nep-mst New Economics Papers
on Market Microstructure
Issue of 2022‒01‒10
three papers chosen by
Thanos Verousis

  1. Do fundamentals shape the price response? A critical assessment of linear impact models By Michele Vodret; Iacopo Mastromatteo; Bence T\'oth; Michael Benzaquen
  2. Trading Institutions in Experimental Asset Markets: Theory and Evidence By Bulent Guler; Volodymyr Lugovskyy; Daniela Puzzello; Steven Tucker
  3. Improved Method of Stock Trading under Reinforcement Learning Based on DRQN and Sentiment Indicators ARBR By Peng Zhou; Jingling Tang

  1. By: Michele Vodret; Iacopo Mastromatteo; Bence T\'oth; Michael Benzaquen
    Abstract: We compare the predictions of the stationary Kyle model, a microfounded multi-step linear price impact model in which market prices forecast fundamentals through information encoded in the order flow, with those of the propagator model, a purely data-driven model in which trades mechanically impact prices with a time-decaying kernel. We find that, remarkably, both models predict the exact same price dynamics at high frequency, due to the emergence of universality at small time scales. On the other hand, we find those models to disagree on the overall strength of the impact function by a quantity that we are able to relate to the amount of excess-volatility in the market. We reveal a crossover between a high-frequency regime in which the market reacts sub-linearly to the signed order flow, to a low-frequency regime in which prices respond linearly to order flow imbalances. Overall, we reconcile results from the literature on market microstructure (sub-linearity in the price response to traded volumes) with those relating to macroeconomically relevant timescales (in which a linear relation is typically assumed).
    Date: 2021–12
  2. By: Bulent Guler (Indiana University); Volodymyr Lugovskyy (Indiana University); Daniela Puzzello (Indiana University); Steven Tucker (University of Waikato)
    Abstract: We report the results of an experiment designed to study the role of trading institutions in the formation of bubbles and crashes in laboratory asset markets. We employ three trading institutions: Call Market, Double Auction and Tâtonnement. The results show that bubbles are significantly smaller in uniform-price institutions than in Double Auction. We reproduce this and other critical patterns of the data by calibrating a heterogeneous agent model with fundamental and myopic-noise traders. The model produces larger bubbles under Double Auction because multiple trades occur within a period, amplifying the impact of myopic traders with positive bias on transaction prices.
    Keywords: experimental asset markets; bubbles; traders' heterogeneity; trading institutions
    JEL: C90 C91 D03 G02 G12
    Date: 2021–12–21
  3. By: Peng Zhou; Jingling Tang
    Abstract: With the application of artificial intelligence in the financial field, quantitative trading is considered to be profitable. Based on this, this paper proposes an improved deep recurrent DRQN-ARBR model because the existing quantitative trading model ignores the impact of irrational investor behavior on the market, making the application effect poor in an environment where the stock market in China is non-efficiency. By changing the fully connected layer in the original model to the LSTM layer and using the emotion indicator ARBR to construct a trading strategy, this model solves the problems of the traditional DQN model with limited memory for empirical data storage and the impact of observable Markov properties on performance. At the same time, this paper also improved the shortcomings of the original model with fewer stock states and chose more technical indicators as the input values of the model. The experimental results show that the DRQN-ARBR algorithm proposed in this paper can significantly improve the performance of reinforcement learning in stock trading.
    Date: 2021–11

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