nep-mst New Economics Papers
on Market Microstructure
Issue of 2019‒07‒22
five papers chosen by
Thanos Verousis

  1. Who Provides Liquidity, and When? By Sida Li; Xin Wang; Mao Ye
  2. From asymptotic properties of general point processes to the ranking of financial agents By Othmane Mounjid; Mathieu Rosenbaum; Pamela Saliba
  3. Extending Deep Learning Models for Limit Order Books to Quantile Regression By Zihao Zhang; Stefan Zohren; Stephen Roberts
  4. Are Analyst Trade Ideas Valuable? By Justin Birru; Sinan Gokkaya; Xi Liu; René M. Stulz
  5. When do low-frequency measures really measure transaction costs? By Mohammad Jahan-Parvar; Filip Zikes

  1. By: Sida Li; Xin Wang; Mao Ye
    Abstract: We model competition for liquidity provision between high-frequency traders (HFTs) and slower execution algorithms designed to minimize transaction costs for buy-side institutions (B-Algos). Under continuous pricing, B-Algos dominate liquidity provision by using aggressive limit orders to stimulate HFTs’ market orders. Under discrete pricing, HFTs dominate liquidity provision if the bid–ask spread is binding at one tick. If the tick size is not binding, B-Algos choose between stimulating HFTs and providing liquidity to other non-HFTs. Flash crashes arise under certain parameter values. Transaction costs can be negatively correlated with the bid–ask spread when all traders can provide liquidity.
    JEL: G1 G12
    Date: 2019–06
  2. By: Othmane Mounjid; Mathieu Rosenbaum; Pamela Saliba
    Abstract: We propose a general non-linear order book model that is built from the individual behaviours of the agents. Our framework encompasses Markovian and Hawkes based models. Under mild assumptions, we prove original results on the ergodicity and diffusivity of such system. Then we provide closed form formulas for various quantities of interest: stationary distribution of the best bid and ask quantities, spread, liquidity fluctuations and price volatility. These formulas are expressed in terms of individual order flows of market participants. Our approach enables us to establish a ranking methodology for the market makers with respect to the quality of their trading.
    Date: 2019–06
  3. By: Zihao Zhang; Stefan Zohren; Stephen Roberts
    Abstract: We showcase how Quantile Regression (QR) can be applied to forecast financial returns using Limit Order Books (LOBs), the canonical data source of high-frequency financial time-series. We develop a deep learning architecture that simultaneously models the return quantiles for both buy and sell positions. We test our model over millions of LOB updates across multiple different instruments on the London Stock Exchange. Our results suggest that the proposed network not only delivers excellent performance but also provides improved prediction robustness by combining quantile estimates.
    Date: 2019–06
  4. By: Justin Birru; Sinan Gokkaya; Xi Liu; René M. Stulz
    Abstract: Using a novel database, we show that the stock-price impact of analyst trade ideas is at least as large as the impact of stock recommendation, target price, and earnings forecast changes, and that investors following trade ideas can earn significant abnormal returns. Trade ideas triggered by forthcoming firm catalyst events are more informative than ideas exploiting temporary mispricing. Institutional investors trade in the direction of trade ideas and commission-paying institutional clients do so earlier than non-clients. Analysts generating trade ideas are more established and are more likely to produce ideas for stocks with high dollar trading commissions in their coverage universe.
    JEL: G11 G12 G14 G20 G23 G24
    Date: 2019–07
  5. By: Mohammad Jahan-Parvar; Filip Zikes
    Abstract: We compare popular measures of transaction costs based on daily data with their high-frequency data-based counterparts. We find that for U.S. equities and major foreign exchange rates, (i) the measures based on daily data are highly upward biased and imprecise; (ii) the bias is a function of volatility; and (iii) it is primarily volatility that drives the dynamics of these liquidity proxies both in the cross section as well as over time. We corroborate our results in carefully designed simulations and show that such distortions arise when the true transaction costs are small relative to volatility. Many financial assets exhibit this property, not only in the last two decades, but also in the previous century. We document that using low-frequency measures as liquidity proxies in standard asset pricing tests may produce sizable biases and spurious inferences about the pricing of aggregate volatility or liquidity risk.
    Keywords: Liquidity Risk ; Transaction Costs ; Volatility
    Date: 2019–07–08

This nep-mst issue is ©2019 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.
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