Summary of Ai in Investment Analysis: Llms For Equity Stock Ratings, by Kassiani Papasotiriou et al.
AI in Investment Analysis: LLMs for Equity Stock Ratings
by Kassiani Papasotiriou, Srijan Sood, Shayleen Reynolds, Tucker Balch
First submitted to arxiv on: 30 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computational Finance (q-fin.CP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The application of Large Language Models (LLMs) in investment analysis can enhance the equity rating process. This paper explores the use of LLMs to generate multi-horizon stock ratings by ingesting diverse datasets. Traditional methods rely on financial analyst expertise, but face challenges like data overload and delayed reactions to market events. Our study addresses these issues by leveraging LLMs for more accurate and consistent stock ratings. We also assess the efficacy of using different data modalities with LLMs in the financial domain. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Investment analysis is crucial in the Financial Services industry. This paper uses machine learning techniques, like Large Language Models (LLMs), to make better stock predictions. Right now, experts do this job, but it can be hard and slow. Our study shows how LLMs can help make more accurate and consistent predictions by using many different types of data. |
Keywords
* Artificial intelligence * Machine learning
