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An IPSO-FW-WSVM Method for Stock Trading Signal Forecasting
1Department of Computer Science and Technology, Tongji University, Shanghai 201804, China.
Entropy (Basel, Switzerland)
|February 25, 2023
Summary
This study introduces a new method for detecting trading signals using piecewise linear representation (PLR) and improved particle swarm optimization (IPSO) with a feature-weighted support vector machine (FW-WSVM). The novel approach demonstrates higher accuracy and profitability in stock market predictions.
Area of Science:
- Financial investment analysis
- Machine learning applications in finance
- Algorithmic trading strategies
Background:
- Trading signal detection is a complex challenge in financial investment.
- Analyzing nonlinear relationships in historical stock data is crucial for accurate predictions.
- Existing methods may not fully capture the intricacies of stock market dynamics.
Purpose of the Study:
- To develop a novel method for effective trading signal detection.
- To analyze nonlinear relationships between trading signals and stock data.
- To improve prediction accuracy and profitability in financial investments.
Main Methods:
- Integrating piecewise linear representation (PLR) to identify trading points (peaks/valleys).
- Formulating turning point prediction as a three-class classification problem.
- Utilizing improved particle swarm optimization (IPSO) to optimize feature-weighted support vector machine (FW-WSVM) parameters.
Main Results:
- The proposed IPSO-FW-WSVM method achieved higher prediction accuracy compared to PLR-ANN.
- Experimental results on 25 stocks showed superior profitability with the novel method.
- The method effectively identified nonlinear relationships for better trading signal prediction.
Conclusions:
- The developed IPSO-FW-WSVM method is effective for trading signal prediction.
- The integration of PLR, IPSO, and FW-WSVM offers a robust approach to financial data analysis.
- This research contributes to advancing algorithmic trading strategies through enhanced prediction capabilities.
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