nep-mst New Economics Papers
on Market Microstructure
Issue of 2020‒06‒15
seven papers chosen by
Thanos Verousis

  1. Low-Latency Trading and Price Discovery: Evidence from the Tokyo Stock Exchange in the Pre-Opening and Opening Periods By Mario Bellia; Loriana Pelizzon; Marti G. Subrahmanyam; Jun Uno; Darya Yuferova
  2. On bid and ask side-specific tick sizes By Baldacci Bastien; Bergault Philippe; Derchu Joffrey; Rosenbaum Mathieu
  3. Institutional trading in volatile markets: evidence from Chinese stock markets By Julia Darby; Hai Zhang; Jinkai Zhang
  4. Structural Interdependence of Price and Demand in a Model of the Foreign Exchange Market with Heterogeneous Speculators: Evidence from High-frequency Data By Leonardo BARGIGLI; Giulio CIFARELLI
  5. Temporal mixture ensemble models for intraday volume forecasting in cryptocurrency exchange markets By Nino Antulov-Fantulin; Tian Guo; Fabrizio Lillo
  6. Stock return comovement when investors are distracted: more, and more homogeneous By Ehrmann, Michael; Jansen, David-Jan
  7. Zeroing in on the Expected Returns of Anomalies By Andrew Y. Chen; Mihail Velikov

  1. By: Mario Bellia (Department of Economics, University Of Venice Cà Foscari; SAFE, Goethe University); Loriana Pelizzon (Department of Economics, University Of Venice Cà Foscari; SAFE, Goethe University); Marti G. Subrahmanyam (Leonard N. Stern School of Business, New York University); Jun Uno (Waseda University; Ca' Foscari University of Venice); Darya Yuferova (Norwegian School of Economics (NHH))
    Abstract: We study whether the presence of low-latency traders (including high-frequency traders (HFTs)) in the pre-opening period contributes to market quality, defined by price discovery and liquidity provision, in the opening auction. We use a unique dataset from the Tokyo Stock Exchange (TSE) based on server-IDs and find that HFTs dynamically alter their presence in different stocks and on different days. In spite of the lack of immediate execution, about one quarter of HFTs participate in the pre-opening period, and contribute significantly to market quality in the pre-opening period, the opening auction that ensues and the continuous trading period. Their contribution is largely different from that of the other HFTs during the continuous period.
    Keywords: High-Frequency Traders (HFTs), Pre-Opening, Opening Call Auction, Price Discovery, Liquidity provision
    JEL: G12 G14
    Date: 2020
  2. By: Baldacci Bastien; Bergault Philippe; Derchu Joffrey; Rosenbaum Mathieu
    Abstract: The tick size, which is the smallest increment between two consecutive prices for a given asset, is a key parameter of market microstructure. In particular, the behavior of high frequency market makers is highly related to its value. We take the point of view of an exchange and investigate the relevance of having different tick sizes on the bid and ask sides of the order book. Using an approach based on the model with uncertainty zones, we show that when side-specific tick sizes are suitably chosen, it enables the exchange to improve the quality of liquidity provision.
    Date: 2020–05
  3. By: Julia Darby (Department of Economics, University of Strathclyde); Hai Zhang (Department of Accountanty & Finance, University of Strathclyde); Jinkai Zhang (Department of Economics, University of Strathclyde)
    Abstract: We investigate daily stock returns of all firms listed on the Shanghai and Shenzhen stock exchanges over the period 2010-2017. Using daily cash flow data on the largest category of trades by value we construct a proxy for institutional trading and demonstrate that institutional trading behaviour consistently destabilizes both markets on extreme market movement days. We go on to highlight the conflating influence of regulator imposed daily limits to individual stocks’ price movements. Specifically, showing that when large institutional trades coincide with upper (lower) price limits being hit on extreme days, the prices of affected stocks continue to increase (decrease) significantly in subsequent days, such that institutional trades on extreme days help predict subsequent abnormal returns. While there is some evidence of longer-run price reversal after stocks hit the lower price limits, this is not the case when upper limits are hit. We conclude that binding price limits act to exacerbate the destabilising effects of institutional trading in Chinese stock markets.
    Keywords: extreme market swings, price limits, cash flow, institutional trading behaviour
    JEL: G11 G12 G13 G14 G28
    Date: 2019–09
  4. By: Leonardo BARGIGLI; Giulio CIFARELLI
    Abstract: We assume that the variations of the exchange rate depend on the current net demand of the base currency as a consequence of market making, and that the current net demand of the base currency depends on current and past variations of the exchange rate as a consequence of how future price expectations are formed by bounded rational agents. We achieve identification supposing that the structural shocks of price variations and demand follow a GARCH process. Using high-frequency transaction data of the EUR/USD market in 2016, we show that the simultaneous effects of price on demand and viceversa are both significant and positive. Our estimates suggest that one important source of heterogeneity in demand might be missing from our model, since the structural errors are negatively correlated.
    Keywords: Asset pricing model, heterogeneous beliefs, market making, foreign exchange market, SVAR-GARCH, high frequency data.
    JEL: G12 D84 F31 C32 C55
    Date: 2020
  5. By: Nino Antulov-Fantulin; Tian Guo; Fabrizio Lillo
    Abstract: We study the problem of the intraday short-term volume forecasting in cryptocurrency exchange markets. The predictions are built by using transaction and order book data from different markets where the exchange takes place. Methodologically, we propose a temporal mixture ensemble model, capable of adaptively exploiting, for the forecasting, different sources of data and providing a volume point estimate, as well as its uncertainty. We provide evidence of the outperformance of our model by comparing its outcomes with those obtained with different time series and machine learning methods. Finally, we discuss the difficulty of volume forecasting when large quantities are abruptly traded.
    Date: 2020–05
  6. By: Ehrmann, Michael; Jansen, David-Jan
    Abstract: This paper tests whether fluctuations in investors' attention affect stock return comovement with national and global markets, and which stocks are most affected. We measure fluctuations in investor attention using 59 high-profile soccer matches played during stock market trading hours at the three editions of the FIFA World Cup between 2010 and 2018. Using intraday data for more than 750 firms in 19 countries, we find that distracted investors shift attention away from firm-specific and from global news. When movements in global stock markets are large, the pricing of global news reverts back to normal, but firm-specific news keep being priced less, leading to increased comovement of stock returns with the national stock market. This increase is economically large, and particularly strong for those stocks that typically comove little with the national market, thereby leading to a convergence in betas across stocks. JEL Classification: G12, G15, G41
    Keywords: comovement, investor attention, stock returns
    Date: 2020–05
  7. By: Andrew Y. Chen; Mihail Velikov
    Abstract: We zero in on the expected returns of long-short portfolios based on 120 stock market anomalies by accounting for (1) effective bid-ask spreads, (2) post-publication effects, and (3) the modern era of trading technology that began in the early 2000s. Net of these effects, the average anomaly's expected return is a measly 8 bps per month. The strongest anomalies return only 10-20 bps after accounting for data-mining with either out-of-sample tests or empirical Bayesian methods. Expected returns are negligible despite cost optimizations that produce impressive net returns in-sample and the omission of additional trading costs like price impact.
    Keywords: Trading costs; Mispricing; Stock return anomalies; Anomaly zoo
    JEL: G10 G11 G12 G14
    Date: 2020–05–22

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