nep-mst New Economics Papers
on Market Microstructure
Issue of 2020‒07‒20
seven papers chosen by
Thanos Verousis

  1. Using full limit order book for price jump prediction By Mynbaev, Kairat
  2. Hidden Markov Models Applied To Intraday Momentum Trading With Side Information By Hugh Christensen; Simon Godsill; Richard Turner
  3. Optimal trade execution in an order book model with stochastic liquidity parameters By Julia Ackermann; Thomas Kruse; Mikhail Urusov
  4. Emissions Trading with Transaction Costs By Marc Baudry; Anouk Faure; Simon Quemin
  5. An overall view of key problems in algorithmic trading and recent progress By Micha\"el Karpe
  6. Optimal Trading with Differing Trade Signals By Ryan Donnelly; Matthew Lorig
  7. Deep Learning modeling of Limit Order Book: a comparative perspective By Antonio Briola; Jeremy Turiel; Tomaso Aste

  1. By: Mynbaev, Kairat
    Abstract: Institutional investors, especially high frequency traders, employ the order information contained in the Limit Order Book (LOB). The main purpose of the paper is to investigate how full information about the LOB can help in predicting the price jump. Normally, a full LOB contains total volumes of orders for hundreds of prices. Using the full information runs into the curse of dimensionality which manifests itself in multicollinearity, insignificant coefficients, inflated estimate variances and high computation time. Due to these problems, order volumes for prices that are distant from ask and bid prices are usually not used in prediction procedures. For this reason we call such information a silent crowd. Here we propose a summary measure of the silent crowd and quantify its influence on trade jump prediction. We use a realistically simulated LOB as a vehicle for experiments and logistic regression as the prediction tool. The full code in Matlab includes 18 blocks.
    Keywords: Simulation, trade jump prediction, high frequency trading, logistic regression, limit order book
    JEL: C25 C61 G12
    Date: 2020–06
  2. By: Hugh Christensen; Simon Godsill; Richard Turner
    Abstract: A Hidden Markov Model for intraday momentum trading is presented which specifies a latent momentum state responsible for generating the observed securities' noisy returns. Existing momentum trading models suffer from time-lagging caused by the delayed frequency response of digital filters. Time-lagging results in a momentum signal of the wrong sign, when the market changes trend direction. A key feature of this state space formulation, is no such lagging occurs, allowing for accurate shifts in signal sign at market change points. The number of latent states in the model is estimated using three techniques, cross validation, penalized likelihood criteria and simulation-based model selection for the marginal likelihood. All three techniques suggest either 2 or 3 hidden states. Model parameters are then found using Baum-Welch and Markov Chain Monte Carlo, whilst assuming a single (discretized) univariate Gaussian distribution for the emission matrix. Often a momentum trader will want to condition their trading signals on additional information. To reflect this, learning is also carried out in the presence of side information. Two sets of side information are considered, namely a ratio of realized volatilities and intraday seasonality. It is shown that splines can be used to capture statistically significant relationships from this information, allowing returns to be predicted. An Input Output Hidden Markov Model is used to incorporate these univariate predictive signals into the transition matrix, presenting a possible solution for dealing with the signal combination problem. Bayesian inference is then carried out to predict the securities $t+1$ return using the forward algorithm. Simple modifications to the current framework allow for a fully non-parametric model with asynchronous prediction.
    Date: 2020–06
  3. By: Julia Ackermann; Thomas Kruse; Mikhail Urusov
    Abstract: We analyze an optimal trade execution problem in a financial market with stochastic liquidity. To this end we set up a limit order book model in which both order book depth and resilience evolve randomly in time. Trading is allowed in both directions and at discrete points in time. We derive an explicit recursion that, under certain structural assumptions, characterizes minimal execution costs. We also discuss several qualitative aspects of optimal strategies, such as existence of profitable round trips or closing the position in one go, and compare our findings with the literature.
    Date: 2020–06
  4. By: Marc Baudry (EconomiX (Université Paris Nanterre) & Chaire Economie du Climat (PSL)); Anouk Faure (EconomiX (Université Paris Nanterre) & Chaire Economie du Climat (PSL)); Simon Quemin (Grantham Research Institute (LSE) & Chaire Economie du Climat (PSL))
    Abstract: We develop an equilibrium model of emissions permit trading in the presence of ï¬ xed and proportional trading costs in which the permit price and ï¬ rms’ participation in and extent of trading are endogenously determined. We analyze the sensitivity of the equilibrium to changes in the trading costs and ï¬ rms’ allocations, and characterize situations where the trading costs alternatively depress or raise permit prices relative to frictionless market conditions. We calibrate our model to annual transaction and compliance data in Phase II of the EU ETS (2008-2012) which we consolidate at the ï¬ rm level. We ï¬ nd that trading costs in the order of 10 k€ per annum plus 1€ per permit traded substantially reduce discrepancies between observations and theoretical predictions for ï¬ rms’ behavior (e.g. autarkic compliance). Our simulations suggest that ignoring trading costs leads to an underestimation of the price impacts of supply-curbing policies, this difference varying with the incidence on ï¬ rms.
    Keywords: Emissions trading, Transaction costs, Policy design and evaluation, EU ETS
    JEL: D22 D23 H23 Q52 Q58
    Date: 2020–06
  5. By: Micha\"el Karpe
    Abstract: We summarize the fundamental issues at stake in algorithmic trading, and the progress made in this field over the last twenty years. We first present the key problems of algorithmic trading, describing the concepts of optimal execution, optimal placement, and price impact. We then discuss the most recent advances in algorithmic trading through the use of Machine Learning, discussing the use of Deep Learning, Reinforcement Learning, and Generative Adversarial Networks.
    Date: 2020–06
  6. By: Ryan Donnelly; Matthew Lorig
    Abstract: We consider the problem of maximizing portfolio value when an agent has a subjective view on asset value which differs from the traded market price. The agent's trades will have a price impact which affect the price at which the asset is traded. In addition to the agent's trades affecting the market price, the agent may change his view on the asset's value if its difference from the market price persists. We also consider a situation of several agents interacting and trading simultaneously when they have a subjective view on the asset value. Two cases of the subjective views of agents are considered, one in which they all share the same information, and one in which they all have an individual signal correlated with price innovations. To study the large agent problem we take a mean-field game approach which remains tractable. After classifying the mean-field equilibrium we compute the cross-sectional distribution of agents' inventories and the dependence of price distribution on the amount of shared information among the agents.
    Date: 2020–06
  7. By: Antonio Briola; Jeremy Turiel; Tomaso Aste
    Abstract: The present work addresses theoretical and practical questions in the domain of Deep Learning for High Frequency Trading, with a thorough review and analysis of the literature and state-of-the-art models. Random models, Logistic Regressions, LSTMs, LSTMs equipped with an Attention mask, CNN-LSTMs and MLPs are compared on the same tasks, feature space, and dataset and clustered according to pairwise similarity and performance metrics. The underlying dimensions of the modeling techniques are hence investigated to understand whether these are intrinsic to the Limit Order Book's dynamics. It is possible to observe that the Multilayer Perceptron performs comparably to or better than state-of-the-art CNN-LSTM architectures indicating that dynamic spatial and temporal dimensions are a good approximation of the LOB's dynamics, but not necessarily the true underlying dimensions.
    Date: 2020–07

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