# What do LLMs Know about Financial Markets? A Case Study on Reddit Market Sentiment Analysis

arXiv: https://arxiv.org/abs/2212.11311 · HTML: https://mlsanity.com/paper/2212.11311/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 24 references that are in the map.
- First posted on arXiv 21 Dec 2022.

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

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [Knowledge Distillation: A Survey](https://mlsanity.com/paper/2006.05525/) (2020)
- [Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?](https://mlsanity.com/paper/2202.12837/) (2022)
- [Least-to-Most Prompting Enables Complex Reasoning in Large Language Models](https://mlsanity.com/paper/2205.10625/) (2022)
- [An Explanation of In-context Learning as Implicit Bayesian Inference](https://mlsanity.com/paper/2111.02080/) (2021)
- [FinBERT: Financial Sentiment Analysis with Pre-trained Language Models](https://mlsanity.com/paper/1908.10063/) (2019)
- [FinBERT: A Pretrained Language Model for Financial Communications](https://mlsanity.com/paper/2006.08097/) (2020)
- [Charformer: Fast Character Transformers via Gradient-based Subword Tokenization](https://mlsanity.com/paper/2106.12672/) (2021)
- [Controlling Linguistic Style Aspects in Neural Language Generation](https://mlsanity.com/paper/1707.02633/) (2017)

## Built on by (most cited first, 27 of 27)

- [Revolutionizing Finance with LLMs: An Overview of Applications and Insights](https://mlsanity.com/paper/2401.11641/) (2024)
- [A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges](https://mlsanity.com/paper/2406.11903/) (2024)
- [Designing Heterogeneous LLM Agents for Financial Sentiment Analysis](https://mlsanity.com/paper/2401.05799/) (2024)
- [Prompt Packer: Deceiving LLMs through Compositional Instruction with Hidden Attacks](https://mlsanity.com/paper/2310.10077/) (2023)
- [FLAG-Trader: Fusion LLM-Agent with Gradient-based Reinforcement Learning for Financial Trading](https://mlsanity.com/paper/2502.11433/) (2025)
- [User Modeling in the Era of Large Language Models: Current Research and Future Directions](https://mlsanity.com/paper/2312.11518/) (2023)
- [LLMs with Industrial Lens: Deciphering the Challenges and Prospects -- A Survey](https://mlsanity.com/paper/2402.14558/) (2024)
- [Large Language Models as Automated Aligners for benchmarking Vision-Language Models](https://mlsanity.com/paper/2311.14580/) (2023)
- [A Comparative Analysis of Instruction Fine-Tuning LLMs for Financial Text Classification](https://mlsanity.com/paper/2411.02476/) (2024)
- [FinTrust: A Comprehensive Benchmark of Trustworthiness Evaluation in Finance Domain](https://mlsanity.com/paper/2510.15232/) (2025)
- [RankAdaptor: Hierarchical Rank Allocation for Efficient Fine-Tuning Pruned LLMs via Performance Model](https://mlsanity.com/paper/2406.15734/) (2024)
- [No Size Fits All: The Perils and Pitfalls of Leveraging LLMs Vary with Company Size](https://mlsanity.com/paper/2408.01444/) (2024)
- [Do LLMs Find Human Answers To Fact-Driven Questions Perplexing? A Case Study on Reddit](https://mlsanity.com/paper/2404.01147/) (2024)
- [A Dutch Financial Large Language Model](https://mlsanity.com/paper/2410.12835/) (2024)
- [QPruner: Probabilistic Decision Quantization for Structured Pruning in Large Language Models](https://mlsanity.com/paper/2412.11629/) (2024)
- [A Review of Large Language Models for Stock Price Forecasting from a Hedge-Fund Perspective](https://mlsanity.com/paper/2605.05211/) (2026)
- ["With Great Power Comes Great Responsibility!": Student and Instructor Perspectives on the influence of LLMs on Undergraduate Engineering Education](https://mlsanity.com/paper/2309.10694/) (2023)
- [Motamot: A Dataset for Revealing the Supremacy of Large Language Models over Transformer Models in Bengali Political Sentiment Analysis](https://mlsanity.com/paper/2407.19528/) (2024)
- [Enhancing Cryptocurrency Sentiment Analysis with Multimodal Features](https://mlsanity.com/paper/2508.15825/) (2025)
- [The Overlooked Repetitive Lengthening Form in Sentiment Analysis](https://mlsanity.com/paper/2604.01268/) (2026)
- [FiSTECH: Financial Style Transfer to Enhance Creativity without Hallucinations in LLMs](https://mlsanity.com/paper/2408.05365/) (2024)
- [Leveraging LLMs for Predictive Insights in Food Policy and Behavioral Interventions](https://mlsanity.com/paper/2411.08563/) (2024)
- [AutoMixQ: Self-Adjusting Quantization for High Performance Memory-Efficient Fine-Tuning](https://mlsanity.com/paper/2411.13814/) (2024)
- [A methodology for analyzing financial needs hierarchy from social discussions using LLM](https://mlsanity.com/paper/2602.06431/) (2026)
- [Leveraging Large Language Models for Sentiment Analysis: Multi-Modal Analysis of Decentraland's MANA Token](https://mlsanity.com/paper/2605.20192/) (2026)
- [Evaluating LLM Usage for Efficient and Explainable Numerical and Classified Implicit Sentiment Analysis of Product Desirability](https://mlsanity.com/paper/2606.23701/) (2026)
- [DysLexLens: A Low-Resource LLM Framework for Analysing Dyslexic Learners Insights from Online Forums](https://mlsanity.com/paper/2606.27619/) (2026)

## Abstract (arXiv)

Market sentiment analysis on social media content requires knowledge of both financial markets and social media jargon, which makes it a challenging task for human raters. The resulting lack of high-quality labeled data stands in the way of conventional supervised learning methods. Instead, we approach this problem using semi-supervised learning with a large language model (LLM). Our pipeline generates weak financial sentiment labels for Reddit posts with an LLM and then uses that data to train a small model that can be served in production. We find that prompting the LLM to produce Chain-of-Thought summaries and forcing it through several reasoning paths helps generate more stable and accurate labels, while using a regression loss further improves distillation quality. With only a handful of prompts, the final model performs on par with existing supervised models. Though production applications of our model are limited by ethical considerations, the model's competitive performance points to the great potential of using LLMs for tasks that otherwise require skill-intensive annotation.