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August 4, 2026International Journal of Internet Protocol Technology

Improving stock price forecasting accuracy on the Shanghai Stock Exchange utilising hybrid LSTM and BRO algorithm

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Key Points

The aim is to enhance the accuracy of stock price forecasting for the Shanghai Stock Exchange using advanced algorithms.
Utilized hybrid LSTM (Long Short-Term Memory) and BRO algorithm for forecasting.
Analyzed stock price data from the Shanghai Stock Exchange to validate model accuracy.
Achieved significant improvement in forecasting accuracy compared to traditional methods.

Implication

Study improves stock price forecasting accuracy on the Shanghai Stock Exchange, suggesting enhanced financial predictions.

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Cite This Study

Mingbo Zhang (2026) studied this question.

synapsesocial.com/papers/6a743773764cddc9499d4bce https://doi.org/10.1504/ijipt.2027.10080365

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Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1Stock market forecasting using a lion-algorithm-optimised hybrid LSTM modelOriginal Research · 2026
2A Hybrid Model for Stock Price Forecasting Integrating XGBoost and LSTM with Financial Indicators2025 · 1 citations
3Modeling opening price spread of Shanghai Composite Index based on ARIMA-GRU/LSTM hybrid model2024 · 35 citations
4Predicting Stock Prices using state-of-art Machine Learning Models with Enhanced Feature Representation and Momentum Indicator SelectionOriginal Research · 2026
5Capability of Hybrid Long Short-Term Memory in Stock Price Prediction: A Comprehensive Literature Review2024 · 4 citations

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