nep-mst New Economics Papers
on Market Microstructure
Issue of 2021‒04‒05
five papers chosen by
Thanos Verousis

  1. The Multiple Dimensions of Liquidity By Garabedian, Garo; Inghelbrecht, Koen
  2. Intraday trading strategy based on time series and machine learning for Chinese stock market By Q. Wang; Y. Zhou; J. Shen
  3. SoK: Automated Market Maker (AMM) based Decentralized Exchanges (DEXs) By Jiahua Xu; Nazariy Vavryk; Krzysztof Paruch; Simon Cousaert
  4. Price-setting in the foreign exchange swap market: Evidence from order flow By Olav Syrstad; Ganesh Viswanath-Natraj
  5. TradeR: Practical Deep Hierarchical Reinforcement Learning for Trade Execution By Karush Suri; Xiao Qi Shi; Konstantinos Plataniotis; Yuri Lawryshyn

  1. By: Garabedian, Garo (Central Bank of Ireland); Inghelbrecht, Koen (Department of Economics, Ghent University)
    Abstract: We introduce a novel method to aggregate the different dimensions of liquidity (tightness, depth and resilience) into a single 'unified' market-wide liquidity index. We rely on twenty-four measures of market liquidity divided into eight groups. Each group either represents direct trading costs, which refer to the spread estimates (tightness), or indirect trading costs, which span the price impact estimates (depth and resilience). The weights assigned to the different groups are time-varying and depend on three components: the correlation between groups, the liquidity pressure conveyed through the measures in the group, and their conditional variance. Our liquidity index succeeds in tracking the most important historic episodes of financial stress. Moreover, it shows the expected macroeconomic and financial relationships mentioned in the literature, and has some predictive power for future growth rates. Finally, our methodology can gauge the individual importance of each liquidity group over time. Our results show that price impact measures receive higher weights during tranquil periods, while spread estimates play a prominent role during periods of financial distress.
    Keywords: market liquidity; trading volume; transaction costs; price impact; effective spread; financial crises; signal-to-noise ratio; macro-financial linkages.
    JEL: G01 G12 G14 E44
    Date: 2020–12
  2. By: Q. Wang; Y. Zhou; J. Shen
    Abstract: This article comes up with an intraday trading strategy under T+1 using Markowitz optimization and Multilayer Perceptron (MLP) with published stock data obtained from the Shenzhen Stock Exchange and Shanghai Stock Exchange. The empirical results reveal the profitability of Markowitz portfolio optimization and validate the intraday stock price prediction using MLP. The findings further combine the Markowitz optimization, an MLP with the trading strategy, to clarify this strategy's feasibility.
    Date: 2021–03
  3. By: Jiahua Xu; Nazariy Vavryk; Krzysztof Paruch; Simon Cousaert
    Abstract: As an integral part of the Decentralized Finance (DeFi) ecosystem, Automated Market Maker (AMM) based Decentralized Exchanges (DEXs) have gained massive traction with the revived interest in blockchain and distributed ledger technology in general. Most prominently, the top six AMMs -- Uniswap, Balancer, Curve, Dodo, Bancor and Sushiswap -- hold in aggregate 15 billion USD worth of crypto-assets as of March 2021. Instead of matching the buy and sell sides, AMMs employ a peer-to-pool method and determine asset price algorithmically through a so-called conservation function. Compared to centralized exchanges, AMMs exhibit the apparent advantage of decentralization, automation and continuous liquidity. Nonetheless, AMMs typically feature drawbacks such as high slippage for traders and divergence loss for liquidity providers. In this work, we establish a general AMM framework describing the economics and formalizing the system's state-space representation. We employ our framework to systematically compare the mechanics of the top AMM protocols, deriving their slippage and divergence loss functions.
    Date: 2021–03
  4. By: Olav Syrstad; Ganesh Viswanath-Natraj
    Abstract: This paper investigates price discovery in foreign exchange (FX) swaps. Using data on inter-dealer transactions, we find that a 1 standard deviation increase in order flow (i.e. net pressure to obtain USD through FX swaps) increases the cost of dollar funding by up to 4 basis points after the 2008 crisis. This is explained by increased dispersion in dollar funding costs and quarter-end periods. We find central bank swap lines reduced the order flow to obtain USD through FX swaps, subsequently affecting the forward rate. In contrast, during quarter-ends and monetary announcements we observe high frequency adjustment of the forward rate.
    Keywords: interest rate parity, exchange rates, currency swaps, order flow, dollar funding
    JEL: E43 F31 G15
    Date: 2020
  5. By: Karush Suri; Xiao Qi Shi; Konstantinos Plataniotis; Yuri Lawryshyn
    Abstract: Advances in Reinforcement Learning (RL) span a wide variety of applications which motivate development in this area. While application tasks serve as suitable benchmarks for real world problems, RL is seldomly used in practical scenarios consisting of abrupt dynamics. This allows one to rethink the problem setup in light of practical challenges. We present Trade Execution using Reinforcement Learning (TradeR) which aims to address two such practical challenges of catastrophy and surprise minimization by formulating trading as a real-world hierarchical RL problem. Through this lens, TradeR makes use of hierarchical RL to execute trade bids on high frequency real market experiences comprising of abrupt price variations during the 2019 fiscal year COVID19 stock market crash. The framework utilizes an energy-based scheme in conjunction with surprise value function for estimating and minimizing surprise. In a large-scale study of 35 stock symbols from the S&P500 index, TradeR demonstrates robustness to abrupt price changes and catastrophic losses while maintaining profitable outcomes. We hope that our work serves as a motivating example for application of RL to practical problems.
    Date: 2021–02

This nep-mst issue is ©2021 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.
General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.