nep-mst New Economics Papers
on Market Microstructure
Issue of 2023‒08‒28
nine papers chosen by
Thanos Verousis

  1. Estimation of an Order Book Dependent Hawkes Process for Large Datasets By Luca Mucciante; Alessio Sancetta
  2. Large Orders in Small Markets: Execution with Endogenous Liquidity Supply By Agostino Capponi; Albert J. Menkveld; Hongzhong Zhang
  3. Understanding the least well-kept secret of high-frequency trading By Sergio Pulido; Mathieu Rosenbaum; Emmanouil Sfendourakis
  4. Liquidity fragmentation on decentralized exchanges By Alfred Lehar; Christine Parlour; Marius Zoican
  5. Towards Generalizable Reinforcement Learning for Trade Execution By Chuheng Zhang; Yitong Duan; Xiaoyu Chen; Jianyu Chen; Jian Li; Li Zhao
  6. Fast and Furious: A High-Frequency Analysis of Robinhood Users' Trading Behavior By David Ardia; Cl\'ement Aymard; Tolga Cenesizoglu
  7. Decentralized Prediction Markets and Sports Books By Hamed Amini; Maxim Bichuch; Zachary Feinstein
  8. Dealer Capacity and U.S. Treasury Market Functionality By Darrell Duffie; Michael J. Fleming; Frank M. Keane; Claire Nelson; Or Shachar; Peter Van Tassel
  9. Is Kyle's equilibrium model stable? By Umut Cetin; Kasper Larsen

  1. By: Luca Mucciante; Alessio Sancetta
    Abstract: A point process for event arrivals in high frequency trading is presented. The intensity is the product of a Hawkes process and high dimensional functions of covariates derived from the order book. Conditions for stationarity of the process are stated. An algorithm is presented to estimate the model even in the presence of billions of data points, possibly mapping covariates into a high dimensional space. The large sample size can be common for high frequency data applications using multiple liquid instruments. Convergence of the algorithm is shown, consistency results under weak conditions is established, and a test statistic to assess out of sample performance of different model specifications is suggested. The methodology is applied to the study of four stocks that trade on the New York Stock Exchange (NYSE). The out of sample testing procedure suggests that capturing the nonlinearity of the order book information adds value to the self exciting nature of high frequency trading events.
    Date: 2023–07
  2. By: Agostino Capponi (Columbia University); Albert J. Menkveld (Vrije Universiteit Amsterdam); Hongzhong Zhang (Columbia University)
    Abstract: We model the execution of large uninformed sell orders in the presence of strategic competitive market makers. We solve for the unique symmetric equilibrium of the model in closed-form. Our equilibrium findings provide a rationale for the empirically observed patterns of (i) short orders exhibiting higher intensity of execution and (ii) price pressure potentially subsiding before execution is completed. The model further generates a liquidity surface where the total price impact depends both on the size and duration of the order. Lastly, our analysis demonstrates that large orders unequivocally benefit market makers, while smaller investors stand to benefit only if the order trades with a sufficiently high intensity.
    Keywords: Liquidity, market makers, welfare covariance matrices
    JEL: G10
    Date: 2023–07–24
  3. By: Sergio Pulido; Mathieu Rosenbaum; Emmanouil Sfendourakis
    Abstract: Volume imbalance in a limit order book is often considered as a reliable indicator for predicting future price moves. In this work, we seek to analyse the nuances of the relationship between prices and volume imbalance. To this end, we study a market-making problem which allows us to view the imbalance as an optimal response to price moves. In our model, there is an underlying efficient price driving the mid-price, which follows the model with uncertainty zones. A single market maker knows the underlying efficient price and consequently the probability of a mid-price jump in the future. She controls the volumes she quotes at the best bid and ask prices. Solving her optimization problem allows us to understand endogenously the price-imbalance connection and to confirm in particular that it is optimal to quote a predictive imbalance. The value function of the market maker's control problem can be viewed as a family of functions, indexed by the level of the market maker's inventory, solving a coupled system of PDEs. We show existence and uniqueness of classical solutions to this coupled system of equations. In the case of a continuous inventory, we also prove uniqueness of the market maker's optimal control policy.
    Date: 2023–07
  4. By: Alfred Lehar; Christine Parlour; Marius Zoican
    Abstract: We study economies of scale in liquidity provision on decentralized exchanges, focusing on the impact of fixed transaction costs such as gas prices on liquidity providers (LPs). Small LPs are disproportionately affected by the fixed cost, resulting in liquidity supply fragmentation between low- and high-fee pools. Analyzing Uniswap data, we find that high-fee pools attract 56% of liquidity supply but execute only 35% of trading volume. Large (institutional) LPs dominate low-fee pools, frequently adjusting positions in response to substantial trading volume. In contrast, small (retail) LPs converge to high-fee pools, accepting lower execution probabilities to mitigate smaller liquidity management costs.
    Date: 2023–07
  5. By: Chuheng Zhang; Yitong Duan; Xiaoyu Chen; Jianyu Chen; Jian Li; Li Zhao
    Abstract: Optimized trade execution is to sell (or buy) a given amount of assets in a given time with the lowest possible trading cost. Recently, reinforcement learning (RL) has been applied to optimized trade execution to learn smarter policies from market data. However, we find that many existing RL methods exhibit considerable overfitting which prevents them from real deployment. In this paper, we provide an extensive study on the overfitting problem in optimized trade execution. First, we model the optimized trade execution as offline RL with dynamic context (ORDC), where the context represents market variables that cannot be influenced by the trading policy and are collected in an offline manner. Under this framework, we derive the generalization bound and find that the overfitting issue is caused by large context space and limited context samples in the offline setting. Accordingly, we propose to learn compact representations for context to address the overfitting problem, either by leveraging prior knowledge or in an end-to-end manner. To evaluate our algorithms, we also implement a carefully designed simulator based on historical limit order book (LOB) data to provide a high-fidelity benchmark for different algorithms. Our experiments on the high-fidelity simulator demonstrate that our algorithms can effectively alleviate overfitting and achieve better performance.
    Date: 2023–05
  6. By: David Ardia; Cl\'ement Aymard; Tolga Cenesizoglu
    Abstract: We analyze Robinhood (RH) investors' trading reactions to intraday hourly and overnight price changes. Contrasting with recent studies focusing on daily behaviors, we find that RH users strongly favor big losers over big gainers. We also uncover that they react rapidly, typically within an hour, when acquiring stocks that exhibit extreme negative returns. Further analyses suggest greater (lower) attention to overnight (intraday) movements and exacerbated behaviors post-COVID-19 announcement. Moreover, trading attitudes significantly vary across firm size and industry, with a more contrarian strategy towards larger-cap firms and a heightened activity on energy and consumer discretionary stocks.
    Date: 2023–07
  7. By: Hamed Amini; Maxim Bichuch; Zachary Feinstein
    Abstract: Prediction markets allow traders to bet on potential future outcomes. These markets exist for weather, political, sports, and economic forecasting. Within this work we consider a decentralized framework for prediction markets using automated market makers (AMMs). Specifically, we construct a liquidity-based AMM structure for prediction markets that, under reasonable axioms on the underlying utility function, satisfy meaningful financial properties on the cost of betting and the resulting pricing oracle. Importantly, we study how liquidity can be pooled or withdrawn from the AMM and the resulting implications to the market behavior. In considering this decentralized framework, we additionally propose financially meaningful fees that can be collected for trading to compensate the liquidity providers for their vital market function.
    Date: 2023–07
  8. By: Darrell Duffie; Michael J. Fleming; Frank M. Keane; Claire Nelson; Or Shachar; Peter Van Tassel
    Abstract: We show a significant loss in U.S. Treasury market functionality when intensive use of dealer balance sheets is needed to intermediate bond markets, as in March 2020. Although yield volatility explains most of the variation in Treasury market liquidity over time, when dealer balance sheet utilization reaches sufficiently high levels, liquidity is much worse than predicted by yield volatility alone. This is consistent with the existence of occasionally binding constraints on the intermediation capacity of bond markets.
    Keywords: Treasury market; liquidity; volatility; dealer intermediation; Value-at-Risk
    JEL: G01 G1 G12 G18 E58
    Date: 2023–08–01
  9. By: Umut Cetin; Kasper Larsen
    Abstract: In the dynamic discrete-time trading setting of Kyle (1985), we prove that Kyle's equilibrium model is stable when there are one or two trading times. For three or more trading times, we prove that Kyle's equilibrium is not stable. These theoretical results are proven to hold irrespectively of all Kyle's input parameters.
    Date: 2023–07

This nep-mst issue is ©2023 by Thanos Verousis. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
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