Quant Paper
Explore cutting-edge quantitative finance papers with AI-powered search
Latest Papers
101 papers
Warp speed price moves: Jumps after earnings announcements
Corporate earnings announcements unpack large bundles of public information that should, in efficient markets, trigger jumps in stock prices. Testing this impli…
Cryptocurrency as an Investable Asset Class: Coming of Age
Cryptocurrencies are coming of age. We organize empirical regularities into ten stylized facts and analyze cryptocurrency through the lens of empirical asset pr…
QuantMind: A Context-Engineering Based Knowledge Framework for Quantitative Finance
Quantitative research increasingly relies on unstructured financial content such as filings, earnings calls, and research notes, yet existing LLM and RAG pipeli…
R&D-Agent-Quant: A Multi-Agent Framework for Data-Centric Factors and Model Joint Optimization
Financial markets pose fundamental challenges for asset return prediction due to their high dimensionality, non-stationarity, and persistent volatility. Despite…
RD-Agent: An LLM-Agent Framework Towards Autonomous Data Science
Recent advances in AI and ML have transformed data science, yet increasing complexity and expertise requirements continue to hinder progress. Although crowd-sou…
From Deep Learning to LLMs: A survey of AI in Quantitative Investment
Quantitative investment (quant) is an emerging, technology-driven approach in asset management, increasingy shaped by advancements in artificial intelligence. R…
ChatGPT and Deepseek: Can They Predict the Stock Market and Macroeconomy?
We study whether ChatGPT and DeepSeek can extract information from the Wall Street Journal to predict the stock market and the macroeconomy. We find that ChatGP…
Follow the Leader: Enhancing Systematic Trend-Following Using Network Momentum
We present a systematic, trend-following strategy, applied to commodity futures markets, that combines univariate trend indicators with cross-sectional trend in…
TradingAgents: Multi-Agents LLM Financial Trading Framework
Significant progress has been made in automated problem-solving using societies of agents powered by large language models (LLMs). In finance, efforts have larg…
Sentiment trading with large language models
We investigate the efficacy of large language models (LLMs) in sentiment analysis of U.S. financial news and their potential in predicting stock market returns.…
An Application of the Ornstein-Uhlenbeck Process to Pairs Trading
We conduct a preliminary analysis of a pairs trading strategy using the Ornstein-Uhlenbeck (OU) process to model stock price spreads. We compare this approach t…
Automate Strategy Finding with LLM in Quant Investment
We present a novel three-stage framework leveraging Large Language Models (LLMs) within a risk-aware multi-agent system for automate strategy finding in quantit…