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on Market Microstructure |
| By: | Hamza Bodor; Laurent Carlier |
| Abstract: | This paper presents an in-depth analysis of stylized facts in the context of futures on German bonds. The study examines four futures contracts on German bonds: Schatz, Bobl, Bund and Buxl, using tick-by-tick limit order book datasets. It uncovers a range of stylized facts and empirical observations, including the distribution of order sizes, patterns of order flow, and inter-arrival times of orders. The findings reveal both commonalities and unique characteristics across the different futures, thereby enriching our understanding of these markets. Furthermore, the paper introduces insightful realism metrics that can be used to benchmark market simulators. The study contributes to the literature on financial stylized facts by extending empirical observations to this class of assets, which has been relatively underexplored in existing research. This work provides valuable guidance for the development of more accurate and realistic market simulators. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.10722&r=mst |
| By: | Andrew W. Macpherson |
| Abstract: | We study a new "laminated" queueing model for orders on batched trading venues such as decentralised exchanges. The model aims to capture and generalise transaction queueing infrastructure that has arisen to organise MEV activity on public blockchains such as Ethereum, providing convenient channels for sophisticated agents to extract value by acting on end-user order flow by performing arbitrage and related HFT activities. In our model, market orders are interspersed with orders created by arbitrageurs that under idealised conditions reset the marginal price to a global equilibrium between each trade, improving predictability of execution for liquidity traders. If an arbitrageur has a chance to land multiple opportunities in a row, he may attempt to manipulate the execution price of the intervening market order by a probabilistic blind sandwiching strategy. To study how bad this manipulation can get, we introduce and bound a price manipulation coefficient that measures the deviation from global equilibrium of local pricing quoted by a rational arbitrageur. We exhibit cases in which this coefficient is well approximated by a "zeta value' with interpretable and empirically measurable parameters. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.08302&r=mst |
| By: | Matthias Hafner; Helmut Dietl |
| Abstract: | Decentralized exchanges are widely used platforms for trading crypto assets. The most common types work with automated market makers (AMM), allowing traders to exchange assets without needing to find matching counterparties. Thereby, traders exchange against asset reserves managed by smart contracts. These assets are provided by liquidity providers in exchange for a fee. Static analysis shows that small price changes in one of the assets can result in losses for liquidity providers. Despite the success of AMMs, it is claimed that liquidity providers often suffer losses. However, the literature does not adequately consider the dynamic effects of fees over time. Therefore, we investigate the impermanent loss problem in a dynamic setting using Monte Carlo simulations. Our findings indicate that price changes do not necessarily lead to losses. Fees paid by traders and arbitrageurs are equally important. In this respect, we can show that an arbitrage-friendly environment benefits the liquidity provider. Thus, we suggest that AMM developers should promote an arbitrage-friendly environment rather than trying to prevent arbitrage. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.07689&r=mst |
| By: | Andrew Ye; James Xu; Yi Wang; Yifan Yu; Daniel Yan; Ryan Chen; Bosheng Dong; Vipin Chaudhary; Shuai Xu |
| Abstract: | We propose the integration of sentiment analysis and deep-reinforcement learning ensemble algorithms for stock trading, and design a strategy capable of dynamically altering its employed agent given concurrent market sentiment. In particular, we create a simple-yet-effective method for extracting news sentiment and combine this with general improvements upon existing works, resulting in automated trading agents that effectively consider both qualitative market factors and quantitative stock data. We show that our approach results in a strategy that is profitable, robust, and risk-minimal -- outperforming the traditional ensemble strategy as well as single agent algorithms and market metrics. Our findings determine that the conventional practice of switching ensemble agents every fixed-number of months is sub-optimal, and that a dynamic sentiment-based framework greatly unlocks additional performance within these agents. Furthermore, as we have designed our algorithm with simplicity and efficiency in mind, we hypothesize that the transition of our method from historical evaluation towards real-time trading with live data should be relatively simple. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.01441&r=mst |