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on Market Microstructure |
| By: | Emilio Said (UdeM - Université de Montréal, ADIA - Abu Dhabi Investment Authority) |
| Abstract: | We propose a theory of the market impact of metaorders based on a coarse-grained approach where the microscopic details of supply and demand is replaced by a single parameter ρ ∈ [0, +∞] shaping the supply-demand equilibrium and the market impact process during the execution of the metaorder. Our model provides an unified explanation of most of the empirical observations that have been reported and establishes a strong connection between the excess volatility puzzle and the order-driven view of the markets through the square-root law. |
| Keywords: | Market microstructure,market impact model,price formation,excess volatility |
| Date: | 2022–05–15 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:hal-03668669&r= |
| By: | Jin Hyuk Choi; Heeyoung Kwon; Kasper Larsen |
| Abstract: | In a continuous-time Kyle setting, we prove global existence of an equilibrium when the insider faces a terminal trading constraint. We prove that our equilibrium model produces output consistent with several empirical stylized facts such as autocorrelated aggregate holdings, decreasing price impacts over the trading day, and U shaped optimal trading patterns. |
| Date: | 2022–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2206.08117&r= |
| By: | Robin Fritsch; Samuel K\"aser; Roger Wattenhofer |
| Abstract: | This paper studies the question whether automated market maker protocols such as Uniswap can sustainably retain a portion of their trading fees for the protocol. We approach the problem by modelling how to optimally choose a pool's take rate, i.e\ the fraction of fee revenue that remains with the protocol, in order to maximize the protocol's revenue. The model suggest that if AMMs have a portion of loyal trade volume, they can sustainably set a non-zero take rate, even without losing liquidity to competitors with a zero take rate. Furthermore, we determine the optimal take rate depending on a number of model parameters including how much loyal trade volume pools have and how high the competitors' take rates are. |
| Date: | 2022–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2206.04634&r= |
| By: | Michele Vodret; Iacopo Mastromatteo; Bence Toth; Michael Benzaquen |
| Abstract: | We relax the strong rationality assumption for the agents in the paradigmatic Kyle model of price formation, thereby reconciling the framework of asymmetrically informed traders with the Adaptive Market Hypothesis, where agents use inductive rather than deductive reasoning. Building on these ideas, we propose a stylised model able to account parsimoniously for a rich phenomenology, ranging from excess volatility to volatility clustering. While characterising the excess-volatility dynamics, we provide a microfoundation for GARCH models. Volatility clustering is shown to be related to the self-excited dynamics induced by traders' behaviour, and does not rely on clustered fundamental innovations. Finally, we propose an extension to account for the fragile dynamics exhibited by real markets during flash crashes. |
| Date: | 2022–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2206.06764&r= |
| By: | Luyao Zhang; Tianyu Wu; Saad Lahrichi; Carlos-Gustavo Salas-Flores; Jiayi Li |
| Abstract: | Recent advances in Artificial Intelligence (AI) have made algorithmic trading play a central role in finance. However, current research and applications are disconnected information islands. We propose a generally applicable pipeline for designing, programming, and evaluating the algorithmic trading of stock and crypto assets. Moreover, we demonstrate how our data science pipeline works with respect to four conventional algorithms: the moving average crossover, volume-weighted average price, sentiment analysis, and statistical arbitrage algorithms. Our study offers a systematic way to program, evaluate, and compare different trading strategies. Furthermore, we implement our algorithms through object-oriented programming in Python3, which serves as open-source software for future academic research and applications. |
| Date: | 2022–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2206.14932&r= |
| By: | Sebastian Frischbier; Jawad Tahir; Christoph Doblander; Arne Hormann; Ruben Mayer; Hans-Arno Jacobsen |
| Abstract: | The DEBS Grand Challenge (GC) is an annual programming competition open to practitioners from both academia and industry. The GC 2022 edition focuses on real-time complex event processing of high-volume tick data provided by Infront Financial Technology GmbH. The goal of the challenge is to efficiently compute specific trend indicators and detect patterns in these indicators like those used by real-life traders to decide on buying or selling in financial markets. The data set Trading Data used for benchmarking contains 289 million tick events from approximately 5500+ financial instruments that had been traded on the three major exchanges Amsterdam (NL), Paris (FR), and Frankfurt am Main (GER) over the course of a full week in 2021. The data set is made publicly available. In addition to correctness and performance, submissions must explicitly focus on reusability and practicability. Hence, participants must address specific nonfunctional requirements and are asked to build upon open-source platforms. This paper describes the required scenario and the data set Trading Data, defines the queries of the problem statement, and explains the enhancements made to the evaluation platform Challenger that handles data distribution, dynamic subscriptions, and remote evaluation of the submissions. |
| Date: | 2022–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2206.13237&r= |
| By: | Xiaoqing Wan; Nichole R. Lighthall |
| Abstract: | Recently enacted regulations aimed to enhance retail investors' understanding about different types of investment accounts. Toward this goal, the Securities and Exchange Commission (SEC) mandated that SEC-registered investment advisors and broker-dealers provide a brief relationship summary (Form CRS) to retail investors. The present study examines the impact of this regulation on investors and considers its market implications. The effects of Form CRS were evaluated based on three outcome variables: perceived helpfulness, comprehension, and decision making. The study also examined whether personal characteristics, such as investment experience, influenced the disclosure's impact on decision making. Results indicated that participants perceived the disclosure as helpful and it significantly enhanced comprehension about the two types of investment accounts. Critically, participants also showed increased preference and choice for broker-dealers after the disclosure. Increased preference for broker-dealers was associated with greater investment experience, greater comprehension gains, and access to more information from a longer disclosure. These findings suggest that Form CRS may promote informed decision making among retail investors while simultaneously increasing the selection of broker-dealer accounts. |
| Date: | 2022–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2206.00117&r= |