mlsanityData to 14 Oct 2026

Unveiling the Potential of Sentiment: Can Large Language Models Predict Chinese Stock Price Movements?

arXiv 2306.14222 ↗First posted 25 Jun 2023cs.CL cs.AI q-fin.STcited by 15

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Abstract (arXiv)

The rapid advancement of Large Language Models (LLMs) has spurred discussions about their potential to enhance quantitative trading strategies. LLMs excel in analyzing sentiments about listed companies from financial news, providing critical insights for trading decisions. However, the performance of LLMs in this task varies substantially due to their inherent characteristics. This paper introduces a standardized experimental procedure for comprehensive evaluations. We detail the methodology using three distinct LLMs, each embodying a unique approach to performance enhancement, applied specifically to the task of sentiment factor extraction from large volumes of Chinese news summaries. Subsequently, we develop quantitative trading strategies using these sentiment factors and conduct back-tests in realistic scenarios. Our results will offer perspectives about the performances of Large Language Models applied to extracting sentiments from Chinese news texts.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 10 of its 25 references that are in the map. First posted on arXiv 25 Jun 2023.

Builds on

10 of 25 references.

References · 10

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2020Language Models are Few-Shot Learnerscited by 32,419
  4. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  5. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
  6. 2023BloombergGPT: A Large Language Model for Financecited by 939
  7. 2023Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Modelscited by 129
  8. 2023Temporal Data Meets LLM -- Explainable Financial Time Series Forecastingcited by 77
  9. 2022Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligencecited by 70
  10. 2021Text Mining of Stocktwits Data for Predicting Stock Pricescited by 7

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 5; 2025: 6; 2026: 2.

15 papers.

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  1. 2024TradingAgents: Multi-Agents LLM Financial Trading Frameworkcited by 188
  2. 2024Large Language Model Agent in Financial Trading: A Surveycited by 60
  3. 2023Empowering Many, Biasing a Few: Generalist Credit Scoring through Large Language Modelscited by 24
  4. 2025Can LLM-based Financial Investing Strategies Outperform the Market in Long Run?cited by 20
  5. 2025Trading-R1: Financial Trading with LLM Reasoning via Reinforcement Learningcited by 15
  6. 2025From Deep Learning to LLMs: A survey of AI in Quantitative Investmentcited by 9
  7. 2025ContestTrade: A Multi-Agent Trading System Based on Internal Contest Mechanismcited by 8
  8. 2025Bridging Language Models and Financial Analysiscited by 4
  9. 2025Trade in Minutes! Rationality-Driven Agentic System for Quantitative Financial Tradingcited by 4
  10. 2023Methods for Acquiring and Incorporating Knowledge into Stock Price Prediction: A Surveycited by 3
  11. 2024What Teaches Robots to Walk, Teaches Them to Trade too -- Regime Adaptive Execution using Informed Data and LLMscited by 3
  12. 2024NIFTY Financial News Headlines Datasetcited by 1
3 more
  1. 2024Boardwalk Empire: How Generative AI is Revolutionizing Economic Paradigmscited by 1
  2. 2026AlphaCrafter: Harnessing Multi-Agent Workflows for Cross-Sectional Quantitative Tradingcited by 1
  3. 2026Agent Memory with Episodic Retrieval for Financial Decision-Makingcited by 0