LSTM-Based Deep Model for Investment Portfolio Assessment and Analysis

Area of Science:

• Quantitative Finance
• Artificial Intelligence
• Machine Learning

Background:

• Quantitative investment models are crucial for financial prediction and strategy development.
• Standard Long Short-Term Memory (LSTM) networks exhibit limitations in accurately modeling fiscal cycle sequences.
• Existing models struggle with the complexity of fiat-delayed financial data series.

Purpose of the Study:

• To enhance the predictive accuracy of quantitative investment models.
• To address the shortcomings of standard LSTM networks in financial time series analysis.
• To develop an improved LSTM model integrated with an attention mechanism and genetic algorithm for superior financial forecasting.

Main Methods:

• An amended Long Short-Term Memory (LSTM) neural network architecture was developed.
• An attention mechanism was integrated into the LSTM model to improve feature focus.
• A genetic algorithm (GA) was employed to optimize the intrinsic parameters of the enhanced LSTM model.
• The model was trained and validated using man stock market data from January 2019 to May 2020.

Main Results:

• The enhanced LSTM model demonstrated superior performance compared to existing designs in financial time series prediction.
• The integration of the attention mechanism and GA optimization led to improved prediction accuracy.
• The model effectively captured complex patterns within the fiscal cycle sequences.
• The proposed model showed significant improvements in generalization aptitude.

Conclusions:

• The improved LSTM model offers a more effective approach to quantitative investment and financial forecasting.
• The model's enhanced predictive capabilities make it suitable for practical investment portfolio design.
• This research provides a robust framework for leveraging advanced machine learning techniques in financial markets.