nep-mst New Economics Papers
on Market Microstructure
Issue of 2020‒03‒23
ten papers chosen by
Thanos Verousis

  1. Informed trading, limit order book and implementation shortfall: equilibrium and asymptotics By Umut \c{C}etin; Henri Waelbroeck
  2. Assessing the Price Impact of Treasury Market Workups By Michael J. Fleming; Giang Nguyen
  3. Equilibrium Model of Limit Order Books: A Mean-field Game View By Jin Ma; Eunjung Noh
  4. Predictive intraday correlations in stable and volatile market environments: Evidence from deep learning By Ben Moews; Gbenga Ibikunle
  5. Time-Varying Pricing of Risk in Sovereign Bond Futures Returns By Barbora Malinska
  6. Transformers for Limit Order Books By James Wallbridge
  7. Adversarial Attacks on Machine Learning Systems for High-Frequency Trading By Micah Goldblum; Avi Schwarzschild; Naftali Cohen; Tucker Balch; Ankit B. Patel; Tom Goldstein
  8. Dealer Trading and Positioning in Floating Rate Notes By Amanda Wahlers; Michael J. Fleming
  9. Market Integration and Price Discovery of Regional Land Markets in Texas By Su, Tian; Dharmasena, Senarath; Leatham, David J.; Gilliland, Charles E
  10. How Liquid Is the Inflation Swap Market? By Michael J. Fleming; John Sporn

  1. By: Umut \c{C}etin; Henri Waelbroeck
    Abstract: We propose a static equilibrium model for limit order book where profit-maximizing investors receive an information signal regarding the liquidation value of the asset and execute via a competitive dealer with random initial inventory, who trades against a competitive limit order book populated by liquidity suppliers. We show that an equilibrium exists for bounded signal distributions, obtain closed form solutions for Bernoulli-type signals and propose a straightforward iterative algorithm to compute the equilibrium order book for the general case. We obtain the exact analytic asymptotics for the market impact of large trades and show that the functional form depends on the tail distribution of the private signal of the insiders. In particular, the impact follows a power law if the signal has fat tails while the law is logarithmic in case of lighter tails. Moreover, the tail distribution of the trade volume in equilibrium obeys a power law in our model. We find that the liquidity suppliers charge a minimum bid-ask spread that is independent of the amount of `noise' trading but increasing in the degree of informational advantage of insiders in equilibrium. The model also predicts that the order book flattens as the amount of noise trading increases converging to a model with proportional transactions costs.. Competition among the insiders leads to aggressive trading causing the aggregate profit to vanish in the limiting case $N\to\infty$. The numerical results also show that the spread increases with the number of insiders keeping the other parameters fixed. Finally, an equilibrium may not exist if the liquidation value is unbounded. We conjecture that existence of equilibrium requires a sufficient amount of competition among insiders if the signal distribution exhibit fat tails.
    Date: 2020–03
  2. By: Michael J. Fleming; Giang Nguyen
    Abstract: The price impact of a trade derives largely from its information content. The ?workup? mechanism, a trading protocol used in the U.S. Treasury securities market, is designed to mitigate the instantaneous price impact of a trade by allowing market participants to trade additional quantities of a security after a buyer and seller first agree on its price. Nevertheless, workup trades are not necessarily free of information. In this post, we assess the role of workups in price discovery, following our recent paper in the Review of Asset Pricing Studies (an earlier version of which was released as a New York Fed staff report).
    Keywords: Workup; size discovery; Treasury market; information share; price impact
    JEL: G1
  3. By: Jin Ma; Eunjung Noh
    Abstract: In this paper we study a continuous time equilibrium model of limit order book (LOB) in which the liquidity dynamics follows a non-local, reflected mean-field stochastic differential equation (SDE) with evolving intensity. Generalizing the basic idea of Ma et al. (2015), we argue that the frontier of the LOB (e.g., the best asking price) is the value function of a mean-field stochastic control problem, as the limiting version of a Bertrand-type competition among the liquidity providers. With a detailed analysis on the $N$-seller static Bertrand game, we formulate a continuous time limiting mean-field control problem of the representative seller. We then validate the dynamic programming principle (DPP), and show that the value function is a viscosity solution of the corresponding Hamilton-Jacobi-Bellman (HJB) equation. We argue that the value function can be used to obtain the equilibrium density function of the LOB, following the idea of Ma et al. (2015).
    Date: 2020–02
  4. By: Ben Moews; Gbenga Ibikunle
    Abstract: Standard methods and theories in finance can be ill-equipped to capture highly non-linear interactions in financial prediction problems based on large-scale datasets, with deep learning offering a way to gain insights into correlations in markets as complex systems. In this paper, we apply deep learning to econometrically constructed gradients to learn and exploit lagged correlations among S&P 500 stocks to compare model behaviour in stable and volatile market environments, and under the exclusion of target stock information for predictions. In order to measure the effect of time horizons, we predict intraday and daily stock price movements in varying interval lengths and gauge the complexity of the problem at hand with a modification of our model architecture. Our findings show that accuracies, while remaining significant and demonstrating the exploitability of lagged correlations in stock markets, decrease with shorter prediction horizons. We discuss implications for modern finance theory and our work's applicability as an investigative tool for portfolio managers. Lastly, we show that our model's performance is consistent in volatile markets by exposing it to the environment of the recent financial crisis of 2007/2008.
    Date: 2020–02
  5. By: Barbora Malinska (Institute of Economic Studies, Faculty of Social Sciences, Charles University in Prague, Smetanovo nabrezi 6, 111 01 Prague 1, Czech Republic)
    Abstract: We examine time-varying explanatory power of realized moments on subsequent bond futures excess returns using more than 12 years of high-frequency data from U.S. and German sovereign bond markets. We detect realized volatility and realized kurtosis to carry valuable information for next-day open-close excess returns on the U.S. market which is not priced in traditional bond return predictors such as term or default spreads. Most importantly, we reveal the bond excess return predictability to be significantly dynamic and to increase during crisis period. Whereas the realized volatlity reveals to have negative effect on next-day excess returns, effect of realized kurtosis is switching from positive effect in the time of 2007-2009 financial crisis to negative values after 2014.
    Keywords: Realized moments, bond pricing, risk-return trade-off, high-frequency data, time-varying coeffcients
    JEL: C32 C55 G12
    Date: 2020–03
  6. By: James Wallbridge
    Abstract: We introduce a new deep learning architecture for predicting price movements from limit order books. This architecture uses a causal convolutional network for feature extraction in combination with masked self-attention to update features based on relevant contextual information. This architecture is shown to significantly outperform existing architectures such as those using convolutional networks (CNN) and Long-Short Term Memory (LSTM) establishing a new state-of-the-art benchmark for the FI-2010 dataset.
    Date: 2020–02
  7. By: Micah Goldblum; Avi Schwarzschild; Naftali Cohen; Tucker Balch; Ankit B. Patel; Tom Goldstein
    Abstract: Algorithmic trading systems are often completely automated, and deep learning is increasingly receiving attention in this domain. Nonetheless, little is known about the robustness properties of these models. We study valuation models for algorithmic trading from the perspective of adversarial machine learning. We introduce new attacks specific to this domain with size constraints that minimize attack costs. We further discuss how these attacks can be used as an analysis tool to study and evaluate the robustness properties of financial models. Finally, we investigate the feasibility of realistic adversarial attacks in which an adversarial trader fools automated trading systems into making inaccurate predictions.
    Date: 2020–02
  8. By: Amanda Wahlers (Research and Statistics Group?); Michael J. Fleming
    Abstract: In January 2014, the U.S. Treasury Department made its first sale of floating rate notes (FRNs), securities whose coupon rates vary over time depending on the course of short-term rates. Now that a few years have passed, we have enough data to analyze dealer trading and positioning in FRNs. In this post, we assess the level of trading and positioning, concentration across issues, and auction cycle effects, comparing these properties to those of other types of Treasury securities.
    Keywords: dealers; trading volume; positions; Floating rate notes
    JEL: G1 G2
  9. By: Su, Tian; Dharmasena, Senarath; Leatham, David J.; Gilliland, Charles E
    Keywords: Farm Management, Agribusiness
  10. By: Michael J. Fleming; John Sporn
    Abstract: Inflation swaps are used to transfer inflation risk and make inferences about the future course of inflation. Despite the importance of this market to inflation hedgers, inflation speculators, and policymakers, there is little evidence on its liquidity. Based on an analysis of new and detailed data in this post we show that the market appears reasonably liquid and transparent despite low trading activity, likely reflecting the high liquidity of related markets for inflation risk. In a previous post, we examined similar issues for the broader interest rate derivatives market.
    Keywords: Derivative; Trading; Transparency
    JEL: G1

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