nep-mst New Economics Papers
on Market Microstructure
Issue of 2023‒01‒16
seven papers chosen by
Thanos Verousis

  1. Broker colocation and the execution costs of customer and proprietary orders By Sagade, Satchit; Scharnowski, Stefan; Westheide, Christian
  2. Systemic fragility in decentralised markets By Alfred Lehar; Christine A Parlour
  3. A machine learning approach to support decision in insider trading detection By Piero Mazzarisi; Adele Ravagnani; Paola Deriu; Fabrizio Lillo; Francesca Medda; Antonio Russo
  4. Drivers and effects of stock market fragmentation - Insights on SME stocks By Lausen, Jens; Clapham, Benjamin; Gomber, Peter; Bender, Micha
  5. Measuring price impact and information content of trades in a time-varying setting By F. Campigli; G. Bormetti; F. Lillo
  6. Short sale bans may improve market quality during crises: New evidence from the 2020 Covid By Fohlin, Caroline; Lu, Zhikun; Zhou, Nan
  7. Strategic Sophistication and Trading Profits: An Experiment with Professional Traders By Marco Angrisani; Marco Cipriani; Antonio Guarino

  1. By: Sagade, Satchit; Scharnowski, Stefan; Westheide, Christian
    Abstract: Colocation services offered by stock exchanges enable market participants to achieve execution costs for large orders that are substantially lower and less sensitive to transacting against high-frequency traders. However, these benefits manifest only for orders executed on the colocated brokers' own behalf, whereas customers' order execution costs are substantially higher. Analyses of individual order executions indicate that customer orders originating from colocated brokers are less actively monitored and achieve inferior execution quality. This suggests that brokers do not make effective use of their technology, possibly due to agency frictions or poor algorithm selection and parameter choice by customers.
    Keywords: Execution Cost,Institutional Investor,Broker,High-Frequency Trading,Colocation
    JEL: G10 G14 G15
    Date: 2022
  2. By: Alfred Lehar; Christine A Parlour
    Abstract: We analyze a unique data set of collateral liquidations on two Decentralized Finance lending platforms – Compound and Aave. Such liquidations require arbitrageurs to repay the loan in return for the discounted collateral. Using Blockchain transaction data, we observe if arbitrageurs liquidate positions out of their own inventory or obtain "flash loans." To repay flash loans, arbitrageurs immediately sell the collateral asset. We document the high frequency price impact of such liquidity trades on nine different decentralized exchanges. Consistent with large block trades in equity markets there is a temporary and permanent price impact of collateral asset sales in DeFi. We document the effect of these trades on return distributions. Our work highlights the systemic fragility of decentralized markets.
    Keywords: Decentralized lending, blockchain, decentralized finance, system risk
    JEL: G1 G23
    Date: 2022–12
  3. By: Piero Mazzarisi; Adele Ravagnani; Paola Deriu; Fabrizio Lillo; Francesca Medda; Antonio Russo
    Abstract: Identifying market abuse activity from data on investors' trading activity is very challenging both for the data volume and for the low signal to noise ratio. Here we propose two complementary unsupervised machine learning methods to support market surveillance aimed at identifying potential insider trading activities. The first one uses clustering to identify, in the vicinity of a price sensitive event such as a takeover bid, discontinuities in the trading activity of an investor with respect to his/her own past trading history and on the present trading activity of his/her peers. The second unsupervised approach aims at identifying (small) groups of investors that act coherently around price sensitive events, pointing to potential insider rings, i.e. a group of synchronised traders displaying strong directional trading in rewarding position in a period before the price sensitive event. As a case study, we apply our methods to investor resolved data of Italian stocks around takeover bids.
    Date: 2022–12
  4. By: Lausen, Jens; Clapham, Benjamin; Gomber, Peter; Bender, Micha
    Abstract: We analyze how market fragmentation affects market quality of SME and other less actively traded stocks. Compared to large stocks, they are less likely to be traded on multiple venues and show, if at all, low levels of fragmentation. Concerning the impact of fragmentation on market quality, we find evidence for a hockey stick effect: Fragmentation has no effect for infrequently traded stocks, a negative effect on liquidity of slightly more active stocks, and increasing benefits for liquidity of large and actively traded stocks. Consequently, being traded on multiple venues is not necessarily harmful for SME stock market quality.
    Keywords: Market Microstructure,Market Fragmentation,Securities Market Regulation,Market Quality,SME Trading
    JEL: G10 G14
    Date: 2022
  5. By: F. Campigli; G. Bormetti; F. Lillo
    Abstract: The estimation of market impact is crucial for measuring the information content of trades and for transaction cost analysis. Hasbrouck's (1991) seminal paper proposed a Structural-VAR (S-VAR) to jointly model mid-quote changes and trade signs. Recent literature has highlighted some pitfalls of this approach: S-VAR models can be misspecified when the impact function has a non-linear relationship with the trade sign, and they lack parsimony when they are designed to capture the long memory of the order flow. Finally, the instantaneous impact of a trade is constant, while market liquidity highly fluctuates in time. This paper fixes these limitations by extending Hasbrouck's approach in several directions. We consider a nonlinear model where we use a parsimonious parametrization allowing to consider hundreds of past lags. Moreover we adopt an observation driven approach to model the time-varying impact parameter, which adapts to market information flow and can be easily estimated from market data. As a consequence of the non-linear specification of the dynamics, the trade information content is conditional both on the local level of liquidity, as modeled by the dynamic instantaneous impact coefficient, and on the state of the market. By analyzing NASDAQ data, we find that impact follows a clear intra-day pattern and quickly reacts to pre-scheduled announcements, such as those released by the FOMC. We show that this fact has relevant consequences for transaction cost analysis by deriving an expression for the permanent impact from the model parameters and connecting it with the standard regression procedure. Monte Carlo simulations and empirical analyses support the reliability of our approach, which exploits the complete information of tick-by-tick prices and trade signs without the need for aggregation on a macroscopic time scale.
    Date: 2022–12
  6. By: Fohlin, Caroline; Lu, Zhikun; Zhou, Nan
    Abstract: In theory, banning short selling stabilizes stock prices but undermines pricing efficiency and has ambiguous impacts on market liquidity. Empirical studies find mixed and conflicting results. This paper leverages cross-country policy variation during the 2020 Covid crisis to assess differential impacts of bans on stock liquidity, prices, and volatility. Results suggest that bans improved liquidity and stabilized prices for illiquid stocks but temporarily diminished liquidity for highly liquid stocks.The findings support theories in which short sale bans may improve liquidity by selectively filtering out informed- potentially predatory-traders. Thus, policies that target the most illiquid stocks may deliver better overall market quality than uniform short sale bans imposed on all stocks.
    Date: 2022
  7. By: Marco Angrisani; Marco Cipriani; Antonio Guarino
    Abstract: We run an experiment where professional traders, endowed with private information, trade an asset over multiple periods. After the trading game, we gather information about the professional traders’ characteristics by having them carry out a series of tasks. We study which of these characteristics predict profits in the trading game. We find that strategic sophistication, as measured in the Guessing Game (for example, through level-k theory), is the only significant determinant of professional traders’ profits. In contrast, profits are not driven by individual characteristics such as cognitive abilities or behavioral traits. Moreover, higher profits are due to the ability to trade at favorable prices rather than to the ability to earn higher dividends. Comparing these results to those of a sample of students, we show that whereas cognitive skills are important for students, they are not for traders, whereas the opposite is the case for strategic sophistication.
    Keywords: experiments; financial markets; professional traders; strategic sophistication
    JEL: C93 G11 G14
    Date: 2022–12–01

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