Abstract
Trading signal detection is a very popular yet challenging research topic in the financial investment area. This paper develops a novel method integrating piecewise linear representation (PLR), improved particle swarm optimization (IPSO) and a feature-weighted support vector machine (FW-WSVM) to analyze the nonlinear relationships between trading signals and the stock data hidden in historical data. First, PLR is applied to generate numerous trading points (valleys or peaks) based on the historical data. These turning points' prediction is formulated as a three-class classification problem. Then, IPSO is utilized to find the optimal parameters of FW-WSVM. Lastly, we conduct a series of comparative experiments between IPSO-FW-WSVM and PLR-ANN on 25 stocks with 2 different investment strategies. The experiment results show that our proposed method achieves higher prediction accuracy and profitability, which indicates the IPSO-FW-WSVM method is effective in the prediction of trading signals.
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