March 1, 2025

Hybrid Machine Learning–driven Financial Forecasting Models: Integrating Lstm, Prophet, and Xgboost for Enhanced Stock Price and Risk Prediction

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Hybrid machine learning models consistently outperform individual methods in stock price and risk forecasting, improving accuracy and reliability.
Normalized tests reveal hybrid models reduce RMSE by nearly 9% and enhance directional accuracy by 5 to 6 percentage points in one-day forecasts.
The review synthesizes 110 studies using a PRISMA protocol, focusing on hybridization and time-respecting evaluations for equity forecasting.
Recommendations include using structural layers and Shapley value attributions to enhance explainability and governance in forecasting models.

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Implication

This review demonstrates enhanced stock price accuracy in forecasting equity risk using hybrid machine learning, indicating significant improvements over single learners.

Cite This Study

Md Shah Ali Dolon (2025) studied this question.

synapsesocial.com/papers/68af7df27567bf4f94ff4cb3 https://doi.org/10.63125/nr1j8527

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Implication

This review demonstrates enhanced stock price accuracy in forecasting equity risk using hybrid machine learning, indicating significant improvements over single learners.

Hybrid Machine Learning–driven Financial Forecasting Models: Integrating Lstm, Prophet, and Xgboost for Enhanced Stock Price and Risk Prediction

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Implication

Cite This Study

Hybrid Machine Learning–driven Financial Forecasting Models: Integrating Lstm, Prophet, and Xgboost for Enhanced Stock Price and Risk Prediction

Key Points

Discussion

Implication

Cite This Study

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