Explore the corpus

Find evidence, not just documents.

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

Hybrid retrieval

Results for “AI & Machine Learning Techniques for Stock Market Forecasting”

30 findings

77match

Finding 55621 source

The study concludes that deep learning techniques are increasingly popular in stock market predictions. Potential applications include improved financial forecasting models. Future research should focus on integrating more diverse data sources and refining machine learning techniques for better accuracy.

LexicalMeaningQualityGraph

Matched: forecasting, learning, machine, market, prediction, stock, techniques

75match

Finding 30191 source

The research suggests that CNNs are highly effective for stock market prediction, outperforming traditional ML models. Future research should focus on complex models and ensemble learning to improve accuracy. Potential applications include better investment decision-making and enhanced financial market analysis.

LexicalMeaningQualityGraph

Matched: forecast, forecasting, learning, machine, market, prediction, stock, techniques

74match

Finding 55101 source

The results suggest that LSTM models are highly effective for stock price prediction, benefiting investors. Future research could explore integrating more external factors. Potential applications include financial market analysis and automated trading systems.

LexicalMeaningQualityGraph

Matched: forecast, forecasting, learning, machine, market, prediction, stock, techniques

73match

Finding 41831 source

The research suggests that the ELR-ML model can significantly aid in stock market forecasting, providing investors with accurate predictions for better decision-making. Future research could explore enhancing the model's accuracy and applying it to other financial indices.

LexicalMeaningQualityGraph

Matched: forecast, forecasting, learning, machine, market, prediction, stock

70match

Finding 20451 source

The study concludes that AI models significantly improve stock market predictions. Potential applications include better investment decisions and financial planning. Future research should explore more advanced models and diverse data sources.

LexicalMeaningQualityGraph

Matched: forecasting, learning, machine, market, prediction, stock, techniques

67match

Finding 23041 source

The study concludes that AI techniques, especially deep reinforcement learning, hold great potential for stock market prediction. Future research should focus on improving data availability, model interpretability, and real-time trading implementations. Potential applications include automated trading systems and enhanced risk management strategies.

LexicalMeaningQualityGraph

Matched: forecasting, learning, machine, market, prediction, stock, techniques

66match

Finding 27041 source

The study concludes that hybrid LSTM-CNN models are highly effective for stock prediction. Potential applications include portfolio management and intraday trading. Future research should focus on integrating sentiment analysis for improved accuracy.

LexicalMeaningQualityGraph

Matched: forecast, forecasting, learning, machine, prediction, stock, techniques

66match

Finding 44001 source

A new forecasting model, CPO-VMD-PConv-Informer, predicts the KBW Nasdaq Financial Technology Index (KFTX) with exceptional accuracy, achieving $R^2$ values of 0.9681 and 0.9757, surpassing traditional machine learning methods.

LexicalMeaningQualityGraph

Matched: forecast, forecasting, learning, machine, market, prediction, stock, techniques

65match

Finding 33331 source

The study concludes that integrating Sentiment Analysis with Technical Analysis enhances stock market predictions. Potential applications include more accurate financial forecasting and investment strategies. Future research could explore additional data sources and further refine the models.

LexicalMeaningQualityGraph

Matched: forecasting, learning, machine, market, prediction, stock, techniques

65match

Finding 38971 source

This paper uniquely applies multi-output machine learning models to estimate industry betas, capturing cross-industry dependencies overlooked by single-output approaches. Its novelty lies in adapting established ML techniques to empirical asset pricing, demonstrating improved forecast accuracy and practical portfolio benefits. The work is compelling for AI-driven finance, offering significant, original insights.

LexicalMeaningQualityGraph

Matched: estimate, forecast, learning, machine, market, prediction, stock, techniques

65match

Finding 72301 source

The study introduces a novel CAB model that efficiently fuses three-dimensional convolution, bidirectional LSTMs, and multi-head attention with econometrics to precisely forecast covariance matrices in multi-asset portfolios over medium-term horizons.

LexicalMeaningQualityGraph

Matched: forecast, forecasting, learning, machine, market, prediction, stock, techniques

65match

Finding 66061 source

The study concludes that deep learning and combinatorial models are promising for stock price forecasting. Future research should focus on integrating textual data and improving model accuracy. Potential applications include better investment strategies and risk management in stock markets.

LexicalMeaningQualityGraph

Matched: forecasting, learning, machine, market, prediction, stock

65match

Finding 23051 source

The research demonstrates that combining deep learning with XAI techniques can improve stock market predictions and provide valuable insights. Potential applications include financial forecasting and investment strategies. Future research could explore other XAI methods and apply the model to different datasets.

LexicalMeaningQualityGraph

Matched: forecasting, learning, machine, market, prediction, stock, techniques

64match

Finding 43991 source

The research demonstrates that hybrid normalization and intersection feature selection improve stock prediction accuracy. Potential applications include better investment strategies and risk management. Future research could explore combining more feature selection techniques for even better results.

LexicalMeaningQualityGraph

Matched: forecast, forecasting, machine, market, prediction, stock, techniques

64match

Finding 44021 source

SHAP analysis reveals a striking shift in predictive factors: before COVID-19, the term structure of the market (TM) dominated, but after the pandemic, the Financial Stress Index (FSI) became the main driver.

LexicalMeaningQualityGraph

Matched: forecast, forecasting, learning, machine, market, prediction, stock, techniques

64match

Finding 82581 source

The model extracts fine-grained temporal features via an innovative 1D-CNN embedding combined with dynamic token embeddings, boosting prediction accuracy and profitability while debuting trading algorithms Stock Direction and Stock Tanh.

LexicalMeaningQualityGraph

Matched: forecasting, learning, machine, market, prediction, stock, techniques

63match

Finding 82601 source

The paper introduces a mutated Transformer, Stockformer, for stock market forecasting through multivariate time series prediction. This approach builds on recent deep learning advances. Although its employment of widely-used Transformer architectures renders the innovation incremental, the method remains original, timely, and compelling, offering fresh perspectives and improvements in financial analysis.

LexicalMeaningQualityGraph

Matched: forecasting, learning, machine, market, prediction, stock, techniques

63match

Finding 44011 source

The models robustness stands out during extreme market events like COVID-19, thanks to its integration of Variational Mode Decomposition (VMD) and Crested Porcupine Optimizer (CPO), maintaining mean absolute percentage errors (MAPE) below 2%.

LexicalMeaningQualityGraph

Matched: forecast, forecasting, learning, machine, market, prediction, stock, techniques

63match

Finding 44181 source

Researchers demonstrate that machine learning techniques, notably deep neural networks with modified DeepLOB architecture integrating CNN and LSTM layers, outperform traditional models, improving intraday trading volume predictability in equity markets.

LexicalMeaningQualityGraph

Matched: forecast, forecasting, learning, machine, market, prediction, stock, techniques

63match

Finding 43931 source

The research demonstrates the potential of NLP in enhancing financial forecasts, suggesting applications in trading strategies and market analysis. Future research could explore more advanced NLP techniques and their long-term impact on market efficiency.

LexicalMeaningQualityGraph

Matched: forecast, forecasting, learning, machine, market, prediction, techniques

62match

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: forecasting, learning, machine, market, prediction, stock, techniques

62match

Finding 44191 source

The study introduces innovative clustered and universal asset models to incorporate crosssectional commonality, while incorporating auxiliary features and order flow imbalance techniques, unveiling enhancements to liquidity dynamics and CMEM performance.

LexicalMeaningQualityGraph

Matched: forecast, forecasting, learning, machine, market, prediction, stock, techniques

62match

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: forecasting, learning, machine, market, prediction, stock, techniques

62match

Finding 33161 source

Authors integrate advanced neural networks with multiple reconciliation techniques, comparing performance via four metrics (AvgRMSE, AvgMAPE, AvgRelRMSE, AvgRelMAPE) through rigorous evaluation to underscore forecast coherence and structural weighting in modeling.

LexicalMeaningQualityGraph

Matched: forecast, forecasting, learning, machine, market, stock, techniques

62match

Finding 44031 source

This paper presents the CPO-VMD-PConv-Informer framework, uniquely integrating eight uncertainty indicators (including EPU, GPR) for KFTX forecasting. Combining VMD, CPO, and Informer, it outperforms traditional models, especially during extreme volatility (e.g., COVID-19). SHAP-based interpretability and robust empirical validation make it a compelling, innovative advancement in quantitative finance.

LexicalMeaningQualityGraph

Matched: forecast, forecasting, learning, machine, market, prediction, stock, techniques

62match

Finding 73001 source

The study concludes that the K-means algorithm is highly effective for financial risk prediction, with potential applications in credit and systemic risk management. Future research may focus on improving algorithm accuracy and integrating emerging technologies like blockchain for enhanced financial risk management.

LexicalMeaningQualityGraph

Matched: estimate, forecasting, learning, machine, market, prediction

62match

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: forecasting, learning, machine, market, prediction, stock, techniques

61match

Finding 23421 source

The research shows that CNN combined with QRM can improve stock option predictions, with potential applications in financial trading strategies. Future work could explore different data sets, additional market factors, and alternative machine learning models to further enhance prediction accuracy.

LexicalMeaningQualityGraph

Matched: forecast, forecasting, learning, machine, market, prediction, stock

61match

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: forecasting, learning, machine, market, prediction, stock, techniques

61match

Finding 39051 source

This paper integrates nonlinear machine learning into profitability decomposition frameworks, enhancing traditional $ROE$ forecasting through AI-driven adaptation. It provides a fresh perspective on merging corporate fundamentals and advanced ML for earnings prediction. Though incremental rather than revolutionary, its pragmatic fusion of classic models and $ML$ delivers valuable forecasting accuracy gains.

LexicalMeaningQualityGraph

Matched: estimate, forecast, forecasting, learning, machine, prediction, stock