arXiv · arXiv q-fin · 2025
To trade tokens in cryptoeconomic systems, automated market makers (AMMs) typically rely on liquidity providers (LPs) that deposit tokens in exchange for rewards. To profit from such rewards, LPs must use effective liquidity provisioning strategies. However, LPs lack guidance for developing such strategies, which often leads them to financial losses. We developed a measurement model based on impermanent loss to analy…
Thanos Drossos, Daniel Kirste, Niclas Kannengießer, Ali Sunyaev
arXiv · arXiv q-fin · 2021
We examine how the introduction of concentrated liquidity has changed the liquidity provision market in automated market makers such as Uniswap. To this end, we compare average liquidity provider returns from trading fees before and after its introduction. Furthermore, we quantify the performance of a number of fundamental concentrated liquidity strategies using historical trade data. We estimate their possible retur…
Robin Fritsch
arXiv · arXiv q-fin · 2010
Credit networks represent a way of modeling trust between entities in a network. Nodes in the network print their own currency and trust each other for a certain amount of each other's currency. This allows the network to serve as a decentralized payment infrastructure---arbitrary payments can be routed through the network by passing IOUs between trusting nodes in their respective currencies---and obviates the need f…
Pranav Dandekar, Ashish Goel, Ramesh Govindan, Ian Post
arXiv · arXiv q-fin · 2026
This study introduces a benchmark framework for evaluating the financial decision-making capabilities of large language models (LLMs) through portfolio optimization problems with mathematically explicit solutions. Unlike existing financial benchmarks that emphasize language-processing tasks, the proposed framework directly tests optimization-based reasoning in investment contexts. A large set of multiple-choice quest…
Hanyong Cho, Jang Ho Kim
arXiv · arXiv q-fin · 2025
Stock trading strategies play a critical role in investment. However, it is challenging to design a profitable strategy in a complex and dynamic stock market. In this paper, we propose an ensemble strategy that employs deep reinforcement schemes to learn a stock trading strategy by maximizing investment return. We train a deep reinforcement learning agent and obtain an ensemble trading strategy using three actor-crit…
Hongyang Yang, Xiao-Yang Liu, Shan Zhong, Anwar Walid
arXiv · arXiv q-fin · 2021
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 · 2020
Optimal order execution is widely studied by industry practitioners and academic researchers because it determines the profitability of investment decisions and high-level trading strategies, particularly those involving large volumes of orders. However, complex and unknown market dynamics pose significant challenges for the development and validation of optimal execution strategies. In this paper, we propose a model…
Michaël Karpe, Jin Fang, Zhongyao Ma, Chen Wang
arXiv · arXiv q-fin · 2025
Accurate volatility forecasts are vital in modern finance for risk management, portfolio allocation, and strategic decision-making. However, existing methods face key limitations. Fully multivariate models, while comprehensive, are computationally infeasible for realistic portfolios. Factor models, though efficient, primarily use static factor loadings, failing to capture evolving volatility co-movements when they ar…
Duo Zhang, Jiayu Li, Junyi Mo, Elynn Chen
arXiv · arXiv q-fin · 2025
We advance market-making strategies by integrating Adversarial Reinforcement Learning (ARL), Hawkes Processes, and variable volatility levels while also expanding the action space available to market makers (MMs). To enhance the adaptability and robustness of these strategies -- which can quote always, quote only on one side of the market or not quote at all -- we shift from the commonly used Poisson process to the H…
Ziyi Wang, Carmine Ventre, Maria Polukarov
arXiv · arXiv q-fin · 2025
Conditional Autoencoders (CAEs) offer a flexible, interpretable approach for estimating latent asset-pricing factors from firm characteristics. However, existing studies usually limit the latent factor dimension to around K=5 due to concerns that larger K can degrade performance. To overcome this challenge, we propose a scalable framework that couples a high-dimensional CAE with an uncertainty-aware factor selection …
Ryan Engel, Yu Chen, Pawel Polak, Ioana Boier
arXiv · arXiv q-fin · 2024
Can AI Agents simulate real-world trading environments to investigate the impact of external factors on stock trading activities (e.g., macroeconomics, policy changes, company fundamentals, and global events)? These factors, which frequently influence trading behaviors, are critical elements in the quest for maximizing investors' profits. Our work attempts to solve this problem through large language model based agen…
Chong Zhang, Xinyi Liu, Zhongmou Zhang, Mingyu Jin, Lingyao Li
arXiv · arXiv q-fin · 2024
We introduce a novel approach to options trading strategies using a highly scalable and data-driven machine learning algorithm. In contrast to traditional approaches that often require specifications of underlying market dynamics or assumptions on an option pricing model, our models depart fundamentally from the need for these prerequisites, directly learning non-trivial mappings from market data to optimal trading s…
Wee Ling Tan, Stephen Roberts, Stefan Zohren
arXiv · arXiv q-fin · 2023
Recent advances in large language models (LLMs) have opened new possibilities for artificial intelligence applications in finance. In this paper, we provide a practical survey focused on two key aspects of utilizing LLMs for financial tasks: existing solutions and guidance for adoption. First, we review current approaches employing LLMs in finance, including leveraging pretrained models via zero-shot or few-shot lear…
Yinheng Li, Shaofei Wang, Han Ding, Hang Chen
arXiv · arXiv q-fin · 2021
Machine learning techniques are playing more and more important roles in finance market investment. However, finance quantitative modeling with conventional supervised learning approaches has a number of limitations. The development of deep reinforcement learning techniques is partially addressing these issues. Unfortunately, the steep learning curve and the difficulty in quick modeling and agile development are impe…
Zechu Li, Xiao-Yang Liu, Jiahao Zheng, Zhaoran Wang, Anwar Walid
arXiv · arXiv q-fin · 2021
We design a system for risk-analyzing and pricing portfolios of non-performing consumer credit loans. The rapid development of credit lending business for consumers heightens the need for trading portfolios formed by overdue loans as a manner of risk transferring. However, the problem is nontrivial technically and related research is absent. We tackle the challenge by building a bottom-up architecture, in which we mo…
Siyi Wang, Xing Yan, Bangqi Zheng, Hu Wang, Wangli Xu
arXiv · arXiv q-fin · 2021
The task we consider is portfolio construction in a speculative market, a fundamental problem in modern finance. While various empirical works now exist to explore deep learning in finance, the theory side is almost non-existent. In this work, we focus on developing a theoretical framework for understanding the use of data augmentation for deep-learning-based approaches to quantitative finance. The proposed theory cl…
Liu Ziyin, Kentaro Minami, Kentaro Imajo
arXiv · arXiv q-fin · 2021
Financial markets are of much interest to researchers due to their dynamic and stochastic nature. With their relations to world populations, global economies and asset valuations, understanding, identifying and forecasting trends and regimes are highly important. Attempts have been made to forecast market trends by employing machine learning methodologies, while statistical techniques have been the primary methods us…
Peter Akioyamen, Yi Zhou Tang, Hussien Hussien
arXiv · arXiv q-fin · 2021
Simulated environments are increasingly used by trading firms and investment banks to evaluate trading strategies before approaching real markets. Backtesting, a widely used approach, consists of simulating experimental strategies while replaying historical market scenarios. Unfortunately, this approach does not capture the market response to the experimental agents' actions. In contrast, multi-agent simulation prese…
Andrea Coletta, Matteo Prata, Michele Conti, Emanuele Mercanti, Novella Bartolini