Forecasting Stock Market Indices Using Integration of Encoder, Decoder, and Attention Mechanism

Area of Science:

• Computational Finance
• Machine Learning
• Time Series Analysis

Background:

• Accurate stock market index forecasting is vital for financial decision-making.
• Traditional methods often struggle with complex temporal dependencies in financial data.

Purpose of the Study:

• To develop a novel framework for enhanced stock market index forecasting.
• To leverage encoder-decoder architectures, attention mechanisms, and Bayesian optimization for improved prediction accuracy.

Main Methods:

• Utilized encoder-decoder architectures to process sequential stock price data.
• Integrated an attention mechanism to focus on relevant input sequence segments.
• Employed Bayesian optimization for hyperparameter tuning to maximize forecast precision.

Main Results:

• The proposed framework demonstrated significant improvements in forecast precision.
• Achieved superior performance compared to traditional recurrent neural networks.
• Effectively captured complex patterns and dependencies in stock price data.

Conclusions:

• The integrated framework shows strong potential for accurate stock market forecasting.
• The combination of advanced neural network architectures and optimization techniques is effective.
• This approach offers a robust solution for analyzing complex financial time series.