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Results for “Utilizing Corporate and Fundamental Data in AI-based Stock Prediction”

30 findings

68match

Finding 40171 source

This paper uniquely integrates technical, fundamental, and macroeconomic data within ensemble machine learning models for banking stocks. Its originality lies in a holistic approach that builds on established techniques rather than radical innovations. The work remains compelling for its balanced methodology, offering nuanced insights into corporate and fundamental data for AI-driven stock predictions.

LexicalMeaningQualityGraph

Matched: based, corporate, data, fundamental, prediction, stock, utilizing

68match

Finding 53781 source

The study concludes that CLIP-based models offer remarkable performance in market forecasting, with potential applications in real-time trading and financial decision-making. Future research could explore other markets and data periods to generalize the findings.

LexicalMeaningQualityGraph

Matched: based, data, fundamental, prediction, stock, utilizing

66match

Finding 31301 source

The study introduces a flexible ML framework with advanced techniques, such as conformalized quantile regression and prediction-powered inference, enabling robust uncertainty quantification and reliable analysis across a massive dataset of thousands of companies in 95 countries.

LexicalMeaningQualityGraph

Matched: based, corporate, data, fundamental, prediction, stock, utilizing

66match

Finding 73581 source

This paper introduces a multi-graph learning framework to fuse heterogeneous market information for stock forecasting, integrating fundamental indicators, financial news, and firm relationships. While moderately original and relevant, it builds on existing data fusion and graph-based learning methods, lacking clear groundbreaking novelty. Its significance lies in comprehensive data integration.

LexicalMeaningQualityGraph

Matched: based, corporate, data, fundamental, prediction, stock, utilizing

66match

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: based, corporate, data, fundamental, prediction, stock, utilizing

65match

Finding 55371 source

Combining AI-driven stock forecasting with extensive corporate fundamentals, this study empirically evaluates feature engineering across a vast signal universe. Its appeal lies in breadth and practical insights, but the incremental signal universe concept and sparse methodological innovation limit true novelty. Nonetheless, it offers valuable guidance for ML-based investment research practitioners.

LexicalMeaningQualityGraph

Matched: based, corporate, data, fundamental, prediction, stock, utilizing

65match

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: based, corporate, data, fundamental, prediction, stock, utilizing

65match

Finding 31311 source

While the approach expands sustainability data access for researchers and policymakers, it works best for large, public firms; high computational demands and voluntary reporting biases remain key limitations, especially for smaller businesses or data-poor regions.

LexicalMeaningQualityGraph

Matched: based, corporate, data, fundamental, prediction, stock, utilizing

64match

Finding 73561 source

The study introduces novel terminology and techniques for integrating heterogeneous data, allowing the model to learn from multiple interconnected graphs representing different market aspects, a significant advance in financial data analysis.

LexicalMeaningQualityGraph

Matched: based, corporate, data, fundamental, prediction, stock, utilizing

63match

Finding 73571 source

Results show this approach significantly outperforms standard models, revealing surprising trends in how interconnected market information boosts forecasting accuracy, though its complexity and high data requirements may hinder immediate widespread adoption.

LexicalMeaningQualityGraph

Matched: based, corporate, data, fundamental, prediction, stock, utilizing

63match

Finding 73551 source

A new multi-graph learning framework fuses diverse market databeyond just financial news and fundamentalsenabling more accurate stock forecasting by capturing complex company interactions often missed by traditional models.

LexicalMeaningQualityGraph

Matched: based, corporate, data, fundamental, prediction, stock, utilizing

63match

Finding 40141 source

Combining technical, fundamental, and macroeconomic indicators significantly boosts prediction accuracy for Indian banking stocks, achieving over 90% performance for mid- and long-term horizons, while technical metrics drive effective short-term forecasts.

LexicalMeaningQualityGraph

Matched: based, corporate, data, fundamental, prediction, stock, utilizing

63match

Finding 40151 source

The study utilizes ensemble machine learning techniques like Random Forest, Gradient Boosting, and XGBoost with a dataset of 30 variables across 20 major banks, bridging technical and fundamental analysis approaches.

LexicalMeaningQualityGraph

Matched: based, corporate, data, fundamental, prediction, stock, utilizing

62match

Finding 76051 source

Presented is a comprehensive study comparing supervised, statistical, and hybrid methods for predicting clean energy stock prices by incorporating fear and fundamental factors. Employing techniques including SHAP validation and PCA, the paper integrates diverse methodologies. Its originality, methodological rigor, and targeted focus deliver truly compelling insights for quantitative finance researchers.

LexicalMeaningQualityGraph

Matched: based, corporate, data, fundamental, prediction, stock, utilizing

62match

Finding 55361 source

Real-time walk-forward testing reveals temporal instability in popular indicators highlights engineered composites stability, yet notes missing transaction cost analysis, high turnover, exclusive reliance on fundamental data and untapped alternative sources.

LexicalMeaningQualityGraph

Matched: based, corporate, data, fundamental, prediction, stock, utilizing

62match

Finding 40161 source

Surprisingly, macroeconomic signals such as inflation and bond yields influence even short-term stock movements, while comprehensive cross-validation and grid-search optimization ensure rigorous analysis from 2013 to 2022 despite overfitting concerns.

LexicalMeaningQualityGraph

Matched: based, corporate, data, fundamental, prediction, stock, utilizing

61match

Finding 76041 source

New proposals use simulated future data via Random Forest and LSTM to address gaps. Prophet-based models excel with simulated data, yet LSTM risks overfitting, emphasizing improvements in model granularity significantly.

LexicalMeaningQualityGraph

Matched: based, corporate, data, fundamental, prediction, stock, utilizing

61match

Finding 76031 source

Innovative integration of media sentiment with financial indicators, supported by XGBoost, SHAP, and PCA techniques, enhances forecast accuracy while explaining significance, offering novel solutions for data scarcity challenges across markets.

LexicalMeaningQualityGraph

Matched: based, corporate, data, fundamental, prediction, stock, utilizing

61match

Finding 55331 source

The research demonstrates that machine learning models can effectively predict long-term stock performance using fundamental analysis. Future research could explore more data, additional algorithms, and incorporate technical and sentiment analysis to further enhance prediction accuracy.

LexicalMeaningQualityGraph

Matched: based, data, fundamental, prediction, stock

61match

Finding 76021 source

Traditional models like ARIMA and VAR provide a robust linear baseline, but combined with feature enhancements using Prophet and LSTM, they accurately capture non-linear trends and improve forecast reliability remarkably.

LexicalMeaningQualityGraph

Matched: based, corporate, data, fundamental, prediction, stock, utilizing

60match

Finding 55351 source

The study introduces a structured pipelinescreening, transformation, aggregation, selectionand a new taxonomy of signal interactions, applying gradient-boosted trees, PCA and shrinkage regularization to generate consistent alpha across diverse asset classes.

LexicalMeaningQualityGraph

Matched: based, corporate, data, fundamental, prediction, stock, utilizing

60match

Finding 55341 source

Researchers blend over 500 raw indicators into compact orthogonal factors using novel feature-engineering, boosting predictive accuracy by 20% compared to standard regression, delivering economically significant out-of-sample returns in real time.

LexicalMeaningQualityGraph

Matched: based, corporate, data, fundamental, prediction, stock, utilizing

60match

Finding 53091 source

Researchers unveil a novel asset management method using a multi-task self-supervised tabular transformer (SAINT), which learns from over 400 US stock features and consistently beats traditional models in predicting returns and Sharpe ratios.

LexicalMeaningQualityGraph

Matched: based, corporate, data, fundamental, prediction, stock, utilizing

60match

Finding 53111 source

The approach combines masked autoencoding, next-row forecasting, and contrastive learning, but faces challenges with non-stationary data and a training-test performance gap; future improvements could include more macroeconomic data and refined portfolio construction.

LexicalMeaningQualityGraph

Matched: based, corporate, data, fundamental, prediction, stock, utilizing

60match

Finding 58021 source

The study introduces the pioneering MiMIC dataset for the Indian market, enabling integrated cascaded models using text and image classifiers within a regression framework that substantially reduces prediction errors effectively.

LexicalMeaningQualityGraph

Matched: based, corporate, data, fundamental, prediction, stock, utilizing

59match

Finding 53101 source

A standout innovation is leveraging transformer attention weights to reveal which firm characteristics drive returns, finding that liquidity and financing variables become crucial during recessions, offering new insights into market downturns.

LexicalMeaningQualityGraph

Matched: based, corporate, data, fundamental, prediction, stock, utilizing

59match

Finding 53121 source

This paper presents a novel multi-task self-supervised framework leveraging tabular transformers to learn firm embeddings from high-dimensional data, uniquely modeling both within-firm and cross-sectional interactions. Transformer attention enables economic interpretability, especially during recessions. Its similarity-based trading strategy outperforms standard ML methods, offering compelling advances for AI-driven quantitative finance.

LexicalMeaningQualityGraph

Matched: based, corporate, data, fundamental, prediction, stock, utilizing

59match

Finding 49511 source

Companies often use upbeat headlines in earnings guidance press releases to shape investor perceptions, even when the underlying news is negative, revealing a strategic approach to impression management in corporate communications.

LexicalMeaningQualityGraph

Matched: based, corporate, data, fundamental, prediction, stock, utilizing

59match

Finding 58041 source

Presenting a pioneering multi-modal dataset, this paper focuses on Indian earnings calls, diverging from US-centric literature. By integrating textual, visual, and tabular data to predict stock prices, it introduces innovative fusion techniques that advance AI-based forecasting, making the work both original and compelling, with significant implications for global finance research.

LexicalMeaningQualityGraph

Matched: based, corporate, data, fundamental, prediction, stock, utilizing

59match

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: based, corporate, data, fundamental, prediction, stock