nep-mst New Economics Papers
on Market Microstructure
Issue of 2016‒01‒29
eight papers chosen by
Thanos Verousis
University of Bath

  1. The Dynamics of Ex-ante High-Frequency Liquidity: An Empirical Analysis By Dionne, Georges; Zhou, Xiaozhou
  2. Centralized trading, transparency and interest rate swap market liquidity: evidence from the implementation of the Dodd-Frank Act By Benos, Evangelos; Payne, Richard; Vasios, Michalis
  3. Extended Abstract: Neural Networks for Limit Order Books By Justin Sirignano
  4. The role of intra-day volatility pattern in jump detection: empirical evidence on how financial markets respond to macroeconomic news announcements By Yao, Wenying; Tian, Jing
  5. Testing for Causality in Continuous Time Bayesian Network Models of High-Frequency Data By Jonas Hallgren; Timo Koski
  6. High frequency characterization of Indian banking stocks By Sayaeed, Mohammad Abu; Dungey, Mardi; Yao, Wenying
  7. Are rankings of financial analysts useful to investors? By Artur Aiguzhinov; Ana Paula Serra; Carlos Soares
  8. Speculative Futures Trading under Mean Reversion By Tim Leung; Jiao Li; Xin Li; Zheng Wang

  1. By: Dionne, Georges (HEC Montreal, Canada Research Chair in Risk Management); Zhou, Xiaozhou (HEC Montreal, Canada Research Chair in Risk Management)
    Abstract: The ex-ante liquidity embedded in an open Limit Order Book (LOB) and its dynamics have been one of the most important issues in financial research and evolves with the development of financial infrastructure. Using the tick-by-tick data and the reconstructed open LOB data from the Xetra trading system, we investigate the impact of trade duration, quote duration and other exogenous variables on ex-ante liquidity embedded in an open LOB. By taking into account Ultra High Frequency (UHF) data, our modeling involves decomposing the joint distribution of the ex-ante liquidity measure into simple and interpretable distributions. The decomposed factors are Activity, Direction and Size. Our results suggest that trade durations and quote durations do influence the ex-ante liquidity changes. Short-run variables, such as spread change and volume, also predict the probability of liquidity changes. The long-term variable trade imbalance is less informative.
    Keywords: Decomposition model; Limit order book; Xetra Liquidity Measure (XLM); Ex-ante liquidity; LogACD process.
    JEL: G11 G12
    Date: 2016–01–14
  2. By: Benos, Evangelos (Bank of England); Payne, Richard (Cass Business School); Vasios, Michalis (Bank of England)
    Abstract: We use transactional data from the USD and EUR segments of the plain vanilla interest rate swap market to assess the impact of the Dodd-Frank mandate that US persons must trade certain swap contracts on Swap Execution Facilities (SEFs). We find that, as a result of SEF trading, activity increases and liquidity improves across the swap market, with the improvement being largest for USD mandated contracts which are most affected by the mandate. The associated reduction in execution costs is economically significant. For example, execution costs in USD mandated contracts, where SEF penetration is highest, drop, for market end-users alone, by $3 million–$4 million daily relative to EUR mandated contracts and in total by about $7 million–$13 million daily. We also find that inter-dealer activity drops concurrently with the improvement in liquidity suggesting that execution costs may have fallen because dealer intermediation chains became shorter. Finally, we document that the Dodd-Frank mandate caused the activity of the EUR segment of the market to geographically fragment. However, this does not appear to have compromised liquidity. Overall, our results suggest that the improvements in transparency brought about by the Dodd-Frank trading mandate have substantially improved interest rate swap market liquidity.
    Keywords: Swap Execution Facilities; transparency; market liquidity
    JEL: G10 G12 G14
    Date: 2016–01–15
  3. By: Justin Sirignano
    Abstract: We design and test neural networks for modeling the dynamics of the limit order book. In addition to testing traditional neural networks originally designed for classification, we develop a new neural network architecture for modeling spatial distributions (i.e., distributions on $\mathbb{R}^d$) which takes advantage of local spatial structure. Model performance is tested on 140 S\&P 500 and NASDAQ-100 stocks. The neural networks are trained using information from deep into the limit order book (i.e., many levels beyond the best bid and best ask). Techniques from deep learning such as dropout are employed to improve performance. Due to the computational challenges associated with the large amount of data, the neural networks are trained using GPU clusters. The neural networks are shown to outperform simpler models such as the naive empirical model and logistic regression, and the new neural network for spatial distributions outperforms the standard neural network.
    Date: 2016–01
  4. By: Yao, Wenying (Tasmanian School of Business & Economics, University of Tasmania); Tian, Jing (Tasmanian School of Business & Economics, University of Tasmania)
    Abstract: This paper examines the effect of adjusting for the intra-day volatility pattern on jump detection. Using tests that identify the intra-day timing of jumps, we show that before the adjustment, jumps in the financial market have high probability of occurring concurrently with pre-scheduled economy-wide news announcements. We demonstrate that adjustment for the U-shaped volatility pattern prior to jump detection effectively removes most of the association between jumps and macroeconomic news announcements. We find empirical evidence that only news that comes with large surprise can cause jumps in the market index after the volatility adjustment, while the effect of other types of news is largely absorbed through the continuous volatility channel. The FOMC meeting announcement is shown to have the highest association with jumps in the market both before and after the adjustment.
    Keywords: volatility pattern, intra-day jumps, news announcements, high frequency data
    JEL: C58 C12 G14
    Date: 2015
  5. By: Jonas Hallgren; Timo Koski
    Abstract: Continuous time Bayesian networks are investigated with a special focus on their ability to express causality. A framework is presented for doing inference in these networks. The central contributions are a representation of the intensity matrices for the networks and the introduction of a causality measure. A new model for high-frequency financial data is presented. It is calibrated to market data and by the new causality measure it performs better than older models.
    Date: 2016–01
  6. By: Sayaeed, Mohammad Abu (Tasmanian School of Business & Economics, University of Tasmania); Dungey, Mardi (Tasmanian School of Business & Economics, University of Tasmania); Yao, Wenying (Tasmanian School of Business & Economics, University of Tasmania)
    Abstract: Using high-frequency stock returns in the Indian banking sector we find that the beta on jump movements substantially exceeds that on the continuous component, and that the majority of the information content for returns lies with the jump beta. We contribute to the debate on strategies to decrease systemic risk, showing that increased bank capital and reduced leverage reduce both jump and continuous beta - with slightly stronger effects for capital on continuous beta and stronger effects for leverage on jump beta. However, changes in these firm characteristics need to be large to create an economically meaningful change in beta.
    Keywords: CAPM, jump, high frequency, India
    JEL: C58 G21 G28
    Date: 2015–02–03
  7. By: Artur Aiguzhinov (Faculty of Economics & cef.up, University of Porto; Institute for Systems and Computer Engineering, Technology and Science); Ana Paula Serra (Faculty of Economics & cef.up, University of Porto); Carlos Soares (Faculty of Engineering, University of Porto; Institute for Systems and Computer Engineering, Technology and Science)
    Abstract: Several institutions issue rankings of financial analysts based on the accuracy of their price and EPS forecasts. Given that these rankings are expost they may not be useful to investors. In this paper we show that trading strategies based on perfect foresight and on past rankings outperform a passive strategy. In addition, we report that investors are better off following analysts that issue accurate price targets rather than following those with accurate EPS forecasts.
    Keywords: Keywords: financial analysts; rankings; target price forecasts; earnings forecasts; portfolio management
    JEL: G11 G14 G24 G29
    Date: 2016–01
  8. By: Tim Leung; Jiao Li; Xin Li; Zheng Wang
    Abstract: This paper studies the problem of trading futures with transaction costs when the underlying spot price is mean-reverting. Specifically, we model the spot dynamics by the Ornstein-Uhlenbeck (OU), Cox-Ingersoll-Ross (CIR), or exponential Ornstein-Uhlenbeck (XOU) model. The futures term structure is derived and its connection to futures price dynamics is examined. For each futures contract, we describe the evolution of the roll yield, and compute explicitly the expected roll yield. For the futures trading problem, we incorporate the investor's timing option to enter or exit the market, as well as a chooser option to long or short a futures upon entry. This leads us to formulate and solve the corresponding optimal double stopping problems to determine the optimal trading strategies. Numerical results are presented to illustrate the optimal entry and exit boundaries under different models. We find that the option to choose between a long or short position induces the investor to delay market entry, as compared to the case where the investor pre-commits to go either long or short.
    Date: 2016–01

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