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

Hybrid retrieval

Results for “Corporate Financial Data & Statements in AI Stock Prediction”

30 findings

69match

Finding 53651 source

Despite strong performance, the study notes potential weaknesses, including heavy reliance on historical data and operational complexity, while offering a notably promising framework bridging predictive accuracy with actionable trading decisions.

LexicalMeaningQualityGraph

Matched: corporate, data, financial, prediction, statements, stock

69match

Finding 53641 source

Extensive backtesting on S&P500 data reveals significantly improved Sharpe ratios compared to traditional methods, with rigorous ablation studies and multiple evaluation metrics like Mean Squared Error and Mean Absolute Error.

LexicalMeaningQualityGraph

Matched: corporate, data, financial, prediction, statements, stock

67match

Finding 53631 source

Researchers introduce a novel dual-graph model combining BiLSTM-GAT-AM architecture with technical and fundamental analyses, using DTW-based graph for price movements and industry similarity graph for longer-term relationships, enhancing stock predictions.

LexicalMeaningQualityGraph

Matched: corporate, data, financial, prediction, statements, stock

66match

Finding 53661 source

Integrating BiLSTM with Graph Attention Networks, the paper introduces a dual-graph approach that captures technical and fundamental relationships, offering a distinct, unified framework for portfolio optimization and market prediction. Its originality and novelty, exemplified by the $DualGraph$ structure, create compelling insights that remarkably advance quantitative and computational finance research.

LexicalMeaningQualityGraph

Matched: corporate, data, financial, prediction, statements, stock

63match

Finding 27921 source

AI has the potential to outperform human analysts in stock price prediction, especially for firms with high investment and uncertainty. Future research could explore more complex models and additional data types to further enhance prediction accuracy.

LexicalMeaningQualityGraph

Matched: data, financial, prediction, statements, stock

62match

Finding 41301 source

The research suggests that the proposed model can significantly improve financial distress predictions for SMEs. Potential applications include better credit risk assessment and early warning systems for lenders. Future research could explore additional variables and refine the model for different industries.

LexicalMeaningQualityGraph

Matched: corporate, data, financial, prediction, statements

59match

Finding 72371 source

Despite robust results and innovative techniques, the studys limited dataset and focus on American stocks constrain broader applicability, urging future research to expand data scope for globally relevant financial forecasting.

LexicalMeaningQualityGraph

Matched: corporate, data, financial, prediction, statements, stock

58match

Finding 44961 source

The study concludes that generative AI can significantly enhance financial market efficiency and decision-making. Potential applications include improved risk management and investment strategies. Future research should address ethical concerns, data security, and regulatory frameworks to maximize AI's benefits while mitigating its risks.

LexicalMeaningQualityGraph

Matched: corporate, data, financial, statements, stock

58match

Finding 72361 source

A teacher model trained on historical and future data transfers knowledge to a student, significantly boosting forecast accuracy by $5.41%$ on US indices via innovative attention fusion and Hilbert-Schmidt loss.

LexicalMeaningQualityGraph

Matched: corporate, data, financial, prediction, statements, stock

57match

Finding 72351 source

Researchers introduce DishFT-GNN, an innovative, future-aware graph neural network that effectively integrates historical and forward-looking data using a teacher-student distillation process, revolutionizing stock trend forecasting by capturing unseen market shifts.

LexicalMeaningQualityGraph

Matched: corporate, data, financial, prediction, statements, stock

57match

Finding 72381 source

Integrating graph neural networks with a teacher--student distillation framework, the paper introduces a future-aware approach that leverages long-term trend information to overcome non-stationary stock prediction challenges. This innovative method extends traditional GNN techniques with original, compelling ideas, providing a novel strategy that enhances predictive learning, adapting to dynamic market conditions.

LexicalMeaningQualityGraph

Matched: corporate, data, financial, prediction, statements, stock

56match

Finding 22801 source

The research suggests that semantic analysis of earnings calls can improve stock price movement predictions, offering potential applications in investment strategies. Future research could further refine these methods and explore additional data sources to enhance predictive accuracy.

LexicalMeaningQualityGraph

Matched: data, financial, statements, stock

55match

Finding 42691 source

Larger, high-quality datasets improve forecasting accuracy, with Transformer models outperforming RNN, LSTM, GRU, CNN and TimesNet to achieve $R^2\approx0.97$ on 35-stock sets, while smaller data treat extra modalities as noise.

LexicalMeaningQualityGraph

Matched: corporate, data, financial, prediction, statements, stock

55match

Finding 42181 source

Surprisingly, AI struggles with seemingly simple tasks like translation and data entry due to hidden spreadsheet logic, highlighting a major gap between current AI capabilities and the demands of real enterprise environments.

LexicalMeaningQualityGraph

Matched: corporate, data, financial, prediction, statements, stock

54match

Finding 42171 source

Despite advances, top AI models such as GPT 5.1 Pro and Claude Sonnet 4.5 pass fewer than 40% of FINCH workflows, with performance plummeting on complex, multi-step, and multimodal tasks.

LexicalMeaningQualityGraph

Matched: corporate, data, financial, prediction, statements, stock

54match

Finding 64721 source

Analyzing risk disclosure text in 48,224 U.S. annual reports (2006--2023) significantly improves financial distress predictions, outperforming traditional models that rely solely on numerical financial, market, and macroeconomic data.

LexicalMeaningQualityGraph

Matched: corporate, data, financial, prediction, statements, stock

54match

Finding 42161 source

The FINCH paper unveils a new benchmark for AI in finance and accounting, using real-world artifacts like Enron spreadsheets and emails to capture the true complexity and chaos of enterprise workflows.

LexicalMeaningQualityGraph

Matched: corporate, data, financial, prediction, statements, stock

54match

Finding 59931 source

The results suggest that META can significantly enhance early prediction of credit rating migrations, benefiting investors and regulators. Potential applications include better investment decision-making and improved risk management. Future research could explore extending META to other financial prediction tasks and further refining its components.

LexicalMeaningQualityGraph

Matched: corporate, data, financial, prediction

54match

Finding 64731 source

The study finds that both the way risks are described and the specific types disclosed offer valuable forecasting insights, with textual analysis becoming more useful than numbers as prediction horizons extend further into the future.

LexicalMeaningQualityGraph

Matched: corporate, data, financial, prediction, statements, stock

53match

Finding 64741 source

A novel approach combining textual attributes and topic modeling highlights the importance of disclosure language, urging investors and regulators to use these early warnings, though findings may differ for non-U.S. firms or regulations.

LexicalMeaningQualityGraph

Matched: corporate, data, financial, prediction, statements, stock

53match

Finding 38041 source

AI acts as an early warning signal for crypto markets, but combining AI and crypto assets may reduce diversification benefits; findings are limited by short data periods and few tokens per sector.

LexicalMeaningQualityGraph

Matched: corporate, data, financial, prediction, statements, stock

53match

Finding 42701 source

The paper establishes a reproducible pipelineweb scraping, XBRL parsing, chart generationintroducing GPT-4.1 trend labels and LSA-based sentiment, coining the large four-modal financial benchmark despite fixed sentiment granularity and tuning limits.

LexicalMeaningQualityGraph

Matched: corporate, data, financial, prediction, statements, stock

53match

Finding 80421 source

SEFDs innovative parsing pipeline preserves complex document structurestables, indentation, visual hierarchiesusing MultiMarkdown, enabling advanced financial reasoning and outperforming plain-text datasets, with less than 0.1% overlap with common web corpora.

LexicalMeaningQualityGraph

Matched: corporate, data, financial, prediction, statements, stock

53match

Finding 31291 source

Machine learning can accurately predict diverse corporate sustainability metricslike carbon emissions and board diversityusing only financial data, outperforming traditional models and working even when direct sustainability disclosures are missing or inconsistent.

LexicalMeaningQualityGraph

Matched: corporate, data, financial, prediction, stock

53match

Finding 31321 source

This paper introduces a flexible, domain-agnostic machine learning framework for estimating diverse firm-level sustainability metrics from standard financial data, uniquely addressing incomplete ESG data. Incorporating prediction uncertainty quantification and prediction-powered inference, it advances methodological robustness, offering significant impact for quantitative finance as sustainability data becomes crucial for investment and risk management.

LexicalMeaningQualityGraph

Matched: corporate, data, financial, prediction, stock

53match

Finding 42191 source

FINCH presents a novel, large-scale benchmark for AI agents in finance, leveraging authentic, multi-modal workflows from major institutions. Its unique LLM-assisted pipeline and expert annotation address real-world complexity, revealing significant gaps in current AI capabilities. The datasets scale, realism, and practical focus make it compelling and highly original.

LexicalMeaningQualityGraph

Matched: corporate, data, financial, prediction, statements, stock

53match

Finding 42681 source

FinMultiTime offers a bilingual, 112.6 GB dataset aligning news text, financial tables, K-line charts and price series across S&P 500 and HS 300 (2009--2025) at minute, daily and quarterly resolutions across granular timeframes.

LexicalMeaningQualityGraph

Matched: corporate, data, financial, prediction, statements, stock

53match

Finding 80431 source

Surprisingly, a few long filings contribute most dataset value, showing high-quality, well-structured data beats sheer quantity in LLM training; SEFD introduces new benchmarks but lacks image coverage and may lag SEC schema changes.

LexicalMeaningQualityGraph

Matched: corporate, data, financial, prediction, statements, stock

53match

Finding 64751 source

This paper innovatively applies textual attribute analysis and topic modeling to 48,224 annual reports, revealing that textual risk disclosures surpass traditional numerical variables in predicting financial distress, especially over longer horizons. Its dual focus and scale offer fresh insights, advancing quantitative risk management, financial AI, and NLP applications in finance.

LexicalMeaningQualityGraph

Matched: corporate, data, financial, prediction, statements, stock

52match

Finding 42711 source

This paper introduces a large bilingual multimodal financial time-series dataset, uniquely spanning diverse markets and modalities. Its originality lies in comprehensive data scale and cross-lingual, multimodal integration. Although not algorithmic, this resource offers fresh foundations for AI-driven trading research, substantially advancing forecasting benchmarks and enabling novel investigations and practical applications.

LexicalMeaningQualityGraph

Matched: corporate, data, financial, prediction, statements, stock