Author(s)

Chen Ting ¹

Affiliation(s)

¹ School of Economics, Shanghai University, Shanghai, China

Corresponding Author

Chen Ting

ABSTRACT

Current stock market price prediction research mainly uses machine and deep learning for historical data, sentiment, and macro - indicators to boost accuracy. Prediction is crucial for investors, risk management, and market stability. LSTM has strengths like handling long - term dependencies in price sequences but has long training times and high resource use. This paper gets index volatility and return data via GARCH, then uses Bayesian optimization on LSTM to enhance prediction. It validates the model by comparing four metrics with others. Using the CSI 1000 Index, the Bayesian - optimized LSTM reduces RMSE by 0.169% compared to the basic LSTM.

KEYWORDS

Stock Prediction, Long Short-Term Memory Network (LSTM), Bayesian Optimization, Volatility Modeling

CITE THIS PAPER

Chen Ting, Stock Market Price Prediction Based on GARCH-BO-LSTM. Financial Engineering and Risk Management (2025) Vol. 8: 187-193. DOI: http://dx.doi.org/10.23977/ferm.2025.080124.

REFERENCES

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