nep-mst New Economics Papers
on Market Microstructure
Issue of 2021‒03‒01
four papers chosen by
Thanos Verousis

  1. Quantifying the High-Frequency Trading “Arms Race†: A Simple New Methodology and Estimates By Matteo Aquilina; Eric Budish
  2. Market Efficiency, Behavior and Information Asymmetry: Empirical Evidence from Cryptocurrency and Stock Markets By Häfner, David
  3. Modeling Price Clustering in High-Frequency Prices By Vladim\'ir Hol\'y; Petra Tomanov\'a
  4. Trading Frictions and the Post-Earnings-Announcement Drift By Josef Fink; Stefan Palan; Erik Theissen

  1. By: Matteo Aquilina (Financial Conduct Authority); Eric Budish (University of Chicago - Booth School of Business; NBER)
    Abstract: We use stock exchange message data to quantify the negative aspect of high-frequency trading, known as “latency arbitrage.†The key difference between message data and widely-familiar limit order book data is that message data contain attempts to trade or cancel that fail. This allows the researcher to observe both winners and losers in a race, whereas in limit order book data you cannot see the losers, so you cannot directly see the races. We find that latency-arbitrage races are very frequent (about one per minute per symbol for FTSE 100 stocks), extremely fast (the modal race lasts 5-10 millionths of a second), and account for a large portion of overall trading volume (about 20%). Race participation is concentrated, with the top 6 firms accounting for over 80% of all race wins and losses. Most races (about 90%) are won by an aggressive order as opposed to a cancel attempt; market participants outside the top 6 firms disproportionately provide the liquidity that gets taken in races (about 60%). Our main estimates suggest that eliminating latency arbitrage would reduce the market’s cost of liquidity by 17% and that the total sums at stake are on the order of $5 billion annually in global equity markets.
    Date: 2020
  2. By: Häfner, David
    Abstract: This dissertation is dedicated to the analysis of three superordinate economic principles in varying market environments: market efficiency, the behavior of market participants and information asymmetry. Sustainability and social responsibility have gained importance as investment criteria in recent years. However, responsible investing can lead to conflicting goals with respect to utility-maximizing behavior and portfolio diversification in efficient markets. Conducting a meta-analysis, this thesis presents evidence that positive (non-monetary) side effects of responsible investing can overcome this burden. Next, the impact of the EU-wide regulation of investment research on the interplay between information asymmetry, idiosyncratic risk, liquidity and the role of financial analysts in stock markets is investigated. An empirical analysis of the emerging primary and secondary market for cryptocurrencies yields further insights about the effects of information asymmetry between investors, issuers and traders. The efficient allocation of resources is dependent on the market microstructure, the behavior of market participants, as well as exogenous shocks. Against this background, this thesis is dedicated to the empirical analysis of limit order books, the rationality of traders and the impact of COVID-19. Due to its young history, the market for cryptocurrencies yields a suitable research subject to test classical financial theories. This doctoral thesis reveals parallels between the microstructure of cryptocurrency and stock markets and uncovers some previously unknown statistical properties of the cryptocurrency market microstructure. An initial examination of the impact of COVID-19 further shows that cryptocurrencies with a high market capitalization seem to react to macroeconomic shocks similar to stock markets. This cumulative dissertation comprises six stand-alone papers, of which three papers have already been published.
    Date: 2021
  3. By: Vladim\'ir Hol\'y; Petra Tomanov\'a
    Abstract: The price clustering phenomenon, i.e. an increased occurence of specific prices, is widely observed and well-documented for various financial instruments in various financial markets. In the literature, however, it is rarely incorporated into price models. We consider that there are several types of agents trading only in specific multiples of the tick size resulting in an increased occurrence of these multiples in prices. For example, stocks on the NYSE and NASDAQ exchanges are traded with precision to one cent but multiples of five cents and ten cents occur much more often in prices. To capture this behaviour, we propose a discrete price model based on a mixture of double Poisson distributions with dynamic volatility and dynamic proportions of agent types. The model is estimated by the maximum likelihood method. In an empirical study of DJIA stocks, we find that higher instantaneous volatility leads to weaker price clustering at the ultra-high frequency. This is in sharp contrast with results at low frequencies which show that daily realized volatility has positive impact on price clustering.
    Date: 2021–02
  4. By: Josef Fink (Institute of Banking and Finance, University of Graz); Stefan Palan (Institute of Banking and Finance, University of Graz); Erik Theissen (Finance Area, University of Mannheim)
    Abstract: We use laboratory experiments to analyze how the existence of trading frictions (a transaction fee and a ban on short selling and margin buying) affects occurrence and strength of the post-earnings-announcement drift. We find lower trading activity and higher asset prices in the presence of frictions. While the initial price reaction to earnings announcements is weaker, the strength of the PEAD is not materially affected. Trading strategies aimed at exploiting the PEAD are less profitable in the presence of frictions.
    Date: 2021–02–16

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