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on Market Microstructure |
| By: | Jiafa He; Cong Zheng; Can Yang |
| Abstract: | We focus on the problem of market making in high-frequency trading. Market making is a critical function in financial markets that involves providing liquidity by buying and selling assets. However, the increasing complexity of financial markets and the high volume of data generated by tick-level trading makes it challenging to develop effective market making strategies. To address this challenge, we propose a deep reinforcement learning approach that fuses tick-level data with periodic prediction signals to develop a more accurate and robust market making strategy. Our results of market making strategies based on different deep reinforcement learning algorithms under the simulation scenarios and real data experiments in the cryptocurrency markets show that the proposed framework outperforms existing methods in terms of profitability and risk management. |
| Date: | 2023–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2306.17179&r=mst |
| By: | Andrea Coletta; Joseph Jerome; Rahul Savani; Svitlana Vyetrenko |
| Abstract: | Limit order books are a fundamental and widespread market mechanism. This paper investigates the use of conditional generative models for order book simulation. For developing a trading agent, this approach has drawn recent attention as an alternative to traditional backtesting due to its ability to react to the presence of the trading agent. Using a state-of-the-art CGAN (from Coletta et al. (2022)), we explore its dependence upon input features, which highlights both strengths and weaknesses. To do this, we use "adversarial attacks" on the model's features and its mechanism. We then show how these insights can be used to improve the CGAN, both in terms of its realism and robustness. We finish by laying out a roadmap for future work. |
| Date: | 2023–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2306.12806&r=mst |
| By: | \'Alvaro Cartea; Fay\c{c}al Drissi; Marcello Monga |
| Abstract: | Automated market makers (AMMs) are a new prototype of trading venues which are revolutionising the way market participants interact. At present, the majority of AMMs are constant function market makers (CFMMs) where a deterministic trading function determines how markets are cleared. A distinctive characteristic of CFMMs is that execution costs are given by a closed-form function of price, liquidity, and transaction size. This gives rise to a new class of trading problems. We focus on constant product market makers and show how to optimally trade a large position in an asset and how to execute statistical arbitrages based on market signals. We employ stochastic optimal control tools to devise two strategies. One strategy is based on the dynamics of prices in competing venues and assumes constant liquidity in the AMM. The other strategy assumes that AMM prices are efficient and liquidity is stochastic. We use Uniswap v3 data to study price, liquidity, and trading cost dynamics, and to motivate the models. Finally, we perform consecutive runs of in-sample estimation of model parameters and out-of-sample liquidation and arbitrage strategies to showcase the performance of the strategies. |
| Date: | 2023–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2307.03499&r=mst |
| By: | Basile Caparros; Amit Chaudhary; Olga Klein |
| Abstract: | Liquidity providers (LPs) on decentralized exchanges (DEXs) can protect themselves from adverse selection risk by updating their positions more frequently. However, repositioning is costly, because LPs have to pay gas fees for each update. We analyze the causal relation between repositioning and liquidity concentration around the market price, using the entry of a blockchain scaling solution, Polygon, as our instrument. Polygon's lower gas fees allow LPs to update more frequently than on Ethereum. Our results demonstrate that higher repositioning intensity and precision lead to greater liquidity concentration, which benefits small trades by reducing their slippage. |
| Date: | 2023–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2306.17742&r=mst |