nep-mst New Economics Papers
on Market Microstructure
Issue of 2023‒09‒25
five papers chosen by
Thanos Verousis

  1. Algorithmic Trading, Price Efficiency and Welfare: An Experimental Approach By Corgnet, Brice; DeSantis, Mark; Siemroth, Christoph
  2. JAX-LOB: A GPU-Accelerated limit order book simulator to unlock large scale reinforcement learning for trading By Sascha Frey; Kang Li; Peer Nagy; Silvia Sapora; Chris Lu; Stefan Zohren; Jakob Foerster; Anisoara Calinescu
  3. Investigating Short-Term Dynamics in Green Bond Markets By Lorenzo Mercuri; Andrea Perchiazzo; Edit Rroji
  4. To the Moon: Analyzing Collective Trading Events on the Wings of Sentiment Analysis By Tim Matthies; Thomas L\"ohden; Stephan Leible; Jun-Patrick Raabe
  5. Dealers' Treasury Market Intermediation and the Supplementary Leverage Ratio By Paul Cochran; Sebastian Infante; Lubomir Petrasek; Zack Saravay; Mary Tian

  1. By: Corgnet, Brice; DeSantis, Mark; Siemroth, Christoph
    Abstract: We develop a novel experimental paradigm to study the causal impact of two classes of trading algorithms on price efficiency, trading volume, liquidity, and welfare. In our design, public information about the asset value is revealed during trading, which gives algorithms a reaction speed advantage. We distinguish market-order (aggressive) and limit-order (passive) algorithms, which replace human traders from the baseline markets. Relative to human-only markets, limit-order algorithms improve welfare, although human traders do not benefit, as the surplus is captured by the algorithms. Market-order algorithms do not change welfare, though they do lower human traders’ profits. Both types of algorithms improve price efficiency, lower volatility, and increase the share of profits for unsophisticated human traders. Our results offer unique evidence that non-exploitative algorithms can enhance welfare and be beneficial to unsophisticated traders.
    Keywords: Algorithmic Trading, Experimental Markets, High-Frequency Trading, Price Efficiency, News Announcements, Welfare
    Date: 2023–08–30
  2. By: Sascha Frey; Kang Li; Peer Nagy; Silvia Sapora; Chris Lu; Stefan Zohren; Jakob Foerster; Anisoara Calinescu
    Abstract: Financial exchanges across the world use limit order books (LOBs) to process orders and match trades. For research purposes it is important to have large scale efficient simulators of LOB dynamics. LOB simulators have previously been implemented in the context of agent-based models (ABMs), reinforcement learning (RL) environments, and generative models, processing order flows from historical data sets and hand-crafted agents alike. For many applications, there is a requirement for processing multiple books, either for the calibration of ABMs or for the training of RL agents. We showcase the first GPU-enabled LOB simulator designed to process thousands of books in parallel, with a notably reduced per-message processing time. The implementation of our simulator - JAX-LOB - is based on design choices that aim to best exploit the powers of JAX without compromising on the realism of LOB-related mechanisms. We integrate JAX-LOB with other JAX packages, to provide an example of how one may address an optimal execution problem with reinforcement learning, and to share some preliminary results from end-to-end RL training on GPUs.
    Date: 2023–08
  3. By: Lorenzo Mercuri; Andrea Perchiazzo; Edit Rroji
    Abstract: The paper investigates the effect of the label green in bond markets from the lens of the trading activity. The idea is that jumps in the dynamics of returns have a specific memory nature that can be well represented through a self-exciting process. Specifically, using Hawkes processes where the intensity is described through a continuous time moving average model, we study the high-frequency dynamics of bond prices. We also introduce a bivariate extension of the model that deals with the cross-effect of upward and downward price movements. Empirical results suggest that differences emerge if we consider periods with relevant interest rate announcements, especially in the case of an issuer operating in the energy market.
    Date: 2023–08
  4. By: Tim Matthies; Thomas L\"ohden; Stephan Leible; Jun-Patrick Raabe
    Abstract: This research investigates the growing trend of retail investors participating in certain stocks by organizing themselves on social media platforms, particularly Reddit. Previous studies have highlighted a notable association between Reddit activity and the volatility of affected stocks. This study seeks to expand the analysis to Twitter, which is among the most impactful social media platforms. To achieve this, we collected relevant tweets and analyzed their sentiment to explore the correlation between Twitter activity, sentiment, and stock volatility. The results reveal a significant relationship between Twitter activity and stock volatility but a weak link between tweet sentiment and stock performance. In general, Twitter activity and sentiment appear to play a less critical role in these events than Reddit activity. These findings offer new theoretical insights into the impact of social media platforms on stock market dynamics, and they may practically assist investors and regulators in comprehending these phenomena better.
    Date: 2023–08
  5. By: Paul Cochran; Sebastian Infante; Lubomir Petrasek; Zack Saravay; Mary Tian
    Abstract: Treasury market intermediation by dealers, including Treasury securities market making and financing, requires regulatory capital. In particular, the six largest U.S. Treasury securities dealers are subsidiaries of large U.S. bank holding companies (BHCs), which are required to maintain a supplementary leverage ratio (SLR) of at least 5 percent at the BHC level.
    Date: 2023–08–03

This nep-mst issue is ©2023 by Thanos Verousis. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.