nep-mst New Economics Papers
on Market Microstructure
Issue of 2018‒04‒09
eight papers chosen by
Thanos Verousis

  1. Optimal liquidity-based trading tactics By Charles-Albert Lehalle; Othmane Mounjid; Mathieu Rosenbaum
  2. The cooling-off effect of price limits in the Chinese stock markets By Yu-Lei Wan; Gang-Jin Wang; Zhi-Qiang Jiang; Wen-Jie Xie; Wei-Xing Zhou
  3. Universal features of price formation in financial markets: perspectives from Deep Learning By Justin Sirignano; Rama Cont
  4. Smart TWAP Trading in Continuous-Time Equilibria By Jin Hyuk Choi; Kasper Larsen; Duane J. Seppi
  5. Central Bank Policy Announcements and Changes in Trading Behavior: Evidence from Bond Futures High Frequency Price Data By Koichiro Kamada; Tetsuo Kurosaki; Ko Miura; Tetsuya Yamada
  6. High dimensional Hawkes processes for limit order books Modelling, empirical analysis and numerical calibration By Xiaofei Lu; Frédéric Abergel
  7. Large large-trader activity weakens the long memory of limit order markets By Kevin Primicerio; Damien Challet
  8. Fearing the Fed: How Wall Street Reads Main Street By Tzuo Hann Law; Dongho Song; Amir Yaron

  1. By: Charles-Albert Lehalle; Othmane Mounjid; Mathieu Rosenbaum
    Abstract: We consider an agent who needs to buy (or sell) a relatively small amount of asset over some fixed short time interval. We work at the highest frequency meaning that we wish to find the optimal tactic to execute our quantity using limit orders, market orders and cancellations. To solve the agent's control problem, we build an order book model and optimize an expected utility function based on our price impact. We derive the equations satisfied by the optimal strategy and solve them numerically. Moreover, we show that our optimal tactic enables us to outperform significantly naive execution strategies.
    Date: 2018–03
  2. By: Yu-Lei Wan (ECUST); Gang-Jin Wang (HNU); Zhi-Qiang Jiang (ECUST); Wen-Jie Xie (ECUST); Wei-Xing Zhou (ECUST)
    Abstract: In this paper, we investigate the cooling-off effect (opposite to the magnet effect) from two aspects. Firstly, from the viewpoint of dynamics, we study the existence of the cooling-off effect by following the dynamical evolution of some financial variables over a period of time before the stock price hits its limit. Secondly, from the probability perspective, we investigate, with the logit model, the existence of the cooling-off effect through analyzing the high-frequency data of all A-share common stocks traded on the Shanghai Stock Exchange and the Shenzhen Stock Exchange from 2000 to 2011 and inspecting the trading period from the opening phase prior to the moment that the stock price hits its limits. A comparison is made of the properties between up-limit hits and down-limit hits, and the possible difference will also be compared between bullish and bearish market state by dividing the whole period into three alternating bullish periods and three bearish periods. We find that the cooling-off effect emerges for both up-limit hits and down-limit hits, and the cooling-off effect of the down-limit hits is stronger than that of the up-limit hits. The difference of the cooling-off effect between bullish period and bearish period is quite modest. Moreover, we examine the sub-optimal orders effect, and infer that the professional individual investors and institutional investors play a positive role in the cooling-off effects. All these findings indicate that the price limit trading rule exerts a positive effect on maintaining the stability of the Chinese stock markets.
    Date: 2018–03
  3. By: Justin Sirignano; Rama Cont
    Abstract: Using a large-scale Deep Learning approach applied to a high-frequency database containing billions of electronic market quotes and transactions for US equities, we uncover nonparametric evidence for the existence of a universal and stationary price formation mechanism relating the dynamics of supply and demand for a stock, as revealed through the order book, to subsequent variations in its market price. We assess the model by testing its out-of-sample predictions for the direction of price moves given the history of price and order flow, across a wide range of stocks and time periods. The universal price formation model is shown to exhibit a remarkably stable out-of-sample prediction accuracy across time, for a wide range of stocks from different sectors. Interestingly, these results also hold for stocks which are not part of the training sample, showing that the relations captured by the model are universal and not asset-specific. The universal model --- trained on data from all stocks --- outperforms, in terms of out-of-sample prediction accuracy, asset-specific linear and nonlinear models trained on time series of any given stock, showing that the universal nature of price formation weighs in favour of pooling together financial data from various stocks, rather than designing asset- or sector-specific models as commonly done. Standard data normalizations based on volatility, price level or average spread, or partitioning the training data into sectors or categories such as large/small tick stocks, do not improve training results. On the other hand, inclusion of price and order flow history over many past observations is shown to improve forecasting performance, showing evidence of path-dependence in price dynamics.
    Date: 2018–03
  4. By: Jin Hyuk Choi; Kasper Larsen; Duane J. Seppi
    Abstract: This paper presents a continuous-time equilibrium model of liquidity provision in a market with multiple strategic investors with intraday trading targets. We show analytically that there are infinitely many Nash equilibria. We solve for the welfare-maximizing equilibrium and the competitive equilibrium, and we illustrate that these equilibria are different. The model is easily computed numerically, and we provide a number of numerical illustrations.
    Date: 2018–03
  5. By: Koichiro Kamada (Deputy Director-General, Institute for Monetary and Economic Studies, Bank of Japan (E-mail:; Tetsuo Kurosaki (Director and Senior Economist, Institute for Monetary and Economic Studies, Bank of Japan (E-mail:; Ko Miura (Research and Statistics Department, Bank of Japan (currently, University of Wisconsin-Madison)); Tetsuya Yamada (Director and Senior Economist, Institute for Monetary and Economic Studies (currently, Financial System and Bank Examination Department), Bank of Japan, (E-mail:
    Abstract: We present a theoretical model to explain how financial traders incorporate public and private information into security prices. We explain that the model enables us to simultaneously identify when public information caused surprises and how large an impact it had on the market. By applying the model to the tick-by-tick data on Japanese government bond futures prices, we show that the Bank of Japan fs introduction of quantitative and qualitative monetary easing was one of the most surprising episodes during the period from 2005 to 2016. We also show that the sensitivity to the Bank fs announcements has strengthened since the introduction of the negative interest rate policy, whereas the sensitivity to economic indicators and surveys has weakened substantially.
    Keywords: Central bank announcements, Government bond futures, Herding behavior, Information efficiency, Market microstructure
    JEL: C14 D40 D83 E58 G12 G14
    Date: 2018–03
  6. By: Xiaofei Lu; Frédéric Abergel (FiQuant - Chaire de finance quantitative - Ecole Centrale Paris)
    Abstract: High-dimensional Hawkes processes with exponential kernels are used to describe limit order books in order-driven financial markets. The dependencies between orders of various types are carefully studied and modelled, based on a thorough empirical analysis. The observation of inhibition effects is particularly interesting, and leads us to the use of non-linear Hawkes processes. A specific attention is devoted to the calibration problem, in order to account for the high dimensionality of the problem and the very poor convexity properties of the MLE. Our analyses show a good agreement between the statistical properties of order book data and those of the model.
    Keywords: non-convex optimization ,Hawkes processes,limit order books,high frequency data
    Date: 2018–01–08
  7. By: Kevin Primicerio; Damien Challet
    Abstract: Using more than 6.7 billions of trades, we explore how the tick-by-tick dynamics of limit order books depends on the aggregate actions of large investment funds on a much larger (quarterly) timescale. In particular, we find that the well-established long memory of market order signs is markedly weaker when large investment funds trade either in a directional way and even weaker when their aggregate participation ratio is large. Conversely, we investigate to what respect a weaker memory of market order signs predicts that an asset is being actively traded by large funds. Theoretical arguments suggest two simple mechanisms that contribute to the observed effect: a larger number of active meta-orders and a modification of the distribution of size of meta-orders. Empirical evidence suggests that the number of active meta-orders is the most important contributor to the loss of market order sign memory.
    Date: 2018–03
  8. By: Tzuo Hann Law (Boston College); Dongho Song (Boston College); Amir Yaron (University of Pennsylvania)
    Abstract: Using intraday stock returns around macroeconomic news announcements (MNAs), we find strong evidence of persistent, cyclical variation in the stock market's response to MNA surprises. The response is particularly strong coming out of recessions and is gradually attenuated as the economy expands. We show that this cyclical pattern can be explained by a regime-switching model. In the model, we find that the direction and shape of the market's response reflect the evolution of beliefs about the state of the economy and monetary policy. The risk of an interest rate hike can entirely mitigate (and even reverse) the effect of positive MNA surprises on returns. This mechanism is consistent with the data -- positive MNA surprises coincide with negative stock market returns when there is substantial uncertainty over monetary policy.
    Date: 2017

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