariant? Trading costs matter By Frédéric Bucci; Fabrizio Lillo; Jean-Philippe Bouchaud; Michael Benzaquen
  4. Informed trading and the dynamics of client-dealer connections in corporate bond markets By Czech, Robert; Pintér, Gábor
  5. The Multivariate Kyle model: More is different By L. C. Garcia Del Molino; I. Mastromatteo; Michael Benzaquen; J.-P. Bouchaud
  6. Sign Matters: Stock Movement Based Trading Decisions of Private Investors By Stefan Muhl; Marc Oliver Rieger; Hung Ling Chen
  7. Zooming In on Equity Factor Crowding By Valerio Volpati; Michael Benzaquen; Zoltán Eisler; Iacopo Mastromatteo; Bence Tóth; Jean-Philippe Bouchaud
  8. Treasury Market Functioning During the COVID-19 Outbreak: Evidence from Collateral Re-use By Sebastian Infante; Zack Saravay

  1. By: Bilodeau, Yann (HEC Montreal, Canada Research Chair in Risk Management)
    Abstract: This paper analyzes the limit order book events arrival dependency structure using high-dimensional Hawkes processes. We seek for recurrent relationships among events from a set of 86 event types which in addition to transactions, includes limit order submissions and cancellations taking place up to the 20th depth level of the order book. We focus on BMW, SAP, and ADS, three liquid DAX 30 index stocks for which we have a microsecond stamped high-frequency dataset covering the 61 trading day period going from February 1 to March 31, 2013. For each stock, we build a tailored descriptive model by selecting recurrent events relationships. Estimated on a daily basis, we find that the selected models offer interesting data fitting performance, particularly for limit order submissions and cancellations occurring on the first five price levels of the order book. Finally, we use the comprehensive sets of estimated parameters to describe a global events arrival dynamics that we relate to the potential behaviors of market participants having different objectives and directional views.
    Keywords: Limit order book; Hawkes process; high-frequency; algorithmic trading; liquidity; Frankfort Stock Exchange
    JEL: C22 C41 C53 G11
    Date: 2020–12–02
  2. By: Joseph Janzen (University of Illinois at Urbana-Champaign [Urbana] - University of Illinois System); Nicolas Legrand (SMART - Structures et Marché Agricoles, Ressources et Territoires - AGROCAMPUS OUEST - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)
    Abstract: Electronic trading in modern commodity markets has extended trading hours, lowered barriers to listing new contracts, broadened participation internationally, and encouraged entry of new trader types, particularly algorithmic traders whose order execution is automated. This paper seeks to understand how these forces have shaped the quantity and timing of trading activity, using the world's multiple wheat futures markets as a laboratory. To do so, we extend existing models for forecasting trading volume found in the literature on volume weighted average price (VWAP) order execution (e.g. Bialkowski, et al 2008 and Humphery-Jenner 2011) to applications beyond trading algorithm design. We consider a setting with multiple trading venues for related commodities, specifically the front-month Chicago Mercantile Exchange Soft Red Wheat and Paris Euronext Milling Wheat futures contracts. We compare a series of nested forecasting models to infer whether past trading history, intraday volume dynamics, cross market trading activity, and other information are useful predictors of trading activity. We assess the value of extended trading hours and the existence of alternative trading venues by testing whether trading volume is more predictable at particular times throughout the trading day.
    Keywords: Trading hours,High-frequency data,Volume predictions
    Date: 2019–05–15
  3. By: Frédéric Bucci; Fabrizio Lillo; Jean-Philippe Bouchaud; Michael Benzaquen (LadHyX - Laboratoire d'hydrodynamique - X - École polytechnique - CNRS - Centre National de la Recherche Scientifique)
    Abstract: We revisit the trading invariance hypothesis recently proposed by Kyle and Obizhaeva [1] by empirically investigating a large dataset of bets, or metaorders, provided by ANcerno. The hypothesis predicts that the quantity I := R/N 3/2 , where R is the exchanged risk (volatility × volume × price) and N is the number of bets, is invariant. We find that the 3/2 scaling between R and N works well and is robust against changes of year, market capitalisation and economic sector. However our analysis clearly shows that I is not invariant. We find a very high correlation R 2 > 0.8 between I and the total trading cost (spread and market impact) of the bet. We propose new invariants defined as a ratio of I and costs and find a large decrease in variance. We show that the small dispersion of the new invariants is mainly driven by (i) the scaling of the spread with the volatility per transaction, (ii) the near invariance of the distribution of metaorder size and of the volume and number fractions of bets across stocks.
    Date: 2020
  4. By: Czech, Robert (Bank of England); Pintér, Gábor (Bank of England)
    Abstract: Using a unique non-anonymous dataset, covering virtually all secondary market trades in the UK corporate bond market, we show that clients outperform when they trade with more dealers. The effect is stronger for informationally sensitive clients (holding CDS positions on the issuer), assets (high-yield bonds), and during informationally intensive days (macro/rating announcements). The results are consistent with clients varying the number of dealers they trade with to conceal private information. Various tests support our information-based interpretation, ruling out alternative explanations related to uninformed demand and supply. Identifying clients who simultaneously trade in government and corporate bonds reveals that the informational role of connections is larger and more persistent in the corporate bond market. Using a Kyle (1989)-type model, we show that both the degree of inter-dealer competition and the magnitude of private information could, in theory, explain the strength of the performance-connection relation. The empirical evidence supports the mechanism related to the magnitude of private information, while the inter-dealer competition channel is rejected by the data.
    Keywords: Informed trading; corporate bonds; client-dealer connections; inter-dealer competition
    JEL: G12 G14 G23 G24
    Date: 2020–11–27
  5. By: L. C. Garcia Del Molino; I. Mastromatteo; Michael Benzaquen (LadHyX - Laboratoire d'hydrodynamique - X - École polytechnique - CNRS - Centre National de la Recherche Scientifique); J.-P. Bouchaud
    Abstract: We reconsider the multivariate Kyle model in a risk-neutral setting with a single, perfectly informed rational insider and a rational competitive market maker, setting the price of n correlated securities. We prove the unicity of a symmetric, positive definite solution for the impact matrix and provide insights on its interpretation. We explore its implications from the perspective of empirical market microstructure, and argue that it provides a sensible inference procedure to cure some pathologies encountered in recent attempts to calibrate cross-impact matrices.
    Date: 2020
  6. By: Stefan Muhl; Marc Oliver Rieger; Hung Ling Chen
    Abstract: This paper studies the relation between the sign of recent returns (anup-down-pattern) and sell and buy decisions of private investors. For our comprehensive data set of Taiwanese private stock market investors we find two striking trading patterns:First, a stock pattern with predominantly positive days triggers significantly more tradesby private investorsthan a pattern with many negative days. Second, following positive days, privateinvestors sell proportionally more stocks than they buy. These results still hold when controlling for returns, absolute returns and stock index returns. To explain this behavior of simultaneously rising or falling buy and sell trades, we construct a simple behavioral model of potential sellersandbuyers. We assume that both groups initially have different expectationstowards their respective sharesand update these before their final decision while observing the price pattern. Together with the well-documented disposition effect, this model can explain the key results and also the observed gender differences.
    Keywords: nvestment behavior; trading decisions; trend following; contrarian; price patterns
    JEL: G11 G12 G1
    Date: 2020
  7. By: Valerio Volpati (CEA - Commissariat à l'énergie atomique et aux énergies alternatives); Michael Benzaquen (LadHyX - Laboratoire d'hydrodynamique - X - École polytechnique - CNRS - Centre National de la Recherche Scientifique); Zoltán Eisler; Iacopo Mastromatteo (SISSA / ISAS - Scuola Internazionale Superiore di Studi Avanzati / International School for Advanced Studies); Bence Tóth; Jean-Philippe Bouchaud (CFM - Capital Fund Management - Capital Fund Management)
    Abstract: Crowding is most likely an important factor in the deterioration of strategy performance, the increase of trading costs and the development of systemic risk. We study the imprints of crowding on both anonymous market data and a large database of metaorders from institutional investors in the U.S. equity market. We propose direct metrics of crowding that capture the presence of investors contemporaneously trading the same stock in the same direction by looking at fluctuations of the imbalances of trades executed on the market. We identify significant signs of crowding in well known equity signals, such as Fama-French factors and especially Momentum. We show that the rebalancing of a Momentum portfolio can explain between 1-2% of order flow, and that this percentage has been significantly increasing in recent years.
    Keywords: market microstructure,momentum,equity factors,crowding
    Date: 2020
  8. By: Sebastian Infante; Zack Saravay
    Abstract: In March 2020, uncertainty over the COVID-19 pandemic caused severe stress in U.S. financial markets. Specifically, Fleming and Ruela (2020) document a severe impairment of Treasury market functioning, as indicated by a sharp increase in bid/ask spreads, a decline in market depth, and an increase in price impact measures.
    Date: 2020–12–04

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