nep-mst New Economics Papers
on Market Microstructure
Issue of 2021‒12‒13
four papers chosen by
Thanos Verousis
University of Essex

  1. The effect of ambiguity on price formation and trading behavior in financial markets By Li, Wenhui; Ockenfels, Peter; Wilde, Christian
  2. Information dynamics of price and liquidity around the 2017 Bitcoin markets crash By Vaiva Vasiliauskaite; Fabrizio Lillo; Nino Antulov-Fantulin
  3. Financial Transaction Taxes and the Informational Efficiency of Financial Markets: A Structural Estimation By Marco Cipriani; Antonio Guarino; Andreas Uthemann
  4. FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance By Xiao-Yang Liu; Hongyang Yang; Jiechao Gao; Christina Dan Wang

  1. By: Li, Wenhui; Ockenfels, Peter; Wilde, Christian
    Abstract: This paper sets up an experimental asset market in the laboratory to investigate the effects of ambiguity on price formation and trading behavior in financial markets. The obtained trading data is used to analyze the effect of ambiguity on various market outcomes (the price level, volatility, trading activity, market liquidity, and the degree of speculative trading) and to test the quality of popular empirical market-based measures for the degree of ambiguity. We find that ambiguity decreases market prices and trading activity; ambiguity leads to lower market liquidity through wider bid-ask spreads; and ambiguity leads to less speculative trading. We also find that popular market-based measures of ambiguity used in the empirical literature do not seem to correctly capture the true degree of ambiguity.
    Keywords: ambiguity,financial market,market price,volatility,trading activity,bidask spread,market-based measure of ambiguity,laboratory experiment
    JEL: D81 G10
    Date: 2021
  2. By: Vaiva Vasiliauskaite; Fabrizio Lillo; Nino Antulov-Fantulin
    Abstract: We study the information dynamics between the largest Bitcoin exchange markets during the bubble in 2017-2018. By analysing high-frequency market-microstructure observables with different information theoretic measures for dynamical systems, we find temporal changes in information sharing across markets. In particular, we study the time-varying components of predictability, memory, and synchronous coupling, measured by transfer entropy, active information storage, and multi-information. By comparing these empirical findings with several models we argue that some results could relate to intra-market and inter-market regime shifts, and changes in direction of information flow between different market observables.
    Date: 2021–11
  3. By: Marco Cipriani; Antonio Guarino; Andreas Uthemann
    Abstract: We develop a new methodology to estimate the impact of a financial transaction tax (FTT) on financial market outcomes. In our sequential trading model, there are price-elastic noise and informed traders. We estimate the model through maximum likelihood for a sample of sixty New York Stock Exchange (NYSE) stocks in 2017. We quantify the effect of introducing an FTT given the parameter estimates. An FTT increases the proportion of informed trading, improves information aggregation, but lowers trading volume and welfare. For some less-liquid stocks, however, an FTT blocks private information aggregation.
    Keywords: financial transaction tax; market microstructure; structural estimation
    JEL: G14 D82 C13
    Date: 2021–12–01
  4. By: Xiao-Yang Liu; Hongyang Yang; Jiechao Gao; Christina Dan Wang
    Abstract: Deep reinforcement learning (DRL) has been envisioned to have a competitive edge in quantitative finance. However, there is a steep development curve for quantitative traders to obtain an agent that automatically positions to win in the market, namely \textit{to decide where to trade, at what price} and \textit{what quantity}, due to the error-prone programming and arduous debugging. In this paper, we present the first open-source framework \textit{FinRL} as a full pipeline to help quantitative traders overcome the steep learning curve. FinRL is featured with simplicity, applicability and extensibility under the key principles, \textit{full-stack framework, customization, reproducibility} and \textit{hands-on tutoring}. Embodied as a three-layer architecture with modular structures, FinRL implements fine-tuned state-of-the-art DRL algorithms and common reward functions, while alleviating the debugging workloads. Thus, we help users pipeline the strategy design at a high turnover rate. At multiple levels of time granularity, FinRL simulates various markets as training environments using historical data and live trading APIs. Being highly extensible, FinRL reserves a set of user-import interfaces and incorporates trading constraints such as market friction, market liquidity and investor's risk-aversion. Moreover, serving as practitioners' stepping stones, typical trading tasks are provided as step-by-step tutorials, e.g., stock trading, portfolio allocation, cryptocurrency trading, etc.
    Date: 2021–11

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