nep-mst New Economics Papers
on Market Microstructure
Issue of 2023‒07‒17
six papers chosen by
Thanos Verousis

  1. Deep Attentive Survival Analysis in Limit Order Books: Estimating Fill Probabilities with Convolutional-Transformers By Alvaro Arroyo; Alvaro Cartea; Fernando Moreno-Pino; Stefan Zohren
  2. Dark trading and alternative execution priority rules By Bernales, Alejandro; Ladley, Daniel; Litos, Evangelos; Valenzuela, Marcela
  3. Optimal execution and speculation with trade signals By Peter Bank; \'Alvaro Cartea; Laura K\"orber
  4. The Cost of Misspecifying Price Impact By Natascha Hey; Jean-Philippe Bouchaud; Iacopo Mastromatteo; Johannes Muhle-Karbe; Kevin Webster
  5. Are Cryptos Different? Evidence from Retail Trading By Shimon Kogan; Igor Makarov; Marina Niessner; Antoinette Schoar
  6. Multivariate Simulation-based Forecasting for Intraday Power Markets: Modelling Cross-Product Price Effects By Simon Hirsch; Florian Ziel

  1. By: Alvaro Arroyo; Alvaro Cartea; Fernando Moreno-Pino; Stefan Zohren
    Abstract: One of the key decisions in execution strategies is the choice between a passive (liquidity providing) or an aggressive (liquidity taking) order to execute a trade in a limit order book (LOB). Essential to this choice is the fill probability of a passive limit order placed in the LOB. This paper proposes a deep learning method to estimate the filltimes of limit orders posted in different levels of the LOB. We develop a novel model for survival analysis that maps time-varying features of the LOB to the distribution of filltimes of limit orders. Our method is based on a convolutional-Transformer encoder and a monotonic neural network decoder. We use proper scoring rules to compare our method with other approaches in survival analysis, and perform an interpretability analysis to understand the informativeness of features used to compute fill probabilities. Our method significantly outperforms those typically used in survival analysis literature. Finally, we carry out a statistical analysis of the fill probability of orders placed in the order book (e.g., within the bid-ask spread) for assets with different queue dynamics and trading activity.
    Date: 2023–06
  2. By: Bernales, Alejandro; Ladley, Daniel; Litos, Evangelos; Valenzuela, Marcela
    Abstract: Traders' choice between lit and dark trading venues depends on market conditions, which are affected by execution priority rules in the dark pool, adverse selection, and traders' competition. We show that dark trading activity has a non-linear relationship with asset volatility and liquidity, which explains previous mixed empirical results regarding the impact of dark pools on market quality. The introduction of dark pools increases welfare only for speculators, while other traders (even large traders) are worse off. Importantly, we show that a size execution priority rule improves global welfare and liquidity relative to a time execution priority for dark orders.
    Keywords: dark pool; limit order market; execution priority rules; liquidity; welfare
    JEL: C63 C73 D40 D81 G11 G14
    Date: 2021–06–18
  3. By: Peter Bank; \'Alvaro Cartea; Laura K\"orber
    Abstract: We propose a price impact model where changes in prices are purely driven by the order flow in the market. The stochastic price impact of market orders and the arrival rates of limit and market orders are functions of the market liquidity process which reflects the balance of the demand and supply of liquidity. Limit and market orders mutually excite each other so that liquidity is mean reverting. We use the theory of Meyer-$\sigma$-fields to introduce a short-term signal process from which a trader learns about imminent changes in order flow. In this setting, we examine an optimal execution problem and derive the Hamilton--Jacobi--Bellman (HJB) equation for the value function. The HJB equation is solved numerically and we illustrate how the trader uses the signal to enhance the performance of execution problems and to execute speculative strategies.
    Date: 2023–06
  4. By: Natascha Hey; Jean-Philippe Bouchaud; Iacopo Mastromatteo; Johannes Muhle-Karbe; Kevin Webster
    Abstract: Portfolio managers' orders trade off return and trading cost predictions. Return predictions rely on alpha models, whereas price impact models quantify trading costs. This paper studies what happens when trades are based on an incorrect price impact model, so that the portfolio either over- or under-trades its alpha signal. We derive tractable formulas for these misspecification costs and illustrate them on proprietary trading data. The misspecification costs are naturally asymmetric: underestimating impact concavity or impact decay shrinks profits, but overestimating concavity or impact decay can even turn profits into losses.
    Date: 2023–06
  5. By: Shimon Kogan; Igor Makarov; Marina Niessner; Antoinette Schoar
    Abstract: Trading in cryptocurrencies has grown rapidly over the last decade, primarily dominated by retail investors. Using a dataset of 200, 000 retail traders from eToro, we show that they have a different model of the underlying price dynamics in cryptocurrencies relative to other assets. Retail traders in our sample are contrarian in stocks and gold, yet the same traders follow a momentum-like strategy in cryptocurrencies. Individual characteristics do not explain the differences in how people trade cryptocurrencies versus stocks, suggesting that our results are orthogonal to differences in investor composition or clientele effects. Furthermore, our findings are not explained by inattention, differences in fees, or preference for lotterylike stocks. We conjecture that retail investors hold a model of cryptocurrency prices, where price changes imply a change in the likelihood of future widespread adoption, which in turn pushes asset prices further in the same direction.
    JEL: G12 G14 G41
    Date: 2023–06
  6. By: Simon Hirsch; Florian Ziel
    Abstract: Intraday electricity markets play an increasingly important role in balancing the intermittent generation of renewable energy resources, which creates a need for accurate probabilistic price forecasts. However, research to date has focused on univariate approaches, while in many European intraday electricity markets all delivery periods are traded in parallel. Thus, the dependency structure between different traded products and the corresponding cross-product effects cannot be ignored. We aim to fill this gap in the literature by using copulas to model the high-dimensional intraday price return vector. We model the marginal distribution as a zero-inflated Johnson's $S_U$ distribution with location, scale and shape parameters that depend on market and fundamental data. The dependence structure is modelled using latent beta regression to account for the particular market structure of the intraday electricity market, such as overlapping but independent trading sessions for different delivery days. We allow the dependence parameter to be time-varying. We validate our approach in a simulation study for the German intraday electricity market and find that modelling the dependence structure improves the forecasting performance. Additionally, we shed light on the impact of the single intraday coupling (SIDC) on the trading activity and price distribution and interpret our results in light of the market efficiency hypothesis. The approach is directly applicable to other European electricity markets.
    Date: 2023–06

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