# AI in Investment Analysis: LLMs for Equity Stock Ratings

arXiv: https://arxiv.org/abs/2411.00856 · HTML: https://mlsanity.com/paper/2411.00856/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 41 references that are in the map.
- First posted on arXiv 30 Oct 2024.

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

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [BloombergGPT: A Large Language Model for Finance](https://mlsanity.com/paper/2303.17564/) (2023)
- [FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design](https://mlsanity.com/paper/2311.13743/) (2023)
- [Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models](https://mlsanity.com/paper/2304.07619/) (2023)
- [Table Meets LLM: Can Large Language Models Understand Structured Table Data? A Benchmark and Empirical Study](https://mlsanity.com/paper/2305.13062/) (2023)
- [A Survey of Large Language Models in Finance (FinLLMs)](https://mlsanity.com/paper/2402.02315/) (2024)
- [Enhancing Financial Sentiment Analysis via Retrieval Augmented Large Language Models](https://mlsanity.com/paper/2310.04027/) (2023)
- [AlphaFin: Benchmarking Financial Analysis with Retrieval-Augmented Stock-Chain Framework](https://mlsanity.com/paper/2403.12582/) (2024)
- [QuantAgent: Seeking Holy Grail in Trading by Self-Improving Large Language Model](https://mlsanity.com/paper/2402.03755/) (2024)
- [Can Large Language Models Beat Wall Street? Unveiling the Potential of AI in Stock Selection](https://mlsanity.com/paper/2401.03737/) (2024)
- [Evaluating LLMs' Mathematical Reasoning in Financial Document Question Answering](https://mlsanity.com/paper/2402.11194/) (2024)
- [Extracting Structured Insights from Financial News: An Augmented LLM Driven Approach](https://mlsanity.com/paper/2407.15788/) (2024)
- [GCNET: graph-based prediction of stock price movement using graph convolutional network](https://mlsanity.com/paper/2203.11091/) (2022)
- [Feature Learning for Stock Price Prediction Shows a Significant Role of Analyst Rating](https://mlsanity.com/paper/2103.09106/) (2021)

## Built on by (most cited first, 10 of 10)

- [MarketSenseAI 2.0: Enhancing Stock Analysis through LLM Agents](https://mlsanity.com/paper/2502.00415/) (2025)
- [Interpretable LLMs for Credit Risk: A Systematic Review and Taxonomy](https://mlsanity.com/paper/2506.04290/) (2025)
- [Integrating Large Language Models in Financial Investments and Market Analysis: A Survey](https://mlsanity.com/paper/2507.01990/) (2025)
- [An Adaptive Multi Agent Bitcoin Trading System](https://mlsanity.com/paper/2510.08068/) (2025)
- [Signal or Noise in Multi-Agent LLM-based Stock Recommendations?](https://mlsanity.com/paper/2604.17327/) (2026)
- [Smoothing-Based Conformal Prediction for Balancing Efficiency and Interpretability](https://mlsanity.com/paper/2509.22529/) (2025)
- [Hierarchical Reranking for Scalable Financial RAG System](https://mlsanity.com/paper/2607.27523/) (2026)
- [Can Open-Weight Models Compete on Financial Text Comprehension?](https://mlsanity.com/paper/2608.08634/) (2026)
- [Buy the Rumor, Sell the News: When Is News Priced In?](https://mlsanity.com/paper/2608.14014/) (2026)
- [Integrating the Analytic Hierarchy Process with Large Language Models for Transparent Multi-Criteria Decision-Making](https://mlsanity.com/paper/2609.16779/) (2026)

## Abstract (arXiv)

Investment Analysis is a cornerstone of the Financial Services industry. The rapid integration of advanced machine learning techniques, particularly Large Language Models (LLMs), offers opportunities to enhance the equity rating process. This paper explores the application of LLMs to generate multi-horizon stock ratings by ingesting diverse datasets. Traditional stock rating methods rely heavily on the expertise of financial analysts, and face several challenges such as data overload, inconsistencies in filings, and delayed reactions to market events. Our study addresses these issues by leveraging LLMs to improve the accuracy and consistency of stock ratings. Additionally, we assess the efficacy of using different data modalities with LLMs for the financial domain. We utilize varied datasets comprising fundamental financial, market, and news data from January 2022 to June 2024, along with GPT-4-32k (v0613) (with a training cutoff in Sep. 2021 to prevent information leakage). Our results show that our benchmark method outperforms traditional stock rating methods when assessed by forward returns, specially when incorporating financial fundamentals. While integrating news data improves short-term performance, substituting detailed news summaries with sentiment scores reduces token use without loss of performance. In many cases, omitting news data entirely enhances performance by reducing bias. Our research shows that LLMs can be leveraged to effectively utilize large amounts of multimodal financial data, as showcased by their effectiveness at the stock rating prediction task. Our work provides a reproducible and efficient framework for generating accurate stock ratings, serving as a cost-effective alternative to traditional methods. Future work will extend to longer timeframes, incorporate diverse data, and utilize newer models for enhanced insights.