nep-mst New Economics Papers
on Market Microstructure
Issue of 2020‒05‒04
eight papers chosen by
Thanos Verousis

  1. Deep reinforcement learning for the optimal placement of cryptocurrency limit orders By Schnaubelt, Matthias
  2. Forecasting directional movements of stock prices for intraday trading using LSTM and random forests By Pushpendu Ghosh; Ariel Neufeld; Jajati Keshari Sahoo
  3. Empirical Study of Market Impact Conditional on Order-Flow Imbalance By Anastasia Bugaenko
  4. Order book dynamics in the presence of liquidity fluctuations By Helder Rojas; Anatoly Yambartsev
  5. Optimal execution with liquidity risk in a diffusive order book market By Hyoeun Lee; Kiseop Lee
  6. How much liquidity would a liquidity-saving mechanism save if a liquidity-saving mechanism could save liquidity? A simulation approach for Canada's large-value payment system Shaun Byck By Shaun Byck; Ronald Heijmans
  7. The Frequency of One-Day Abnormal Returns and Price Fluctuations in the FOREX By Guglielmo Maria Caporale; Alex Plastun; Viktor Oliinyk
  8. Propagation of Political Information By Daniel Bradley; Sinan Gokkaya; Xi Liu; Roni Michaely

  1. By: Schnaubelt, Matthias
    Abstract: This paper presents the first large-scale application of deep reinforcement learning to optimize the placement of limit orders at cryptocurrency exchanges. For training and out-of-sample evaluation, we use a virtual limit order exchange to reward agents according to the realized shortfall over a series of time steps. Based on the literature, we generate features that inform the agent about the current market state. Leveraging 18 months of high-frequency data with 300 million historic trades and more than 3.5 million order book states from major exchanges and currency pairs, we empirically compare state-of-the-art deep reinforcement learning algorithms to several benchmarks. We find proximal policy optimization to reliably learn superior order placement strategies when compared to deep double Q-networks and other benchmarks. Further analyses shed light into the black box of the learned execution strategy. Important features are current liquidity costs and queue imbalances, where the latter can be interpreted as predictors of short-term mid-price returns. To preferably execute volume in limit orders to avoid additional market order exchange fees, order placement tends to be more aggressive in expectation of unfavorable price movements.
    Keywords: Finance,Optimal Execution,Limit Order Markets,Machine learning,Deep Reinforcement Learning
    Date: 2020
  2. By: Pushpendu Ghosh; Ariel Neufeld; Jajati Keshari Sahoo
    Abstract: We employ both random forests and LSTM networks (more precisely CuDNNLSTM) as training methodologies to analyze their effectiveness in forecasting out-of-sample directional movements of constituent stocks of the S&P 500 from January 1993 till December 2018 for intraday trading. We introduce a multi-feature setting consisting not only of the returns with respect to the closing prices, but also with respect to the opening prices and intraday returns. As trading strategy, we use Krauss et al. (2017) and Fischer & Krauss (2018) as benchmark and, on each trading day, buy the 10 stocks with the highest probability and sell short the 10 stocks with the lowest probability to outperform the market in terms of intraday returns -- all with equal monetary weight. Our empirical results show that the multi-feature setting provides a daily return, prior to transaction costs, of 0.64% using LSTM networks, and 0.54% using random forests. Hence we outperform the single-feature setting in Fischer & Krauss (2018) and Krauss et al. (2017) consisting only of the daily returns with respect to the closing prices, having corresponding daily returns of 0.41% and of 0.39% with respect to LSTM and random forests, respectively.
    Date: 2020–04
  3. By: Anastasia Bugaenko
    Abstract: In this research we have empirically investigated the key drivers affecting liquidity in equity markets. We illustrated how theoretical models, such as Kyle's model, of agents' interplay in the financial markets, are aligned with the phenomena observed in publicly available trades and quotes data. Specifically, we confirmed that for small signed order-flows, the price impact grows linearly with increase in the order-flow imbalance. We have, further, implemented a machine learning algorithm to forecast market impact given a signed order-flow. Our findings suggest that machine learning models can be used in estimation of financial variables; and predictive accuracy of such learning algorithms can surpass the performance of traditional statistical approaches. Understanding the determinants of price impact is crucial for several reasons. From a theoretical stance, modelling the impact provides a statistical measure of liquidity. Practitioners adopt impact models as a pre-trade tool to estimate expected transaction costs and optimize the execution of their strategies. This further serves as a post-trade valuation benchmark as suboptimal execution can significantly deteriorate a portfolio performance. More broadly, the price impact reflects the balance of liquidity across markets. This is of central importance to regulators as it provides an all-encompassing explanation of the correlation between market design and systemic risk, enabling regulators to design more stable and efficient markets.
    Date: 2020–04
  4. By: Helder Rojas; Anatoly Yambartsev
    Abstract: We propose a stochastic model for a limit order book with liquidity fluctuations. Our model shows how severe intermittencies in the liquidity can affect the order book dynamics. The law of large numbers (LLN), central limit theorem (CLT) and large deviations (LD) are proved for our model. Our results allow us to satisfactorily explain the volatility and local trends in the prices, relevant empirical characteristics that are observed in this type of markets. Furthermore, it shows us how these local trends and volatility are determined by the typical values of the bid-ask spread. In addition, we use our model to show how large deviations occur in the spread as a direct result of severe liquidity fluctuations.
    Date: 2020–04
  5. By: Hyoeun Lee; Kiseop Lee
    Abstract: We study the optimal order placement strategy with the presence of a liquidity cost. In this problem, a stock trader wishes to clear her large inventory by a predetermined time horizon $T$. A trader uses both limit and market orders, and a large market order faces an adverse price movement caused by the liquidity risk. First, we study a single period model where the trader places a limit order and/or a market order at the beginning. We show the behavior of optimal amount of market order, $m^*$, and optimal placement of limit order, $y^*$, under different market conditions. Next, we extend it to a multi-period model, where the trader makes sequential decisions of limit and market orders at multiple time points.
    Date: 2020–04
  6. By: Shaun Byck; Ronald Heijmans
    Abstract: Canada's Large Value Transfer System (LVTS) is in the process of being replaced by a real-time gross settlement (RTGS) system. A pure RTGS system typically requires participants to hold large amounts of intraday liquidity in order to settle their payment obligations. Implementing one or more liquidity-saving mechanisms (LSMs) can reduce the amount of liquidity participants need to hold. This paper investigates how much liquidity requirements can be reduced with the implementation of different LSMs in the Financial Network Analytics simulation engine using LVTS transaction data from 2018. These LSMs include: 1) Bilateral offsetting, 2) FIFO-Bypass, 3) Multilateral offsetting, and 4) a combination of all LSMs. We simulate two different scenarios. In the first scenario, all payments from Tranche 1, which are considered time-critical, are settled in a pure RTGS payment stream, while less time-critical Tranche 2 payments are settled in a payment stream with LSMs. In the second scenario, we settle all payments (Tranches 1 and 2) in the LSM stream. Our results show that when there is ample liquidity available in the system, there is minimal benefit from LSMs as payments are settled without much delay-the effectiveness of LSMs increases as the amount of intraday liquidity decreases. A combination of LSMs shows a reduction in liquidity requirements that is larger than any one individual LSM.
    Keywords: Liquidity Saving Mechanism; Simulation; LVTS; RTGS; Financial Market Infrastructure; Intraday Liquidity; Collateral
    JEL: E42 E50 E58 E59 G21
    Date: 2020–04
  7. By: Guglielmo Maria Caporale; Alex Plastun; Viktor Oliinyk
    Abstract: This paper analyses the explanatory power of the frequency of abnormal returns in the FOREX for the EURUSD, GBRUSD, USDJPY, EURJPY, GBPCHF, AUDUSD and USDCAD exchange rates over the period 1994-2019. Abnormal returns are detected using a dynamic trigger approach; then the following hypotheses are tested: their frequency is a significant driver of price movements (H1); it does not exhibit seasonal patterns (H2); it is stable over time (H3). For our purposes a variety of statistical methods (both parametric and non-parametric) are applied including ADF tests, Granger causality tests, correlation analysis, (multiple) regression analysis, Probit and Logit regression models. No evidence is found of either seasonal patterns or instability. However, there appears to be a strong positive (negative) relationship between returns in the FOREX and the frequency of positive (negative) abnormal returns. On the whole, the results suggest that the latter is an important driver of price dynamics in the FOREX, is informative about crises and can be the basis of profitable trading strategies, which is inconsistent with market efficiency.
    Keywords: FOREX, anomalies, price dynamics, frequency of abnormal returns
    JEL: G12 G17 C63
    Date: 2020
  8. By: Daniel Bradley (University of South Florida); Sinan Gokkaya (Department of Finance, Ohio University); Xi Liu (Miami University of Ohio - Richard T. Farmer School of Business Administration); Roni Michaely (University of Geneva - Geneva Finance Research Institute (GFRI); Swiss Finance Institute)
    Abstract: We identify an important channel through which political information propagates into capital markets—Washington policy analysts (WAs). WAs monitor political developments and produce research to interpret the impact of these events. Institutional clients generate superior returns on their trades and channel more commissions to brokerages providing policy research. WA policy research reports evoke significant market reactions, and sell-side analysts with access to WA research issue superior stock recommendations. These effects are particularly acute in politically sensitive industries, in periods of high political uncertainty, and when the quality of WA is higher. Overall, we uncover a new conduit through which political information filters into asset prices.
    Keywords: Policy analysts; policy research; political uncertainty; trading commissions; institutional trading; sell-side analysts
    JEL: L50 G10 G18 G20 G23
    Date: 2020–04

This nep-mst issue is ©2020 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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