nep-mst New Economics Papers
on Market Microstructure
Issue of 2022‒11‒07
five papers chosen by
Thanos Verousis
University of Essex

  1. Fast and Slow Optimal Trading with Exogenous Information By Alessandro Micheli; Eyal Neuman
  2. Inattentive Price Discovery in ETFs By Mariia Kosar; Sergei Mikhalishchev
  3. Algorithmic Trading Using Continuous Action Space Deep Reinforcement Learning By Naseh Majidi; Mahdi Shamsi; Farokh Marvasti
  4. Towards Multi-Agent Reinforcement Learning driven Over-The-Counter Market Simulations By Nelson Vadori; Leo Ardon; Sumitra Ganesh; Thomas Spooner; Selim Amrouni; Jared Vann; Mengda Xu; Zeyu Zheng; Tucker Balch; Manuela Veloso
  5. Tweeting for money: Social media and mutual fund flows By Javier Gil-Bazo; Juan F. Imbet

  1. By: Alessandro Micheli; Eyal Neuman
    Abstract: We consider a stochastic game between a slow institutional investor and a high-frequency trader who are trading a risky asset and their aggregated order-flow impacts the asset price. We model this system by means of two coupled stochastic control problems, in which the high-frequency trader exploits the available information on a price predicting signal more frequently, but is also subject to periodic "end of day" inventory constraints. We first derive the optimal strategy of the high-frequency trader given any admissible strategy of the institutional investor. Then, we solve the problem of the institutional investor given the optimal signal-adaptive strategy of the high-frequency trader, in terms of the resolvent of a Fredholm integral equation, thus establishing the unique multi-period Stackelberg equilibrium of the game. Our results provide an explicit solution to the game, which shows that the high-frequency trader can adopt either predatory or cooperative strategies in each period, depending on the tradeoff between the order-flow and the trading signal. We also show that the institutional investor's strategy is considerably more profitable when the order-flow of the high-frequency trader is taken into account in her trading strategy.
    Date: 2022–10
  2. By: Mariia Kosar; Sergei Mikhalishchev
    Abstract: This paper studies the information choice of exchange-traded funds (ETF) investors, and its impact on the price efficiency of underlying stocks. First, we show that the learning of stock-specific information can occur at the ETF level. Our results suggest that ETF investors respond endogenously to changes in the fundamental value of underlying stocks, in line with the rational inattention theory. Second, we provide evidence that ETFs facilitate propagation of idiosyncratic shocks across its constituents.
    Keywords: Exchange-Traded Fund; ETF; Price Efficiency; Rational Inattention; Information Acquisition; Comovement;
    JEL: G12 G14 D82
    Date: 2022–09
  3. By: Naseh Majidi; Mahdi Shamsi; Farokh Marvasti
    Abstract: Price movement prediction has always been one of the traders' concerns in financial market trading. In order to increase their profit, they can analyze the historical data and predict the price movement. The large size of the data and complex relations between them lead us to use algorithmic trading and artificial intelligence. This paper aims to offer an approach using Twin-Delayed DDPG (TD3) and the daily close price in order to achieve a trading strategy in the stock and cryptocurrency markets. Unlike previous studies using a discrete action space reinforcement learning algorithm, the TD3 is continuous, offering both position and the number of trading shares. Both the stock (Amazon) and cryptocurrency (Bitcoin) markets are addressed in this research to evaluate the performance of the proposed algorithm. The achieved strategy using the TD3 is compared with some algorithms using technical analysis, reinforcement learning, stochastic, and deterministic strategies through two standard metrics, Return and Sharpe ratio. The results indicate that employing both position and the number of trading shares can improve the performance of a trading system based on the mentioned metrics.
    Date: 2022–10
  4. By: Nelson Vadori; Leo Ardon; Sumitra Ganesh; Thomas Spooner; Selim Amrouni; Jared Vann; Mengda Xu; Zeyu Zheng; Tucker Balch; Manuela Veloso
    Abstract: We study a game between liquidity provider and liquidity taker agents interacting in an over-the-counter market, for which the typical example is foreign exchange. We show how a suitable design of parameterized families of reward functions coupled with associated shared policy learning constitutes an efficient solution to this problem. Precisely, we show that our deep-reinforcement-learning-driven agents learn emergent behaviors relative to a wide spectrum of incentives encompassing profit-and-loss, optimal execution and market share, by playing against each other. In particular, we find that liquidity providers naturally learn to balance hedging and skewing as a function of their incentives, where the latter refers to setting their buy and sell prices asymmetrically as a function of their inventory. We further introduce a novel RL-based calibration algorithm which we found performed well at imposing constraints on the game equilibrium, both on toy and real market data.
    Date: 2022–10
  5. By: Javier Gil-Bazo; Juan F. Imbet
    Abstract: We investigate whether asset management firms use social media to persuade investors. Combining a database of almost 1.6 million Twitter posts by U.S. mutual fund families with textual analysis, we find that flows of money to mutual funds respond positively to tweets with a positive tone. Consistently with the persuasion hypothesis, positive tweets work best when they convey advice or views on the market and when investor sentiment is higher. Using a high-frequency approach, we are able to identify a short-lived impact of families' tweets on ETF share prices. Finally, we reject the alternative hypothesis that asset management companies use social media to alleviate information asymmetries by either lowering search costs or disclosing privately observed information.
    Keywords: Social media, Twitter, persuasion, mutual funds, mutual fund, flows, machine learning, textual analysis
    JEL: G11 G23 D83
    Date: 2022–10

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