A Review of Large Language Models for Stock Price Forecasting from a Hedge-Fund Perspective

• URL: http://arxiv.org/abs/2605.05211v1
• Date: Fri, 10 Apr 2026 17:36:04 GMT
• Title: A Review of Large Language Models for Stock Price Forecasting from a Hedge-Fund Perspective
• Abstract summary: Large language models (LLMs) are increasingly deployed in quantitative finance for stock price forecasting.<n>This review synthesizes recent applications of LLMs in this domain, including extracting sentiment from financial news and social media.
• Score: 1.84273475762818
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) are increasingly deployed in quantitative finance for stock price forecasting. This review synthesizes recent applications of LLMs in this domain, including extracting sentiment from financial news and social media, analyzing financial reports and earnings-call transcripts, tokenizing or symbolizing stock price series, and constructing multi-agent trading systems. Particular attention is paid to practical pitfalls that are often understated in the literature, such as fragility in sentiment analysis, dataset and horizon design, performance evaluation metrics, data leakage, illiquidity premia, and limits of stock price predictability. Organized from a hedge-fund perspective, the review is intended to guide both academic researchers and hedge fund managers in integrating LLMs into real-world trading pipelines and in stress-testing their robustness under realistic market frictions.

Related papers

• CN-Buzz2Portfolio: A Chinese-Market Dataset and Benchmark for LLM-Based Macro and Sector Asset Allocation from Daily Trending Financial News [21.498084305999992]
  Large Language Models (LLMs) are rapidly transitioning from static Natural Language Processing (NLP) tasks to acting as dynamic decision-making agents.<n>Direct live trading is irreproducible and prone to outcome bias by confounding luck with skill, whereas existing static benchmarks are often confined to entity-level stock picking and ignore broader market attention.<n>This work provides new insights into the alignment between general reasoning and financial decision-making, and all data, codes, and experiments are released to promote sustainable financial agent research.
  arXiv  Detail & Related papers  (2026-03-18T02:31:28Z)
• Generalized Stock Price Prediction for Multiple Stocks Combined with News Fusion [55.80546131166094]
  This paper introduces an approach that integrates Large Language Models (LLMs) with daily financial news for stock price prediction.<n>We encode news articles using a pre-trained LLM and implement three attention-based pooling techniques to filter news based on stock relevance.<n> Experimental results demonstrate a 7.11% reduction in Mean Absolute Error (MAE) compared to the baseline.
  arXiv  Detail & Related papers  (2026-03-08T09:05:10Z)
• PriceSeer: Evaluating Large Language Models in Real-Time Stock Prediction [47.70107097572211]
  We introduce PriceSeer, a benchmark specifically designed for large language models performing stock prediction tasks.<n>PriceSeer includes 110 U.S. stocks from 11 industrial sectors, with each containing 249 historical data points.<n>We evaluate six cutting-edge LLMs under different prediction horizons, demonstrating their potential in generating investment strategies.
  arXiv  Detail & Related papers  (2025-12-31T08:35:46Z)
• Deriving Strategic Market Insights with Large Language Models: A Benchmark for Forward Counterfactual Generation [55.2788567621326]
  We introduce a novel benchmark, FIN-FORCE-FINancial FORward Counterfactual Evaluation.<n>By curating financial news headlines, FIN-FORCE supports LLM based forward counterfactual generation.<n>This paves the way for scalable and automated solutions for exploring and anticipating future market developments.
  arXiv  Detail & Related papers  (2025-05-26T02:41:50Z)
• FinTSB: A Comprehensive and Practical Benchmark for Financial Time Series Forecasting [58.70072722290475]
  Financial time series (FinTS) record the behavior of human-brain-augmented decision-making.<n>FinTSB is a comprehensive and practical benchmark for financial time series forecasting.
  arXiv  Detail & Related papers  (2025-02-26T05:19:16Z)
• Agent Trading Arena: A Study on Numerical Understanding in LLM-Based Agents [69.58565132975504]
  Large language models (LLMs) have demonstrated remarkable capabilities in natural language tasks.<n>We present the Agent Trading Arena, a virtual zero-sum stock market in which LLM-based agents engage in competitive multi-agent trading.
  arXiv  Detail & Related papers  (2025-02-25T08:41:01Z)
• RAG-IT: Retrieval-Augmented Instruction Tuning for Automated Financial Analysis [1.2891210250935148]
  RAG-IT (Retrieval-Augmented Instruction Tuning) is a novel framework designed to automate the generation of earnings report analyses.<n>Our approach integrates retrieval augmentation with instruction-based fine-tuning to enhance factual accuracy, contextual relevance, and domain adaptability.
  arXiv  Detail & Related papers  (2024-12-11T08:09:42Z)
• AlphaFin: Benchmarking Financial Analysis with Retrieval-Augmented Stock-Chain Framework [48.3060010653088]
  We release AlphaFin datasets, combining traditional research datasets, real-time financial data, and handwritten chain-of-thought (CoT) data. We then use AlphaFin datasets to benchmark a state-of-the-art method, called Stock-Chain, for effectively tackling the financial analysis task.
  arXiv  Detail & Related papers  (2024-03-19T09:45:33Z)
• Numerical Claim Detection in Finance: A New Financial Dataset, Weak-Supervision Model, and Market Analysis [4.575870619860645]
  We construct a new financial dataset for the claim detection task in the financial domain. We propose a novel weak-supervision model that incorporates the knowledge of subject matter experts (SMEs) in the aggregation function. Here, we observe the dependence of earnings surprise and return on our optimism measure.
  arXiv  Detail & Related papers  (2024-02-18T22:55:26Z)
• Enhancing Financial Sentiment Analysis via Retrieval Augmented Large Language Models [11.154814189699735]
  Large Language Models (LLMs) pre-trained on extensive corpora have demonstrated superior performance across various NLP tasks. We introduce a retrieval-augmented LLMs framework for financial sentiment analysis. Our approach achieves 15% to 48% performance gain in accuracy and F1 score.
  arXiv  Detail & Related papers  (2023-10-06T05:40:23Z)
• Instruct-FinGPT: Financial Sentiment Analysis by Instruction Tuning of General-Purpose Large Language Models [18.212210748797332]
  We introduce a simple yet effective instruction tuning approach to address these issues. In the experiment, our approach outperforms state-of-the-art supervised sentiment analysis models.
  arXiv  Detail & Related papers  (2023-06-22T03:56:38Z)
• Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models [48.87381259980254]
  We document the capability of large language models (LLMs) like ChatGPT to predict stock market reactions from news headlines without direct financial training.<n>Using post-knowledge-cutoff headlines, GPT-4 captures initial market responses, achieving approximately 90% portfolio-day hit rates for the non-tradable initial reaction.
  arXiv  Detail & Related papers  (2023-04-15T19:22:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.