August 11, 2025Open Access

Multi-Model Approach for Stock Price Prediction and Trading Recommendations

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Authors

ZCZhenrui ChenUniversity of Science and Technology of China ZDZhifei DaiPeking University HXHui XingThe University of Queensland

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Implication

This analysis demonstrates improved stock market predictions using a multi-model framework, indicating the value of machine learning techniques in finance.

Key Points

ARIMA excels on volatile assets, showing its effectiveness in capturing financial market behavior.
LSTM models significantly benefit from input normalization, leading to improved prediction accuracy on selected stocks.
The integration of classical and modern methods offers a robust approach for financial time series analysis.
An extensible framework contributes valuable insights for both academic researchers and practical traders in the market.

Cite This Study

Chen et al. (2025) studied this question.

synapsesocial.com/papers/68a360d60a429f79733290f1 https://doi.org/10.20944/preprints202501.1003.v4

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Implication

This analysis demonstrates improved stock market predictions using a multi-model framework, indicating the value of machine learning techniques in finance.

Multi-Model Approach for Stock Price Prediction and Trading Recommendations

Discussion

Member takes

Implication

Key Points

Cite This Study

Multi-Model Approach for Stock Price Prediction and Trading Recommendations

Discussion

Member takes

Implication

Key Points

Cite This Study

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