A novel model by evolving partially connected neural network for stock price trend forecasting
Expert Systems with Applications: An International Journal
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Stock turning points detection is a very interesting subject arising in numerous financial and economic planning problems. In this paper, a piecewise linear representation method with Dynamics Time Warping system for stock turning points detection is presented. The piecewise linear representation method is able to generate numerous stocks turning points from the historic data base, then the Dynamic Time Warping system will be applied to retrieve similar stock price patterns from historic data for training the system. These turning points represent short-term trading signals for selling or buying stocks from the market. A Back-Propagation neural network (B.P.N) is further applied to learn the connection weights from these historic turning points and afterwards it is applied to forecast the future turning points from the set of test data. Experimental results demonstrate that the system integrating PLR and neural networks can make a significant amount of profit when compared with other approaches using stock data.