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Results for “competitiveness” · papers 18 · wiki 1
Academic Papers · 18arXiv q-fin live 18 · desk corpus 0
arXiv · arXiv q-fin · 2023

FLAIR: A Metric for Liquidity Provider Competitiveness in Automated Market Makers

This paper aims to enhance the understanding of liquidity provider (LP) returns in automated market makers (AMMs). LPs face market risk as well as adverse selection due to risky asset holdings in the pool that they provide liquidity to and the informational asymmetry between informed traders (arbitrageurs) and AMMs. Loss-versus-rebalancing (LVR) quantifies the adverse selection cost (Milionis et al., 2022a), and is a

Jason Milionis, Xin Wan, Austin Adams
arXiv · arXiv q-fin · 2026

When large trades are not (automatically) news: Liquidity tail risk and price discovery

When is a large trade news, and when is it a liquidity shock? We study this question in a sequential competitive limit order book with asymmetric information. In our model, liquidity suppliers observe aggregate order flow but not its decomposition into informed demand and uninformed liquidity demand. We model uninformed order flow with Student-$t$ tails, interpreted as a reduced form for rare liquidity regimes. The t

Umut Çetin, Mingwei Lin, Giulia Livieri
arXiv · arXiv q-fin · 2025

Optimal Fees for Liquidity Provision in Automated Market Makers

Passive liquidity providers (LPs) in automated market makers (AMMs) face losses due to adverse selection (LVR), which static trading fees often fail to offset in practice. We study the key determinants of LP profitability in a dynamic reduced-form model where an AMM operates in parallel with a centralized exchange (CEX), traders route their orders optimally to the venue offering the better price, and arbitrageurs exp

Steven Campbell, Philippe Bergault, Jason Milionis, Marcel Nutz
arXiv · arXiv q-fin · 2025

Liquidity Competition Between Brokers and an Informed Trader

We study a multi-agent setting in which brokers transact with an informed trader. Through a sequential Stackelberg-type game, brokers manage trading costs and adverse selection with an informed trader. In particular, supplying liquidity to the informed traders allows the brokers to speculate based on the flow information. They simultaneously attempt to minimize inventory risk and trading costs with the lit market bas

Ryan Donnelly, Zi Li
arXiv · arXiv q-fin · 2024

The Impact of Designated Market Makers on Market Liquidity and Competition: A Simulation Approach

This paper conducts an empirical investigation into the effects of Designated Market Makers (DMMs) on key market quality indicators, such as liquidity, bid-ask spreads, and order fulfillment ratios. Through agent-based simulations, this study explores the impact of varying competition levels and incentive structures among DMMs on market dynamics. It aims to demonstrate that DMMs are crucial for enhancing market liqui

Cong Zhou
arXiv · arXiv q-fin · 2023

Fragmentation and optimal liquidity supply on decentralized exchanges

We investigate how liquidity providers (LPs) choose between high- and low-fee trading venues, in the face of a fixed common gas cost. Analyzing Uniswap data, we find that high-fee pools attract 58% of liquidity supply yet execute only 21% of volume. Large LPs dominate low-fee pools, frequently adjusting out-of-range positions in response to informed order flow. In contrast, small LPs converge to high-fee pools, accep

Alfred Lehar, Christine Parlour, Marius Zoican
arXiv · arXiv q-fin · 2018

Liquidity in Competitive Dealer Markets

We study a continuous-time version of the intermediation model of Grossman and Miller (1988). To wit, we solve for the competitive equilibrium prices at which liquidity takers' demands are absorbed by dealers with quadratic inventory costs, who can in turn gradually transfer these positions to an exogenous open market with finite liquidity. This endogenously leads to transient price impact in the dealer market. Smoot

Peter Bank, Ibrahim Ekren, Johannes Muhle-Karbe
arXiv · arXiv q-fin · 2026

Deep Reinforcement Learning Framework for Diversified Portfolio Management Across Global Equity Markets

This study develops and evaluates a deep reinforcement learning framework for dynamic portfolio allocation across global equity markets. The Soft Actor-Critic algorithm is used to learn continuous portfolio weights within a Markov Decision Process, incorporating transaction costs, turnover penalties, and diversification constraints into the reward function. Five model configurations are compared, varying in reward fo

Kamil Kashif, Robert Ślepaczuk
arXiv · arXiv q-fin · 2026

A Limit Order Market with Uncertain Informed Trading Participation

We study a one period limit order market with informed traders, noise traders, and competitive liquidity suppliers, in which the number of informed traders is random. Liquidity suppliers know the distribution of the informed trader count, but not its realization, and therefore face uncertainty about both the presence and the intensity of informed trading. We characterize equilibrium by a fixed point integral equation

Umut Çetin, Mingwei Lin
arXiv · arXiv q-fin · 2025

Market-Dependent Communication in Multi-Agent Alpha Generation

Multi-strategy hedge funds face a fundamental organizational choice: should analysts generating trading strategies communicate, and if so, how? We investigate this using 5-agent LLM-based trading systems across 450 experiments spanning 21 months, comparing five organizational structures from isolated baseline to collaborative and competitive conversation. We show that communication improves performance, but optimal c

Jerick Shi, Burton Hollifield
arXiv · arXiv q-fin · 2024

Adaptive Curves for Optimally Efficient Market Making

Automated Market Makers (AMMs) are essential in Decentralized Finance (DeFi) as they match liquidity supply with demand. They function through liquidity providers (LPs) who deposit assets into liquidity pools. However, the asset trading prices in these pools often trail behind those in more dynamic, centralized exchanges, leading to potential arbitrage losses for LPs. This issue is tackled by adapting market maker bo

Viraj Nadkarni, Sanjeev Kulkarni, Pramod Viswanath
arXiv · arXiv q-fin · 2021

FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance

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 fir

Xiao-Yang Liu, Hongyang Yang, Jiechao Gao, Christina Dan Wang
arXiv · arXiv q-fin · 2018

Inventory Management for High-Frequency Trading with Imperfect Competition

We study Nash equilibria for inventory-averse high-frequency traders (HFTs), who trade to exploit information about future price changes. For discrete trading rounds, the HFTs' optimal trading strategies and their equilibrium price impact are described by a system of nonlinear equations; explicit solutions obtain around the continuous-time limit. Unlike in the risk-neutral case, the optimal inventories become mean-re

Sebastian Herrmann, Johannes Muhle-Karbe, Dapeng Shang, Chen Yang
arXiv · arXiv q-fin · 2013

Simulating the Synchronizing Behavior of High-Frequency Trading in Multiple Markets

Nearly one-half of all trades in financial markets are executed by high-speed, autonomous computer programs -- a type of trading often called high-frequency trading (HFT). Although evidence suggests that HFT increases the efficiency of markets, it is unclear how or why it produces this outcome. Here we create a simple model to study the impact of HFT on investors who trade similar securities in different markets. We

Benjamin Myers, Austin Gerig
arXiv · arXiv q-fin · 2023

Market Crowds' Trading Behaviors, Agreement Prices, and the Implications of Trading Volume

It has been long that literature in financial academics focuses mainly on price and return but much less on trading volume. In the past twenty years, it has already linked both price and trading volume to economic fundamentals, and explored the behavioral implications of trading volume such as investor's attitude toward risks, overconfidence, disagreement, and attention etc. However, what is surprising is how little

Leilei Shi, Bing Han, Yingzi Zhu, Liyan Han, Yiwen Wang
arXiv · arXiv q-fin · 2019

Fools Rush In: Competitive Effects of Reaction Time in Automated Trading

We explore the competitive effects of reaction time of automated trading strategies in simulated financial markets containing a single exchange with public limit order book and continuous double auction matching. A large body of research conducted over several decades has been devoted to trading agent design and simulation, but the majority of this work focuses on pricing strategy and does not consider the time taken

Henry Hanifan, John Cartlidge
arXiv · arXiv q-fin · 2015

On Optimal Pricing Model for Multiple Dealers in a Competitive Market

In this paper, the optimal pricing strategy in Avellande-Stoikov's for a monopolistic dealer is extended to a general situation where multiple dealers are present in a competitive market. The dealers' trading intensities, their optimal bid and ask prices and therefore their spreads are derived when the dealers are informed the severity of the competition. The effects of various parameters on the bid-ask quotes and pr

Wai-Ki Ching, Jia-Wen Gu, Qing-Qing Yang, Tak-Kuen Siu
arXiv · arXiv q-fin · 2011

Inside Trading, Public Disclosure and Imperfect Competition

In this paper, we present a multi-period trading model in the style of Kyle (1985)'s inside trading model, by assuming that there are at least two insiders in the market with long-lived private information, under the requirement that each insider publicly discloses his stock trades after the fact. Based on this model, we study the influences of "public disclosure" and "competition among insiders" on the trading behav

Fuzhou Gong, Hong Liu
Wiki Entities · 1
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