nep-mst New Economics Papers
on Market Microstructure
Issue of 2022‒03‒14
four papers chosen by
Thanos Verousis

  1. On Market Design and Latency Arbitrage By Wolfgang Kuhle
  2. Flow Trading By Eric Budish; Peter Cramton; Albert S. Kyle; Jeongmin Lee; David Malec
  3. Dynamic Autoregressive Liquidity (DArLiQ) By Hafner, C. M.
  4. Dual-CLVSA: a Novel Deep Learning Approach to Predict Financial Markets with Sentiment Measurements By Jia Wang; Hongwei Zhu; Jiancheng Shen; Yu Cao; Benyuan Liu

  1. By: Wolfgang Kuhle
    Abstract: We argue that contemporary stock market designs are, due to traders' inability to fully express their preferences over the execution times of their orders, prone to latency arbitrage. In turn, we propose a new order type which allows traders to specify the time at which their orders are executed after reaching the exchange. Using this order type, traders can synchronize order executions across different exchanges, such that high-frequency traders, even if they operate at the speed of light, can no-longer engage in latency arbitrage.
    Date: 2021–12
  2. By: Eric Budish (University of Chicago, Booth School of Business); Peter Cramton (University of Cologne and University of Maryland); Albert S. Kyle (University of Maryland); Jeongmin Lee (Olin Business School, Washington University in St.Louis); David Malec (University of Cologne and University of Maryland)
    Abstract: We propose a new market design for trading financial assets. The design combines three elements: (1) Orders are downward-sloping linear demand curves with quantities expressed as flows; (2)Markets clear in discrete time using uniform-price batch auctions; (3) Traders may submit orders for portfolios of assets, expressed as arbitrary linear combinations with positive and negative weights. Thus, relative to the status quo design: time is discrete instead of continuous, prices and quantities are continuous instead of discrete, and traders can directly trade arbitrary portfolios. Clearing prices and quantities are shown to exist, with the latter unique, despite the wide variety of preferences that can be expressed via portfolio orders; calculating prices and quantities is shown to be computationally feasible; microfoundations for portfolio orders are provided. The proposal addresses six concerns with the current market design: (1) sniping and the speed race; (2) the complexities and inefficiencies caused by tick-size constraints; (3) the cost and complexity of trading large quantities over time, (4) of trading portfolios, and (5) of providing liquidity in correlated assets; (6) fairness and transparency of optimal execution.
    Date: 2022–02
  3. By: Hafner, C. M.
    Abstract: We introduce a new class of semiparametric dynamic autoregressive models for the Amihud illiquidity measure, which captures both the long-run trend in the illiquidity series with a nonparametric component and the short-run dynamics with an autoregressive component. We develop a GMM estimator based on conditional moment restrictions and an efficient semiparametric ML estimator based on an iid assumption. We derive large sample properties for both estimators. We further develop a methodology to detect the occurrence of permanent and transitory breaks in the illiquidity process. Finally, we demonstrate the model performance and its empirical relevance on two applications. First, we study the impact of stock splits on the illiquidity dynamics of the five largest US technology company stocks. Second, we investigate how the different components of the illiquidity process obtained from our model relate to the stock market risk premium using data on the S&P 500 stock market index.
    Keywords: Nonparametric, Semiparametric, Splits, Structural Change
    JEL: C12 C14
    Date: 2022–02–23
  4. By: Jia Wang; Hongwei Zhu; Jiancheng Shen; Yu Cao; Benyuan Liu
    Abstract: It is a challenging task to predict financial markets. The complexity of this task is mainly due to the interaction between financial markets and market participants, who are not able to keep rational all the time, and often affected by emotions such as fear and ecstasy. Based on the state-of-the-art approach particularly for financial market predictions, a hybrid convolutional LSTM Based variational sequence-to-sequence model with attention (CLVSA), we propose a novel deep learning approach, named dual-CLVSA, to predict financial market movement with both trading data and the corresponding social sentiment measurements, each through a separate sequence-to-sequence channel. We evaluate the performance of our approach with backtesting on historical trading data of SPDR SP 500 Trust ETF over eight years. The experiment results show that dual-CLVSA can effectively fuse the two types of data, and verify that sentiment measurements are not only informative for financial market predictions, but they also contain extra profitable features to boost the performance of our predicting system.
    Date: 2022–01

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