# Integrating Stock Features and Global Information via Large Language Models for Enhanced Stock Return Prediction

arXiv: https://arxiv.org/abs/2310.05627 · HTML: https://mlsanity.com/paper/2310.05627/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 33 references that are in the map.
- First posted on arXiv 9 Oct 2023.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Efficient and Effective Text Encoding for Chinese LLaMA and Alpaca](https://mlsanity.com/paper/2304.08177/) (2023)
- [Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models](https://mlsanity.com/paper/2304.07619/) (2023)
- [Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction](https://mlsanity.com/paper/1712.02136/) (2017)
- [The Wall Street Neophyte: A Zero-Shot Analysis of ChatGPT Over MultiModal Stock Movement Prediction Challenges](https://mlsanity.com/paper/2304.05351/) (2023)
- [CSL: A Large-scale Chinese Scientific Literature Dataset](https://mlsanity.com/paper/2209.05034/) (2022)
- [Deep Learning in Asset Pricing](https://mlsanity.com/paper/1904.00745/) (2019)
- [CLUECorpus2020: A Large-scale Chinese Corpus for Pre-training Language Model](https://mlsanity.com/paper/2003.01355/) (2020)
- [A Robust Transferable Deep Learning Framework for Cross-sectional Investment Strategy](https://mlsanity.com/paper/1910.01491/) (2019)
- [HiSA-SMFM: Historical and Sentiment Analysis based Stock Market Forecasting Model](https://mlsanity.com/paper/2203.08143/) (2022)

## Built on by (most cited first, 17 of 17)

- [TradingAgents: Multi-Agents LLM Financial Trading Framework](https://mlsanity.com/paper/2412.20138/) (2024)
- [Large Language Models for Time Series: A Survey](https://mlsanity.com/paper/2402.01801/) (2024)
- [Large Language Model Agent in Financial Trading: A Survey](https://mlsanity.com/paper/2408.06361/) (2024)
- [Automate Strategy Finding with LLM in Quant Investment](https://mlsanity.com/paper/2409.06289/) (2024)
- [Can LLM-based Financial Investing Strategies Outperform the Market in Long Run?](https://mlsanity.com/paper/2505.07078/) (2025)
- [Trading-R1: Financial Trading with LLM Reasoning via Reinforcement Learning](https://mlsanity.com/paper/2509.11420/) (2025)
- [FinTrust: A Comprehensive Benchmark of Trustworthiness Evaluation in Finance Domain](https://mlsanity.com/paper/2510.15232/) (2025)
- [Can AI Help with Your Personal Finances?](https://mlsanity.com/paper/2412.19784/) (2024)
- [Bridging Language Models and Financial Analysis](https://mlsanity.com/paper/2503.22693/) (2025)
- [Predicting Liquidity-Aware Bond Yields using Causal GANs and Deep Reinforcement Learning with LLM Evaluation](https://mlsanity.com/paper/2502.17011/) (2025)
- [Summoning the Oracle to Slay It: Mitigating Look-Ahead Bias in Financial Backtesting with Large Language Models](https://mlsanity.com/paper/2605.24564/) (2026)
- [From Time Series Analysis to Question Answering: A Survey in the LLM Era](https://mlsanity.com/paper/2506.11512/) (2025)
- [H3M-SSMoEs: Hypergraph-based Multimodal Learning with LLM Reasoning and Style-Structured Mixture of Experts](https://mlsanity.com/paper/2510.25091/) (2025)
- [WebCryptoAgent: Agentic Crypto Trading with Web Informatics](https://mlsanity.com/paper/2601.04687/) (2026)
- [A Bipartite Graph Approach to U.S.-China Cross-Market Return Forecasting](https://mlsanity.com/paper/2603.10559/) (2026)
- [TradingMoE: Routing the Right Experts in Evolving Markets](https://mlsanity.com/paper/2608.11785/) (2026)
- [Agent Memory with Episodic Retrieval for Financial Decision-Making](https://mlsanity.com/paper/2609.28771/) (2026)

## Abstract (arXiv)

The remarkable achievements and rapid advancements of Large Language Models (LLMs) such as ChatGPT and GPT-4 have showcased their immense potential in quantitative investment. Traders can effectively leverage these LLMs to analyze financial news and predict stock returns accurately. However, integrating LLMs into existing quantitative models presents two primary challenges: the insufficient utilization of semantic information embedded within LLMs and the difficulties in aligning the latent information within LLMs with pre-existing quantitative stock features. We propose a novel framework consisting of two components to surmount these challenges. The first component, the Local-Global (LG) model, introduces three distinct strategies for modeling global information. These approaches are grounded respectively on stock features, the capabilities of LLMs, and a hybrid method combining the two paradigms. The second component, Self-Correlated Reinforcement Learning (SCRL), focuses on aligning the embeddings of financial news generated by LLMs with stock features within the same semantic space. By implementing our framework, we have demonstrated superior performance in Rank Information Coefficient and returns, particularly compared to models relying only on stock features in the China A-share market.