A curated list of Quantitative Finance papers.
Due to the increasing interest in my project, I have updated the new format and address some issues with the first version. In this new version, I have updated the table format with brief descriptions and organized it in a separate folder which can be accessed here.
Please reach out to me at [email protected] for any further questions. I will update this monthly and display the top 20 recent papers here.
| Paper | Author(s) | Description | Source | Date |
|---|---|---|---|---|
| Position Building in Competition Is A Game with Incomplete Information | Neil A. Chriss | This paper examines strategic trading under incomplete information, where firms lack full knowledge of key aspects of their competitors’ trading strategies such as target sizes and market impact models. | arxiv-2501.01241 | 2025-01-02 |
| TradingAgents: Multi-Agents LLM Financial Trading Framework | Yijia Xiao; Edward Sun; Di Luo; Wei Wang | TradingAgents proposes a novel stock trading framework inspired by trading firms, featuring LLM-powered agents in specialized roles such as fundamental analysts, sentiment analysts, technical analysts, and traders with varied risk profiles. | arxiv-2412.20138 | 2024-12-28 |
| Parameters Optimization of Pair Trading Algorithm | Charles Barthelemy; Ruoyu Chen; Edward Lucyszyn | This study explores the mathematical foundations of pair trading, focusing on identifying cointegrated pairs, constructing trading signals, and optimizing model parameters to maximize returns. | arxiv-2412.12555 | 2024-12-17 |
| An Application of The Ornstein-Uhlenbeck Process to Pairs Trading | Jirat Suchato; Sean Wiryadi; Danran Chen; Ava Zhao; Michael Yue | We conduct a preliminary analysis of a pairs trading strategy using the Ornstein-Uhlenbeck (OU) process to model stock price spreads. | arxiv-2412.12458 | 2024-12-16 |
| STORM: A Spatio-Temporal Factor Model Based on Dual Vector Quantized Variational Autoencoders for Financial Trading | YILEI ZHAO et. al. | As a result, the learned factors are of low quality and lack diversity, reducing their effectiveness and robustness across different trading periods. To address these issues, we propose a Spatio-Temporal factOR Model based on dual vector quantized variational autoencoders, named STORM, which extracts features of stocks from temporal and spatial perspectives, then fuses and aligns these features at the fine-grained and semantic level, and represents the factors as multi-dimensional embeddings. | arxiv-2412.09468 | 2024-12-12 |
| Research on Cross-provincial Power Trading Strategy Considering The Medium and Long-term Trading Plan | Sizhe Yan; Weiqing Wang; Xiaozhu Li; Hang He; Xin Zhao | To accommodate China’s electricity market reforms integrating medium and long-term (MLT) transactions and spot transactions, and to boost renewable energy consumption through the spot market, this paper proposes an optimized cross-provincial electricity trading strategy model based on a two-layer game framework. | pubmed-39627406 | 2024-12-03 |
| Calculating Profits and Losses for Algorithmic Trading Strategies: A Short Guide | James B. Glattfelder; Thomas Houweling | We present a series of equations that track the total realized and unrealized profits and losses at any time, incorporating the spread. | arxiv-2411.14068 | 2024-11-21 |
| A Study of Hybrid Deep Learning Model for Stock Asset Management | Yuanzhi Huo; Mengjie Jin; Sicong You | Abstract:Crafting a lucrative stock trading strategy is pivotal in the realm of investments. However, the task of devising such a strategy becomes challenging task the intricate and … | pubmed-39678294 | 2024-11-19 |
| Financial News-Driven LLM Reinforcement Learning for Portfolio Management | Ananya Unnikrishnan | This study explores the integration of sentiment analysis, derived from large language models (LLMs), into RL frameworks to enhance trading performance. | arxiv-2411.11059 | 2024-11-17 |
| Deep Recurrent Q-network Algorithm for Carbon Emission Allowance Trading Strategy | Chao Wu; Wenjie Bi; Haiying Liu | pubmed-39549452 | 2024-11-15 | |
| A Financial Market Simulation Environment for Trading Agents Using Deep Reinforcement Learning | Chris Mascioli; Anri Gu; Yongzhao Wang; Mithun Chakraborty; Michael P. Wellman | Abstract:We present PyMarketSim , a financial market simulation environment designed for training and evaluating trading agents using deep reinforcement learning (dRL). Our agent-based … | doi.org_10.1145_3677052.3698639 | 2024-11-14 |
| Hybrid Vector Auto Regression and Neural Network Model for Order Flow Imbalance Prediction in High Frequency Trading | Abdul Rahman; Neelesh Upadhye | This paper introduces a hybrid predictive model that combines Vector Auto Regression (VAR) with a simple feedforward neural network (FNN) to forecast OFI and assess trading intensity. | arxiv-2411.08382 | 2024-11-13 |
| CryptoTrade: A Reflective LLM-based Agent to Guide Zero-shot Cryptocurrency Trading | YUAN LI et. al. | This work aims to bridge the gap by developing an LLM-based trading agent, CryptoTrade, which uniquely combines the analysis of on-chain and off-chain data. | emnlp-2024.emnlp-main.63-2024-11-11 | 2024-11-11 |
| Optimization of High-Frequency Trading Strategies Using Deep Reinforcement Learning | Guanghe Cao; Yitian Zhang; Qi Lou; Gaike Wang | Abstract:This study presents a new method for optimising high-risk trading (HFT) strategies using deep learning (DRL). We propose a multi-time DRL framework integrating advanced neural … | doi.org_10.60087_jaigs.v6i1.247 | 2024-11-05 |
| Peer-to-Peer Energy Transactions for Prosumers Based on Improved Deep Deterministic Policy Gradient Algorithm | Hao Xiao; Xiaowei Pu; Wei Pei; Li Ma | Abstract:With the evolution of the power market, the active involvement of prosumers in both consuming renewable energy and maximizing financial gains has emerged as a pivotal and … | doi.org_10.1109_tsg.2024.3419122 | 2024-11-01 |
| PENGARUH VOLUME DAN FREKUENSI PERDAGANGAN TERHADAP HARGA SAHAM PADA BANK INDEX LQ45 – PERIODE 2020 – 2024 | Hartanto Hartanto | Abstract:Abstract: The purpose of this study is to examine and analyze whether there is an effect of the independent variables, Trading Volume and Frequency Transactions, and the … | doi.org_10.30811_ekonis.v26i2.6044 | 2024-10-31 |
| Inferring Option Movements Through Residual Transactions: A Quantitative Model | Carl von Havighorst; Vincil Bishop III | This research presents a novel approach to predicting option movements by analyzing residual transactions, which are trades that deviate from standard hedging activities. | arxiv-2410.16563 | 2024-10-21 |
| Cross-contextual Sequential Optimization Via Deep Reinforcement Learning for Algorithmic Trading | KAIMING PAN et. al. | Existing works mainly focus on capturing temporal relations while ignoring deriving essential factors across features. Therefore, we propose a DRL-based cross-contextual sequential optimization (CCSO) method for algorithmic trading. | cikm-3627673.3680101-2024-10-21 | 2024-10-21 |