nep-mst New Economics Papers
on Market Microstructure
Issue of 2018‒11‒19
five papers chosen by
Thanos Verousis

  1. High frequency trading and ghost liquidity By Hans Degryse; Rudy De Winne; Carole Gresse; Richard Payne
  2. Deep Learning can Replicate Adaptive Traders in a Limit-Order-Book Financial Market By Arthur le Calvez; Dave Cliff
  3. Essays on corporate bond market liquidity and dealer behavior By Rapp, Andreas
  4. A Temporal Analysis of Intraday Volatility of Nifty Futures on the National Stock Exchange By Singh, Ritvik; Gangwar, Rachna
  5. Another law of small numbers: patterns of trading prices in experimental markets By Tristan Roger; Wael Bousselmi; Patrick Roger; Patrick Roger; Marc Willinger

  1. By: Hans Degryse (K.U. LEUVEN - KU Leuven - Katholieke Universiteit Leuven); Rudy De Winne (Louvain School of Management - UCL - Université Catholique de Louvain); Carole Gresse (DRM - Dauphine Recherches en Management - Université Paris-Dauphine - CNRS - Centre National de la Recherche Scientifique); Richard Payne (Cass Business School - City University London - City University London)
    Abstract: We measure the extent to which consolidated liquidity in modern fragmented equity markets overstates true liquidity due to a phenomenon that we call Ghost Liquidity (GL). GL exists when traders place duplicate limit orders on competing venues, intending for only one of the orders to execute, and when one does execute, duplicates are cancelled. We employ data from 2013, covering 91 stocks trading on their primary exchanges and three alternative platforms and where order submitters are identified consistently across venues, to measure the incidence of GL and to investigate its determinants. On average, for every 100 shares pending on an order book, slightlymore than 8 shares are immediately cancelled by the same liquidity supplier on a different venue.This percentage is significantly greater for HFTs than for non-HFTs and for those trading as principal. Overall, GL represents a significant fraction of total liquidity, implying that simply measured consolidated liquidity greatly exceeds true consolidated liquidity.
    Keywords: Ghost Liquidity,High Frequency Trading (HFT),Algorithmic Trading (AT),Fragmentation
    Date: 2018–05
  2. By: Arthur le Calvez; Dave Cliff
    Abstract: We report successful results from using deep learning neural networks (DLNNs) to learn, purely by observation, the behavior of profitable traders in an electronic market closely modelled on the limit-order-book (LOB) market mechanisms that are commonly found in the real-world global financial markets for equities (stocks & shares), currencies, bonds, commodities, and derivatives. Successful real human traders, and advanced automated algorithmic trading systems, learn from experience and adapt over time as market conditions change; our DLNN learns to copy this adaptive trading behavior. A novel aspect of our work is that we do not involve the conventional approach of attempting to predict time-series of prices of tradeable securities. Instead, we collect large volumes of training data by observing only the quotes issued by a successful sales-trader in the market, details of the orders that trader is executing, and the data available on the LOB (as would usually be provided by a centralized exchange) over the period that the trader is active. In this paper we demonstrate that suitably configured DLNNs can learn to replicate the trading behavior of a successful adaptive automated trader, an algorithmic system previously demonstrated to outperform human traders. We also demonstrate that DLNNs can learn to perform better (i.e., more profitably) than the trader that provided the training data. We believe that this is the first ever demonstration that DLNNs can successfully replicate a human-like, or super-human, adaptive trader operating in a realistic emulation of a real-world financial market. Our results can be considered as proof-of-concept that a DLNN could, in principle, observe the actions of a human trader in a real financial market and over time learn to trade equally as well as that human trader, and possibly better.
    Date: 2018–11
  3. By: Rapp, Andreas (Tilburg University, School of Economics and Management)
    Abstract: The thesis consists of three chapters and studies the role of corporate bond dealers as liquidity providers in decentralized over-the-counter markets. The first two empirical chapters explore the impact of dealers' inventory financing constraints on their ability to act as middlemen in corporate bond markets. Specifically, the first chapter provides empirical evidence that dealers' financing constraints are a crucial determinant of the costs of their liquidity provision. The second chapter demonstrates that bonds handled by dealers with higher financing constraints are associated with substantially larger and abrupt price declines and slower price reversals in case of a rating downgrade from investment to non-investment grades. The third theoretical chapter studies the effects of post-trade disclosure on a dealer's dynamic trading strategy in a two-period dealership market and shows that in terms of customer welfare neither a regime with full nor one without post-trade transparency is universally dominating.
    Date: 2018
  4. By: Singh, Ritvik; Gangwar, Rachna
    Abstract: This paper aims to establish trends in intraday volatility in context of the Indian stock market and analyze the impact of development in the Indian economy on its stock market volatility. One minute tick data of Nifty 50 futures from Jan 1, 2011 to Aug 31, 2018 was used for the purpose of this research. Volatility was computed for each day of week and various time intervals. Our analysis shows evidence of the expected U-shaped pattern of intraday volatility (higher at the beginning and end of the day). We also observed a decline in the hourly volatility over the time period studied. However, sufficient evidence to determine the impact of development in the Indian economy on volatility in the stock market was not found.
    Keywords: Risk Analysis; Intraday Volatility; National Stock Exchange of India; Nifty Futures; Temporal Analysis
    JEL: G10 G13 G15
    Date: 2018–09–18
  5. By: Tristan Roger; Wael Bousselmi; Patrick Roger; Patrick Roger; Marc Willinger
    Abstract: Conventional finance models indicate that the magnitude of stock prices should not influence portfolio choices or future returns. This view is contradicted, however, by empirical evidence. In this paper, we report the results of an experiment showing that trading prices, in experimental markets, are processed differently by participants, depending on their magnitude. Our experiment has two consecutive treatments. One where the fundamental value is a small number (the small price market) and a second one where the fundamental value is a large number (the large price market). Small price markets exhibit greater mispricing than large price markets. We obtain this result both between-participants and within-participants. Our findings show that price magnitude influences the way people perceive the distribution of future returns. This result is at odds with standard finance theory but is consistent with: (1) a number of observations in the empirical finance and accounting literature; and (2) evidence in neuropsychology on the use of different mental scales for small and large numbers.
    Keywords: experimental markets, number perception, behavioral bias, stock price magnitude,mental scales
    Date: 2018–10

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