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Search Minerva’s current Findings using transparent hybrid retrieval. Results remain strictly inside the configured corpus.

Hybrid retrieval

Results for “LLMs for Stock Analysis and Prediction”

30 findings

73match

Finding 53431 source

The SEP framework enhances explainable stock predictions and portfolio construction, with potential applications in financial analysis and investment strategies. Future research could focus on increasing robustness and incorporating additional data sources. The study's limitations include potential cumulative errors in the SEP process.

LexicalMeaningQualityGraph

Matched: analysis, generative, language, large, llms, models, prediction, stock

73match

Finding 46451 source

Researchers unveil a pioneering generative-AI framework for finance, leveraging Large Language Models (LLMs) to analyze equity markets cross-sectional structure, offering a fresh, nuanced perspective on complex financial data and stock relationships.

LexicalMeaningQualityGraph

Matched: generative, language, large, llms, models, stock

72match

Finding 45291 source

Despite impressive results, the paper notes limited discussion on transparency and performance in other markets or smaller deals, raising questions about broader applicability and real-world challenges for generative AI in finance.

LexicalMeaningQualityGraph

Matched: analysis, generative, language, large, llms, models, stock

72match

Finding 70561 source

The research demonstrates the effectiveness of LLMs in predicting stock returns and developing investment strategies. Potential applications include enhancing market prediction and investment decision-making methodologies, with implications for asset managers, institutional investors, and financial regulators.

LexicalMeaningQualityGraph

Matched: analysis, language, large, llms, models, prediction, stock

70match

Finding 46481 source

The input text lacks substantive content, providing only a title, author details, and an incomplete sentence about a generative-AI framework for finance using LLMs to encode cross-sectional equity structure. Without further information, it is impossible to assess originality, novelty, or impact, making meaningful evaluation or engagement impossible.

LexicalMeaningQualityGraph

Matched: generative, language, large, llms, models

70match

Finding 45271 source

Large language models like OpenAI's o3, GPT-5, and GPT-5.1 can accurately predict short-term stock market reactions to major U.S. M&A announcements, outperforming traditional methods such as logistic regression.

LexicalMeaningQualityGraph

Matched: analysis, generative, language, large, llms, models, stock

70match

Finding 52671 source

This paper innovatively fuses large language models with equity market analysis, synthesizing and categorizing research trends into a dual framework. Its novelty lies in bridging AI with quantitative finance, offering fresh perspectives for professionals despite its review nature. The work remains engaging, relevant, and valuable for practitioners seeking market insights.

LexicalMeaningQualityGraph

Matched: analysis, language, large, llms, models, prediction, stock

69match

Finding 81031 source

The research highlights ChatGPT's limitations in stock prediction tasks and suggests future work should focus on improving multimodal data integration. Potential applications include enhancing financial market analysis and leveraging social media sentiment for better investment strategies.

LexicalMeaningQualityGraph

Matched: analysis, language, large, llms, models, prediction, stock

69match

Finding 52641 source

Survey reveals LLMs rapidly reshape equity markets by blending structured and unstructured data, enhancing prediction accuracy and decision-making through innovative quantitative methods combined with prompting, fine-tuning, reinforcement learning, and systems.

LexicalMeaningQualityGraph

Matched: analysis, language, large, llms, models, prediction, stock

69match

Finding 52651 source

Researchers integrate qualitative news insights by converting them into numerical signals, powering stock trend prediction through frameworks like Ploutos, StockTime, and benchmarks such as InvestorBench, despite scalability and interpretability challenges.

LexicalMeaningQualityGraph

Matched: analysis, language, large, llms, models, prediction, stock

69match

Finding 26541 source

Researchers unveil a novel financial forecasting method, merging knowledge graphs, large language models, and adaptive deep learning, which dramatically boosts accuracy in sentiment analysis and stock prediction compared to traditional approaches.

LexicalMeaningQualityGraph

Matched: analysis, language, large, llms, models, prediction, stock

69match

Finding 45301 source

This paper uniquely applies state-of-the-art LLMs (OpenAIs o3, GPT-5, GPT-5.1) to M& A announcement return prediction, surpassing traditional logistic regression and naive benchmarks in both predictive accuracy and portfolio performance. Its reasoning-based approach to M& A events offers compelling innovation, practical relevance, and significant impact for empirical finance and investment management.

LexicalMeaningQualityGraph

Matched: analysis, generative, language, large, llms, models, prediction, stock

69match

Finding 50561 source

The research shows that instruction tuning LLMs can significantly enhance financial sentiment analysis. Potential applications include improved market prediction tools and financial news analysis. Future research could explore broader financial tasks and further refine instruction tuning methods.

LexicalMeaningQualityGraph

Matched: analysis, language, large, llms, models, prediction

68match

Finding 27951 source

The research demonstrates the value of ChatGPT in predicting stock returns, suggesting potential applications in enhancing financial decision-making and quantitative trading strategies. Future research could focus on developing more sophisticated LLMs tailored for finance.

LexicalMeaningQualityGraph

Matched: analysis, language, large, llms, models, stock

68match

Finding 43071 source

FinSphere, a new AI agent for real-time stock analysis, outperforms both general-purpose and finance-specific large language models, as well as existing agent-based systems, in generating professional-grade stock analysis reports.

LexicalMeaningQualityGraph

Matched: analysis, language, large, llms, models, stock

67match

Finding 52661 source

The review of 84 studies introduces hybrid modeling and knowledge generation prompting, highlighting significant strengths and limitations while charting LLMs' disruptive potential and prompting future improvements across global financial ecosystems.

LexicalMeaningQualityGraph

Matched: analysis, language, large, llms, models, prediction, stock

67match

Finding 63951 source

The research suggests that Ploutos can significantly improve stock movement prediction accuracy and interpretability. Potential applications include financial investment strategies and market analysis. Future research could explore optimizing computational costs and incorporating additional data types.

LexicalMeaningQualityGraph

Matched: analysis, language, large, llms, models, prediction, stock

67match

Finding 46471 source

Results reveal the framework uncovers hidden market patterns and boosts predictive accuracy, a promising trend, though the paper lacks detailed empirical results and discussion of limitations like data quality or model interpretability.

LexicalMeaningQualityGraph

Matched: generative, language, large, llms, models

67match

Finding 46461 source

The studys standout innovation is adapting LLMstraditionally used for textto encode intricate financial datasets, bridging machine learning and finance, and introducing new terminology that could reshape financial modeling and analysis.

LexicalMeaningQualityGraph

Matched: analysis, generative, language, large, llms, models

67match

Finding 42841 source

The research suggests that FinPT can significantly enhance financial risk prediction, with potential applications in default, fraud, and churn prediction. Future research could explore other tuning strategies and expand the benchmark. The study highlights the importance of unified benchmarks and advanced models in financial risk prediction.

LexicalMeaningQualityGraph

Matched: generative, language, large, llms, models, prediction

67match

Finding 45591 source

A major study used advanced language models like ChatGPT, Claude, and Gemini to analyze sentiment in over 11,000 Japanese 10-K reports, covering 70 million words from 2014 to 2023.

LexicalMeaningQualityGraph

Matched: analysis, language, large, llms, models, stock

67match

Finding 45611 source

The research highlights how sophisticated NLP tools can uncover predictive signals missed by older models, but notes practical challenges in real-world trading and the risk of overfitting remain unresolved.

LexicalMeaningQualityGraph

Matched: analysis, language, large, llms, models, stock

67match

Finding 45281 source

The study shows reasoning-based LLMs interpret complex deal, company, and economic data from 2012-2022, enabling investment portfolios with higher risk-adjusted returns and signaling a major shift in financial decision-making.

LexicalMeaningQualityGraph

Matched: analysis, generative, language, large, llms, models, stock

67match

Finding 38251 source

The study concludes that the proposed framework significantly enhances financial sentiment analysis accuracy. Potential applications include improved market movement forecasting and investment decision-making. Future research could integrate macroeconomic and microeconomic data for even more precise predictions.

LexicalMeaningQualityGraph

Matched: analysis, language, large, llms, models

66match

Finding 46651 source

The research demonstrates that deep generative models are effective for financial applications, such as risk analysis and portfolio optimization. Future work could explore additional architectures and features. Potential applications include creating optimal portfolios and improving risk management strategies.

LexicalMeaningQualityGraph

Matched: analysis, generative, language, large, models

66match

Finding 84011 source

Leveraging a multimodal, training-free approach, the paper uniquely merges visual line graph representations with textual numerical data using vision-language models for stock forecasting. Its innovative zero-shot capabilities and chain-of-thought prompting strategy add novelty to traditional methods. Relevant for quantitative finance, the method is fresh, compelling, combining LLM and VLM insights.

LexicalMeaningQualityGraph

Matched: analysis, language, llms, models, prediction, stock

66match

Finding 83501 source

The research demonstrates that language-specific pre-training can significantly enhance the performance of LLMs in extracting sentiment factors from Chinese financial news. This has potential applications in developing more effective quantitative trading strategies. Future research could explore further fine-tuning and pre-training techniques.

LexicalMeaningQualityGraph

Matched: analysis, language, large, llms, models, stock

66match

Finding 45601 source

Surprisingly, LLM-derived sentiment scores showed a significant negative correlation with future stock returnsmore negative sentiment predicted better performancechallenging the efficient market hypothesis and outperforming traditional dictionary-based methods.

LexicalMeaningQualityGraph

Matched: analysis, language, large, llms, models, stock

66match

Finding 43031 source

FinSphere revolutionizes financial analysis by integrating real-time market data, advanced quantitative tools, and the expert-curated Stocksis dataset, remarkably delivering superior stock analysis that outperforms both general-purpose and domain-specific language models.

LexicalMeaningQualityGraph

Matched: analysis, language, large, llms, models, stock

66match

Finding 45621 source

This paper uniquely applies advanced LLMs (ChatGPT, Claude, Gemini) to sentiment analysis of Japanese 10-K reports, an underexplored dataset. Its findingthat LLM-derived sentiment predicts future stock returns while traditional methods do notchallenges the efficient market hypothesis, offering novel insights and significant implications for both academic research and investment practice.

LexicalMeaningQualityGraph

Matched: analysis, language, large, llms, models, stock