nep-mst New Economics Papers
on Market Microstructure
Issue of 2020‒12‒07
three papers chosen by
Thanos Verousis

  1. Price Discovery in the U.S. Treasury Cash Market: On Principal Trading Firms and Dealers By James Collin Harkrader; Michael Puglia
  2. Price formation and optimal trading in intraday electricity markets with a major player By Olivier F\'eron; Peter Tankov; Laura Tinsi
  3. Pattern recognition in trading behaviors before stock price jumps: new method based on multivariate time series classification By Ao Kong; Robert Azencott; Hongliang Zhu

  1. By: James Collin Harkrader; Michael Puglia
    Abstract: We explore the following question: does the trading activity of registered dealers on Treasury interdealer broker (IDB) platforms differ from that of principal trading firms (PTFs), and if so, how and to what effect on market liquidity? To do so, we use a novel dataset that combines Treasury cash transaction reports from FINRA’s Trade Reporting and Compliance Engine (TRACE) and publicly available limit order book data from BrokerTec. We find that trades conducted in a limit order book setting have high permanent price impact when a PTF is the passive party, playing the role of liquidity provider. Conversely, we find that dealer trades have higher price impact when the dealer is the aggressive party, playing the role of liquidity taker. Trades in which multiple firms (whether dealers or PTFs) participate on one or both sides, however, have relatively low price impact. We interpret these results in light of theoretical models suggesting that traders with only a “small†informational advantage prefer to use (passive) limit orders, while traders with a comparatively large informational advantage prefer to use (aggressive) market orders. We also analyze the events that occurred in Treasury markets in March 2020, during the onset of the COVID-19 pandemic.
    Keywords: Treasury markets; High frequency trading; Market microstructure; Price discovery; Price impact; PTFs; Dealers; TRACE; BrokerTec
    JEL: G12 G14 G32
    Date: 2020–11–16
  2. By: Olivier F\'eron; Peter Tankov; Laura Tinsi
    Abstract: We study price formation in intraday electricity markets in the presence of intermittent renewable generation. We consider the setting where a major producer may interact strategically with a large number of small producers. Using stochastic control theory we identify the optimal strategies of agents with market impact and exhibit the Nash equilibrium in closed form in the asymptotic framework of mean field games with a major player. This is a companion paper to [F\'eron, Tankov, and Tinsi, Price formation and optimal trading in intraday electricity markets, arXiv:2009.04786, 2020], where a similar model is developed in the setting of identical agents.
    Date: 2020–11
  3. By: Ao Kong; Robert Azencott; Hongliang Zhu
    Abstract: This paper extends the work of Boudt and Pertitjean(2014) and investigates the trading patterns before price jumps in the stock market based on a new multivariate time classification technique. Different from Boudt and Pertitjean(2014), our analyzing scheme can explore the "time-series information" embedded in the trading-related attributes and provides a set of jump indicators for abnormal pattern recognition. In addition to the commonly used liquidity measures, our analysis also involves a set of technical indicators to describe the micro-trading behaviors. An empirical study is conducted on the level-2 data of the constituent stocks of China Security Index 300. It is found that among all the candidate attributes, several volume and volatility-related attributes exhibit the most significant abnormality before price jumps. Though some of the abnormalities start just shortly before the occurrence of the jumps, some start much earlier. We also find that most of our attributes have low mutual dependencies with each other from the perspective of time-series analysis, which allows various perspectives to study the market trading behaviors. To this end, our experiment provides a set of jump indicators that can effectively detect the stocks with extremely abnormal trading behaviors before price jumps. More importantly, our study offers a new framework and potential useful directions for trading-related pattern recognition problem using the time series classification techniques.
    Date: 2020–11

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